Reviewerly/exHarmony huggingface dataset:
Reducing motion to photon latency in multi-focal augmented reality near-eye display

It is foreseen that the most convenient hardware for depiction of augmented reality (AR) will be optical seethrough head-mounted displays. Currently such systems are utilizing single focal plane and are inflicting vergenceaccommodation conflict to the human visual system – limiting wide acceptance. In this work, we analyze an optical seethrough AR head-mounted display prototype which has four focal planes operating in time-sequential mode thus mitigating limitation of single focal plane devices. Nevertheless, optical see-through nature implies requirement of very short motion-to-photon latency not to cause noticeable misalignment between the digital content and real-world scene. The utilized prototype display relies on commercial visual-SLAM spatial tracking module (Intel realsense T265) and within this work we analyzed factors improving motion-to-photon latency with the provided hardware setup. The performance analysis of the T265 module revealed slight translational and angular jitter – on the order of <1 mm and <15 arcseconds, and velocity readout of few cm/s from a completely still IMU. The experimentally determined motion-tophoton latency and render-to-photon latency was 46±6 ms and 38 ms respectively. To overcome IMU positional jitter, pose averaging with variable width of the averaging window was implemented. Based on immediate acceleration and velocity data the size of the averaging window was adjusted. To perform pose prediction a basic rotational-axis offset model was verified. Based on prerecorded head movements, a training model reduced the error between the predicted and actual recorded pose. The optimization parameters were corresponding offset values of the IMU’s rotational axis, translational and angular velocity as well as angular acceleration. As expected, the highest weight for the most accurate predictions was observed for velocities following angular acceleration. The role of offset values wasn’t significant. For improved perceived experience and motion-to-photon latency reduction we consider further investigation of simple trained neural networks for more accurate real-time pose prediction as well as investigation of content-driven adaptive image output overriding default order of image plane output in a time-sequential sequence.
Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units

Machine learning (ML) is successful in achieving human-level performance in various fields. However, it lacks the ability to explain an outcome due to its black-box nature. While existing explainable ML is promising, almost all of these methods focus on formatting interpretability as an optimization problem. Such a mapping leads to numerous iterations of time-consuming complex computations, which limits their applicability in real-time applications. In this paper, we propose a novel framework for accelerating explainable ML using Tensor Processing Units (TPUs). The proposed framework exploits the synergy between matrix convolution and Fourier transform, and takes full advantage of TPU's natural ability in accelerating matrix computations. Specifically, this paper makes three important contributions. (1) To the best of our knowledge, our proposed work is the first attempt in enabling hardware acceleration of explainable ML using TPUs. (2) Our proposed approach is applicable across a wide variety of ML algorithms, and effective utilization of TPU-based acceleration can lead to real-time outcome interpretation. (3) Extensive experimental results demonstrate that our proposed approach can provide an order-of-magnitude speedup in both classification time (25x on average) and interpretation time (13x on average) compared to state-of-the-art techniques.
Hey Alexa, What Should I Read? Comparing the Use of Social and Algorithmic Recommendations for Different Reading Genres

Users often seek reading recommendations for what to read, across a variety of topics of interest and genres. While there has been extensive research on the development of recommender algorithms, our understanding of social factors relating to reading recommendation in the digital era is poor. We have no holistic view of how readers interact with diverse resources, social and digital, to obtain reading recommendations. Users can consult computer-generated summaries and human-created reviews. How much or how often the typical user relies on one or other source, or what variations there are by genre of intended reading, are both open questions. To narrow these research gaps, we conducted a diary study to capture a comprehensive picture of readers' use of algorithm- and social-sourced information to inform their future reading choices. Based on a qualitative analysis of these diaries, we produced a survey to investigate in-depth readers' recommendation preferences across fictional reading, factual reading, academic resources, and news and articles. We show that users rely on different sources of recommendation information in different ways across different genres, and that modern social media plays an increasing role alongside established mass media, especially for fiction.
An Adaptive Differential Evolution Algorithm with Restart for Solving Continuous Optimization Problems

