Valence

valence

Gamified Surveys and Cognitive Load Detection in mHealth: Conclusion, Acknowledgment, and References

Authors:
(1) Lenore Blum (lblum@cs.cmu.edu);
(2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it We tried to make the CtmR Up-Tree competition deterministic, but it turns out to necessarily be probabilistic. This is because any deterministic competition must be made increasingly complex to realize essential properties. For example, this is the case if we want chunks of near equal |weight| to have near equal chances of getting onto the STM stage. Consider a deterministic competition which makes decisions based on a chunks weight. Suppose chunk A is pinned to weight 11, chunk B to weight 9, and all other chunks to weight 0. In the deterministic CtmR competition, A always wins and B never does. And as long as weights don’t change, CtmR remains totally unconscious of chunk B. In the probabilistic CtmR competition described below (with the parameter d = 0), chunk A wins with probability 11/20 while B win with probability 9/20. So A and B each have roughly equal probability of winning a competition, and a new independent competition is begun at every clock tick. In that case, CtmR will likely become conscious of both. In the probabilistic CtmR competition (a variant of the standard tennis or chess tournament) it is proved that a chunk wins the competition with probability proportional to (a function of) its weight. As a consequence, the winning chunk is independent of where processors are located! This is a property of CtmR’s probabilistic competition. (It is a property that would be difficult if not impossible to achieve in tennis and chess tournaments.) We now describe the probabilistic competition. First recall that a chunk is a tuple, <pointer, time, gist, weight, auxiliary information> consisting of a pointer to the originating processor, the time the chunk was put into the competition, a succinct Brainish gist of information, a valenced weight (to indicate the importance/ value/ confidence the originating processor assigns its gist), and some auxiliary information. For the probabilistic CtmR, the auxiliary information is a pair of numbers which we call (intensity, mood). At the start of the competition, each LTM processor enters a chunk into its leaf node with intensity = |weight| and mood = weight. In the probabilistic competition, each non-leaf node of the Up-Tree contains a coin-toss neuron. The coin-toss neuron probabilistically chooses the local winner of the two (competing) chunks in the node’s two children based on their f values, where f(chunk) = intensity + d•(mood) and -1 ≤ d ≤ +1. Here d is a constant called CtmR’s disposition. If C1 and C2 are the two competing chunks, then the coin toss neuron will choose Ci to be the local winner with probability f(Ci)/f(C1)+f(C2). The local winner will move up one level in a single clock tick. The first four parameters of this new chunk are the same as the local winner’s. But its intensity is the sum of two competing chunks’ intensities. Similarly for its mood. Thus, as a chunk moves up the tree, the intensity never decreases. This is not (necessarily) the case for the mood. In this way, the winning chunk’s auxiliary information at the end of the competition will contain the sum of all submitted chunks’ intensities (|weights|) and the sum of all submitted moods (weights). If the disposition is d > 0, CtmR will be “upbeat” in the sense that positively valenced chunks will have a higher probability of winning than negatively weighted chunks of the same |weight|. If its disposition is d = +1, CtmR is manic: only positively valenced chunks can win the competition. The CtmR knows only what is positive in its life, as long as anything is positive. If the disposition is d < 0, CtmR will be “downbeat”. If d = -1, CtmR is “hopelessly depressed”, only negatively valenced chunks can win the competition. There is no way out of this horrible state except with a reboot, i.e., to “shock” the system to get a less extreme disposition.[35] This paper is available on arxiv under CC BY 4.0 DEED license. [35] If d=+1, CtmR is in the manic state, and CtmR theory suggests that there too, a reboot is warranted. In humans, electroconvulsive therapy (ECT) is used primarily for depression. The CtmR model suggests that ECT might work as well for mania. Despite the fact that “ECT is a rapid and highly effective treatment of manic episodes”, current guidelines only endorse ECT “for pharmacotherapy-resistant mania but often as second- or third-line treatment.” (Elias, Thomas, & Sackeim, 2021). Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Authors: Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 5 Summary and Conclusions 6 Acknowledgements 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References References 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it We tried to make the CtmR Up-Tree competition deterministic, but it turns out to necessarily be probabilistic. This is because any deterministic competition must be made increasingly complex to realize essential properties. For example, this is the case if we want chunks of near equal |weight| to have near equal chances of getting onto the STM stage. Consider a deterministic competition which makes decisions based on a chunks weight. Suppose chunk A is pinned to weight 11, chunk B to weight 9, and all other chunks to weight 0. In the deterministic CtmR competition, A always wins and B never does. And as long as weights don’t change, CtmR remains totally unconscious of chunk B. In the probabilistic CtmR competition described below (with the parameter d = 0), chunk A wins with probability 11/20 while B win with probability 9/20. So A and B each have roughly equal probability of winning a competition, and a new independent competition is begun at every clock tick. In that case, CtmR will likely become conscious of both. In the probabilistic CtmR competition (a variant of the standard tennis or chess tournament) it is proved that a chunk wins the competition with probability proportional to (a function of) its weight. As a consequence, the winning chunk is independent of where processors are located! This is a property of CtmR’s probabilistic competition. (It is a property that would be difficult if not impossible to achieve in tennis and chess tournaments.) We now describe the probabilistic competition. First recall that a chunk is a tuple, <pointer, time, gist, weight, auxiliary information> consisting of a pointer to the originating processor, the time the chunk was put into the competition, a succinct Brainish gist of information, a valenced weight (to indicate the importance/ value/ confidence the originating processor assigns its gist), and some auxiliary information. For the probabilistic CtmR, the auxiliary information is a pair of numbers which we call (intensity, mood) . auxiliary information (intensity, mood) At the start of the competition , each LTM processor enters a chunk into its leaf node with At the start of the competition intensity = |weight| and mood = weight. intensity = |weight| mood = weight. In the probabilistic competition, each non-leaf node of the Up-Tree contains a coin-toss neuron. coin-toss neuron. The coin-toss neuron probabilistically chooses the local winne r of the two (competing) chunks in the node’s two children based on their f values, where local winne f f(chunk) = intensity + d•(mood) and -1 ≤ d ≤ +1. f(chunk) = intensity + d•(mood) and -1 ≤ d ≤ +1. Here d is a constant called CtmR’s disposition . d disposition If C1 and C2 are the two competing chunks, then the coin toss neuron will choose Ci to be the local winner with probability f(Ci)/f(C1)+f(C2). The local winner will move up one level in a single clock tick. The first four parameters of this new chunk are the same as the local winner’s. But its intensity is the sum of two competing chunks’ intensities. Similarly for its mood. Thus, as a chunk moves up the tree, the intensity never decreases. This is not (necessarily) the case for the mood. In this way, the winning chunk’s auxiliary information at the end of the competition will contain the sum of all submitted chunks’ intensities (|weights|) and the sum of all submitted moods (weights). If the disposition is d > 0 , CtmR will be “upbeat” in the sense that positively valenced chunks will have a higher probability of winning than negatively weighted chunks of the same |weight|. If its disposition is d = +1 , CtmR is manic: only positively valenced chunks can win the competition. The CtmR knows only what is positive in its life, as long as anything is positive. d > 0 d = +1 If the disposition is d < 0 , CtmR will be “downbeat”. If d = -1 , CtmR is “hopelessly depressed”, only negatively valenced chunks can win the competition. There is no way out of this horrible state except with a reboot, i.e., to “shock” the system to get a less extreme disposition.[35] d < 0 d = -1 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [35] If d=+1 , CtmR is in the manic state, and CtmR theory suggests that there too, a reboot is warranted. d=+1 In humans, electroconvulsive therapy (ECT) is used primarily for depression. The CtmR model suggests that ECT might work as well for mania. Despite the fact that “ECT is a rapid and highly effective treatment of manic episodes”, current guidelines only endorse ECT “for pharmacotherapy-resistant mania but often as second- or third-line treatment.” (Elias, Thomas, & Sackeim, 2021).

