Luke Combs and Tracy Chapman performed 'Fast Car' at the Grammys for the first time together. I hope the duet offers deeper meaning for our country.

Tracy Chapman, Luke Combs drove me to tears with 'Fast Car' Grammys duet. It's a good thing.

One day after the 2024 Grammy Awards, Tracy Chapman's "Fast Car" rose to No. 1 on the iTunes charts. On Sunday, Chapman performed the hit with Luke Combs, who released a cover of the song in April 2023.

Tracy Chapman’s ‘Fast Car’ Hits No. 1 on iTunes After Duet With Luke Combs at 2024 Grammys

Duet

duet

Artificial Intelligence (AI) has revolutionised the way we work, pushing the boundaries of what is possible in the realm of software development. As the demand for more efficient and collaborative coding solutions grows, developers are turning to innovative tools like GitHub Copilot and Duet AI. In this post, we’ll explore the features, strengths, and potential use cases of these two powerful AI-driven coding assistants. GitHub Copilot GitHub Copilot, developed by GitHub in collaboration with OpenAI, is a groundbreaking AI-powered coding assistant that integrates seamlessly with popular code editors like Visual Studio Code. Powered by OpenAI’s GPT (Generative Pre-trained Transformer) technology, Copilot assists developers by suggesting entire lines or blocks of code based on the context of the code being written. It supports a wide range of programming languages, making it versatile for various development projects. Strengths of GitHub Copilot Code Autocompletion: Copilot offers real-time suggestions as developers type, significantly speeding up the coding process Language Agnostic: With support for multiple programming languages, Copilot caters to the diverse needs of developers working on different projects Context-Aware Suggestions: Copilot understands the context of the code being written and generates relevant suggestions, reducing the need for manual corrections. Use Cases for GitHub Copilot Rapid Prototyping: Copilot is excellent for quickly prototyping ideas, allowing developers to focus on the logic rather than the syntax Learning and Teaching: It serves as a valuable learning tool for beginners, providing instant feedback and guidance on coding practices. Duet AI Duet AI is another AI coding assistant that operates on a collaborative model. It leverages AI to enhance the coding experience by offering suggestions and automating repetitive tasks. Unlike Copilot, Duet AI is a standalone application that can be used alongside any code editor. Strengths of Duet AI Collaborative Coding: Duet AI is designed with collaboration in mind, enabling multiple developers to work on the same codebase simultaneously Versatility: It supports a wide array of languages and frameworks, making it adaptable to various development environments Customisation: Developers can customise Duet AI to align with their coding style and preferences, tailoring the AI assistance to their specific needs. Use Cases for Duet AI Team Collaboration: Duet AI is ideal for teams working on complex projects, promoting a collaborative coding environment Codebase Maintenance: It can assist in refactoring code and maintaining a clean and consistent codebase. Both GitHub Copilot and Duet AI represent significant strides in the field of AI-driven coding assistance. GitHub Copilot excels in rapid prototyping and individual development scenarios, providing intelligent autocompletion and context-aware suggestions. On the other hand, Duet AI stands out for its collaborative features, making it a powerful tool for team-based coding projects. Ultimately, the choice between Copilot and Duet AI depends on the specific needs of developers and the nature of the projects they are working on. As AI continues to evolve, these tools are poised to become indispensable allies in the journey towards more efficient and collaborative software development. Also appears . here

Unveiling the Power of AI: A Deep Dive into Copilot and Duet AI

Google Cloud x Gemini: Accomplish More in the Cloud with Generative AI

Helloooooooo! Hope you're doing great! This is SMY! 👋 Let's jump right in 🚀 Although there are other options like physically connecting with adapters or purchasing software like Duet. But, if you're like me, you would want a seamless Just Work solution and have a second screen setup in no time. That's where Deskreen comes into play! Follow the steps to easily set up your screen. Contents: ⚡ Downloading Deskreen
⚡ Sharing your screen and Streaming on Mobile Step 1: Downloading Deskreen Head over to the following link, and download & install Deskreen for your platform: https://deskreen.com/lang-en Step 2: Allowing Deskreen For Mac users: Allow Screen Recording Permission on MacOS is in System Preferences -> Security & Privacy -> Privacy -> Screen Recording For Windows users: need to add 'Private Network Access' for Deskreen on the first start or in firewall settings Step 3: Connect your Phone Open up Deskreen, and connect your Phone by scanning a QR or going to the given address: It will automatically ask for approval once you open the page on the Phone's browser. Click on Allow, and your Phone will be connected! Step 4: Share Your Desired Screen Once Connected, share your entire screen or desired window: Once done, confirm the options, and your screen will be streamed on your Phone! Wrapping Up: We just did the steps to Connect our Screen for Streaming on the Phone's browser. 🚀 ..... Now, you can Experience Seamless Multitasking 🚀 That's it, folks! I hope it was a good read for you. Thank you! ✨ 👉 Follow me GitHub LinkedIn Helloooooooo! Helloooooooo! Hope you're doing great! This is SMY! 👋 Let's jump right in 🚀 Although there are other options like physically connecting with adapters or purchasing software like Duet . But, if you're like me, you would want a seamless Just Work solution and have a second screen setup in no time. Duet Just Work That's where Deskreen comes into play! Follow the steps to easily set up your screen. Deskreen Contents: ⚡ Downloading Deskreen ⚡ Sharing your screen and Streaming on Mobile ⚡ Downloading Deskreen Downloading Deskreen ⚡ Sharing your screen and Streaming on Mobile Sharing your screen and Streaming on Mobile Step 1: Downloading Deskreen Head over to the following link, and download & install Deskreen for your platform: Deskreen https://deskreen.com/lang-en https://deskreen.com/lang-en Step 2: Allowing Deskreen For Mac users: Allow Screen Recording Permission on MacOS is in System Preferences -> Security & Privacy -> Privacy -> Screen Recording For Windows users: need to add 'Private Network Access' for Deskreen on the first start or in firewall settings Step 3: Connect your Phone Open up Deskreen , and connect your Phone by scanning a QR or going to the given address: Deskreen It will automatically ask for approval once you open the page on the Phone's browser. Click on Allow, and your Phone will be connected! Step 4: Share Your Desired Screen Once Connected, share your entire screen or desired window: Once done, confirm the options, and your screen will be streamed on your Phone! Wrapping Up: We just did the steps to Connect our Screen for Streaming on the Phone's browser. 🚀 ..... Now, you can Experience Seamless Multitasking 🚀 That's it, folks! I hope it was a good read for you. Thank you! ✨ 👉 Follow me GitHub GitHub LinkedIn LinkedIn

