Reviewerly/exHarmony huggingface dataset:
Enhanced Privacy Duties for Dominant Technology Companies

This paper explores two case studies in order to extract the elements of a legislative approach in the United States for stronger privacy regulation for dominant technology companies. The Federal Communication Commission’s now-repealed broadband privacy rules required broadband companies to obtain opt-in consent for data use beyond what was needed for providing broadband service. In an abuse of dominance case, the German Federal Cartel Office required Facebook to get separate affirmative consent from its users for combining third-party and affiliate data with data from its own social network service. Informed by these two cases, this paper outlines a new privacy law for dominant technology companies in the United States. It would establish enhanced privacy duties for dominant technology companies to ensure that the consumer interest in effective data protection is vindicated in a context where consumers are less able to exercise their general privacy rights. It develops several economic factors to test whether a company is dominant in its line of business and covers companies providing search, social media, online retailing and video exchange services. The paper proposes that such dominant technology companies would need to have a legal basis for their data use and describes consent, contractual necessity and legitimate interest as three possible bases. In addition, dominant technology companies would not be allowed to use unreasonably deceptive, abusive or dangerous designs, as described in Woodward Hartzog’s recent work. Furthermore, they would be required to satisfy enhanced duties of information fiduciaries as outlined in Jack Balkin’s work. The law would also impose restrictions on data sharing with service providers and affiliates and hold open the possibility of banning specific data uses.The law would put in the hands of an industry-specialist regulatory agency the authority to take into account the specified factors to determine when a technology company was dominant and so subject to enhanced privacy duties. The specialist agency would also be authorized to write and enforce implementing regulations in connection with the requirements specified in the legislation.
Teaching and Learning Computer-Intelligent Games

Games, whether created for entertainment, simulation, or education, provide great opportunities for machine learning (ML). Due to game engine design limitations and the rapid game development and publication cycle, current commercial game engines don’t offer the opportunity for developers to integrate true machine-intelligence algorithms to personalize a complete game play experience. Although open-source application programming interfaces have been developed to connect game builds to external ML frameworks and tools, like player experience modeling (PEM) or adaptive non-player character behaviors, their effective uses have been imperfect outside of scholarly research. PEM involves the observation, analysis, and simulation of a player’s actions and tactical and strategic decisions, all modulated by behaviors discovered in game play across a single game, level, quest, or checkpoint. The chapter then discusses The State of Artificial-Intelligence (AI) in game engine integration. AI-focused learning game engine where the AI engine is at the center of the game engine design entirely hosted in a cloud server.
Touched by vision: how heterosexual, gay, and lesbian people react to the view of their avatar being caressed on taboo body parts

Embodying an artificial agent through immersive virtual reality (IVR) may lead to feeling somatosensory stimuli on one’s body which are in fact never delivered. What remains unknown is whether the vicarious touch in virtual reality reflects the basic individual and social features of real-life interpersonal interactions. Here, we explored the subjective and objective reactivity of heterosexual men/women (Study 1) and of gay men/lesbian women (Study 2) to the observation of a gender-matched virtual body being touched on different body parts, including the breast and genitalia, by male and female avatars. In addition to the illusory feeling of being touched and of owning the virtual body, participants reported on different aspects of the experience (e.g., appropriateness, erogeneity). Physiological measures of autonomic reactivity were also collected. Heterosexual men rated the female avatar’s touch as more appropriate and erogenous, while heterosexual women rated female and male avatar touches as equally appropriate, with the latter being most erogenous. Interestingly, gay men exhibited the same pattern of appropriateness and erogeneity as heterosexual women. In contrast, lesbian women rated more appropriate and erogenous the touches of the female avatar. For all participants, the most appropriate and erogenous regions were the social and the intimate ones, respectively. Importantly, touches on the virtual body’s intimate areas elicited the highest skin conductance response when participants were touched by a female avatar. Thus, IVR may easily induce vicarious experiences and ultimately allow the direct exploration of sensitive societal and individual issues that can otherwise be explored only through imagination.
Robust Value Iteration for Continuous Control Tasks

