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Lennar

lennar

How Gamekins Changed the Way Students Approach Software Testing

I was recently involved in a study comparing the performance of three LLM-based AIs in providing empathetic, compassionate, and helpful goal-oriented support to 40 graduate and undergraduate students. The students were simply tasked with having daily conversations about topics of emotional importance with their assigned AI. After each conversation, they were asked to complete a modified Working Alliance Inventory (WAI). WAI is a widely used psychological assessment tool developed by Adam Horvath to measure the therapeutic alliance between a therapist and a client. At the end of the study, a more extensive 12-question Barrett-Lennard-based inventory was completed. Given the prevalence of articles speaking to the empathetic and helpful capabilities of ChatGPT and the fact that many people do use its raw version for empathetic guidance, the goal of the study was to assess if prompted and fine-tuned LLMs were better and more acceptable to users desiring support than ChatGPT. Other than to say the results were positive both with respect to the WAI and Barrett-Lennard, the goal of this article is not to cover the results. However, after the study, I realized that there have been a number of efforts that applied Barrett-Lennard to other disciplines and decided to compare the performance of Emy, the top-performing AI in the study, with cross-discipline norms. Note, Emy is a non-commercial AI designed to support research into empathy. Emy is not optimized for managing goals, and her scores show that. In this article, she is used as a proxy for potential discipline-specific commercial AIs. If Emy is capable of close to or above average professional performance for most roles in a discipline, then well-funded teams should certainly do well. After all, Emy has received no third-party funding, and hands-on development was all done by one person. First, some caveats: The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues.