The Probabilistic Competition for Conscious Attention and the Influence of Disposition on It

Addressing Kevin Mitchell’s Questions From the Perspective of Conscious Turing Machine Robots

Authors:
(1) Lenore Blum (lblum@cs.cmu.edu);
(2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR Importantly, CtmR’s Model-of-the-World processor (MotWp) constructs models of CtmR’s inner and outer worlds that are collectively called the Model-of-the-World (MotW). The MotW has CtmR’s current and continuing view of CtmR’s world. [KM1] [KM3] The MotWp, collaborating with other processors, plays an important role in planning, predicting, exploring, testing and correcting/learning. The models it constructs contain sketches of referents in CtmR’s inner and outer worlds. Sketches are labeled with succinct Brainish gists. These labels indicate what CtmR learns or “thinks” about those referents over time. In particular, the sketch “CtmR” whose referent is CtmR itself will develop from scratch and eventually be labeled with SELF, CONSCIOUS, and FEELS. [17] [KM1] [KM3] [KM9] Both the Model-of-the-World and Brainish evolve over time and play an essential role in the feeling of “what it is like” to be a CtmR. Thomas Nagel’s “what it is like” (Nagel, 1974) is often taken to be the canonical definition of phenomenological consciousness. [KM1] [KM3] [KM9] The infant CtmR has only a very foggy MotW that does not even include a sketch of itself. Sketches in the MotW develop gradually, become refined, and gather labels. For example, when the infant CtmR discovers it can move its left leg actuator by the “power of thought”, the MotWp labels the sketch of that leg actuator in the MotW as SELF. [18] (Built into each processor is an algorithm that tracks what happens in the outer world when the processor tries to do something. This is built in because tracking is necessary for making predictions and corrections in the MotW. When the infant’s planning processor gets its motor processor to move its left leg and the infant detects the movement via its sensors, the MotWp will label the left leg as SELF.)[KM3] [KM16] Even earlier, “feelings” of pain and pleasure start to develop. When the infant CtmR’s fuel gauge gets low, some sketch (which becomes the sketch of the fuel gauge) in the MotW gets labeled with the Brainish word LOW FUEL/PAIN (or HUNGER) and this information with a large negatively valenced weight wins the competition and gets globally broadcast. This information triggers a processor to activate the fuel pump processor. The infant CtmR learns that the fuel pump relieves the pain when the fuel gauge indicates “low fuel” (hunger). The “fuel pump” in the MotW is labeled PAIN RELIEVER, and may also get labeled PLEASURE PROVIDER.[19] [KM3][KM12] Formal definition 2. Conscious awareness and feelings of consciousness in CtmR are consequences of Brainish-labeled MotW sketches being globally broadcast.[20] In other words, CtmR becomes consciously aware of a chunk with a Brainish labeled gist, when CtmR pays conscious attention to that chunk.[21] [KM2] As CtmR becomes increasingly consciously aware, the MotWp will label the sketch “CtmR” in the MotW as CONSCIOUSLY AWARE or simply CONSCIOUS. Looking at CtmR from the viewpoint of the outside world, we see that something about CtmR is conscious; specifically, the CtmR considers itself conscious. What is conscious cannot be the MotWp or any other processor, as processors have no feelings; they are just machines running algorithms. Our proposal that CtmR as a whole feels it is conscious is a consequence in part of the fact that the MotWp views the “CtmR” in its MotW as conscious, and that this view is broadcast to all processors. and thus, to all processors associated with feelings of consciousness. This is CtmR’s phenomenal consciousness. This paper is available on arxiv under CC BY 4.0 DEED license. [17] We are often asked, isn’t this process recursive? Doesn’t the sketch of CtmR have a sketch of CtmR have a sketch of CtmR, etc. ? Yes, up to a point. But, at each iteration the current sketch is degraded, so the process soon becomes null. [18] Certain pathologies will occur if a breakdown in CtmR causes its MotWp to mislabel. For example, if the sketch of that leg actuator gets labeled NOT-SELF at some point, CtmR might beg to get its leg amputated, even if it is a still functioning properly. This would be an example of CtmR body integrity dysphoria. Other pathologies due to faulty labeling in the MotW: CtmR phantom limb syndrome (a sketch of an amputated arm actuator is mislabeled SELF), Cotard’s syndrome (the sketch of CtmR is labeled DEAD), paranoia (sketch of CtmR’s best friend is labeled SPY), … . [K10] [19] When a human mother gives a breast to her infant, the infant learns that the breast relieves the pain of hunger. The breast gets incorporated into the infant’s MotW labeled with PAIN RELIEVER and PLEASURE. [20] We call a sequence of such broadcasted chunks, a stream of consciousness. CtmR dreams are such streams during which the input sensors and output actuators are essentially inactive, and CtmR’s Dream processor gets to work. Although dreams are “felt” as real, they can also be fantastical since their predictions are not being tested in the world. We propose that (testing for) dreaming is a (partial) test for consciousness. [21] Is the infant consciously aware? An infant CTM’s memory of an instance doesn’t even have the label SELF associated with its MotW sketch, but soon enough that recollection will get labeled SELF, after which future recollections will have itself in that