When transferring a control policy from simulation to a physical system, the policy needs to be robust to variations in the dynamics to perform well. Commonly, the optimal policy overfits to the approximate model and the corresponding state-distribution, often resulting in failure to trasnfer underlying distributional shifts. In this paper, we present Robust Fitted Value Iteration, which uses dynamic programming to compute the optimal value function on the compact state domain and incorporates adversarial perturbations of the system dynamics. The adversarial perturbations encourage a optimal policy that is robust to changes in the dynamics. Utilizing the continuous-time perspective of reinforcement learning, we derive the optimal perturbations for the states, actions, observations and model parameters in closed-form. Notably, the resulting algorithm does not require discretization of states or actions. Therefore, the optimal adversarial perturbations can be efficiently incorporated in the min-max value function update. We apply the resulting algorithm to the physical Furuta pendulum and cartpole. By changing the masses of the systems we evaluate the quantitative and qualitative performance across different model parameters. We show that robust value iteration is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm. Videos of the experiments are shown at this https URL
Rule-Based Dialog Management for Voice Assistents in Automotive Environments

We describe a rule-based voice dialog system for in-vehicle infotainment.The system is specified by a declarative model comprising the dialog definition,dialog management logic, and domain knowledge, which are backed by anRDFS ontology. The approach simplifies coping with dialog phenomena such asanaphora or implicit confirmation, that can be addressed without writing code. Weintroduce the concept of a virtual knowledge base to accommodate the diversity ofdialog-relevant knowledge sources in a car. It unifies the knowledge access for thedialog logic, no matter whether an internal knowledge base or an external sourcelike a web service is being used. The virtual KB thus supports the model’s portabilityand re-usability. Dialog models can be exported and deployed to on-marketvoice assistants like Amazon Alexa and Google Assistant, which essentially meansto extend their built-in dialog management capabilities. The dialog managementrun-time system is deployed on a separate cloud server and acts as a back-end forthe respective voice assistant.
Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II. However, existing approaches to tackle full games have high computational costs, usually requiring the use of thousands of GPUs and CPUs for weeks. This paper has two main contributions to address this issue: 1) We introduce Gym-$\mu$RTS (pronounced gym-micro-RTS) as a fast-to-run RL environment for full-game RTS research and 2) we present a collection of techniques to scale DRL to play full-game $\mu$RTS as well as ablation studies to demonstrate their empirical importance. Our best-trained bot can defeat every $\mu$RTS bot we tested from the past $\mu$RTS competitions when working in a single-map setting, resulting in a state-of-the-art DRL agent while only taking about 60 hours of training using a single machine (one GPU, three vCPU, 16GB RAM).
On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future.

Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures. In spite of this, due to the great performance provided by the architectures which are found, these methods are widely applied. The final outcome of neuroevolutionary processes is the best structure found during the search, and the rest of the procedure is commonly omitted in the literature. However, a good amount of residual information consisting of valuable knowledge that can be extracted is also produced during these searches. In this paper, we propose an approach that extracts this information from neuroevolutionary runs, and use it to build a metamodel that could positively impact future neural architecture searches. More specifically, by inspecting the best structures found during neuroevolutionary searches of generative adversarial networks with varying characteristics (e.g., based on dense or convolutional layers), we propose a Bayesian network-based model which can be used to either find strong neural structures right away, conveniently initialize different structural searches for different problems, or help future optimization of structures of any type to keep finding increasingly better structures where uninformed methods get stuck into local optima.
An Analysis of the Performance of Websockets in Various Programming Languages and Libraries

As the demand for real-time data increases, so does the use of websockets. It is crucial to consider the speed along with the reliability of a language and websocket library before implementing it in an application. This study aims to benchmark various websocket servers in order to determine which one offers the fastest round trip time of a request, as well as the reliability of the websocket server under load.
On self-orthogonality and self-duality of matrix-product codes over commutative rings