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Trump v. United States Court Filing, retrieved on July 1, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 17 of 21. VII Today’s decision to grant former Presidents immunity for their official acts is deeply wrong. As troubling as this criminal immunity doctrine is in theory, the majority’s application of the doctrine to the indictment in this case is perhaps even more troubling. In the hands of the majority, this new official-acts immunity operates as a one-way ratchet. First, the majority declares all of the conduct involving the Justice Department and the Vice President to be official conduct, see ante, at 19–24, yet it refuses to designate any course of conduct alleged in the indictment as private, despite concessions from Trump’s counsel.[6] Trump’s counsel conceded, for example, that the allegation that Trump “turned to a private attorney who was willing to spread knowingly false claims of election fraud to spearhead his challenges to the election results” “sounds private.” Tr. of Oral Arg. 29. He likewise conceded that the allegation that Trump “conspired with another private attorney who caused the filing in court of a verification signed by [Trump] that contained false allegations to support a challenge” “sounds private.” Ibid.; see also id., at 36–37 (Trump’s counsel explaining that it is not “disputed” that such conduct is “unofficial”). Again, when asked about allegations that “[t]hree private actors . . . helped implement a plan to submit fraudulent slates of presidential electors to obstruct the certification proceeding, and [Trump] and a co-conspirator attorney directed that effort,” Trump’s counsel conceded the alleged conduct was “private.” Id., at 29–30. Only the majority thinks that organizing fraudulent slates of electors might qualify as an official act of the President, see ante, at 24–28, or at least an act so “interrelated” with other allegedly official acts that it might warrant protection, ante, at 28. If the majority’s sweeping conception of “official acts” has any real limits, the majority is unwilling to reveal them in today’s decision. Second, the majority designates certain conduct immune while refusing to recognize anything as prosecutable. It shields large swaths of conduct involving the Justice Department with immunity, see ante, at 19–21; see also Part V, supra, but it does not give an inch in the other direction. The majority admits that the Vice President’s responsibility “‘presiding over the Senate’” is “‘not an “executive branch” function,’” and it further admits that the President “plays no direct constitutional or statutory role” in the counting of electoral votes. Ante, at 23–24. Yet the majority refuses to conclude that Trump lacks immunity for his alleged attempts to “enlist the Vice President to use his ceremonial role at the January 6 certification proceeding to fraudulently alter the election results.” App. 187, Indictment ¶10(d). Instead, it worries that a prosecution for this conduct might make it harder for the President to use the Vice President “to advance [his] agenda in Congress.” Ante, at 24. Such a prosecution, according to the majority, “may well hinder the President’s ability to perform his constitutional functions.” Ibid. Whether a prosecution for this conduct warrants immunity should have been an easy question, but the majority turns it into a debatable one. Remarkably, the majority goes further and declines to deny immunity even for the allegations that Trump organized fraudulent elector slates, pressured States to subvert the legitimate election results, and exploited violence at the Capitol to influence the certification proceedings. It is not conceivable that a prosecution for these alleged efforts to overturn a Presidential election, whether labeled official or unofficial under the majority’s test, would pose any “‘dangers of intrusion on the authority and functions of the Executive Branch,’” ante, at 14, and the majority could have said as much. Instead, it perseverates on a threshold question that should be immaterial. Looking beyond the fate of this particular prosecution, the long-term consequences of today’s decision are stark. The Court effectively creates a law-free zone around the President, upsetting the status quo that has existed since the Founding. This new official-acts immunity now “lies about like a loaded weapon” for any President that wishes to place his own interests, his own political survival, or his own financial gain, above the interests of the Nation. Korematsu v. United States, 323 U. S. 214, 246 (1944) (Jackson, J., dissenting). The President of the United States is the most powerful person in the country, and possibly the world. When he uses his official powers in any way, under the majority’s reasoning, he now will be insulated from criminal prosecution. Orders the Navy’s Seal Team 6 to assassinate a political rival? Immune. Organizes a military coup to hold onto power? Immune. Takes a bribe in exchange for a pardon? Immune. Immune, immune, immune. Let the President violate the law, let him exploit the trappings of his office for personal gain, let him use his official power for evil ends. Because if he knew that he may one day face liability for breaking the law, he might not be as bold and fearless as we would like him to be. That is the majority’s message today. Even if these nightmare scenarios never play out, and I pray they never do, the damage has been done. The relationship between the President and the people he serves has shifted irrevocably. In every use of official power, the President is now a king above the law. * * * The majority’s single-minded fixation on the President’s need for boldness and dispatch ignores the countervailing need for accountability and restraint. The Framers were not so single-minded. In the Federalist Papers, after “endeavor[ing] to show” that the Executive designed by the Constitution “combines . . . all the requisites to energy,” Alexander Hamilton asked a separate, equally important question: “Does it also combine the requisites to safety, in a republican sense, a due dependence on the people, a due responsibility?” The Federalist No. 77, p. 507 (J. Harvard Library ed. 2009). The answer then was yes, based in part upon the President’s vulnerability to “prosecution in the common course of law.” Ibid. The answer after today is no. Never in the history of our Republic has a President had reason to believe that he would be immune from criminal prosecution if he used the trappings of his office to violate the criminal law. Moving forward, however, all former Presidents will be cloaked in such immunity. If the occupant of that office misuses official power for personal gain, the criminal law that the rest of us must abide will not provide a backstop. With fear for our democracy, I dissent. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on July 1, 2024, supremecourt.gov is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. [6] The majority protests that it is “adher[ing] to time-tested practices” by “deciding what is required to dispose of this case and remanding” to lower courts to sort out the details. Ante, at 41. Yet it implicitly acknowledges that it reaches far beyond what any lower court considered or any party briefed by designating certain conduct official in the first instance. See ibid. (noting “the lack of factual analysis in the lower courts, and the lack of briefing on how to categorize the conduct alleged”). In reaching out to shield some conduct as official while refusing to recognize any conduct as unofficial, the majority engages in judicial activism, not judicial restraint. Trump v. United States Court Filing, retrieved on July 1, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 17 of 21. Trump v. United States Court Filing, retrieved on July 1, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 17 of 21. HackerNoon’s Legal PDF Series here VII Today’s decision to grant former Presidents immunity for their official acts is deeply wrong. As troubling as this criminal immunity doctrine is in theory, the majority’s application of the doctrine to the indictment in this case is perhaps even more troubling. In the hands of the majority, this new official-acts immunity operates as a one-way ratchet. First, the majority declares all of the conduct involving the Justice Department and the Vice President to be official conduct, see ante, at 19–24, yet it refuses to designate any course of conduct alleged in the indictment as private, despite concessions from Trump’s counsel.[6] Trump’s counsel conceded, for example, that the allegation that Trump “turned to a private attorney who was willing to spread knowingly false claims of election fraud to spearhead his challenges to the election results” “sounds private.” Tr. of Oral Arg. 29. He likewise conceded that the allegation that Trump “conspired with another private attorney who caused the filing in court of a verification signed by [Trump] that contained false allegations to support a challenge” “sounds private.” Ibid.; see also id., at 36–37 (Trump’s counsel explaining that it is not “disputed” that such conduct is “unofficial”). Again, when asked about allegations that “[t]hree private actors . . . helped implement a plan to submit fraudulent slates of presidential electors to obstruct the certification proceeding, and [Trump] and a co-conspirator attorney directed that effort,” Trump’s counsel conceded the alleged conduct was “private.” Id., at 29–30. Only the majority thinks that organizing fraudulent slates of electors might qualify as an official act of the President, see ante, at 24–28, or at least an act so “interrelated” with other allegedly official acts that it might warrant protection, ante, at 28. If the majority’s sweeping conception of “official acts” has any real limits, the majority is unwilling to reveal them in today’s decision. Second, the majority designates certain conduct immune while refusing to recognize anything as prosecutable. It shields large swaths of conduct involving the Justice Department with immunity, see ante, at 19–21; see also Part V, supra, but it does not give an inch in the other direction. The majority admits that the Vice President’s responsibility “‘presiding over the Senate’” is “‘not an “executive branch” function,’” and it further admits that the President “plays no direct constitutional or statutory role” in the counting of electoral votes. Ante, at 23–24. Yet the majority refuses to conclude that Trump lacks immunity for his alleged attempts to “enlist the Vice President to use his ceremonial role at the January 6 certification proceeding to fraudulently alter the election results.” App. 187, Indictment ¶10(d). Instead, it worries that a prosecution for this conduct might make it harder for the President to use the Vice President “to advance [his] agenda in Congress.” Ante, at 24. Such a prosecution, according to the majority, “may well hinder the President’s ability to perform his constitutional functions.” Ibid. Whether a prosecution for this conduct warrants immunity should have been an easy question, but the majority turns it into a debatable one. Remarkably, the majority goes further and declines to deny immunity even for the allegations that Trump organized fraudulent elector slates, pressured States to subvert the legitimate election results, and exploited violence at the Capitol to influence the certification proceedings. It is not conceivable that a prosecution for these alleged efforts to overturn a Presidential election, whether labeled official or unofficial under the majority’s test, would pose any “‘dangers of intrusion on the authority and functions of the Executive Branch,’” ante, at 14, and the majority could have said as much. Instead, it perseverates on a threshold question that should be immaterial. Looking beyond the fate of this particular prosecution, the long-term consequences of today’s decision are stark. The Court effectively creates a law-free zone around the President, upsetting the status quo that has existed since the Founding. This new official-acts immunity now “lies about like a loaded weapon” for any President that wishes to place his own interests, his own political survival, or his own financial gain, above the interests of the Nation. Korematsu v. United States, 323 U. S. 214, 246 (1944) (Jackson, J., dissenting). The President of the United States is the most powerful person in the country, and possibly the world. When he uses his official powers in any way, under the majority’s reasoning, he now will be insulated from criminal prosecution. Orders the Navy’s Seal Team 6 to assassinate a political rival? Immune. Organizes a military coup to hold onto power? Immune. Takes a bribe in exchange for a pardon? Immune. Immune, immune, immune. Let the President violate the law, let him exploit the trappings of his office for personal gain, let him use his official power for evil ends. Because if he knew that he may one day face liability for breaking the law, he might not be as bold and fearless as we would like him to be. That is the majority’s message today. Even if these nightmare scenarios never play out, and I pray they never do, the damage has been done. The relationship between the President and the people he serves has shifted irrevocably. In every use of official power, the President is now a king above the law. * * * The majority’s single-minded fixation on the President’s need for boldness and dispatch ignores the countervailing need for accountability and restraint. The Framers were not so single-minded. In the Federalist Papers, after “endeavor[ing] to show” that the Executive designed by the Constitution “combines . . . all the requisites to energy,” Alexander Hamilton asked a separate, equally important question: “Does it also combine the requisites to safety, in a republican sense, a due dependence on the people, a due responsibility?” The Federalist No. 77, p. 507 (J. Harvard Library ed. 2009). The answer then was yes, based in part upon the President’s vulnerability to “prosecution in the common course of law.” Ibid. The answer after today is no. Never in the history of our Republic has a President had reason to believe that he would be immune from criminal prosecution if he used the trappings of his office to violate the criminal law. Moving forward, however, all former Presidents will be cloaked in such immunity. If the occupant of that office misuses official power for personal gain, the criminal law that the rest of us must abide will not provide a backstop. With fear for our democracy, I dissent. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on July 1, 2024, supremecourt.gov is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on July 1, 2024, supremecourt.gov is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. supremecourt.gov [6] The majority protests that it is “adher[ing] to time-tested practices” by “deciding what is required to dispose of this case and remanding” to lower courts to sort out the details. Ante, at 41. Yet it implicitly acknowledges that it reaches far beyond what any lower court considered or any party briefed by designating certain conduct official in the first instance. See ibid. (noting “the lack of factual analysis in the lower courts, and the lack of briefing on how to categorize the conduct alleged”). In reaching out to shield some conduct as official while refusing to recognize any conduct as unofficial, the majority engages in judicial activism, not judicial restraint.