A new adaptive differential evolution algorithm with restart (ADE-R) is proposed as a general-purpose method for solving continuous optimization problems. Its design aims at simplicity of use, efficiency and robustness. ADE-R simulates a population evolution of real vectors using vector mixing operations with an adaptive parameter control based on the switching of two selected intervals of values for each scaling factor and crossover rate of the basic differential evolution algorithm. It also incorporates a restart technique to supply new contents to the population to prevent premature convergence and stagnation. The method is tested on several benchmark functions covering various types of functions and compared with some well-known and state-of-art methods. The experimental results show that ADE-R is effective and outperforms the compared methods.
Stereo Matching with Spatiotemporal Disparity Refinement Using Simple Linear Iterative Clustering Segmentation

Stereo matching is a challenging problem, especially for computer vision, e.g., three-dimensional television (3DTV) or 3D visualization. The disparity maps from the video streams must be estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in poor visual quality for the synthesized video and reduce the video coding information. In order to solve this problem, we here propose a spatiotemporal disparity refinement method for local stereo matching using the simple linear iterative clustering (SLIC) segmentation strategy, outlier detection, and refinements of the temporal and spatial domains. In the outlier detection, the segmented region in the initial disparity is used to distinguish errors in the binocular disparity. Based on the color similarity and disparity difference, we recalculate the aggregated cost to determine adaptive disparities to recover the disparity errors in disparity sequences. The flickering errors are also effectively removed, and the object boundaries are well preserved. Experiments using public datasets demonstrated that our proposed method creates high-quality disparity maps and obtains a high peak signal-to-noise ratio compared to state-of-the-art methods.
A Decentralized and Autonomous Model to Administer University Examinations

Administering standardized examinations is a challenging task, especially for those universities for which colleges affiliated to it are geographically distributed over a wide area. Some of the challenges include maintaining integrity and confidentiality of examination records, preventing mal-practices, issuing unique identification numbers to a large student population and managing assets required for the smooth conduct of examinations. These challenges aggravate when colleges affiliated to universities demand academic and administrative autonomy by demonstrating best practices consistently over a long period. In this chapter, we describe a model for decentralized and autonomous examination system to provide the necessary administrative support. The model is based on two emerging technologies of Blockchain Technology and Internet of Things (IoT). We adopt a software architecture approach to describe the model. The prescriptive architecture consists of {\em architectural mappings} which map functional and non-functional requirements to architectural elements of blockchain technology and IoT. In architectural mappings, first, we identify common use-cases in administering standardized examinations. Then we map these use-cases to the core elements of blockchain, i.e. distributed ledgers, cryptography, consensus protocols and smart-contracts and IoT. Such kind of prescriptive architecture guide downstream software engineering processes of implementation and testing
Some Current Aspects of Big Data Evolution

The paper discusses the current trends in digitalization process. A number of technologies that are critical for the digital ecosystem formation (such as big data and artificial intelligence technologies) are discussed. The current approach to digital technology regulation is described. A number of characteristics of the big data life cycle are performed. The complementary connection between science and digital technologies based on it and the social life is shown. The state policy that promotes innovative development is proved to be based on setting general norms and prospects. The methods and approaches from the scientific field are supposed to be the basic tool to form digital society. A number of examples of the introduction of digital technologies in the scientific field are discussed.
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems

Access to high-quality education at scale is limited by the difficulty of providing student feedback on open-ended assignments in structured domains like computer programming, graphics, and short response questions. This problem has proven to be exceptionally difficult: for humans, it requires large amounts of manual work, and for computers, until recently, achieving anything near human-level accuracy has been unattainable. In this paper, we present generative grading: a novel computational approach for providing feedback at scale that is capable of accurately grading student work and providing nuanced, interpretable feedback. Our approach uses generative descriptions of student cognition, written as probabilistic programs, to synthesise millions of labelled example solutions to a problem; we then learn to infer feedback for real student solutions based on this cognitive model. We apply our methods to three settings. In block-based coding, we achieve a 50% improvement upon the previous best results for feedback, achieving super-human accuracy. In two other widely different domains -- graphical tasks and short text answers -- we achieve major improvement over the previous state of the art by about 4x and 1.5x respectively, approaching human accuracy. In a real classroom, we ran an experiment where we used our system to augment human graders, yielding doubled grading accuracy while halving grading time.
Incorporating individual and group privacy preferences in the internet of things