memory. [KM2], [KM3] Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Authors: Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 5 Summary and Conclusions 6 Acknowledgements 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References References 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR Importantly, CtmR’s Model-of-the-World processor (MotWp) constructs models of CtmR’s inner and outer worlds that are collectively called the Model-of-the-World (MotW). The MotW has CtmR’s current and continuing view of CtmR’s world. [KM1] [KM3] The MotWp, collaborating with other processors, plays an important role in planning, predicting, exploring, testing and correcting/learning. The models it constructs contain sketches of referents in CtmR’s inner and outer worlds. Sketches are labeled with succinct Brainish gists. These labels indicate what CtmR learns or “thinks” about those referents over time. In particular, the sketch “CtmR” whose referent is CtmR itself will develop from scratch and eventually be labeled with SELF, CONSCIOUS, and FEELS. [17] [KM1] [KM3] [KM9] Both the Model-of-the-World and Brainish evolve over time and play an essential role in the feeling of “what it is like” to be a CtmR. Thomas Nagel’s “what it is like” (Nagel, 1974) is often taken to be the canonical definition of phenomenological consciousness. [KM1] [KM3] [KM9] The infant CtmR has only a very foggy MotW that does not even include a sketch of itself. Sketches in the MotW develop gradually, become refined, and gather labels. For example, when the infant CtmR discovers it can move its left leg actuator by the “power of thought”, the MotWp labels the sketch of that leg actuator in the MotW as SELF. [18] (Built into each processor is an algorithm that tracks what happens in the outer world when the processor tries to do something. This is built in because tracking is necessary for making predictions and corrections in the MotW. When the infant’s planning processor gets its motor processor to move its left leg and the infant detects the movement via its sensors, the MotWp will label the left leg as SELF.)[KM3] [KM16] Even earlier, “feelings” of pain and pleasure start to develop. When the infant CtmR’s fuel gauge gets low, some sketch (which becomes the sketch of the fuel gauge) in the MotW gets labeled with the Brainish word LOW FUEL/PAIN (or HUNGER) and this information with a large negatively valenced weight wins the competition and gets globally broadcast. This information triggers a processor to activate the fuel pump processor. The infant CtmR learns that the fuel pump relieves the pain when the fuel gauge indicates “low fuel” (hunger). The “fuel pump” in the MotW is labeled PAIN RELIEVER, and may also get labeled PLEASURE PROVIDER.[19] [KM3][KM12] Formal definition 2. Conscious awareness and feelings of consciousness in CtmR are consequences of Brainish-labeled MotW sketches being globally broadcast.[20] Formal definition 2. In other words, CtmR becomes consciously aware of a chunk with a Brainish labeled gist, when CtmR pays conscious attention to that chunk.[21] [KM2] As CtmR becomes increasingly consciously aware, the MotWp will label the sketch “CtmR” in the MotW as CONSCIOUSLY AWARE or simply CONSCIOUS. Looking at CtmR from the viewpoint of the outside world, we see that something about CtmR is conscious; specifically, the CtmR considers itself conscious. What is conscious cannot be the MotWp or any other processor, as processors have no feelings; they are just machines running algorithms. Our proposal that CtmR as a whole feels it is conscious is a consequence in part of the fact that the MotWp views the “CtmR” in its MotW as conscious, and that this view is broadcast to all processors. and thus, to all processors associated with feelings of consciousness. This is CtmR’s phenomenal consciousness. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [17] We are often asked, isn’t this process recursive? Doesn’t the sketch of CtmR have a sketch of CtmR have a sketch of CtmR, etc. ? Yes, up to a point. But, at each iteration the current sketch is degraded, so the process soon becomes null. [18] Certain pathologies will occur if a breakdown in CtmR causes its MotWp to mislabel. For example, if the sketch of that leg actuator gets labeled NOT-SELF at some point, CtmR might beg to get its leg amputated, even if it is a still functioning properly. This would be an example of CtmR body integrity dysphoria. Other pathologies due to faulty labeling in the MotW: CtmR phantom limb syndrome (a sketch of an amputated arm actuator is mislabeled SELF), Cotard’s syndrome (the sketch of CtmR is labeled DEAD), paranoia (sketch of CtmR’s best friend is labeled SPY), … . [K10] [19] When a human mother gives a breast to her infant, the infant learns that the breast relieves the pain of hunger. The breast gets incorporated into the infant’s MotW labeled with PAIN RELIEVER and PLEASURE. [20] We call a sequence of such broadcasted chunks, a stream of consciousness. CtmR dreams are such streams during which the input sensors and output actuators are essentially inactive, and CtmR’s Dream processor gets to work. Although dreams are “felt” as real, they can also be fantastical since their predictions are not being tested in the world. We propose that (testing for) dreaming is a (partial) test for consciousness. [21] Is the infant consciously aware? An infant CTM’s memory of an instance doesn’t even have the label SELF associated with its MotW sketch, but soon enough that recollection will get labeled SELF, after which future recollections will have itself in that memory. [KM2], [KM3]