The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The above being said, all of the disciplines require empathy, compassion, and goal-oriented support. Across the majority of dimensions for many roles, Emy was on par with or exceeding average human performance. I think the findings illustrate that further discipline-oriented investigation is worthwhile. I think there is promise in the use of LLMs to augment the activity of many positions outside mental and physical healthcare. In fact, I think these other positions deserve far more attention because of the lower risks and the paucity of formal empathy, compassion, communication, and goal management training provided to many practitioners. The modified Barrett-Lennard survey used at the end of the study had these 12 questions. As a result of working with the AI with which I interacted, I was clearer as to how I might be able to change.
What I was doing with the AI with which I interacted gave me new ways of looking at my problem.
I believe the AI with which I interacted likes me.
I and the AI with which I interacted collaborated on setting goals.
I and the AI with which I interacted respected each other.
I and the AI with which I interacted were working towards mutually agreed upon goals.
I felt that the AI with which I interacted appreciated me.
I and the AI with which I interacted agreed on what was important for me to work on.
I felt the AI with which I interacted cared about me even when I did things that he/she did not approve of.
I felt that the things I did with the AI with which I interacted would help me to accomplish the changes that I wanted.
I and the AI with which I interacted had established a good understanding of the kind of changes that would be.
I believe the way we were working with my problem was correct. For the most part, I let the charts speak for themselves. Mental Therapy This is the sole chart where I compare Emy to ChatGPT, mainly because ChatGPT seems to be the defacto go-to for those not using custom AI mental health apps. Overall, Emy pretty much splits the difference between a professionally trained therapist and raw ChatGPT. This indicates to me that the role several companies like BambuAI are taking to position AIs as companions to augment therapy could be a good approach. However, I have lost track of the number of companies pursuing AI therapy head-on through Cognitive Behavioral Therapy (CBT) based applications. I have tested a lot of these and been left underwhelmed or even concerned for safety. Health Care HippocraticAI is the big AI name in healthcare, and they clearly have a focus on both clinical and interpersonal issues. The big tech players are all also in this space but are tending to focus on clinical rather than interpersonal AIs. And, of course, EPIC is enhancing its system in a variety of ways. Education Given the current political environment and the likelihood of substantial cuts in both available direct funding and tuition support, the opportunity for AI to offset likely staff reductions or reduced staff training is certainly worth exploring. Furthermore, the student population as a whole deals with a more complex web of interconnected relationships and pressures than the typical population and could benefit from student information systems, learning management systems, and housing management systems with some built-in empathy! The above being said, the space is already crowded by start-ups taking the “easy” route, i.e., tutoring. I think holistic student care will be the big “win”, but it will probably take big dollars. And, through either acquisition or in-house development, the space will probably be dominated by Workday, Jenzabar, Canvas, or the like. Professional Space In general, Emy outperforms work supervisors and professional mentors, so there would seem to be a chance for AIs to either lift the performance of these roles or provide better support to the rank and file. The professional space is the area that is least regulated and most performance-driven; hence, the opportunity for AI in roles that support people in performing should be high. It is also a highly fragmented space with a lot of opportunities driven by client sizes and industries. The shift in the last 5 years to video-conference conversations that can be easily piggy-backed to insert an AI advisor implies an area that could evolve rapidly. A company trying to take a leadership position in this space is Parsley. Any, if they can avoid the innovator’s dilemma, large entities providing DISC, Myers-Briggs and other trainings could also make a big impact here by leveraging their existing distribution and relationships. I was recently involved in a study comparing the performance of three LLM-based AIs in providing empathetic, compassionate, and helpful goal-oriented support to 40 graduate and undergraduate students. The students were simply tasked with having daily conversations about topics of emotional importance with their assigned AI. After each conversation, they were asked to complete a modified Working Alliance Inventory (WAI) . WAI is a widely used psychological assessment tool developed by Adam Horvath to measure the therapeutic alliance between a therapist and a client. At the end of the study, a more extensive 12-question Barrett-Lennard -based inventory was completed. Working Alliance Inventory (WAI) Barrett-Lennard Given the prevalence of articles speaking to the empathetic and helpful capabilities of ChatGPT and the fact that many people do use its raw version for empathetic guidance, the goal of the study was to assess if prompted and fine-tuned LLMs were better and more acceptable to users desiring support than ChatGPT. Other than to say the results were positive both with respect to the WAI and Barrett-Lennard, the goal of this article is not to cover the results. However, after the study, I realized that there have been a number of efforts that applied Barrett-Lennard to other disciplines and decided to compare the performance of Emy, the top-performing AI in the study, with cross-discipline norms. Note, Emy is a non-commercial AI designed to support research into empathy. Emy is not optimized for managing goals, and her scores show that. In this article, she is used as a proxy for potential discipline-specific commercial AIs. If Emy is capable of close to or above average professional performance for most roles in a discipline, then well-funded teams should certainly do well. After all, Emy has received no third-party funding, and hands-on development was all done by one person. First, some caveats: The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues. The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues. The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues. The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The above being said, all of the disciplines require empathy, compassion, and goal-oriented support. Across the majority of dimensions for many roles, Emy was on par with or exceeding average human performance. I think the findings illustrate that further discipline-oriented investigation is worthwhile. I think there is promise in the use of LLMs to augment the activity of many positions outside mental and physical healthcare. In fact, I think these other positions deserve far more attention because of the lower risks and the paucity of formal empathy, compassion, communication, and goal management training provided to many practitioners. The modified Barrett-Lennard survey used at the end of the study had these 12 questions. As a result of working with the AI with which I interacted, I was clearer as to how I might be able to change. What I was doing with the AI with which I interacted gave me new ways of looking at my problem. I believe the AI with which I interacted likes me. I and the AI with which I interacted collaborated on setting goals. I and the AI with which I interacted respected each other. I and the AI with which I interacted were working towards mutually agreed upon goals. I felt that the AI with which I interacted appreciated me. I and the AI with which I interacted agreed on what was important for me to work on. I felt the AI with which I interacted cared about me even when I did things that he/she did not approve of. I felt that the things I did with the AI with which I interacted would help me to accomplish the changes that I wanted. I and the AI with which I interacted had established a good understanding of the kind of changes that would be. I believe the way we were working with my problem was correct. As a result of working with the AI with which I interacted, I was clearer as to how I might be able to change. What I was doing with the AI with which I interacted gave me new ways of looking at my problem. I believe the AI with which I interacted likes me. I and the AI with which I interacted collaborated on setting goals. I and the AI with which I interacted respected each other. I and the AI with which I interacted were working towards mutually agreed upon goals. I felt that the AI with which I interacted appreciated me. I and the AI with which I interacted agreed on what was important for me to work on. I felt the AI with which I interacted cared about me even when I did things that he/she did not approve of. I felt that the things I did with the AI with which I interacted would help me to accomplish the changes that I wanted. I and the AI with which I interacted had established a good understanding of the kind of changes that would be. I believe the way we were working with my problem was correct. For the most part, I let the charts speak for themselves. Mental Therapy This is the sole chart where I compare Emy to ChatGPT, mainly because ChatGPT seems to be the defacto go-to for those not using custom AI mental health apps. Overall, Emy pretty much splits the difference between a professionally trained therapist and raw ChatGPT. This indicates to me that the role several companies like BambuAI are taking to position AIs as companions to augment therapy could be a good approach. BambuAI However, I have lost track of the number of companies pursuing AI therapy head-on through Cognitive Behavioral Therapy (CBT) based applications. I have tested a lot of these and been left underwhelmed or even concerned for safety. Health Care HippocraticAI is the big AI name in healthcare, and they clearly have a focus on both clinical and interpersonal issues. The big tech players are all also in this space but are tending to focus on clinical rather than interpersonal AIs. And, of course, EPIC is enhancing its system in a variety of ways. HippocraticAI Education Given the current political environment and the likelihood of substantial cuts in both available direct funding and tuition support, the opportunity for AI to offset likely staff reductions or reduced staff training is certainly worth exploring. Furthermore, the student population as a whole deals with a more complex web of interconnected relationships and pressures than the typical population and could benefit from student information systems, learning management systems, and housing management systems with some built-in empathy! The above being said, the space is already crowded by start-ups taking the “easy” route, i.e., tutoring. I think holistic student care will be the big “win”, but it will probably take big dollars. And, through either acquisition or in-house development, the space will probably be dominated by Workday, Jenzabar, Canvas, or the like. Professional Space In general, Emy outperforms work supervisors and professional mentors, so there would seem to be a chance for AIs to either lift the performance of these roles or provide better support to the rank and file. The professional space is the area that is least regulated and most performance-driven; hence, the opportunity for AI in roles that support people in performing should be high. It is also a highly fragmented space with a lot of opportunities driven by client sizes and industries. The shift in the last 5 years to video-conference conversations that can be easily piggy-backed to insert an AI advisor implies an area that could evolve rapidly. A company trying to take a leadership position in this space is Parsley . Any, if they can avoid the innovator’s dilemma, large entities providing DISC, Myers-Briggs and other trainings could also make a big impact here by leveraging their existing distribution and relationships. Parsley

