Reviewerly/exHarmony huggingface dataset:
Neural circuits for dynamics-based segmentation of time series

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model-based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error, and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on datasets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.
Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Motivations: Recent research has emerged on generally how to improve AI product user experiences, but relatively little is known about an AI product's inclusivity. For example, what kinds of users does it support well, and who does it leave out? And what changes in the product would make it more inclusive? Objectives: Our overall objective is to help fill this gap, investigating what kinds of diverse users an AI product leaves out, and how to act upon that knowledge. To bring actionability to our findings, we focus on users' diversity of problem-solving attributes. Thus, our specific objectives were: (1) to reveal whether participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants' problem-solving diversity to their demographic diversity, specifically, gender and age. Methods: We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants. Each experiment compared two AI products: one deliberately violating an HAI guideline and the other applying the guideline. For our first objective, we analyzed how much each AI product gained/lost inclusivity compared to its counterpart, where inclusivity was supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants' problem-solving styles aligned with their demographics, namely their genders and ages. Results & Implications: Participants' diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline-but the "who" that got most of the inclusivity varied widely by guideline and by problem-solving style...
Visual Hide and Seek

We train embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator. We place a variety of obstacles in the environment for the prey to hide behind, and we only give the agents partial observations of their environment using an egocentric perspective. Although we train the model to play this game from scratch, experiments and visualizations suggest that the agent learns to predict its own visibility in the environment. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, effect the learned policy. Our results suggest that, although agent weaknesses make the learning problem more challenging, they also cause more useful features to be learned. Our project website is available at: http://www.cs.columbia.edu/ ~bchen/visualhideseek/.
Video Person Re-identification using Attribute-enhanced Features

Video-based person re-identification (Re-ID) which aims to associate people across non-overlapping cameras using surveillance video is a challenging task. Pedestrian attribute, such as gender, age and clothing characteristics contains rich and supplementary information but is less explored in video person Re-ID. In this work, we propose a novel network architecture named Attribute Salience Assisted Network (ASA-Net) for attribute-assisted video person Re-ID, which achieved considerable improvement to existing works by two methods.First, to learn a better separation of the target from background, we propose to learn the visual attention from middle-level attribute instead of high-level identities. The proposed Attribute Salient Region Enhance (ASRE) module can attend more accurately on the body of pedestrian. Second, we found that many identity-irrelevant but object or subject-relevant factors like the view angle and movement of the target pedestrian can greatly influence the two dimensional appearance of a pedestrian. This problem can be mitigated by investigating both identity-relevant and identity-irrelevant attributes via a novel triplet loss which is referred as the Pose~\&~Motion-Invariant (PMI) triplet loss.
Towards Robust Deep Learning using Entropic Losses

Current deep learning solutions are well known for not informing whether they can reliably classify an example during inference. One of the most effective ways to build more reliable deep learning solutions is to improve their performance in the so-called out-of-distribution detection task, which essentially consists of "know that you do not know" or "know the unknown". In other words, out-of-distribution detection capable systems may reject performing a nonsense classification when submitted to instances of classes on which the neural network was not trained. This thesis tackles the defiant out-of-distribution detection task by proposing novel loss functions and detection scores. Uncertainty estimation is also a crucial auxiliary task in building more robust deep learning systems. Therefore, we also deal with this robustness-related task, which evaluates how realistic the probabilities presented by the deep neural network are. To demonstrate the effectiveness of our approach, in addition to a substantial set of experiments, which includes state-of-the-art results, we use arguments based on the principle of maximum entropy to establish the theoretical foundation of the proposed approaches. Unlike most current methods, our losses and scores are seamless and principled solutions that produce accurate predictions in addition to fast and efficient inference. Moreover, our approaches can be incorporated into current and future projects simply by replacing the loss used to train the deep neural network and computing a rapid score for detection.
Blockchain Governance using Reference Architectures

Although Blockchain has grown in importance, great barriers still remain, including high failure rates, governance concerns and issues with quality attributes. These barriers have complex interactions and many trade-offs which suggests the need for design tools such as reference governance models and reference architectures. However, these tools are scarce and research in this area, especially governance, is sparse. The object of this research is thus to propose and then validate a new reference governance model and reference architecture to better design systems with desired attributes. The research will be conducted using the design science methodology and validated through case studies.
PECULIARITIES OF ORGANIZATION OF REMOTE INTERACTION IN GENERAL SECONDARY EDUCATION IN A COVID-19 PANDEMIC