Conscious Awareness and the Feeling of Consciousness in Conscious Turing Machine Robots

Authors:
(1) Lenore Blum (lblum@cs.cmu.edu);
(2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References 2.1 Formal Definition of CtmR CtmR is defined formally as a 7-tuple, (STM, LTM, Up-Tree, Down-Tree, Links, Input, Output). The seven components each have well-defined properties. We indicate these properties here. For CtmR, the stage in the theater model is represented by a Short Term Memory (STM) that at any moment in time contains CtmR’s current conscious content. STM is not a processor; it is merely a buffer and broadcasting station. The N audience members[6] are represented by a massive collection of initially independent powerful (unconscious) processors that comprise CtmR's computational machinery and Long Term Memory, together called LTM. These processors compete to get their information on stage to be immediately broadcast to the audience.[7] (See Appendix 7.2 for a discussion of CtmR’s competition.) CtmR has	a finite lifetime T. 8 At time t = 0, all but the input and output LTM processors are “generic” with certain basic built-in properties,	e.g.,	some learning/prediction correction algorithms, as well as a preference for choosing the positive over the negative. [9] Their functionalities evolve over time. But for now, we designate some important LTM processors built in. These include: a Model-of-theWorld processor (MotWp), actually a collection of processors, for building models of CtmR’s inner and outer worlds; Sensation processors (with input from CtmR’s outer world via its various sensors[10]); Motor processors (with output to CtmR’s outer world via motor actuators[11]); and so on. We also allow off-the-shelf processors (like ChatGPT and Google) that extend CtmR’s capabilities. All processors are in LTM so when we speak of a processor, we mean an LTM processor. While each processor may have its own distinct language, processors communicate within CtmR in Brainish, Brainish being CtmR’s rich multimodal inner language. Brainish words, gists, and phrases fuse sensory modalities (e.g., sight, sounds, smells, tactile) as well as dynamical processes. [12] The Brainish language evolves over CtmR’s lifetime.[13] Brainish can differ from one CtmR to another. LTM processors compete in a well-defined (fast and natural) probabilistic competition (Appendix 7.2) to get their questions, answers, and information onto the stage (STM). The competition is hosted by the Up-Tree, a perfect binary tree[14] of height h which has a leaf in each LTM processor and root in STM. At each clock tick, a new competition starts with each processor putting a chunk of information into its Up-Tree leaf node. A chunk is defined formally to be a tuple,<pointer, time, gist, weight, auxiliary information> , consisting of (in order of importance) a succinct Brainish gist of information, a valenced weight (to indicate the importance/urgency/value/confidence the originating processor assigns the gist), a pointer to the originating processor, and the time the chunk was created, plus some auxiliary information. Each submitted chunk competes locally with its neighbor; a variation of the local winner moves up one level of the Up-Tree in one clock tick, now to compete with its neighbor. The process continues until a chunk reaches the Up-Tree root node in STM. The competition takes h clock ticks. Notable about CtmR’s probabilistic competition is that the winner is independent of its submitting processor’s location. (See Appendix 7.2.) Clearly this is an important feature for a machine or brain; no moving around of processors is needed. And also, it is fairer than other binary tournaments (e.g., tennis or chess). The chunk that gets onto the stage (STM) , i.e., the winning chunk, is called CtmR’s current conscious content and is immediately globally broadcast (in one clock tick) via the Down-Tree (a bush of height 1 with a root in STM and N branches, one leaf in each LTM processor) to the audience (of all LTM processors). [KM2] [KM5] The single chunk in STM to be globally broadcast will enable CtmR to focus attention on the winning gist. “One” is not the “magical number” 7±2 proposed by George Miller (Miller G. A., 1956), but we are looking for simplicity and one chunk will do. This paper is available on arxiv under CC BY 4.0 DEED license. [7] As an example of the theater analogy, consider the “What’s her name?” scenario: Suppose at a party, we see someone we know but cannot recall her name. Greatly embarrassed, we rack our brain to remember. An hour later when we are home, her name pops into our head (unfortunately too late). What’s going on? Racking our brain to remember caused the urgent request “What’s her name?” coming from LTM processor p to rise to the stage (STM) which in turn was immediately broadcast to the audience. Some (LTM) processors try to answer the query. One such processor recalls we met in a neuroscience class; this information gets to the stage and is broadcast triggering another processor to recall that what’s-her-name is interested in “consciousness”, which is broadcast. Another processor p’ sends information to the stage asserting that her name likely begins with S. But sometime later the stage receives information from processor p’’ that her name begins with T which prompts processor p’’’ (who has been paying attention to all the broadcasted information) to claim with great certainty that her name is Tina – which is correct. The name is broadcast from the stage, our audience of processors receives it, and we finally remember her name. Our conscious self has no idea how her name was found. (Based on the correct outcome, learning algorithms internal to each processor, cause processor p’ to lower the importance (|weight|) it gives its information and cause p’’ to increase the importance.) [8] For the general CtmR theory, both T (lifetime) and N (number of LTM processors) are parameters. Time t = 0, 1, 2, 3, … , T is measured in discrete clock ticks. [9] This built-in preference creates a predilection for survival. [10] Ears, eyes, nose, skin, … . [11] Arms, hands, legs, … . [12] A succinct Brainish gist is like a frame in a dream. [13] Paul Liang is developing a computational framework for Brainish (Liang, 2022). [14] A perfect binary tree is a binary tree in which all leaf nodes are at the same depth. This depth is also the height of the tree. If h is the height of a perfect binary tree, then the tree has N = 2h leaves. Each node, except the root node, has a unique neighbor. For simplicity, we choose a perfect binary tree in this chapter. Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Authors: Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 5 Summary and Conclusions 6 Acknowledgements 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References References 2.1 Formal Definition of CtmR CtmR is defined formally as a 7-tuple, ( STM, LTM, Up-Tree, Down-Tree, Links, Input, Output ). The seven components each have well-defined properties. We indicate these properties here. STM, LTM, Up-Tree, Down-Tree, Links, Input, Output For CtmR, the stage in the theater model is represented by a Short Term Memory ( STM ) that at any moment in time contains CtmR’s current conscious content. STM is not a processor; it is merely a buffer and broadcasting station. The N audience members[6] are represented by a massive collection of initially independent powerful (unconscious) processors that comprise CtmR's computational machinery and Long Term Memory, together called LTM . These processors compete to get their information on stage to be immediately broadcast to the audience.[7] (See Appendix 7.2 for a discussion of CtmR’s competition.) STM N LTM CtmR has	a finite lifetime T. 8 At time t = 0, all but the input and output LTM processors are “generic” with certain basic built-in properties,	e.g.,	some learning/prediction correction algorithms, as well as a preference for choosing the positive over the negative. [9] Their functionalities evolve over time. But for now, we designate some important LTM processors built in. These include: a Model-of-theWorld processor (MotWp), actually a collection of processors, for building models of CtmR’s inner and outer worlds; Sensation processors (with input from CtmR’s outer world via its various sensors[10]); Motor processors (with output to CtmR’s outer world via motor actuators[11]); and so on. We also allow off-the-shelf processors (like ChatGPT and Google) that extend CtmR’s capabilities. All processors are in LTM so when we speak of a processor, we mean an LTM processor. While each processor may have its own distinct language, processors communicate within CtmR in Brainish, Brainish being CtmR’s rich multimodal inner language. Brainish words, gists, and phrases fuse sensory modalities (e.g., sight, sounds, smells, tactile) as well as dynamical processes. [12] The Brainish language evolves over CtmR’s lifetime.[13] Brainish can differ from one CtmR to another. LTM processors compete in a well-defined (fast and natural) probabilistic competition (Appendix 7.2) to get their questions, answers, and information onto the stage (STM). The competition is hosted by the Up-Tree, a perfect binary tree[14] of height h which has a leaf in each LTM processor and root in STM. At each clock tick, a new competition starts with each processor putting a chunk of information into its Up-Tree leaf node. Up-Tree, h A chunk is defined formally to be a tuple, <pointer, time, gist, weight, auxiliary information> , consisting of (in order of importance) a succinct Brainish gist of information, a valenced weight (to indicate the importance/urgency/value/confidence the originating processor assigns the gist), a pointer to the originating processor, and the time the chunk was created, plus some auxiliary information. <pointer, time, gist, weight, auxiliary information> Each submitted chunk competes locally with its neighbor; a variation of the local winner moves up one level of the Up-Tree in one clock tick, now to compete with its neighbor. The process continues until a chunk reaches the Up-Tree root node in STM. The competition takes h clock ticks. h Notable about CtmR’s probabilistic competition is that the winner is independent of its submitting processor’s location. (See Appendix 7.2.) Clearly this is an important feature for a machine or brain; no moving around of processors is needed. And also, it is fairer than other binary tournaments (e.g., tennis or chess). The chunk that gets onto the stage (STM) , i.e., the winning chunk, is called CtmR’s current conscious content and is immediately globally broadcast (in one clock tick) via the Down-Tree (a bush of height 1 with a root in STM and N branches, one leaf in each LTM processor) to the audience (of all LTM processors). [KM2] [KM5] Down-Tree 1 The single chunk in STM to be globally broadcast will enable CtmR to focus attention on the winning gist. “One” is not the “magical number” 7±2 proposed by George Miller (Miller G. A., 1956), but we are looking for simplicity and one chunk will do. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [7] As an example of the theater analogy, consider the “What’s her name?” scenario: Suppose at a party, we see someone we know but cannot recall her name. Greatly embarrassed, we rack our brain to remember. An hour later when we are home, her name pops into our head (unfortunately too late). What’s going on? Racking our brain to remember caused the urgent request “What’s her name?” coming from LTM processor p to rise to the stage (STM) which in turn was immediately broadcast to the audience. p Some (LTM) processors try to answer the query. One such processor recalls we met in a neuroscience class; this information gets to the stage and is broadcast triggering another processor to recall that what’s-her-name is interested in “consciousness”, which is broadcast. Another processor p ’ sends information to the stage asserting that her name likely begins with S . p S But sometime later the stage receives information from processor p ’’ that her name begins with T which prompts processor p ’’’ (who has been paying attention to all the broadcasted information) to claim with great certainty that her name is Tina – which is correct. The name is broadcast from the stage, our audience of processors receives it, and we finally remember her name. Our conscious self has no idea how her name was found. p T p Tina (Based on the correct outcome, learning algorithms internal to each processor, cause processor p’ to lower the importance (|weight|) it gives its information and cause p’’ to increase the importance.) [8] For the general CtmR theory, both T (lifetime) and N (number of LTM processors) are parameters. Time t = 0, 1, 2, 3, … , T is measured in discrete clock ticks. T N t = 0, 1, 2, 3, … , T [9] This built-in preference creates a predilection for survival. [10] Ears, eyes, nose, skin, … . [11] Arms, hands, legs, … . [12] A succinct Brainish gist is like a frame in a dream. [13] Paul Liang is developing a computational framework for Brainish (Liang, 2022). [14] A perfect binary tree is a binary tree in which all leaf nodes are at the same depth. This depth is also the height of the tree. If h is the height of a perfect binary tree, then the tree has N = 2h leaves. Each node, except the root node, has a unique neighbor. For simplicity, we choose a perfect binary tree in this chapter.