Justice Sotomayor on Trump v United States: Today's Decision... Is Deeply Wrong

Authors:
(1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu};
(2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu};
(3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro
Related work
Dataset
Experiments
Conclusions and References II. RELATED WORK The University of Rochester Multi-Modal Music Performance Dataset (URMP) [1] is a dataset with 44 Multi-instrument video recordings of classical music pieces. Each instrument present in a piece was recorded separately, both with video and high-quality audio with a stand-alone microphone, in order to have ground-truth individual tracks. Although playing separately, the instruments were coordinated by using a conducting video with a pianist playing in order to set the common timing for the different players. After synchronization, the audio of the individual videos was replaced by the high-quality audio of the microphone and then different recordings were assembled to create the mixture: the individual high-quality audio recordings were added up to create the audio mixture and the visual content was composited in a single video with a common background where all players were arranged at the same level from left to right. For each piece, the dataset provides the musical score in MIDI format, the high-quality individual instrument audio recordings and the videos of the assembled pieces. The instruments present in the dataset, shown in Figure 1, are common instruments in chamber orchestras. In spite of all its good characteristics, it is a small dataset and thus not appropriate for training deep learning architectures. Two other datasets of audio-visual recordings of musical instruments performances have been presented recently: Music [23] and MusicES [31]. Music consists of 536 recordings of solos and 149 videos of duets across 11 categories: accordion, acoustic guitar, cello, clarinet, erhu, flute, saxophone, trumpet, tuba, violin and xylophone. This dataset was gathered by querying YouTube. MusicES [31] is an extension of MUSIC to around the triple of its original size with approximately 1475 recordings but spread in 9 categories instead: accordion, guitar, cello, flute, saxophone, trumpet, tuba, violin and xylophone. There are 7 common categories in MUSIC and Solos: violin, cello, flute, clarinet, saxophone, trumpet and tuba. The common categories between MusicES and Solos are 6 (the former ones except clarinet). Solos and MusicES are complementary. There is only an small intersection of 5% between both, which means both datasets can be combined into a bigger one. We can find in the literature several examples which show the utility of audio-visual datasets. The Sound of Pixels [23] performs audio source separation generating audio spectral components which are further smartly selected by using visual features coming from the video stream to obtain separated sources. This idea was further extended in [20] in order to separate the different sounds present in the mixture in a recursive way. At each stage, the system separates the most salient source from the ones remaining in the mixture. The Sound of Motions [19] uses dense trajectories obtained from optical flow to condition audio source separation, being able even to separate same-instrument mixtures. Visual conditioning is also used in [18] to separate different instruments; during training, a classification loss is used on the separated sounds to enforce object consistency and a co-separation loss forces the estimated individual sounds to produce the original mixtures once reassembled. In [17], the authors developed an energy-based method which minimizes a Non-Negative Matrix Factorization term with an activation matrix which is forced to be aligned to a matrix containing per-source motion information. This motion matrix contains the average magnitude velocities of the clustered motion trajectories in each player bounding box. Recent works show the rising use of skeletons in audiovisual tasks. In Audio to body dynamics [29] authors show it is possible to predict skeletons reproducing the movements of players playing instruments such as piano or violin. Skeletons have proven to be useful for establishing audio-visual correspondences, such as body or finger motion with note onsets or pitch fluctuations, in chamber music performances [21]. A recent work [32] tackles the source separation problem in a similar to Sound of Motions [19] but replacing the dense trajectories by skeleton information. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Authors: Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro Related work Dataset Experiments Conclusions and References Abstract and Intro Abstract and Intro Related work Related work Dataset Dataset Experiments Experiments Conclusions and References Conclusions and References II. RELATED WORK The University of Rochester Multi-Modal Music Performance Dataset (URMP) [1] is a dataset with 44 Multi-instrument video recordings of classical music pieces. Each instrument present in a piece was recorded separately, both with video and high-quality audio with a stand-alone microphone, in order to have ground-truth individual tracks. Although playing separately, the instruments were coordinated by using a conducting video with a pianist playing in order to set the common timing for the different players. After synchronization, the audio of the individual videos was replaced by the high-quality audio of the microphone and then different recordings were assembled to create the mixture: the individual high-quality audio recordings were added up to create the audio mixture and the visual content was composited in a single video with a common background where all players were arranged at the same level from left to right. For each piece, the dataset provides the musical score in MIDI format, the high-quality individual instrument audio recordings and the videos of the assembled pieces. The instruments present in the dataset, shown in Figure 1, are common instruments in chamber orchestras. In spite of all its good characteristics, it is a small dataset and thus not appropriate for training deep learning architectures. Two other datasets of audio-visual recordings of musical instruments performances have been presented recently: Music [23] and MusicES [31]. Music consists of 536 recordings of solos and 149 videos of duets across 11 categories: accordion, acoustic guitar, cello, clarinet, erhu, flute, saxophone, trumpet, tuba, violin and xylophone. This dataset was gathered by querying YouTube. MusicES [31] is an extension of MUSIC to around the triple of its original size with approximately 1475 recordings but spread in 9 categories instead: accordion, guitar, cello, flute, saxophone, trumpet, tuba, violin and xylophone. There are 7 common categories in MUSIC and Solos: violin, cello, flute, clarinet, saxophone, trumpet and tuba. The common categories between MusicES and Solos are 6 (the former ones except clarinet). Solos and MusicES are complementary. There is only an small intersection of 5% between both, which means both datasets can be combined into a bigger one. We can find in the literature several examples which show the utility of audio-visual datasets. The Sound of Pixels [23] performs audio source separation generating audio spectral components which are further smartly selected by using visual features coming from the video stream to obtain separated sources. This idea was further extended in [20] in order to separate the different sounds present in the mixture in a recursive way. At each stage, the system separates the most salient source from the ones remaining in the mixture. The Sound of Motions [19] uses dense trajectories obtained from optical flow to condition audio source separation, being able even to separate same-instrument mixtures. Visual conditioning is also used in [18] to separate different instruments; during training, a classification loss is used on the separated sounds to enforce object consistency and a co-separation loss forces the estimated individual sounds to produce the original mixtures once reassembled. In [17], the authors developed an energy-based method which minimizes a Non-Negative Matrix Factorization term with an activation matrix which is forced to be aligned to a matrix containing per-source motion information. This motion matrix contains the average magnitude velocities of the clustered motion trajectories in each player bounding box. Recent works show the rising use of skeletons in audiovisual tasks. In Audio to body dynamics [29] authors show it is possible to predict skeletons reproducing the movements of players playing instruments such as piano or violin. Skeletons have proven to be useful for establishing audio-visual correspondences, such as body or finger motion with note onsets or pitch fluctuations, in chamber music performances [21]. A recent work [32] tackles the source separation problem in a similar to Sound of Motions [19] but replacing the dense trajectories by skeleton information. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Solos: A Dataset for Audio-Visual Music Analysis - Related work