This paper presents a new privacy negotiation mechanism for an IoT environment that is both efficient and practical to cope with the IoT special need of seamlessness. This mechanism allows IoT users to express and enforce their personal privacy preferences in a seamless manner while interacting with IoT deployments. A key contribution of the paper is that it addresses the privacy concerns of individual users as well as a group of users where privacy preferences of all individual users are combined into a group privacy profile to be negotiated with the IoT owner. In addition, the proposed mechanism satisfies the privacy requirements of the IoT deployment owner. Finally, the proposed privacy mechanism is agnostic to the actual IoT architecture and can be used over a user-managed, edge-managed or a cloud-managed IoT architecture. Prototypes of the proposed mechanism have been implemented for each of these three architectures, and the results show the capability of the protocol to negotiate privacy while adding insignificant time overhead.
A Computational Approach to Historical Ontologies

This paper presents a use case exploring the application of the Archival Resource Key (ARK) persistent identifier for promoting and maintaining ontologies. In particular we look at improving computation with an in-house ontology server in the context of temporally aligned vocabularies. This effort demonstrates the utility of ARKs in preparing historical ontologies for computational archival science.
Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning.

Overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little attention in the multi-agent setting. In this work, we empirically demonstrate that QMIX, a popular $Q$-learning algorithm for cooperative multi-agent reinforcement learning (MARL), suffers from a particularly severe overestimation problem which is not mitigated by existing approaches. We rectify this by designing a novel regularization-based update scheme that penalizes large joint action-values deviating from a baseline and demonstrate its effectiveness in stabilizing learning. We additionally propose to employ a softmax operator, which we efficiently approximate in the multi-agent setting, to further reduce the potential overestimation bias. We demonstrate that our Softmax with Regularization (SR) method, when applied to QMIX, accomplishes its goal of avoiding severe overestimation and significantly improves performance in a variety of cooperative multi-agent tasks. To demonstrate the versatility of our method, we apply it to other $Q$-learning based MARL algorithms and achieve similar performance gains. Finally, we show that our method provides a consistent performance improvement on a set of challenging StarCraft II micromanagement tasks.
CLASSICAL DIDACTIC PRINCIPLES AND ELECTRONIC LEARNING ENVIRONMENTS

The article discusses the didactic aspects of the use of distance learning tools on the example of the electronic information and educational environment (EIEE) of the Volgograd State Technical University (VolgSTU). It is shown that classical didactic principles are also applicable when using information and communication technologies.
Certifying Proofs in the First-Order Theory of Rewriting

Abstract The first-order theory of rewriting is a decidable theory for linear variable-separated rewrite systems. The decision procedure is based on tree automata techniques and recently we completed a formalization in the Isabelle proof assistant. In this paper we present a certificate language that enables the output of software tools implementing the decision procedure to be formally verified. To show the feasibility of this approach, we present , a reincarnation of the decision tool with certifiable output, and the formally verified certifier .
Visual exploration of visual parser execution

Abstract In this paper, we present ParVis , an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by simply defining a JavaScript module that maps each logged parser instruction into a set of events driving the visual system interface. The result is a set of interacting graphical/text windows that allows users to explore logged parser executions and helps them to have a complete understanding of how the parser behaves during its execution on a given input. We used our system to visualize the behavior of textual as well as visual parsers and describe here two of these uses. Moreover, in order to validate the efficacy of our system, we ran a user experiment where students analyzed a CUP-generated parser both with ParVis , instantiated to LALR parsers, and the standard CUP debug facilities. The results show that students can indeed analyze parser behavior and find mistakes in parser specifications more easily and quickly using ParVis . In particular, in some parser design tasks, using ParVis participants achieved a higher success rate of 50 % in 42 % less time with respect to the baseline system.
A two-way communication system with Morse code medium for people with multiple disabilities

This paper proposes an innovative, Morse code based two-way communication system with four user-accessible modes. The four modes are determined to provide speech to Morse vibrations and Morse to speech conversion. The proposed system is embedded with touch, gesture, vibration, microphone, and speaker modules coupled with a microprocessing unit. An S-Morse dictionary convert Morse code pattern read from the touch sensor to voice and speech from microphone to Morse vibration. A gesture recognition model using the SVM algorithm is implemented for the selection of user-accessible modes. The system modes also send and receive text data from an android application through a cloud network. The received and sent data will be converted into vibrations of Morse code pattern and voice forms respectively. The proposed method is strengthened by validating Morse conversion, machine learning algorithm accuracy, gesture recognition accuracy, Morse time analysis for alphabets, word and gesture analysis for mode selection.
An implementation of virtual white board using open CV for virtual classes