Formal Definition of the Conscious Turing Machine Robot

Effective Bias Detection and Mitigation: Key Findings from BiasPainter’s Evaluation

The Crowded AI Spaces Still Have Room for One More Genius

জনাকীর্ণ এআই স্পেসগুলিতে এখনও আরও একটি প্রতিভা থাকার জায়গা রয়েছে

In den überfüllten KI-Räumen ist noch Platz für ein weiteres Genie

Los abarrotados espacios de IA todavía tienen espacio para un genio más

Les espaces IA bondés ont encore de la place pour un génie supplémentaire

भीड़-भाड़ वाले एआई स्पेस में अभी भी एक और प्रतिभा के लिए जगह है

混雑したAI空間にはまだもう一人の天才の余地がある

혼잡한 AI 공간에는 아직 천재가 한 명 더 들어갈 여지가 있습니다

Os lotados espaços de IA ainda têm espaço para mais um gênio

В переполненных пространствах ИИ все еще есть место для еще одного гения

Kalabalık Yapay Zeka Alanlarında Hala Bir Dahiye Daha Yer Var

Không gian AI đông đúc vẫn còn chỗ cho một thiên tài nữa

拥挤的人工智能领域仍有空间容纳更多天才

Authors:
(1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India;
(2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India
(3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India
(4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Table of Links Abstract and Intro
Background and Related Work
EMTD Dataset
Proposed Methodology
Experiments
Conclusion and References 6. Conclusion This work extends the idea of a novel holistic approach to the movie genre classification problem that includes affective and cognitive levels by considering multiple modalities, including situation from the frame, dialogues from speech, and meta-data (movie plot and description). We also built a Hollywood English movie trailers dataset EMTD that includes around 2000 trailers from 5 genres, namely action, comedy, horror, romance, science fiction, to pursue this study. We experimented with various model architectures as discussed in Section 5.2 and also validated our final framework on EMTD and on standard LMTD-9 [2] that achieves AU (PRC) values of 0.92 and 0.82 respectively. Our study's main aim is to build a robust framework to classify a movie genre from its short clip i.e., trailer. Although our study includes English speech as a feature, it can also be applied to some Non-English trailers. For Non-English ones, our model can incorporate the video features only, so on the basis of that, predictions can be made by our architecture. For extension of our proposed model, background audio studies based on vocals can also be incorporated. Hence, in the future, we plan to build a framework considering background vocals in audio along with our current framework to better extract and use most features from movie trailers. We also plan to add some more genres to our study for multi-label classification. 7. References [1] A. Hanjalic and L. Q. Xu, “Affective video content representation and modeling,” IEEE Trans. Multimed., vol. 7, no. 1, 2005. [2] J. Wehrmann and R. C. Barros, “Convolutions through time for multi-label movie genre classification,” in Proceedings of the ACM Symposium on Applied Computing, 2017, vol. Part F1280, pp. 114–119. [3] Z. Rasheed, Y. Sheikh, and M. Shah, “On the use of computable features for film classification,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 1, pp. 52–64, Jan. 2005. [4] L. H. Chen, Y. C. Lai, and H. Y. Mark Liao, “Movie scene segmentation using background information,” Pattern Recognit., vol. 41, no. 3, 2008. [5] S. K. Jain and R. S. Jadon, “Movies genres classifier using neural network,” 2009. [6] L. Canini, S. Benini, and R. Leonardi, “Affective recommendation of movies based on selected connotative features,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, 2013. [7] M. Xu, C. Xu, X. He, J. S. Jin, S. Luo, and Y. Rui, “Hierarchical affective content analysis in arousal and valence dimensions,” Signal Processing, vol. 93, no. 8, 2013. [8] A. Yadav and D. K. Vishwakarma, “A unified framework of deep networks for genre classification using movie trailer,” Appl. Soft Comput. J., vol. 96, 2020. [9] K. Choroś, “Video genre classification based on length analysis of temporally aggregated video shots,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11056 LNAI, pp. 509–518. [10] A. M. Ertugrul and P. Karagoz, “Movie Genre Classification from Plot Summaries Using Bidirectional LSTM,” in Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018, 2018, vol. 2018-January. [11] G. Païs, P. Lambert, D. Beauchêne, F. Deloule, and B. Ionescu, “Animated movie genre detection using symbolic fusion of text and image descriptors,” 2012. [12] A. Shahin and A. Krzyżak, “Genre-ous: The Movie Genre Detector,” in Communications in Computer and Information Science, 2020, vol. 1178 CCIS. [13] N. Kumar, A. Harikrishnan, and R. Sridhar, “Hash Vectorizer Based Movie Genre Identification,” in Lecture Notes in Electrical Engineering, 2020, vol. 605. [14] P. G. Shambharkar, P. Thakur, S. Imadoddin, S. Chauhan, and M. N. Doja, “Genre Classification of Movie Trailers using 3D Convolutional Neural Networks,” 2020. [15] W. T. Chu and H. J. Guo, “Movie genre classification based on poster images with deep neural networks,” 2017. [16] G. S. Simões, J. Wehrmann, R. C. Barros, and D. D. Ruiz, “Movie genre classification with Convolutional Neural Networks,” in Proceedings of the International Joint Conference on Neural Networks, 2016, vol. 2016-October. [17] J. Li, L. Deng, R. Haeb-Umbach, and Y. Gong, “Chapter 2 - Fundamentals of speech recognition,” in Robust Automatic Speech Recognition, J. Li, L. Deng, R. HaebUmbach, and Y. Gong, Eds. Oxford: Academic Press, 2016, pp. 9–40. [18] S. Pratt, M. Yatskar, L. Weihs, A. Farhadi, and A. Kembhavi, “Grounded Situation Recognition,” in Computer Vision -- ECCV 2020, 2020, pp. 314–332. [19] B. Beel, Joeran and Langer, Stefan and Gipp, “TF-IDuF: A Novel Term-Weighting Sheme for User Modeling based on Users’ Personal Document Collections,” Proc. iConference 2017, 2017. [20] J. Wehrmann, R. C. Barros, G. S. Simoes, T. S. Paula, and D. D. Ruiz, “(Deep) Learning from Frames,” 2017. [21] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 2015. [22] E. Fish, A. Gilbert, and J. Weinbren, “Rethinking movie genre classification with finegrained semantic clustering,” arXiv Prepr. arXiv2012.02639, 2020. [23] F. Álvarez, F. Sánchez, G. Hernández-Peñaloza, D. Jiménez, J. M. Menéndez, and G. Cisneros, “On the influence of low-level visual features in film classification,” PLoS One, vol. 14, no. 2, 2019. [24] J. Wehrmann, M. A. Lopes, and R. C. Barros, “Self-attention for synopsis-based multilabel movie genre classification,” 2018. [25] J. Wehrmann and R. C. Barros, “Movie genre classification: A multi-label approach based on convolutions through time,” Appl. Soft Comput. J., vol. 61, 2017. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India; (2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Authors: Authors: (1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India; (2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Table of Links Abstract and Intro Background and Related Work EMTD Dataset Proposed Methodology Experiments Conclusion and References Abstract and Intro Abstract and Intro Background and Related Work Background and Related Work EMTD Dataset EMTD Dataset Proposed Methodology Proposed Methodology Experiments Experiments Conclusion and References Conclusion and References 6. Conclusion This work extends the idea of a novel holistic approach to the movie genre classification problem that includes affective and cognitive levels by considering multiple modalities, including situation from the frame, dialogues from speech, and meta-data (movie plot and description). We also built a Hollywood English movie trailers dataset EMTD that includes around 2000 trailers from 5 genres, namely action, comedy, horror, romance, science fiction, to pursue this study. We experimented with various model architectures as discussed in Section 5.2 and also validated our final framework on EMTD and on standard LMTD-9 [2] that achieves AU (PRC) values of 0.92 and 0.82 respectively. Our study's main aim is to build a robust framework to classify a movie genre from its short clip i.e., trailer. Although our study includes English speech as a feature, it can also be applied to some Non-English trailers. For Non-English ones, our model can incorporate the video features only, so on the basis of that, predictions can be made by our architecture. For extension of our proposed model, background audio studies based on vocals can also be incorporated. Hence, in the future, we plan to build a framework considering background vocals in audio along with our current framework to better extract and use most features from movie trailers. We also plan to add some more genres to our study for multi-label classification. 7. References [1] A. Hanjalic and L. Q. Xu, “Affective video content representation and modeling,” IEEE Trans. Multimed., vol. 7, no. 1, 2005. [2] J. Wehrmann and R. C. Barros, “Convolutions through time for multi-label movie genre classification,” in Proceedings of the ACM Symposium on Applied Computing, 2017, vol. Part F1280, pp. 114–119. [3] Z. Rasheed, Y. Sheikh, and M. Shah, “On the use of computable features for film classification,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 1, pp. 52–64, Jan. 2005. [4] L. H. Chen, Y. C. Lai, and H. Y. Mark Liao, “Movie scene segmentation using background information,” Pattern Recognit., vol. 41, no. 3, 2008. [5] S. K. Jain and R. S. Jadon, “Movies genres classifier using neural network,” 2009. [6] L. Canini, S. Benini, and R. Leonardi, “Affective recommendation of movies based on selected connotative features,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, 2013. [7] M. Xu, C. Xu, X. He, J. S. Jin, S. Luo, and Y. Rui, “Hierarchical affective content analysis in arousal and valence dimensions,” Signal Processing, vol. 93, no. 8, 2013. [8] A. Yadav and D. K. Vishwakarma, “A unified framework of deep networks for genre classification using movie trailer,” Appl. Soft Comput. J., vol. 96, 2020. [9] K. Choroś, “Video genre classification based on length analysis of temporally aggregated video shots,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11056 LNAI, pp. 509–518. [10] A. M. Ertugrul and P. Karagoz, “Movie Genre Classification from Plot Summaries Using Bidirectional LSTM,” in Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018, 2018, vol. 2018-January. [11] G. Païs, P. Lambert, D. Beauchêne, F. Deloule, and B. Ionescu, “Animated movie genre detection using symbolic fusion of text and image descriptors,” 2012. [12] A. Shahin and A. Krzyżak, “Genre-ous: The Movie Genre Detector,” in Communications in Computer and Information Science, 2020, vol. 1178 CCIS. [13] N. Kumar, A. Harikrishnan, and R. Sridhar, “Hash Vectorizer Based Movie Genre Identification,” in Lecture Notes in Electrical Engineering, 2020, vol. 605. [14] P. G. Shambharkar, P. Thakur, S. Imadoddin, S. Chauhan, and M. N. Doja, “Genre Classification of Movie Trailers using 3D Convolutional Neural Networks,” 2020. [15] W. T. Chu and H. J. Guo, “Movie genre classification based on poster images with deep neural networks,” 2017. [16] G. S. Simões, J. Wehrmann, R. C. Barros, and D. D. Ruiz, “Movie genre classification with Convolutional Neural Networks,” in Proceedings of the International Joint Conference on Neural Networks, 2016, vol. 2016-October. [17] J. Li, L. Deng, R. Haeb-Umbach, and Y. Gong, “Chapter 2 - Fundamentals of speech recognition,” in Robust Automatic Speech Recognition, J. Li, L. Deng, R. HaebUmbach, and Y. Gong, Eds. Oxford: Academic Press, 2016, pp. 9–40. [18] S. Pratt, M. Yatskar, L. Weihs, A. Farhadi, and A. Kembhavi, “Grounded Situation Recognition,” in Computer Vision -- ECCV 2020, 2020, pp. 314–332. [19] B. Beel, Joeran and Langer, Stefan and Gipp, “TF-IDuF: A Novel Term-Weighting Sheme for User Modeling based on Users’ Personal Document Collections,” Proc. iConference 2017, 2017. [20] J. Wehrmann, R. C. Barros, G. S. Simoes, T. S. Paula, and D. D. Ruiz, “(Deep) Learning from Frames,” 2017. [21] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 2015. [22] E. Fish, A. Gilbert, and J. Weinbren, “Rethinking movie genre classification with finegrained semantic clustering,” arXiv Prepr. arXiv2012.02639, 2020. [23] F. Álvarez, F. Sánchez, G. Hernández-Peñaloza, D. Jiménez, J. M. Menéndez, and G. Cisneros, “On the influence of low-level visual features in film classification,” PLoS One, vol. 14, no. 2, 2019. [24] J. Wehrmann, M. A. Lopes, and R. C. Barros, “Self-attention for synopsis-based multilabel movie genre classification,” 2018. [25] J. Wehrmann and R. C. Barros, “Movie genre classification: A multi-label approach based on convolutions through time,” Appl. Soft Comput. J., vol. 61, 2017. 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