একক: অডিও-ভিজ্যুয়াল সঙ্গীত বিশ্লেষণের জন্য একটি ডেটাসেট - সম্পর্কিত কাজ

Solos: Ein Datensatz für audiovisuelle Musikanalyse - Verwandte Arbeiten

Solos: un conjunto de datos para el análisis de música audiovisual - Trabajo relacionado

Solos : un ensemble de données pour l'analyse de la musique audiovisuelle - Travaux connexes

सोलोस: ऑडियो-विज़ुअल संगीत विश्लेषण के लिए एक डेटासेट - संबंधित कार्य

Solos: オーディオビジュアル音楽分析のためのデータセット - 関連研究

Solos: 시청각 음악 분석을 위한 데이터세트 - 관련 작업

Solos: um conjunto de dados para análise musical audiovisual - Trabalhos relacionados

Соло: набор данных для аудиовизуального музыкального анализа — соответствующая работа

Sololar: Görsel-İşitsel Müzik Analizi için Bir Veri Kümesi - İlgili çalışma

Solos: Bộ dữ liệu để phân tích âm nhạc nghe nhìn - Công việc liên quan

Solos：视听音乐分析数据集 - 相关工作

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Kiriaki Frangias;
(2) Andrew Lin;
(3) Ellen Vitercik;
(4) Manolis Zampetakis. Table of Links Abstract and Introduction Warm-up: Agents with known equal disutility Agents with unknown disutilites Experiments Conclusions and future directions, References A Summary of notation B Omitted proofs from Section 2 C Omitted proofs from Section 3 D Additional Information about Experiments 5 Conclusions and future directions We studied the multifaceted problem of designing a crowdsourcing mechanism that efficiently and accurately ranks a set of items using pairwise comparisons from rational agents. We based our approach on the classic principal-agent model from contract theory. To distribute comparisons among agents, we used an unexpected connection to the social golfer problem which allowed us to simultaneously minimize the agents’ workload and ensure enough agents evaluate each comparison. We showed that by optimizing the payment mechanisms, the principal can incentivize enough agents to exert effort, ensuring our algorithm recovers the ground-truth ranking. Our experiments showed that even when noise is added to our original model, our method consistently aids the principal in retrieving the accurate ground-truth ordering. llude, which allowed us to assign the same set of verified comparisons to all agents. How should our mechanism change if agents can collude? Moreover, we assumed that agents who do not exert effort will return comparisons consistent with a uniformly-random permutation of the items. What other models of agent behavior can we study under this framework? Finally, as is standard in contract theory, we assumed that the principal knows the probability π that an effortful agent is good. How should we handle the case where π is unknown? References [1] Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, and Gergely V. Zaruba. Crowdsourcing pareto-optimal object finding by pairwise comparisons. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), 2015. [2] Ralph Allan Bradley and Milton E Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39(3/4):324–345, 1952. [3] Yang Cai, Constantinos Daskalakis, and Christos Papadimitriou. Optimum statistical estimation with strategic data sources. In Conference on Learning Theory (COLT), 2015. [4] Xi Chen, Paul N Bennett, Kevyn Collins-Thompson, and Eric Horvitz. Pairwise ranking aggregation in a crowdsourced setting. In Proceedings of the International Conference on Web Search and Data Mining, pages 193–202, 2013. [5] Yuxin Chen and Changho Suh. Spectral MLE: Top-k rank aggregation from pairwise comparisons. In International Conference on Machine Learning (ICML), 2015. [6] Paul Duetting, Tomer Ezra, Michal Feldman, and Thomas Kesselheim. Combinatorial contracts. In Symposium on Foundations of Computer Science (FOCS), 2022. [7] Anindya Ghose, Panagiotis G Ipeirotis, and Beibei Li. Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3):493–520, 2012. [8] John Guiver and Edward Snelson. Bayesian inference for plackett-luce ranking models. In International Conference on Machine Learning (ICML), 2009. [9] Stephen Guo, Aditya Parameswaran, and Hector Garcia-Molina. So who won? dynamic max discovery with the crowd. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, page 385–396, New York, NY, USA, 2012. Association for Computing Machinery. ISBN 9781450312479. doi: 10.1145/2213836.2213880. URL https://doi.org/10.1145/2213836.2213880. [10] Guru Guruganesh, Jon Schneider, and Joshua Wang. Contracts under moral hazard and adverse selection. In ACM Conference on Economics and Computation (EC), 2021. [11] R. Jurca and B. Faltings. Mechanisms for making crowds truthful. Journal of Artificial Intelligence Research, 34:209–253, mar 2009. doi: 10.1613/jair.2621. URL https://doi.org/10.1613%2Fjair.2621. [12] Radu Jurca and Boi Faltings. Robust incentive-compatible feedback payments. In Workshop on Trading Agent Design and Analysis, 2006. [13] Radu Jurca and Boi Faltings. Minimum payments that reward honest reputation feedback. In ACM Conference on Economics and Computation (EC), 2006. [14] Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye, Dong In Kim, and Jun Zhao. Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory. IEEE Transactions on Vehicular Technology, 68(3):2906–2920, 2019. [15] Ngai Meng Kou, Yan Li, Hao Wang, Leong Hou U, and Zhiguo Gong. Crowdsourced top-k queries by confidence-aware pairwise judgments. Proceedings of the 2017 ACM International Conference on Management of Data, 2017. URL https://api.semanticscholar.org/CorpusID:36647621. [16] Jean-Jacques Laffont and David Martimort. Moral Hazard: The Basic Trade-Offs, pages 145–186. Princeton University Press, 2002. URL http://www.jstor.org/stable/j.ctv7h0rwr.8. [17] Shili Lin. Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555–570, 2010. [18] Tyler Lu and Craig Boutilier. Learning mallows models with pairwise preferences. In International Conference on Machine Learning (ICML), 2011. [19] C. L. Mallows. Non-null ranking models. i. Biometrika, 44:114–130, 1957. URL https://api.semanticscholar.org/CorpusID:121527283. [20] Fei Mi and Dit-Yan Yeung. Probabilistic graphical models for boosting cardinal and ordinal peer grading in moocs. In AAAI Conference on Artificial Intelligence, 2015. [21] Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, and Daphne Koller. Tuned models of peer assessment in moocs. In Proceedings of The 6th International Conference on Educational Data Mining (EDM), 2013. [22] R. L. Plackett. The Analysis of Permutations. Journal of the Royal Statistical Society Series C, 24(2):193–202, June 1975. doi: 10.2307/2346567. URL https://ideas.repec.org/a/bla/jorssc/v24y1975i2p193-202.html. [23] Karthik Raman and Thorsten Joachims. Methods for ordinal peer grading. In International Conference on Knowledge Discovery and Data Mining (KDD), 2014. [24] Karthik Raman and Thorsten Joachims. Bayesian ordinal peer grading. In Proceedings of the ACM Conference on Learning at Scale, 2015. [25] Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. Learning from crowds. Journal of machine learning research, 11(4), 2010. [26] Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, and Martin Wainwright. Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2015. [27] Nihar B Shah, Joseph K Bradley, Abhay Parekh, Martin Wainwright, and Kannan Ramchandran. A case for ordinal peer-evaluation in moocs. In NIPS Workshop on Data-Driven Education, 2013. [28] Louis Leon Thurstone. The method of paired comparisons for social values. The Journal of Abnormal and Social Psychology, 21:384–400, 1927. URL https://api.semanticscholar.org/CorpusID:144595330. [29] Markus Triska. Solution Methods for the Social Golfer Problem. PhD thesis, Vienna University of Technology, March 2008. Available from https://www.metalevel.at/mst.pdf. [30] Kristi Tsukida, Maya R Gupta, et al. How to analyze paired comparison data. Department of Electrical Engineering University of Washington, Tech. Rep. UWEETR-2011-0004, 1, 2011. [31] Bo Waggoner and Yiling Chen. Output agreement mechanisms and common knowledge. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1): 220–226, Sep. 2014. doi: 10.1609/hcomp.v2i1.13151. URL https://ojs.aaai.org/index.php/HCOMP/article/view/13151. [32] Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, and Michael I. Jordan. The sample complexity of online contract design. In ACM Conference on Economics and Computation (EC), 2023. This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. This paper is available on arxiv under CC 4.0 license. Authors : Authors (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. Table of Links Abstract and Introduction Abstract and Introduction Warm-up: Agents with known equal disutility Warm-up: Agents with known equal disutility Agents with unknown disutilites Agents with unknown disutilites Experiments Experiments Conclusions and future directions, References Conclusions and future directions, References A Summary of notation A Summary of notation B Omitted proofs from Section 2 B Omitted proofs from Section 2 C Omitted proofs from Section 3 C Omitted proofs from Section 3 D Additional Information about Experiments D Additional Information about Experiments 5 Conclusions and future directions We studied the multifaceted problem of designing a crowdsourcing mechanism that efficiently and accurately ranks a set of items using pairwise comparisons from rational agents. We based our approach on the classic principal-agent model from contract theory. To distribute comparisons among agents, we used an unexpected connection to the social golfer problem which allowed us to simultaneously minimize the agents’ workload and ensure enough agents evaluate each comparison. We showed that by optimizing the payment mechanisms, the principal can incentivize enough agents to exert effort, ensuring our algorithm recovers the ground-truth ranking. Our experiments showed that even when noise is added to our original model, our method consistently aids the principal in retrieving the accurate ground-truth ordering. llude, which allowed us to assign the same set of verified comparisons to all agents. How should our mechanism change if agents can collude? Moreover, we assumed that agents who do not exert effort will return comparisons consistent with a uniformly-random permutation of the items. What other models of agent behavior can we study under this framework? Finally, as is standard in contract theory, we assumed that the principal knows the probability π that an effortful agent is good. How should we handle the case where π is unknown? References [1] Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, and Gergely V. Zaruba. Crowdsourcing pareto-optimal object finding by pairwise comparisons. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), 2015. [2] Ralph Allan Bradley and Milton E Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39(3/4):324–345, 1952. [3] Yang Cai, Constantinos Daskalakis, and Christos Papadimitriou. Optimum statistical estimation with strategic data sources. In Conference on Learning Theory (COLT), 2015. [4] Xi Chen, Paul N Bennett, Kevyn Collins-Thompson, and Eric Horvitz. Pairwise ranking aggregation in a crowdsourced setting. In Proceedings of the International Conference on Web Search and Data Mining, pages 193–202, 2013. [5] Yuxin Chen and Changho Suh. Spectral MLE: Top-k rank aggregation from pairwise comparisons. In International Conference on Machine Learning (ICML), 2015. [6] Paul Duetting, Tomer Ezra, Michal Feldman, and Thomas Kesselheim. Combinatorial contracts. In Symposium on Foundations of Computer Science (FOCS), 2022. [7] Anindya Ghose, Panagiotis G Ipeirotis, and Beibei Li. Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3):493–520, 2012. [8] John Guiver and Edward Snelson. Bayesian inference for plackett-luce ranking models. In International Conference on Machine Learning (ICML), 2009. [9] Stephen Guo, Aditya Parameswaran, and Hector Garcia-Molina. So who won? dynamic max discovery with the crowd. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, page 385–396, New York, NY, USA, 2012. Association for Computing Machinery. ISBN 9781450312479. doi: 10.1145/2213836.2213880. URL https://doi.org/10.1145/2213836.2213880. [10] Guru Guruganesh, Jon Schneider, and Joshua Wang. Contracts under moral hazard and adverse selection. In ACM Conference on Economics and Computation (EC), 2021. [11] R. Jurca and B. Faltings. Mechanisms for making crowds truthful. Journal of Artificial Intelligence Research, 34:209–253, mar 2009. doi: 10.1613/jair.2621. URL https://doi.org/10.1613%2Fjair.2621. [12] Radu Jurca and Boi Faltings. Robust incentive-compatible feedback payments. In Workshop on Trading Agent Design and Analysis, 2006. [13] Radu Jurca and Boi Faltings. Minimum payments that reward honest reputation feedback. In ACM Conference on Economics and Computation (EC), 2006. [14] Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye, Dong In Kim, and Jun Zhao. Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory. IEEE Transactions on Vehicular Technology, 68(3):2906–2920, 2019. [15] Ngai Meng Kou, Yan Li, Hao Wang, Leong Hou U, and Zhiguo Gong. Crowdsourced top-k queries by confidence-aware pairwise judgments. Proceedings of the 2017 ACM International Conference on Management of Data, 2017. URL https://api.semanticscholar.org/CorpusID:36647621. [16] Jean-Jacques Laffont and David Martimort. Moral Hazard: The Basic Trade-Offs, pages 145–186. Princeton University Press, 2002. URL http://www.jstor.org/stable/j.ctv7h0rwr.8. [17] Shili Lin. Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555–570, 2010. [18] Tyler Lu and Craig Boutilier. Learning mallows models with pairwise preferences. In International Conference on Machine Learning (ICML), 2011. [19] C. L. Mallows. Non-null ranking models. i. Biometrika, 44:114–130, 1957. URL https://api.semanticscholar.org/CorpusID:121527283. [20] Fei Mi and Dit-Yan Yeung. Probabilistic graphical models for boosting cardinal and ordinal peer grading in moocs. In AAAI Conference on Artificial Intelligence, 2015. [21] Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, and Daphne Koller. Tuned models of peer assessment in moocs. In Proceedings of The 6th International Conference on Educational Data Mining (EDM), 2013. [22] R. L. Plackett. The Analysis of Permutations. Journal of the Royal Statistical Society Series C, 24(2):193–202, June 1975. doi: 10.2307/2346567. URL https://ideas.repec.org/a/bla/jorssc/v24y1975i2p193-202.html. [23] Karthik Raman and Thorsten Joachims. Methods for ordinal peer grading. In International Conference on Knowledge Discovery and Data Mining (KDD), 2014. [24] Karthik Raman and Thorsten Joachims. Bayesian ordinal peer grading. In Proceedings of the ACM Conference on Learning at Scale, 2015. [25] Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. Learning from crowds. Journal of machine learning research, 11(4), 2010. [26] Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, and Martin Wainwright. Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2015. [27] Nihar B Shah, Joseph K Bradley, Abhay Parekh, Martin Wainwright, and Kannan Ramchandran. A case for ordinal peer-evaluation in moocs. In NIPS Workshop on Data-Driven Education, 2013. [28] Louis Leon Thurstone. The method of paired comparisons for social values. The Journal of Abnormal and Social Psychology, 21:384–400, 1927. URL https://api.semanticscholar.org/CorpusID:144595330. [29] Markus Triska. Solution Methods for the Social Golfer Problem. PhD thesis, Vienna University of Technology, March 2008. Available from https://www.metalevel.at/mst.pdf. [30] Kristi Tsukida, Maya R Gupta, et al. How to analyze paired comparison data. Department of Electrical Engineering University of Washington, Tech. Rep. UWEETR-2011-0004, 1, 2011. [31] Bo Waggoner and Yiling Chen. Output agreement mechanisms and common knowledge. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1): 220–226, Sep. 2014. doi: 10.1609/hcomp.v2i1.13151. URL https://ojs.aaai.org/index.php/HCOMP/article/view/13151. [32] Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, and Michael I. Jordan. The sample complexity of online contract design. In ACM Conference on Economics and Computation (EC), 2023. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Algorithmic Contract Design for Crowdsourced Ranking: Conclusions, Future Directions, and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. 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Predicting a Protein’s Stability
under a Million Mutations: Conclusion, Acknowledgement & References