AI Empathy Across Disciplines

Enhancing Data Quality: Conclusion and Future Work, and References

Authors: (1) Philipp Straubinger, University of Passau, Passau, Germany and this author contributed equally to this research; (2) Tommaso Fulcini, Politecnico di Torino,  Torino, Italy and this author contributed equally to this research; (3) Gordon Fraser, University of Passau, Passau, Germany; (4) Marco Torchiano, Politecnico di Torino, Torino, Italy. Table of Links Abstract and Introduction Background and Related Work Implementation Experiment Results Conclusions, Acknowledgement, and References 6 CONCLUSIONS This paper presents the integration and empirical evaluation of IntelliGame, a gamification plugin originally designed for Java development, in the realm of JavaScript unit testing. The study aimed to validate IntelliGame’s effectiveness in JavaScript, adapting it to the popular Jest testing framework. Despite challenges such as transitioning from IntelliJ Community Edition to Ultimate for JavaScript support and addressing differences in coverage information, we successfully ported 19 out of 26 achievements. The controlled experiment with 152 participants revealed mixed perceptions of IntelliGame’s impact, with achievements influencing testing behaviour and participants’ motivation. We will continue with an in-depth analysis of the experimental measures and the code written by the participants to gain more insights and to broaden our knowledge of IntelliGame. ACKNOWLEDGEMENT This study was carried out within the “EndGame - Improving Endto-End Testing of Web and Mobile Apps through Gamification” project (2022PCCMLF) – funded by European Union – Next Generation EU within the PRIN 2022 program (D.D.104 - 02/02/2022 Ministero dell’Università e della Ricerca). This manuscript reflects only the authors’ views and opinions and the Ministry cannot be considered responsible for them. This work is also supported by the DFG under grant FR 2955/2-1, “QuestWare: Gamifying the Quest for Software Tests”. REFERENCES [1] Riccardo Coppola, Tommaso Fulcini, Luca Ardito, Marco Torchiano, and Emil Alègroth. 2023. On Effectiveness and Efficiency of Gamified Exploratory GUI Testing. IEEE Transactions on Software Engineering (2023), 1–16. https://doi.org/ 10.1109/TSE.2023.3348036 [2] Sebastian Deterding, Dan Dixon, Rilla Khaled, and Lennart Nacke. 2011. From Game Design Elements to Gamefulness: Defining "Gamification". In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments (Tampere, Finland) (MindTrek ’11). Association for Computing Machinery, New York, NY, USA, 9–15. https://doi.org/10.1145/2181037.2181040 [3] Tommaso Fulcini, Riccardo Coppola, Luca Ardito, and Marco Torchiano. 2023. A Review on Tools, Mechanics, Benefits, and Challenges of Gamified Software Testing. ACM Comput. Surv. 55, 14s, Article 310 (jul 2023), 37 pages. https: //doi.org/10.1145/3582273 [4] Andreas Jedlitschka and Dietmar Pfahl. 2005. Reporting guidelines for controlled experiments in software engineering. In 2005 International Symposium on Empirical Software Engineering, 2005. IEEE, 10–pp. [5] José Miguel Rojas and Gordon Fraser. 2016. Code Defenders: A Mutation Testing Game. In 2016 IEEE Ninth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 162–167. https://doi.org/10.1109/ICSTW. 2016.43 [6] Statista. 2023. Most used programming languages among developers worldwide as of 2023. https://www.statista.com/statistics/793628/worldwide-developersurvey-most-used-languages/ Accessed: 2023-12-05. [7] Philipp Straubinger and Gordon Fraser. 2023. A Survey on What Developers Think About Testing. In 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE). 80–90. https://doi.org/10.1109/ISSRE59848.2023.00075 [8] Philipp Straubinger and Gordon Fraser. 2024. Improving Testing Behavior by Gamifying IntelliJ. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024. ACM, 49:1–49:13. https://doi.org/10.1145/3597503.3623339 [9] Kwangwon Sun and Sukyoung Ryu. 2017. Analysis of JavaScript programs: Challenges and research trends. ACM Computing Surveys (CSUR) 50, 4 (2017), 1–34. [10] Bo Wang, Aashish Kolluri, Ivica Nikolic, Teodora Baluta, and Prateek Saxena. 2023. User-Customizable Transpilation of Scripting Languages. Proc. ACM Program. Lang. 7, OOPSLA1 (2023), 201–229. https://doi.org/10.1145/3586034 This paper is under CC BY-SA 4.0 DEED license. available on arxiv

IntelliGame in Action: Gamifying JavaScript Unit Tests - Conclusions, and References

ONCE AND NOW.