Multilevel Profiling of Situation and Dialogue-based Deep Networks: Conclusion and References

পরিস্থিতি এবং সংলাপ-ভিত্তিক গভীর নেটওয়ার্কগুলির মাল্টিলেভেল প্রোফাইলিং: উপসংহার এবং উল্লেখ

Multilevel-Profiling situations- und dialogbasierter Deep Networks: Fazit und Referenzen

Elaboración de perfiles multinivel de redes profundas basadas en situaciones y diálogos: conclusiones y referencias

Profilage multiniveau de la situation et des réseaux profonds basés sur le dialogue : conclusion et références

स्थिति और संवाद-आधारित गहरे नेटवर्क की बहुस्तरीय प्रोफाइलिंग: निष्कर्ष और संदर्भ

状況と対話に基づくディープネットワークのマルチレベルプロファイリング：結論と参考文献

상황 및 대화 기반 심층 네트워크의 다단계 프로파일링: 결론 및 참고 자료

Perfil multinível de redes profundas baseadas em situação e diálogo: conclusão e referências

Многоуровневое профилирование глубоких сетей, основанных на ситуациях и диалоге: выводы и ссылки

Durum ve Diyaloğa Dayalı Derin Ağların Çok Düzeyli Profilinin Oluşturulması: Sonuç ve Referanslar

Lập hồ sơ đa cấp về tình huống và mạng lưới sâu dựa trên đối thoại: Kết luận và tài liệu tham khảo

基于情境和对话的深度网络的多层次剖析：结论和参考文献

VEATIC: Familiarity and Enjoyment Ratings & References

VEATIC: পরিচিতি এবং উপভোগ রেটিং এবং রেফারেন্স

VEATIC: Vertrautheits- und Genussbewertungen und Referenzen

VEATIC: Calificaciones y referencias de familiaridad y disfrute

VEATIC : Notes et références de familiarité et de plaisir

VEATIC: परिचितता और आनंद रेटिंग और संदर्भ

VEATIC: 親しみやすさと楽しさの評価と参考文献

VEATIC: 친숙함과 즐거움 평가 및 참고자료

VEATIC: classificações e referências de familiaridade e prazer

VEATIC: рейтинги знакомства и удовольствия, а также ссылки

VEATIC: Aşinalık ve Eğlence Derecelendirmeler ve Referanslar

VEATIC: Đánh giá và tham khảo về sự quen thuộc và thích thú

VEATIC：熟悉度和享受度评级及参考

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 10. Subject Agreement Across Videos A benefit of the VEATIC dataset is that it has multiple annotators for each video with the minimum number of annotators for any given video being 25 and the maximum being 73. Emotion perception is subjective and observers judgments can vary across multiple people. Many of the previously published emotion datasets have a very low number of annotators, often having only single digit (n < 10) number of annotators. Having so few annotators is problematic because of the increased variance across observers. To show this, we calculated how the average rating for each video in our dataset varied if we randomly sampled, with replacement, five versus all annotators. We repeated this process 1000 times for each video and calculated the standard deviation of the recalculated average rating. Figure 12a shows how the standard deviation of the consensus rating across videos varies if we use either five or all annotators for each video. This analysis shows that having more annotators leads to much smaller standard deviations in the consensus rating which can lead to more accurate representations of the ground truth emotion in the videos. Additionally, We investigated how observers’ responses varied across videos by calculating the standard deviation across observers for each video. Figure 12b shows the standard deviations across videos. We find that the standard deviations for both valence and arousal dimensions were small with valence having an average standard deviation of µ = 0.248 and a median of 0.222 and arousal having an average standard deviation of µ = 0.248 and a median of 0.244, which are comparable with the valence and arousal rating variance from EMOTIC [32]. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 10. Subject Agreement Across Videos A benefit of the VEATIC dataset is that it has multiple annotators for each video with the minimum number of annotators for any given video being 25 and the maximum being 73. Emotion perception is subjective and observers judgments can vary across multiple people. Many of the previously published emotion datasets have a very low number of annotators, often having only single digit (n < 10) number of annotators. Having so few annotators is problematic because of the increased variance across observers. To show this, we calculated how the average rating for each video in our dataset varied if we randomly sampled, with replacement, five versus all annotators. We repeated this process 1000 times for each video and calculated the standard deviation of the recalculated average rating. Figure 12a shows how the standard deviation of the consensus rating across videos varies if we use either five or all annotators for each video. This analysis shows that having more annotators leads to much smaller standard deviations in the consensus rating which can lead to more accurate representations of the ground truth emotion in the videos. Additionally, We investigated how observers’ responses varied across videos by calculating the standard deviation across observers for each video. Figure 12b shows the standard deviations across videos. We find that the standard deviations for both valence and arousal dimensions were small with valence having an average standard deviation of µ = 0.248 and a median of 0.222 and arousal having an average standard deviation of µ = 0.248 and a median of 0.244, which are comparable with the valence and arousal rating variance from EMOTIC [32]. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Subject Agreement Across Videos

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: ভিডিও জুড়ে বিষয় চুক্তি

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