With its brief and enjoyable video clips, TikTok has amassed a massive following on a global scale. It can be difficult to come up with original and captivating video ideas, regardless of experience level on TikTok. We'll look at many TikTok video ideas in this post that will help you stand out from the competition and inspire your creativity. Prepare to produce captivating content that will captivate your audience and entice them to return for more! Dance Challenges: Bust a Move and Go Viral 1. Dance Tutorial Make a tutorial video to showcase your dancing prowess to a global audience. Clearly demonstrate a well-known dancing move so that others can easily follow along. To expand your audience, don't forget to utilize well-known dancing hashtags like #dancechallenge and #learnfromme. 2. Duet with a Pro Locate a well-known dancer on TikTok and pair their video with your own. Try to replicate their movements perfectly, or add your own special touch to the exercise. Through this partnership, you will reach a larger audience and show your abilities. Try a great video editor like to synchronize your audio and video and make your videos stand out! Clipfly Lip Sync and Acting: Channel Your Inner Performer 3. Play a Movie Star Choose an unforgettable scene from your favorite film and reproduce it in your own unique manner. To make your video stand out, focus on the clothing, accessories, and facial expressions of the characters. Remember to add pertinent hashtags such as #actingchallenge and #movierecreation. 4. Lip Sync Battle Choose a well-known song, then precisely and enthusiastically lip-sync to it. To make your performance stand out, incorporate original choreography or artistic aspects. Make connections with other TikTokers who enjoy lip-syncing by using hashtags like #singalong and #lipsyncbattle. Comedy and Skits: Make Them Laugh With Your Talent 5. Prank Your Friends Play light-hearted practical jokes on your loved ones and record their responses on video. Make sure it's all in good humor, whether it's a cleverly hidden camera stunt or a humorous practical joke. To get more views, hashtag your video with #prank and #funnyvideos. 6. Comedy Use your creativity to the fullest when creating impromptu comedic routines. On the spot, come up with amusing speeches, scenarios, and characters. Don't be scared to act on whims and show off your inherent sense of comedy. On TikTok, use hashtags like #improvcomedy and #funny to draw in comedy fans. DIY and Life Hacks: Share Your Skills and Expertise 7. Crafting Tutorial Transform your artistic side by providing a detailed guide for a do-it-yourself craft project. Show off your talents, from jewelry-making to painting techniques, and encourage others to be creative. Use hashtags to connect with other craft enthusiasts, such as #DIY and #crafting. 8. Life Hacks Exchange useful hints and techniques to simplify daily living. Whether it's a time-saving method or a clever housekeeping tip, offer insightful information that will help your audience. Using hashtags like #lifehacks and #tipsandtricks will increase the number of people who see your video. Challenges and Trends: Join the TikTok Community 9. Trendy Challenges Keep up with the most recent TikTok challenges and participate in the enjoyment. Accept and personalize the prevailing trends, whether they be fitness regimens, dance challenges, or cosmetics fads. Make the most of the exposure of your film by including pertinent hashtags. 10. Question and Answer Organize a Q&A session to foster interaction with your followers. Invite them to leave you questions in the comments section, and answer them in a different video. This will increase interaction on your profile and assist you in building a connection with your audience. In The End You won't run out of original material to share with your followers if you use these inspiring TikTok video ideas. Anyone who enjoys having fun, dancing, acting, comedy, do-it-yourself projects, or just plain having fun can find a video idea. Now, grab your phone, let your imagination go wild, and be ready to dominate TikTok. To create a vibrant TikTok community around your content, don't forget to add pertinent hashtags and interact with your followers. Cheers to TikTok!