Self-orthogonal codes and self-dual codes, on the one hand, and matrix-product codes, on the other, form important and sought-after classes of linear codes. Combining the two constructions would be advantageous. Adding to this combination the relaxation of the underlying algebraic structures to be commutative rings instead of fields would be even more advantageous. The current article paves a path in this direction. The authors study the problem of self-orthogonality and self-duality of matrix-product codes over a commutative ring with identity. Some methods as well as special matrices are introduced for the construction of such codes. A characterization of such codes in some cases is also given. Some concrete examples as well as applications to torsion codes are presented.
DiBS: Differentiable Bayesian Structure Learning.

Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Building on recent advances in variational inference, we use DiBS to devise an efficient method for approximating posteriors over structural models. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and the conditional distribution parameters. This makes our method directly applicable to posterior inference of nonstandard Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks. In evaluations on simulated and real-world data, DiBS significantly outperforms related approaches to joint posterior inference.
Human-Understandable Decision Making for Visual Recognition

The widespread use of deep neural networks has achieved substantial success in many tasks. However, there still exists a huge gap between the operating mechanism of deep learning models and human-understandable decision making, so that humans cannot fully trust the predictions made by these models. To date, little work has been done on how to align the behaviors of deep learning models with human perception in order to train a human-understandable model. To fill this gap, we propose a new framework to train a deep neural network by incorporating the prior of human perception into the model learning process. Our proposed model mimics the process of perceiving conceptual parts from images and assessing their relative contributions towards the final recognition. The effectiveness of our proposed model is evaluated on two classical visual recognition tasks. The experimental results and analysis confirm our model is able to provide interpretable explanations for its predictions, but also maintain competitive recognition accuracy.
Conditional Level Generation and Game Blending.

Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data. We build on such models by exploring the level design affordances and applications enabled by conditional VAEs (CVAEs). CVAEs augment VAEs by allowing them to be trained using labeled data, thus enabling outputs to be generated conditioned on some input. We studied how increased control in the level generation process and the ability to produce desired outputs via training on labeled game level data could build on prior PCGML methods. Through our results of training CVAEs on levels from Super Mario Bros., Kid Icarus and Mega Man, we show that such models can assist in level design by generating levels with desired level elements and patterns as well as producing blended levels with desired combinations of games.
Efektifitas Penggunaan Aplikasi Zoom Meeting Dalam Pembelajaran  Di Masa Pandemi Covid-19

Abstract: In the current situation, with the threat of Covid-19, it is hoped that educational institutions (university) are required to implement online learning activities. Through the use of the zoom meeting application provided by the university, it is possible to support online learning activities. In essence, this study aims to see to what extent the effectiveness of distance learning using the Zoom Meeting application for students majoring in Social Sciences, FKIP-Unpatti. The method used in this research is qualitative with a grounded theory approach. With data collection techniques used through interviews and WhatsApp for 40 students majoring in Social Sciences FKIP-Unpatti. Based on the research results obtained, distance learning using zoom meetings is certainly not effective during the lecture process. However, considering the current situation with the outbreak of Covid-19, the use of the zoom meeting application is very relevant in supporting the learning process.
Learning Bounded Subsets of <i>Lₚ</i>

We study learning problems in which the underlying class is a bounded subset of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> and the target Y belongs to L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> . Previously, minimax sample complexity estimates were known under such boundedness assumptions only when p=∞. We present a sharp sample complexity estimate that holds for any p > 4; it is based on a learning procedure that is suited for heavy-tailed problems.
Educational standards: new challenges