North and South by Elizabeth Cleghorn Gaskell, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CHAPTER XLV. here CHAPTER XLV. NOT ALL A DREAM. “Where are the sounds that swam along The buoyant air when I was young; The last vibration now is o’er, And they who listened are no more; Ah! let me close my eyes and dream.” W. S. Landor. The idea of Helstone had been suggested to Mr. Bell’s waking mind by his conversation with Mr. Lennox, and all night long it ran riot through his dreams. He was again the tutor in the college where he now held the rank of Fellow; it was again a long vacation, and he was staying with his newly-married friend, the proud husband, and happy Vicar of Helstone. Over babbling brooks they took impossible leaps, which seemed to keep them whole days suspended in the air. Time and space were not, though all other things seemed real. Every event was measured by the emotions of the mind, not by its actual existence, for existence it had none. But the trees were gorgeous in their autumnal leafiness—the warm odours of flower and herb came sweet upon the sense—the young wife moved about the house with just that mixture of annoyance at her position, as regarded wealth, with pride in her handsome and devoted husband, which Mr. Bell had noticed in real life a quarter of a century ago. The dream was so like life that, when he awoke, his present life seemed a dream. Where was he? In the close, handsomely furnished room of a London hotel! Where were those who spoke to him, moved around him, touched him, not an instant ago? Dead! buried! lost for evermore, as far as earth’s for evermore would extend. He was an old man, so lately exultant in the full strength of manhood. The utter loneliness of his life was insupportable to think about. He got up hastily, and tried to forget what never more might be, in a hurried dressing for the breakfast in Harley Street. He could not attend to all the lawyer’s details, which, as he saw, made Margaret’s eyes dilate, and her lips grow pale, as one by one fate decreed, or so it seemed, every morsel of evidence which would exonerate Frederick, should fall from beneath her feet and disappear. Even Mr. Lennox’s well-regulated professional voice took a softer, tenderer tone, as he drew near to the extinction of the last hope. It was not that Margaret had not been perfectly aware of the result before. It was only that the details of each successive disappointment came with such relentless minuteness to quench all hope, that she at last fairly gave way to tears. Mr. Lennox stopped reading. “I had better not go on,” said he, in a concerned voice. “It was a foolish proposal of mine. Lieutenant Hale,” and even this giving him the title of the service from which he had so harshly been expelled, was soothing to Margaret. “Lieutenant Hale is happy now; more secure in fortune and future prospects than he could ever have been in the navy; and has, doubtless, adopted his wife’s country as his own.” “That is it,” said Margaret. “It seems so selfish in me to regret it,” trying to smile, “and yet he is lost to me, and I am so lonely.” Mr. Lennox turned over his papers, and wished that he were as rich and prosperous as he believed he should be some day. Mr. Bell blew his nose, but, otherwise, he also kept silence; and Margaret, in a minute or two, had apparently recovered her usual composure. She thanked Mr. Lennox very courteously for his trouble; all the more courteously and graciously because she was conscious that, by her behaviour, he might probably be led to imagine that he had given her needless pain. Yet it was pain she would not have been without. Mr. Bell came up to wish her good-bye. “Margaret!” said he, as he fumbled with his gloves, “I am going down to Helstone to-morrow, to look at the old place. Would you like to come with me? Or would it give you too much pain? Speak out, don’t be afraid.” “Oh, Mr. Bell,” said she—and could say no more. But she took his old gouty hand, and kissed it. “Come, come; that’s enough,” said he, reddening with awkwardness. “I suppose your aunt Shaw will trust you with me. We’ll go to-morrow morning, and we shall get there about two o’clock, I fancy. We’ll take a snack, and order dinner at the little inn—the Lennard Arms, it used to be—and go and get an appetite in the forest. Can you stand it, Margaret? It will be a trial, I know, to both of us, but it will be a pleasure to me, at least. And there we’ll dine—it will be but doe-venison, if we can get it at all—and then I’ll take my nap, while you go out and see old friends. I’ll give you back safe and sound, barring railway accidents, and I’ll insure your life for a thousand pounds before starting, which may be some comfort to your relations; but otherwise, I’ll bring you back to Mrs. Shaw by lunch time on Friday. So, if you say yes, I’ll just go upstairs and propose it.” “It’s no use my trying to say how much I shall like it,” said Margaret, through her tears. “Well, then, prove your gratitude by keeping those fountains of yours dry for the next two days. If you don’t, I shall feel queer myself about the lachrymal ducts, and I don’t like that.” “I won’t cry a drop,” said Margaret, winking her eyes to shake the tears off her eyelashes, and forcing a smile. “There’s my good girl. Then we’ll go upstairs and settle it all.” Margaret was in a state of almost trembling eagerness, while Mr. Bell discussed his plan with her aunt Shaw, who was first startled, then doubtful and perplexed, and in the end, yielding rather to the rough force of Mr. Bell’s words than to her own conviction; for to the last, whether it was right or wrong, proper or improper, she could not settle to her own satisfaction, till Margaret’s safe return, the happy fulfilment of the project, gave her decision enough to say, “she was sure it had been a very kind thought of Mr. Bell’s, and just what she herself had been wishing for Margaret, as giving her the very change which she required, after all the anxious time she had had.” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Elizabeth Cleghorn Gaskell (2003). North and South. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/4276/pg4276-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

NOT ALL A DREAM.