10 Best Exciting TikTok Video Ideas To Make Your Videos Stand Out

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Do you find yourself exhausted from wrestling with intricate data for hours on end? Are you yearning for AI that comprehends beyond mere words? Google's Gemini AI emerges as a transformative force, revolutionizing the landscape of AI model development and integration. Upon completion, in various sizes and functionalities, allowing you to select the ideal match for your requirements, whether tackling extensive projects or concentrated assignments. This guarantees that organizations can opt for the model that perfectly aligns with their objectives, be it for expansive applications or specific tasks. Google will present Gemini AI What exactly is Google Gemini AI? Google Gemini AI is an advanced platform harnessing deep learning algorithms to produce remarkably lifelike and inventive results. It propels AI models forward, enhancing their flexibility and efficiency through continuous learning from fresh data. Diverse iterations Offered in – Google Gemini AI accommodates the distinct requirements of individuals and businesses. Whether it's optimizing processes or conducting advanced data analysis, Gemini ensures there's a suitable version tailored to your needs. three variations – Ultra, Pro, and Nano The Gemini AI Model Central to Google's generative AI platform is the Gemini AI model. Utilizing cutting-edge deep learning algorithms, it assimilates extensive datasets to produce authentic outputs. With its adaptability, the model can excel across diverse domains such as natural language processing (NLP), image generation, text composition, music creation, and beyond. Key Features Simplified: Human-like Conversational Proficiency: Gemini demonstrates remarkable conversational abilities akin to humans, . ideal for chatbots and virtual assistants It provides an immersive, seamless, and organic conversational experience. Redefining benchmarks for human-like interactions within digital environments. Image understanding and interpretation: Gemini deciphers image content, responds to inquiries, and produces contextual text. It is exceptionally suited for image search and captioning, establishing pioneering benchmarks in AI. This amplifies user satisfaction and broadens the horizons of image-centric applications. Prolific code generation: Gemini excels in generating high-quality code across different programming languages. It is vital for optimizing coding workflows, guaranteeing both efficiency and precision. This innovation revolutionizes developers' approaches to software development. Data analytics expertise: Empowers users in the realms of data analytics and business intelligence. Critical for making data-driven decisions, unlocking the full potential of data. Serves as a catalyst for driving transformative advancements in analytics. 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Gemini AI by Google: Characteristics, Applications, and Industrial Influence