Examined is the problem of the content of educational standards of various levels of training: secondary vocational education (SPO), bachelor’s degree, specialty, master’s degree. Shown by the example of one enlarged group of specialties and directions, that the standards do not differ much after the changes in 2020. Noted is, that educational programs will differ after the educational organization determines the training profiles and professional competencies. Proposed is to develop generalized standards for all levels of higher education and to form a qualification characteristic for each direction or specialty, indicating the training profiles, the list of professional competencies and the professional standards recommended for use. Also proposed is to form a qualification characteristic for each direction or specialty, indicating the training profiles, the list of professional competencies and the professional standards recommended for use.
A Neural Network Approach to Navigation of a Mobile Robot and Obstacle Avoidance in Dynamic and Unknown Environments

Mobile robot navigation and obstacle avoidance in dynamic and unknown environments is one of the most challenging problems in the field of robotics. Considering that a robot must be able to interact with the surrounding environment and respond to it in real time, and given the limited sensing range, inaccurate data, and noisy sensor readings, this problem becomes even more acute. In this paper, we attempt to develop a neural network approach equipped with statistical dimension reduction techniques to perform exact and fast robot navigation, as well as obstacle avoidance in such a manner. In order to increase the speed and precision of the network learning and reduce the noise, kernel principal component analysis is applied to the training patterns of the network. The proposed method uses two feed-forward neural networks based on function approximation with a back-propagation learning algorithm. Two different data sets are used for training the networks. In order to visualize the robot environment, 180◦ laser range sensor (SICK) readings are employed. The method is tested on real-world data and experimental results are included to verify the effectiveness of the proposed method.
Discrete logarithms in quasi-polynomial time in finite fields of fixed characteristic.

We prove that the discrete logarithm problem can be solved in quasi-polynomial expected time in the multiplicative group of finite fields of fixed characteristic. More generally, we prove that it can be solved in the field of cardinality pn in expected time (pn)2log2(n)+O(1).
Pedestrian attribute recognition using two-branch trainable Gabor wavelets network

Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This multi-label problem is extremely challenging even for human observers and has rightly garnered attention from the computer vision community. Towards a solution to this problem, in this paper, we adopt trainable Gabor wavelets (TGW) layers and cascade them with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We propose a two-branch neural network where mixed layers, a combination of the TGW and convolutional layers, make up the building block of our deep neural network. We test our method on twoo challenging publicly available datasets and compare our results with state of the art.
Feature Reuse and Fusion for Real-Time Semantic Segmentation

For real-time semantic segmentation, how to increase the speed while maintaining high resolution is a problem that has been discussed and solved. Backbone design and fusion design have always been two essential parts of real-time semantic segmentation. We hope to design a light-weight network based on previous design experience and reach the level of state-of-the-art real-time semantic segmentation without any pre-training. To achieve this goal, a encoder-decoder architectures are proposed to solve this problem by applying a decoder network onto a backbone model designed for real-time segmentation tasks and designed three different ways to fuse semantics and detailed information in the aggregation phase. We have conducted extensive experiments on two semantic segmentation benchmarks. Experiments on the Cityscapes and CamVid datasets show that the proposed FRFNet strikes a balance between speed calculation and accuracy. It achieves 72% Mean Intersection over Union (mIoU%) on the Cityscapes test dataset with the speed of 144 on a single RTX 1080Ti card. The Code is available at https://github.com/favoMJ/FRFNet.
Innovation and changes in education management

This article examines the relevance of modernization of today`s education, in particular, the reform of activities in the management of educational organizations. Modernization of management of educational organizations involves the introduction of innovative approaches in the management of educational organizations. It was revealed that one of the priority areas that characterize the activities of modern educational institutions is the transformation of its management cycle. As one of the effective management methods, in contrast to the existing traditional vertical command-administrative system, is considered a horizontal-cyclical organizational structure (HCOS). This transformation has aimed to the implementation of decentralized coordination of the actions of the relevant organizational units (the formed departments) in the process of performing common tasks. The presented work will investigate the implementation of HCOS on the example of the Institute of Natural sciences and geography of the Abai Kazakh National Pedagogical University.