In the current pandemic situation, we have seen one of the reasons for spreading of corona virus is surface touching, and to avoid this sort of spread in the virus we have come up with a project which is titled as "Drawing on Air", As the title itself indicates, we are not going to touch the surface but we are going to draw on air and what we have drawn on air will be displayed on the screen / monitor. This will help us reduce the spread of the virus in the pandemic situations. In this pandemic we have seen maximum of the transactions are been made online but for deposit of money or for transfer of money in person we see people use ATMs which is very risky and there are chances of spreading of the virus at such point of times we can use this as a prevention method to a great extent. Not only in ATMs but also in offices for biometrics or in cyber cafes and many more areas we can use this project.
EU Liability Rules for the Age of Artificial Intelligence

When Artificial Intelligence (AI) systems possess the characteristics of unpredictability and autonomy, they present challenges for the existing liability framework. Two questions about the liability of AI deserve attention from policymakers: 1) Do existing civil liability rules adequately cover risks arising in the context of AI systems? 2) How would modified liability rules for producers, owners, and users of AI play out? This report addresses the two questions for EU non-contractual liability rules. It considers how liability rules affect the incentives of producers, users, and others that may be harmed by AI. The report provides concrete recommendations for updating the EU Product Liaiblity Directive and for the possible legal standard and scope of EU liability rules for owners and users of AI.
Presenting Trends In Petrochemical Process Control Systems

Petrochemical operators view the distributed control system process through operating graphics that are displayed on four or more cathode ray tube displays that are monitored 24/7. The graphics vary in both design and content, however there has not been extensive research done on the ability of vector arrows to display this trend information. The purpose of this study was to understand the current trend representation methods and to develop efficient trend representation displays. Results indicate that providing vector arrows for trend representation can be effective in getting the attention of the user and can help in reducing change blindness. Results also indicate that between 10 and 20 degrees of change a participant notices change and that any rate of change increase beyond that does not improve their recognition, illustrating the importance of capturing the rate of change within this range in representing trend data. We were able to demonstrate that use of vector arrows for at-a-glance initial trend analysis was easy to use and effective in petrochemical industry systems.
Second language vocabulary acquisition through fan fiction on the Archive of Our Own

With the widespread diffusion of the internet and online archives, fan fiction is increasingly consumed by fans who do not speak English as a first language. It is therefore relevant to argue that fan fiction, especially as found on the fan-run Archive of Our Own, may work as a space for second language vocabulary acquisition. The high motivation and extensive engagement with forms of reading of fans who read and write fan fiction helps with vocabulary acquisition.
ANALYSIS OF INFORMATION PREFERENCES OF AGRICULTURAL SCIENTISTS IN SIBERIA

The article presents the results of a survey of employees of 32 scientific organizations ofagricultural profile in Siberia in order to study their information needs. The assessment of significance for scholars of the library as an indispensable social institution in the system of scholarly communication and the Siberian Scientific Agricultural Library of the branch of SPSL SB RAS as a critical link in a single information space of the agricultural science of Siberia. Promising directions of development of the resource base and service of SibNSHB as a branch academic library for improving the quality of information support for scientific research on agricultural issues are identified.
Does SGD Implicitly Optimize for Smoothness?

Modern neural networks can easily fit their training set perfectly. Surprisingly, despite being “overfit” in this way, they tend to generalize well to future data, thereby defying the classic bias–variance trade-off of machine learning theory. Of the many possible explanations, a prevalent one is that training by stochastic gradient descent (SGD) imposes an implicit bias that leads it to learn simple functions, and these simple functions generalize well. However, the specifics of this implicit bias are not well understood. In this work, we explore the smoothness conjecture which states that SGD is implicitly biased towards learning functions that are smooth. We propose several measures to formalize the intuitive notion of smoothness, and we conduct experiments to determine whether SGD indeed implicitly optimizes for these measures. Our findings rule out the possibility that smoothness measures based on first-order derivatives are being implicitly enforced. They are supportive, though, of the smoothness conjecture for measures based on second-order derivatives.