Artificial Intelligence tools such as Bard, ChatGPT, and Bing Chat are the current big names in the category which is on the rise. Large Language Model (LLM) LLMs are trained on vast data sets to be able to communicate by using everyday human language as a chat prompt. Given the flexibility and potential of LLMs, companies are integrating them into many workflows inside the tech industry to make our lives better and easier. One of the main AI-workflow integrations is an auto-complete code plugin, providing the ability to foresee and generate matching code patterns in order to code faster and more efficiently. In the current market of AI code-generator tools, there are many players, including , , , , and more. This blog post outlines examples of common security pitfalls that developers face while using such tools. GitHub Copilot Amazon CodeWhisperer Google Cloud Code (Duet AI) Blackbox AI Code Generation The goal of this article is to raise awareness and emphasize that auto-generated code cannot be blindly trusted, and still requires a security review to avoid introducing software vulnerabilities. Note: some vendors suggest that their auto-complete tools may contain insecure code snippets. Many articles have already emphasized that auto-complete tools generate known vulnerabilities such as , SQL injection, and XSS. This post will highlight other vulnerability types that depend more on the code’s context, where there is a high probability for AI auto-complete to slip bugs into your code. IDOR AI models trained for code are usually trained on large code bases with the main mission of producing functioning code. Security and AI Code Generating Tools Many security issues have a known and defined way of mitigation without compromising the performance side (e.g. SQL injections could be avoided by not concatenating user input/parameters directly into the query) - and therefore, can be eradicated as there is no reason to write the code in an insecure manner. However, many security issues are which could be perfectly safe when implemented as internal functionality, while when facing external input from a client, become a vulnerability, and are therefore hard to distinguish through the AI’s learning dataset. context-dependent In such vulnerabilities, the specific usage of the code usually depends on other parts of the code and the application’s overall purpose. Here are some common use cases and examples that we tested, showcasing some of these types of vulnerabilities: Use Case: Fetching Files from User Input - Arbitrary File Read Use Case: Comparing Secret Tokens - Type Juggling/Coercion Use Case: Forgot Password Mechanism - Unicode Case Mapping Collisions Use Case: Generating Configuration File - Bad Security Practices Use Case: Configuration Objects - Inconsistencies Leading to Insecure Deserialization After seeing how the AI code generation tools handle the above cases, we tried to test their ability to create proper security filters and other mitigation mechanisms. Here are some common use cases and examples that we tested by deliberately requesting secure code: Secure Code Request Use Case: Reading Files - Avoiding Directory Traversal Secure Code Request - File Upload - Restricted Extensions List Use Case: Fetching Files from User Input - Arbitrary File Read Many applications include code that fetches a file back to the user based on a filename. Using directory traversal for reading arbitrary files on the server is a common method bad actors use to gain unauthorized information. It may seem obvious to sanitize file name/file path input coming from the user before fetching the file back, but we assume it is a hard task for an AI model trained on generic codebases -  this is because some of the datasets as part of their functionality (it may even downgrade performance or harm functionality) or are not harmed by unsanitized path as they are intended to be only for internal usage. do not need to use sanitized paths Let’s try to generate a basic file fetching function from user input, using Google Cloud Code - Duet AI auto-complete. We will let Duet AI auto-complete our code, based on our function & route names - AI Tool Suggestion (Google Cloud Code - Duet AI): As we can see in gray, the autocomplete suggestion is to use Flask’s “ ” function, which is Flask’s basic file-handling function. send_file But even Flask’s documentation states “ ”, meaning that while inserting user input directly to the “send_file” function the user will have the ability to read any file on the filesystem, for example by using directory traversal characters such as “../” in the filename. Never pass file paths provided by a user Proper Implementation: Replacing the “send_file” function with Flask’s safe “send_from_directory” function will mitigate the directory traversal risk. It will only allow fetching files from a specific hard-coded directory (“exports” in this case). Use Case: Comparing Secret Tokens - Type Juggling/Coercion In some programming languages, such as PHP and NodeJS, it is possible to make comparisons between two different types of variables and the program will do a behind the scenes to complete the action. type conversion Loose comparisons do not check the variable’s type behind the scenes and therefore are more prone to type juggling attack. However, the use of loose comparisons is not considered an insecure coding practice by itself - it depends on the context of the code. And again, it is difficult for AI models to tell the cases apart. The most common example of PHP type juggling is loose comparisons of secret tokens/hashes where user input which is read via a JSON request (which enables control of the input’s type) reaches a loose comparison (“ =”). AI Tool Suggestion (Amazon CodeWhisperer): ”) instead of a strict one (“ As it appears, CodeWhisperer generates loose comparison conditions in auto-complete suggestions: The loose comparison of the secret_token allows an adversary to bypass the comparison $data[“secret_token”] == “secret_token”. While submitting a JSON object with the value of “secret_token” being True (boolean), the (even on newer PHP versions). loose comparison result is True as well Even when “hinting” the tool (using a comment) to write the check “securely”, it did not generate a code snippet containing a strict comparison - Proper Implementation: Only when specifying a strict comparison (“===”) we are protected against type juggling attacks. Use Case: Forgot Password Mechanism - Unicode Case Mapping Collisions Vulnerabilities in the “Forgot Password” mechanism are very common in SaaS applications and were the root cause behind CVEs such as (Wordpress), (Django), and (GitLab). CVE-2017-8295 CVE-2019-19844 CVE-2023-7028 Specifically, a Unicode Case Mapping Collision vulnerability occurs when user input is either uppercased or lowercased in a comparison expression while its original value is also used in the code. Some different characters will result in the same char code when transformed. For example, “ß” and “SS” will both transform into “0x00DF” when uppercased. Such cases are usually used to bypass/mislead some security checks by tricking the comparison check and later using the original value which is different than expected. These cases are quite elusive to grasp, especially when many implementations are possible. A mechanism that’s particularly susceptible to this vulnerability type is the . It is common to normalize the user’s input into uppercase or lowercase (in email addresses or domain names) when performing the check in order to avoid unwelcome mismatches. AI Tool Suggestion (GitHub Copilot): forgot password mechanism As an example, taking a look at the Copilot-generated code for the forgot password function (below) shows that it is vulnerable to this issue. Copilot’s code first looks up the lowercased email address: Then, when following through with the rest of the process it suggests the following: As we can see, the autocomplete suggestion generates a random password and sends it to the user’s email address. However, it initially uses a lowercase version of the user’s email for fetching the user account, and then continues to use the original user’s email ‘as-is’ (without a lowercase conversion) as the target of the recovery email. For example, lets say the attacker owns the inbox for “miKe@example.com” (the “K” is a unicode character) and uses this email for the “Forgot Password” mechanism. The emails “miKe@example.com” and “mike@example.com”, are equivalent ‘as-is’, but after lowercase conversion they will be - Kelvin Sign not Using the email address “miKe@example.com” will fetch the account information for “mike@example.com” but the reset password will be sent to the attacker’s inbox (“miKe@example.com”), allowing takeover of the “mike@example.com” account. The email address used for fetching the user account must exactly equal the recovery email address (i.e. send_email parameter will also use email.lower()). Proper Implementation: In addition, as a precaution, it is recommended to use the value that was already stored in the server instead of the one supplied by the user. Use Case: Generating Configuration File - Bad Security Practices Another topic that could confuse auto-complete tools is writing configuration files, especially for complicated cloud infrastructures. The main reason is that indeed there are security best practice guidelines, but not everybody follows them, and in some cases, it is needed to go against them. Such code samples might find their way into the AI training dataset.  As a result, some auto-complete outputs will violate the recommended security guidelines. In the following example, we will create a configuration file for a , the smallest execution unit in Kubernetes. Kubernetes Pod In this example, we start creating a Pod configuration file. AI Tool Suggestion (Blackbox AI Code Generation): The suggestion creates the capabilities section and adds a Linux kernel capability (SYS_ADMIN) to the container which is essentially equivalent to running the pod as the root user. Is it better to just omit the securityContext then? No - since many other capabilities will then be added by default, violating the . Proper Implementation: principle of least privilege According to , the most secure setup is to first drop all Linux kernel capabilities and only afterward add the ones required for your container. Docker security best practices In order to fix the issues mentioned above, the capabilities section begins by dropping all capabilities, and will then gradually add the needed ones for our container. Use Case: Configuration Objects - Inconsistencies Leading to Insecure Deserialization Many vulnerabilities require defining a configuration object, which decides how the application will handle the required component. The example we chose is deserialization library in C#, which can be used securely or insecurely depending on the JsonSerializerSettings object’s configuration, and specifically, the . Newtonsoft’s JSON TypeNameHandling While we tested the deserialization code, we noticed that the configuration object’s definition is quite random, and subtle changes can lead to generating a different configuration set. The following examples display an inconsistent behavior that is based only on the method names used by the developer: AI Tool Suggestions (Amazon CodeWhisperer): We can see two different configurations that both result in insecure JSON deserialization due to the TypeNameHandling property being “Auto” and “ALL”. These properties allow the JSON document to define the deserialized class - letting attackers deserialize arbitrary classes. This can be easily leveraged for remote code execution, for example by deserializing the class “System.Diagnostics.Process”. The only difference in the developer’s original code are the method names. Proper Implementation: By default, a JsonSerializerSettings object is instantiated with “TypeNameHandling = TypeNameHandling.None”, which is considered to be secure. Thus, we use JsonSerializerSettings’s default constructor which will result in a safe TypeNameHandling value. Similar to the first use case, where we examined the vulnerable code produced by Copilot for fetching files, here we attempt to request a secure version of a file reading mechanism. Secure Code Request Use Case: Reading Files - Avoiding Directory Traversal AI Tool Suggestion (GitHub Copilot): We can see that the security check is really naive, only avoiding dot-dot-slash sequences that are needed for conducting directory traversal attempts. The path parameter can be an absolute path - pointing to any desired path on the system (for example, /etc/passwd), which defeats the purpose of the security check. Proper Implementation: Here, the secure_file_read function gets a (relative) filename parameter along with a directory (safe_dir) where the filename should reside (and which should not be escaped).
It creates an absolute path by joining safe_dir and filename and proceeds to get its canonicalized form by calling realpath. It then gets the canonicalized form of the safe_dir folder. Then, the file contents are returned only if the canonicalized path starts with the canonicalized safe_dir. It is a common use case to only accept specific file types for upload based on their extension. Secure Code Request - File Upload - Restricted Extensions List In the following example, we asked the AI auto-completer to generate a function tasked with filtering dangerous file extensions. AI Tool Suggestion (Blackbox AI Code Generation): The suggested Python code takes the filename, separates the extension, and compares it against a list of dangerous extensions. At first glance, this code snippet looks secure. However, forbid filenames ending with a dot, and when supplying a filename ending with a dot (“.”) during file creation, the dot will be discarded. Meaning, that the generated filter could be bypassed on Windows when submitting a file with a malicious extension followed by a dot (ex. “example.exe.”). Windows filename conventions Usually the better approach is to use a , which checks the file extension only against allowed extensions. However, since the tools took a blacklist approach (which is needed in some cases) we will outline a safer blacklist implementation: Proper Implementation: whitelist In the following snippet, we strip away as much control the user had on the input by stripping the extension from all non-alphanumeric characters and concatenating it to a newly generated filename. Automatic co-programmers are great for suggesting ideas, auto-completing code, and speeding up the software development process. We expect these tools to keep evolving as time goes on, but for now as demonstrated with the use cases in this post, auto-complete AI solutions have many challenges to overcome until we can ease our minds and trust their output without double-checking for bugs. Bottom line - use with caution A possible way to reduce the likelihood of introducing vulnerabilities is deliberately requesting the AI code generation tool to generate secure code. Although this may be useful in some use cases, it is not foolproof - the “seemingly secure” output should still be reviewed manually by someone proficient in software security. To help secure your software, we recommend both manually reviewing code produced by AI generative tools, as well as incorporating security solutions such as Static Application Security Testing (SAST) which can trustfully discover vulnerabilities as you go. As seen in the example above, SAST tools can alert and catch the unicode case mapping collision described in use case #3. This proactive approach is essential to ensuring the security of your software. JFrog Security Research Team Recommendations The security research team’s findings and research play an important role in improving the JFrog Platform’s application software security capabilities. Stay up-to-date with JFrog Security Research Follow the latest discoveries and technical updates from the JFrog Security Research team on our , and on X . research website @JFrogSecurity

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