The article reveals the peculiarities of the organization of distance interaction in general secondary education institutions in the context of the COVID-19 pandemic. The author used methods of analysis, synthesis, systematization, comparison, generalization, categorical and functional analysis. The study, based on the analysis of a wide range of sources and literature, clarifies the concept of remote interaction, which is considered as the interaction of students, parents and teachers at a distance between specific Internet technologies or other web tools that provide interactivity. It was found that the features of distance interaction in schools include: democratization of education and openness; complete exclusion or reduction of nonverbal information about the interlocutor; dependence on the level of technical equipment and digital competence of participants in the educational process; the risk of distorted perception of the received information, distortion of its interpretation. Based on a survey of teachers, it was found that teachers appreciate their willingness to interact remotely with students. Teachers emphasize the importance of quality technical equipment, a clear plan for remote activities, quality and efficiency of feedback. Among the promising areas of distance interaction with parents, teachers identify: parent meetings, consultations, educational seminars, and some teachers have a positive experience of remote implementation of project activities of students and joint projects of children and parents. At the same time, teachers note that the situation with the coronavirus required all participants in the educational process to very quickly restructure their interaction in general secondary education, for which they were not psychologically ready. Key words: school, distance interaction, COVID-19 pandemic.
Segmented Learning for Class-of-Service Network Traffic Classification

Class-of-service (CoS) network traffic classification (NTC) classifies a group of similar traffic applications. The CoS classification is advantageous in resource scheduling for Internet service providers and avoids the necessity of remodelling. Our goal is to find a robust, lightweight, and fast-converging CoS classifier that uses fewer data in modelling and does not require specialized tools in feature extraction. The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification. We represent the segmented traffic in the vector form using the EVR. Then, the segmented traffic is modelled for classification using random forest. Our solution's success relies on finding the optimal segment size and a minimum number of segments required in modelling. The solution is validated on multiple datasets for various CoS services, including virtual reality (VR). Significant findings of the research work are i) Synchronous services that require acknowledgment and request to continue communication are classified with 99% accuracy, ii) Initial 1,000 packets in any session are good enough to model a CoS traffic for promising results, and we therefore can quickly deploy a CoS classifier, and iii) Test results remain consistent even when trained on one dataset and tested on a different dataset. In summary, our solution is the first to propose segmentation learning NTC that uses fewer features to classify most CoS traffic with an accuracy of 99%. The implementation of our solution is available on GitHub.
MODELING OF PROFESSIONAL TRAINING OF STUDENTS – FUTURE TEACHERS OF TECHNICAL DISCIPLINES TAKING INTO ACCOUNT WORLDSKILLS STANDARDS

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Doing More Complex Tasks

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
A Map of Diverse Synthetic Stable Roommates Instances

Focusing on Stable Roommates (SR) instances, we contribute to the toolbox for conducting experiments for stable matching problems. We introduce a polynomial-time computable pseudometric to measure the similarity of SR instances, analyze its properties, and use it to create a map of SR instances. This map visualizes 460 synthetic SR instances (each sampled from one of ten different statistical cultures) as follows: Each instance is a point in the plane, and two points are close on the map if the corresponding SR instances are similar to each other. Subsequently, we conduct several exemplary experiments and depict their results on the map, illustrating the map's usefulness as a non-aggregate visualization tool, the diversity of our generated dataset, and the need to use instances sampled from different statistical cultures. Lastly, to demonstrate that our framework can also be used for other matching problems under preference, we create and analyze a map of Stable Marriage instances.
Rankability and Linear Ordering Problem: New Probabilistic Insight and Algorithms

The linear ordering problem (LOP), which consists in ordering M objects from their pairwise comparisons, is commonly applied in many areas of research. While efforts have been made to devise efficient LOP algorithms, verification of whether the data are rankable, that is, if the linear ordering problem (LOP) solutions have a meaningful interpretation, received much less attention. To address this problem, we adopt a probabilistic perspective where the results of pairwise comparisons are modeled as Bernoulli variables with a common parameter and we estimate the latter from the observed data. The brute-force approach to the required enumeration has a prohibitive complexity of O(M !), so we reformulate the problem and introduce a concept of the Slater spectrum that generalizes the Slater index, and then devise an algorithm to find the spectrum with complexity O(M^3 2^M) that is manageable for moderate values of M. Furthermore, with a minor modification of the algorithm, we are able to find all solutions of the LOP with the complexity O(M 2^M). Numerical examples are shown on synthetic and real-world data, and the algorithms are publicly available.
Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki

This article presents a protocol for investigating the role of visual imagery in the bouba/kiki-effect, whether training in noticing the bouba/kiki shape-audio regularities affects the bouba/kiki-effect and the recognition of individual bouba and kiki shapes, and finally what mental images these regularities produce. To generate bouba/kiki shape-audio regularities, there were two types of shapes (filled; outlined) and two types of audio (word; non-word sound). Three groups of individuals participated in three experiments: Blind, blindfold, and vision. The experiments were conducted in fixed order across participants, with no break between them. In Experiment 1 (pre-test-post-test design with three repeated within-group measures) the participants were asked to pick out the shape they associated with the auditory bouba/kiki; in Experiment 2 (within-subject design), to name one shape and some audio (sometimes congruous; sometimes incongruous) as ‘bouba’ or ‘kiki;’ and in Experiment 3 (post-test only design), to draw the shape they associated with the auditory bouba/kiki. The results suggest that the blindfold-group draw upon visual imagery to solve new problems, but not long term; that training in noticing bouba/kiki shape-audio regularities affects the bouba/kiki-effect and the recognition of individual bouba and kiki shapes, but differently in each experimental group; and that all experimental groups create mental images of the most characteristic shape feature of bouba (curve) and kiki (angle). In fact, the effect of visual imagery is robust across tasks, but not long term; the effect of learning shape-audio regularities is robust long term, but not across tasks. The presented protocol is appropriate for investigating the effect of visual imagery and learning shape-audio regularities, when they occur and how robust they are; in specific individuals and groups of individuals. This protocol is unique in that it keeps under control both the visual imagery and the sensory information during training and testing.
Rate-Distortion Optimized Quantization in Motion JPEG