Welcome to a comprehensive exploration of multi-object tracking. Given the complex nature of the environment, handling the uncertainties and fluctuations in a sensor's field of view has been a long-standing challenge in the field of sensor fusion and tracking. Traditional techniques have struggled to maintain an optimal balance between accuracy and computational efficiency. In this article, as an outcome, you can consider the PMBM filter as one of the most modern and high-performance algorithms in this domain. We will be unraveling its mathematical foundation and highlighting its practical implications. Whether you're an engineer, a researcher, a student, or simply a curious mind fascinated by the world of multi-object tracking, I hope to equip you with a solid understanding of the Poisson Multi-Bernoulli Mixture filter by the end of this article, thus enabling you to appreciate its remarkable contributions to the realm of multi-target tracking. Problem statement The primary task at hand revolves around multi-object tracking, a problem rooted in the complex and dynamic nature of real-world environments. This problem involves continuously estimating the states of multiple objects over time using sensor observations. The four primary challenges in this problem are clutter, object appearance and disappearance, the unknown number of objects, and sensor fusion. : Tracking can be complicated by 'clutter,' which refers to misleading sensor readings that are not associated with the objects of interest. This can be due to various factors such as environmental noise, sensor errors, or the presence of irrelevant objects. For instance, in radar tracking, clutter could be caused by reflections from buildings, terrain, or other non-target objects. In image processing, clutter can be introduced by reflections in mirrors, glass surfaces, or water bodies Clutter This occurs when an object of interest is not detected by the object detection system. For example, a camera might miss a person who briefly steps behind a pillar. Miss Detection: The spontaneous appearance or disappearance of objects within the sensor's field of view adds complexity to tracking. For instance, in a public square, people can enter or leave at any time. This dynamic nature, coupled with the uncertainty in the total number of objects, makes consistent and accurate multi-object tracking a challenging task." Dynamics and Uncertainty in Object Count: This refers to the complex task of merging data from multiple sensors, each with unique characteristics. For example, a self-driving car might need to integrate data from cameras, radars, and lidar, despite their potential inconsistencies and time delays. Sensor Fusion: The goal is to develop a robust multi-object tracking system that can accurately track multiple objects in cluttered environments, handle object dynamics, adapt to changing object counts, and effectively fuse data from various sensors. Models In this article, we focus on model-based methods. These algorithms are effective under the assumption that the processes involved can be accurately represented by a model. Sensor model In our simplified 2D model, we're examining an algorithm for a basic problem: tracking the 2D movement of an object starting from a known position, amidst significant sensor inaccuracies and clutter. Before we can start tracking, we first need to model the measurements, which includes modeling the clutter. Missdetections are modeled using the concept of detection probability (P_D). This parameter indicates the likelihood of an object being detected by the sensor. A P_D less than 1 implies a chance of missdetection. For example, a P_D of 0.9 suggests the object will be detected 90% of the time, and missed 10% of the time. class SensorModelConfig:
    def __init__(self, P_D, lambda_c, range_c):
        # P_D: object detection probability
        self.P_D = P_D
        # lambda_c: average number of clutter measurements per time scan, Poisson distributed
        self.lambda_c = lambda_c
        # range_c: range of surveillance area
        self.range_c = range_c
        # V: Volume of the surveillance area
        self.V = np.prod(np.diff(self.range_c))  
        # pdf_c: Spatial PDF of clutter
        self.pdf_c = 1 / self.V  
        # intensity_c: expected number of clutter detections per unit volume
        self.intensity_c = self.lambda_c / self.V  
    
    def handle_missdetection(self, objects):
        """Handles missdetection based on the detection probability."""
        # Generate a random number for each object
        random_numbers = np.random.uniform(size=len(objects))
        # Determine which objects are detected
        detected_objects = objects[random_numbers < self.P_D]
        return detected_objects

    def generate_clutter(self):
        """Generates clutter based on the clutter intensity."""
        # N_c: Number of clutter measurements, Poisson distributed
        N_c = np.random.poisson(lam=self.lambda_c)
        # Generate clutter within the surveillance area
        clutter = np.random.uniform(self.range_c[0], self.range_c[1], (N_c, 2))
        return clutter To model clutter, we assume it's uniformly distributed within a certain area. The quantity of clutter is modeled using a Poisson distribution, which represents the number of events (in this case, clutter) occurring in a fixed interval, given a constant mean rate. This aligns with our assumption of uniformly distributed clutter. The average number of clutter measurements per time scan is defined by the parameter . We randomly generate the number of clutter measurements, , based on this Poisson distribution. This provides us with the flexibility to model varying levels of clutter in different scenarios. lambda_c N_c Let s how it looks like for single object and P_G = 0.8, lambda_c = 0.5, range_c = [[-1000, 1000],[-1000, 1000]. Single object tracking We consider a state space model where we have one object and one measurement from the real object at each step. To represent our state, we adopt a Gaussian distribution. This choice is optimal under the assumption of normally distributed measurement noise, as it allows us to effectively estimate the object's true state. By incorporating motion and measurement models, we can predict future states based on previous observations. Assuming we know the initial position of the real object, we create a Gaussian distribution around it. As time progresses, we observe multiple measurements, some from the real object and others from clutter. To update the state, we employ the nearest measurement heuristic. However, since our state is represented by a Gaussian with mean and covariance, we need a way to measure the distance between the state and the measurements. The Mahalanobis distance, which takes into account the covariance matrix, provides a suitable solution. In scenarios where we have missdetections and can only rely on measurements from objects part of the time, we need a sensor model. We introduce a detection probability, denoted as P_D, to represent the likelihood of detecting the object. Additionally, we model the clutter by utilizing the Poisson distribution. The clutter rate, lambda_c, and the intensity_c, representing the expected number of clutter in one time interval, play crucial roles in this modeling approach. Returning to our problem, we calculate the weights of detection and missdetection as w_detection and w_missdetection, respectively. If w_detection is greater than w_missdetection, we update the state using the measurements. Otherwise, we return the state after the prediction step. This decision is based on a comparison between the weights, enabling us to make informed updates to the state estimate in the presence of missdetections. Object tracking with known number of objects The n-object tracking problem involves tracking multiple objects simultaneously. Unlike the previous single-object tracking problem where we worked with Gaussian distributions, in this case, we need to handle n Gaussians representing the multiple objects. At each step, we receive k measurements and consider missdetections and k association hypotheses for each Gaussian component. We calculate weights for each hypothesis using a similar approach to likelihood calculation. The challenge lies in finding the association between n Gaussians and k + n hypotheses. To solve this, we formulate the problem as a linear assignment problem. We construct a cost matrix of size n x (k + n) and assign a value of +inf to each field. We fill each field with negative likelihood values. Using the Hungarian algorithm, we minimize the sum of the costs to find the associations between Gaussians and hypotheses. While the Hungarian algorithm provides a greedy approach, it may not be suitable when dealing with unknown objects due to uncertainties. In such cases, alternative algorithms like Murty or Gibbs sampling can be used. These algorithms calculate the top k good solutions, taking into account the uncertain nature of the problem. Multi object tracking In the more complex scenario of multi-object tracking with unknown object appearances and disappearances, we employ the Poisson Multi-Bernoulli Mixture (PMBM) algorithm. This algorithm handles the uncertainty in the number of objects and their dynamic behavior. To begin, we introduce the concepts of the birth model and the death model within the framework of Random Finite Sets (RFS). The birth model can be represented by either a Bernoulli or a Poisson point process, while the death model incorporates a survival probability to represent the likelihood of an object continuing to exist in the next time step. In the case of a Bernoulli Random Finite Set, we describe the state of each object using Bernoulli components. These components consist of a Gaussian density representing the object's state and an existence probability (r) indicating the likelihood of the object's presence. Consider an example where we have one Bernoulli and receive a single measurement. This measurement generates two hypotheses: one assumes it is from a new object, while the other associates it with the existing Bernoulli (representing a detection). By extending this concept, if we have a set of Bernoullis, we create new Bernoullis for missdetections and associations for each existing Bernoulli, resulting in a Multi-Bernoulli Mixture. The PMBM algorithm allows us to consider uncertainty in associations, providing multiple possible outcomes. Each association represents a global hypothesis. We construct a cost matrix and aim to find the top m associations since the problem may not have a unique solution. The output of the tracker is the most probable hypothesis. For each global hypothesis, we repeat the same procedures in the next step, resulting in an exponential growth of global hypotheses. However, we can store only the most probable ones. The core of the PMBM filter relies on conjugate distributions. By parameterizing a distribution, the model remains unchanged after an update, and only the parameters are updated. This enables us to calculate these parameters and utilize them in subsequent steps. The PMBM algorithm involves several steps: Prediction Step: Predict the states using the motion model, including the prediction methods for both the Poisson Point Process and the Multi-Bernoulli Mixture. Update Step: Update the predicted states based on new measurements. Pruning Step: Remove unlikely hypotheses to manage computational complexity. Estimation Step: Generate estimates of the current state based on the updated and pruned hypotheses. By following these steps, the PMBM algorithm effectively handles the complexities of multi-object tracking with unknown object appearances and disappearances. References García-Fernández, Ángel F., Jason L. Williams, Karl Granström, and Lennart Svensson. 2017. “Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation.” . arXiv. https://doi.org/10.1109/TAES.2018.2805153. arXiv [cs.CV] . Multiple Object Tracking Youtube Channel