Rate-Distortion Optimized Quantization (RDOQ) is an encoding optimization technique that may be applied to any transform-based compression technique preserving bitstream compliance with the standard. In the paper, the application of the RDOQ to Motion JPEG is described and evaluated. The proposed solution includes block- level optimization with picture-level Lagrange multiplier estimation. Performed evaluation results in higher compression ratios as compared to typical Motion JPEG.
HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Autonomous robots can benefit greatly from human-provided semantic characterizations of uncertain task environments and states. However, the development of integrated strategies which let robots model, communicate, and act on such 'soft data' remains challenging. Here, the Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams to address these gaps by formally combining the benefits of online sampling-based POMDP policies, multimodal semantic interaction, and Bayesian data fusion. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments by sketching and labeling arbitrary landmarks across the environment. Dynamic updating of the environment model while during search allows robotic agents to actively query humans for novel and relevant semantic data, thereby improving beliefs of unknown environments and states for improved online planning. Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates required for interception versus conventional planning based solely on robotic sensing. Human subject studies in the same environment (n = 36) demonstrate an average doubling in dynamic target capture rate compared to the lone robot case, and highlight the robustness of active probabilistic reasoning and semantic sensing over a range of user characteristics and interaction modalities.
Lesson Exemplars Using Original Cebuano Stories in Teaching Mother Tongue-Based Multilingual Education (MTB-MLE)

The implementation of the Mother Tongue-Based Multilingual Education (MTB-MLE) as a subject and as a Medium of Instruction (MOI) for Grades 1, 2 and 3 gave rise to language issues in teaching and learning.This study presents the lesson exemplars using new original Cebuano stories to facilitate the teaching of MTB-MLE subject among Grade one students.This study used the quasi-experimental research design that employed the control group and the experimental group of Grade 1 students that investigated the effects of using Cebuano stories and exercises in MTB-MLE class.A researchermade, expert-validated pretest instrument was used to gather baseline data on participants' performance in MTB-MLE of both groups.Results showed that there is a significant difference between students' MTB-MLE performance and participation before and after.During their MTBMLE classes, the experimental group was more motivated to listen and learn new words through Cebuano stories told in class than simply unlocking unfamiliar words in the control group.The lesson exemplars using Cebuano stories served as an effective means of reinforcing lessons in the MTB-MLE subject.Thus, lesson exemplars were proposed to promote the teaching and learning in MTB-MLE subject, enhancing the students' performance and participation in class.
TIC: Text-Guided Image Colorization

Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make the colorization pipeline automatic, these processes often produce unrealistic results due to a lack of conditioning. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To the best of our knowledge, this is one of the first attempts to incorporate textual conditioning in the colorization pipeline. To do so, we have proposed a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. We have evaluated our proposed model using different metrics and found that it outperforms the state-of-the-art colorization algorithms both qualitatively and quantitatively.
Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan

This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021. The main goal of the challenge is to evaluate new technologies for text-dependent (TD) and text-independent (TI) speaker verification (SV) in a short duration scenario. The proposed challenge evaluates SdSV with varying degree of phonetic overlap between the enrollment and test utterances (cross-lingual). It is the first challenge with a broad focus on systematic benchmark and analysis on varying degrees of phonetic variability on short-duration speaker recognition. We expect that modern methods (deep neural networks in particular) will play a key role.
Semi-Supervised Text Classification via Self-Pretraining

We present a neural semi-supervised learning model termed Self-Pretraining. Our model is inspired by the classic self-training algorithm. However, as opposed to self-training, Self-Pretraining is threshold-free, it can potentially update its belief about previously labeled documents, and can cope with the semantic drift problem. Self-Pretraining is iterative and consists of two classifiers. In each iteration, one classifier draws a random set of unlabeled documents and labels them. This set is used to initialize the second classifier, to be further trained by the set of labeled documents. The algorithm proceeds to the next iteration and the classifiers' roles are reversed. To improve the flow of information across the iterations and also to cope with the semantic drift problem, Self-Pretraining employs an iterative distillation process, transfers hypotheses across the iterations, utilizes a two-stage training model, uses an efficient learning rate schedule, and employs a pseudo-label transformation heuristic. We have evaluated our model in three publicly available social media datasets. Our experiments show that Self-Pretraining outperforms the existing state-of-the-art semi-supervised classifiers across multiple settings. Our code is available at https://github.com/p-karisani/self_pretraining.