Demystifying the Poisson Multi-Bernoulli Mixture Filter: A Leap Forward in Multi-Target Tracking

Photo by on Chris Lawton Unsplash Citing a as evidence of a nationwide problem, the U.S. Department of Justice, Consumer Financial Protection Bureau, and other agencies announced a new initiative Friday to combat discriminatory mortgage lending practices. recent investigation by The Markup , Rohit Chopra, the director of the CFPB, said an all-hands approach is required to combat the “endemic” practice of redlining, the term for denying mortgages in neighborhoods with high populations of Black people and immigrants, once encouraged by the federal government. He said that includes fighting “digital redlining” by algorithms used to decide who gets a mortgage. At the press conference “These algorithms are black boxes behind brick walls,” Chopra said. “When families and regulators do not know how decisions are made by these algorithms, we are unable to participate in a fair and competitive market free from illegal bias.” The new multi-agency effort will increase government analyses of lending patterns with an eye toward fair lending, task state attorneys general with enforcing fair lending practices more stringently, and strengthen reporting pipelines between the Department of Justice and “financial regulatory agencies” to identify fair lending violations. The Department of Justice described the Combating Redlining Initiative as its “most aggressive” attempt to curb the practice. This push comes after The Markup’s August investigation, which held constant 17 variables, including factors that the lending industry previously said would explain away lending disparities. We found the disparities continued. Lenders nationwide were more likely to deny people of color than White applicants. They were 80 percent more likely to deny Black applicants than similarly qualified White applicants with similar incomes, debt-to-income, combined loan-to-value ratios, and many other key factors. Chopra cited that finding in his remarks—and criticized the mortgage industry’s response. At the time, the industry argued that The Markup’s analysis wasn’t complete because it didn’t include credit scores, a key component in their decision-making. “Their defense illuminates the problem,” he said. “Black-box underwriting decisions are not necessarily creating a more level playing field and actually may be exacerbating the biases that feed into them.” The Mortgage Bankers Association did not respond to requests for comment on Friday, and the American Bankers Association declined to comment. The Home Mortgage Disclosure Act requires financial institutions to report applicants’ credit scores, but the CFPB does not make them available in the public version of the dataset, citing consumer privacy. The government agency also withholds the decisions made by algorithms required by Fannie Mae and Freddie Mac, the two quasi-governmental agencies that buy about half of the mortgages made in the country. As part of the new effort, the Department of Justice announced a proposed settlement with a Mississippi-based lender, Trustmark National Bank. to “majority-white neighborhoods” in Memphis, Tenn., between 2014 and 2018, discriminating against Black and Latino prospective borrowers. The bank was accused of targeting its financial services , and a court still needs to approve the agreement for it to take effect. The bank did not admit fault as part of the settlement If approved, Trustmark would pay a $5 million civil penalty to the Consumer Financial Protection Bureau and Office of the Comptroller of the Currency, pay $3.85 million to create a fund to subsidize loans for residents of Black and Latino neighborhoods in the metro area, and pay for outreach and financial education efforts around the area. “We fully cooperated with the agencies and have entered into these settlements to avoid the distraction of protracted litigation,” Duane Dewey said in a statement, “and because we share the common goals of breaking down barriers to home financing and exploring innovative ways to help residents of underserved areas achieve the dream of homeownership.” Trustmark CEO As part of our investigation, The Markup also looked at the lending patterns of individual lenders and between people of color and their White counterparts in 2019. found seven financial institutions that produced the widest disparities Among them were three lenders associated with the largest homebuilders in the country, DHI Mortgage, Lennar Mortgage, and Pulte Mortgage. These three companies were more likely to deny Black and Latino applicants than similarly qualified White applicants. The seven lenders previously told The Markup that they don’t deny people based on their race or ethnicity, that they follow the law, and that The Markup’s findings did not accurately reflect their lending patterns. Two lenders said that the data used for the analysis can’t provide an accurate and complete picture of their lending patterns because of the data’s limitations. “Today, we are committing ourselves to address modern-day redlining by making far more robust use of our fair lending authorities,” U.S. attorney general Merrick Garland said at Friday’s multi-agency press conference. “We will spare no resource to ensure that federal fair lending laws are vigorously enforced and that financial institutions provide equal opportunity for every American to obtain credit.” Correction An earlier version of this story described Fannie Mae and Freddie Mac as nonprofit corporations. They are quasi-governmental agencies. Written by: and Emmanuel Martinez Malena Carollo Originally published . here

Will the U.S. Government do Anything About Mortgage Rate Discrimination?

This story was initially published on May 12, 2020 It’s here! The time has finally come for the Bitcoin halving. The most eagerly anticipated crypto event of the year, possibly of the century (and therefore, ever!) is finally upon us. But what does it mean? Will it affect Bitcoin’s price? And what are some key figures from the crypto community (and further afield) saying about it? What is the Bitcoin halving? This particular Bitcoin halving is the third to take place. They have occurred every 210,000 blocks that have been mined on the blockchain, which is roughly at four-year intervals. The first halving took place on November 28, 2012, and the second halving took place on July 9, 2016. Every halving that occurs halves the number of Bitcoins that miners receive for their efforts. In 2012, the reward was cut in half to 25 coins from its original 50. In 2016, the reward was cut in half to 12.5 coins. It’s now being cut in half again to 6.25 coins. Hence, the name – the Bitcoin halving. Will it affect Bitcoin’s price? Bitcoin’s (the amount of power miners use to validate transactions on the blockchain) has – with some fluctuations along the way – been increasing over time. On May 3, less than two weeks before the halving, it hash rate hit record levels. A higher hash rate is good news for the security of the Bitcoin network, as it becomes harder for rogues to launch a (controlling more than 50% of the network’s mining hash rate). However, it is not good news for Bitcoin miners. This is because the higher the hash rate the more difficult it comes to calculate new blocks, and the more difficult therefore it becomes to produce more Bitcoins. This is a clear driving factor for an increase in Bitcoin’s price. However, this may take some time. 51% attack The greater mining difficulty will necessitate miners acquiring more sophisticated mining equipment to mine cost-effectively, and force miners who can’t afford to do so off the network entirely, as was the case in the aftermath of the last two halvings. It took up to a year for miners to acquire the more sophisticated mining equipment previously, and it is expected it will take up a similar amount of time this time around. In the meantime, the hash rate is expected to decrease. As the hash rate starts to increase with the acquiring of the new equipment, miners hold onto their Bitcoins in anticipation of selling them off during the bullish price spikes that occur as a cumulative effect. Those bullish price spikes occurred around a year or so after the previous two halvings, most noticeably last time with the legendary bull run of 2017. As can be seen on the chart below, prices fluctuated up and down before significant bull spikes in Oct 2013 and May – December 2017, 11 months, and 10-17 months, after the first and second halvings respectively. In theory then at least, this should also happen this time. There seems to be a . Of course, nothing’s for certain and there are other factors that can come into play. In the short term at least, up and down spikes in both the hash rate and price of Bitcoin seem inevitable. But what are the viewpoints on what will happen from some of the key figures in crypto? correlation between the hash rates and price of Bitcoin in the long term The view of the Bitcoin halving from some of crypto’s key figures “With the Bitcoin halving fast approaching, we believe a short-term pullback is highly likely immediately post-halving, as traders begin taking profits.” said Bitcoin researcher Lennard Neo, quoted in a article about the Bitcoin halving. However, Neo went on to say that he expects to see price appreciation as 2020 draws to a close and into 2021: Forbes “In the longer-term we can expect Bitcoin to register significant price appreciation towards the end of 2020 and early 2021. Further turmoil in the broader economies could accelerate [Bitcoin’s] upward trajectory.” In a recent interview with our CEO Ben was bullish about the impact the Bitcoin halving will have on its price, despite recent world events (the COVID-19 crisis): Crypto Briefing, “[In light of recent events] it would be somewhat foolish of me to proclaim that the broader economy won’t have any further impact on crypto markets, but I think ultimately other factors will be more important, as they would have been if none of this has happened. The Bitcoin halving is due to happen in May, and I’m confident that this will have a positive impact on the price of Bitcoin, just as it did the previous two times. Also, I am still very bullish about mainstream cryptocurrency and blockchain adoption.” There has been much debate in the crypto community on if the Bitcoin halving has been ‘priced in’ (if the event has already been discounted into the current valuation of the asset), and there’s some opposing views on the matter. Speaking to the , Scott Freeman, co-founder of financial services firm JST Capital, thinks that as the halving has been known about for a long time, it may not have as a significant effect on Bitcoin’s price as some may expect: Coin Telegraph “The halving has been on everyone’s radar screen for a long time and as such, the effect on markets should already be factored into the price of BTC. The halving may affect the profitability of some miners, but we expect at this point that every miner has already made adjustments to their business models.” However, many within the world of crypto disagree, saying it’s impossible for it to have been already been priced in. The view of Bitcoin halving in the ‘normal’ world Data from Google Trends, which analyzes how popular search queries are over periods of time, clearly shows that worldwide searches for the Bitcoin halving have been rising dramatically recently. It currently has a value of 100 (the maximum possible value for a search term, indicating very high popularity): US billionaire hedge fund manager Paul Tudor Jones has revealed he has bought into Bitcoin, holding between 1-2% of his total assets in the cryptocurrency. Calling it a “great speculation”, it’s a sure sign that Jones, someone who is used to making lots of money, is bullish about Bitcoin’s prospects in the aftermath of the halving. “Every day that goes by that Bitcoin survives, the trust in it will go up.” Whatever happens with the Bitcoin halving in the short run, and the long run, it’s a dead cert it’s going to make for some interesting viewing. With the cinemas closed at the moment (in many countries due to the COVID-19 crisis) and the probability you’ve running out of Netflix films to watch, it’s something else to get your popcorn out for!

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