Reviewerly/exHarmony huggingface dataset:
Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures

We study the estimation of risk-sensitive policies in reinforcement learning problems defined by a Markov Decision Process (MDPs) whose state and action spaces are countably finite. Prior efforts are predominately afflicted by computational challenges associated with the fact that risk-sensitive MDPs are time-inconsistent. To ameliorate this issue, we propose a new definition of risk, which we call caution, as a penalty function added to the dual objective of the linear programming (LP) formulation of reinforcement learning. The caution measures the distributional risk of a policy, which is a function of the policy's long-term state occupancy distribution. To solve this problem in an online model-free manner, we propose a stochastic variant of primal-dual method that uses Kullback-Lieber (KL) divergence as its proximal term. We establish that the number of iterations/samples required to attain approximately optimal solutions of this scheme matches tight dependencies on the cardinality of the state and action spaces, but differs in its dependence on the infinity norm of the gradient of the risk measure. Experiments demonstrate the merits of this approach for improving the reliability of reward accumulation without additional computational burdens.
Distinguishing Aerial Intruders from Ambient Trajectory Data: Model-Based and Data-Driven Approaches

The identification of intruders to protected airspace (e.g., birds vs. drones) is pursued, using ambient fluctuations in their speed responses. The identification problem is posed as a statistical hypothesis testing or detection problem, wherein inertial feedback-controlled objects subject to stochastic actuation must be distinguished from speed data. The maximum a posteriori probability detector is obtained, and is then simplified to an explicit computation based on two points in the sample autocorrelation of the data. We also show that this special structure additionally permits an entirely data-based approach for constructing and applying the detector (classifier). Simulations based on synthesized data are presented to illustrate and supplement the formal analyses.
Predicting Solar Insolation Incident on Horizontal Surface for Performance Assessment of Solar Systems

To monitor the performance of solar systems solar insolation is need to measure. Measuring instrument used for the measurement has very high cost and uncertainty involved is higher. Availability of such instruments in the rural area is again a challenge. This paper discusses results of developed NARX ANN model. This model will use for forecasting the solar insolation falling on the surface of the solar system developed. Many studies are going on in nearby areas of North Maharashtra University and the College of Engineering Bambhori. SSBT College of Engineering has installed solar PV with capacity 200 kW. Performance of this system is required to be monitor and identify the maintenance need. For this purpose it is necessary to know all sky solar insolation falling on the system. ANN prediction model developed which has MSE (Mean squared error) 0.01553. The regression coefficient observed was 0.9399, 0.899 and 0.880 for training, validation and testing. The overall regression coefficient was 0.92 thus the model has a good predictability and useful for predicting the solar insolation and it would be easy to monitor the performance of a installed PV plant.
Development and Application of Power Grid Disaster Intelligent Perception and Emergency Command System

Research and development of multi-information intelligent acquisition module of power grid disaster loss, power grid disaster loss information fusion analysis and prediction module, and multi-person information real-time interaction module. Based on these three modules, a prototype system of power grid disaster intelligent perception and emergency command is developed for the application scenarios of emergency drill, training, and command operation. Develop the integrated interface of the prototype system, realize the software and hardware integration of intelligent mobile terminal, background equipment monitoring system and electric power emergency command platform, and realize the field application verification in the company headquarters and typical electric power emergency drill, rescue and command site of provincial company, realize the intelligent perception of power grid disaster and auxiliary decision-making and visual command under the condition of missing information in the emergency site, so as to realize the real-time monitoring of power grid disaster. It can effectively improve the efficiency of emergency command in disaster judgment, operation command, and material dispatching.
Active and Sparse Methods in Smoothed Model Checking

Smoothed model checking based on Gaussian process classification provides a powerful approach for statistical model checking of parametric continuous time Markov chain models. The method constructs a model for the functional dependence of satisfaction probability on the Markov chain parameters. This is done via Gaussian process inference methods from a limited number of observations for different parameter combinations. In this work we incorporate sparse variational methods and active learning into the smoothed model checking setting. We use these methods to improve the scalability of smoothed model checking. In particular, we see that active learning-based ideas for iteratively querying the simulation model for observations can be used to steer the model-checking to more informative areas of the parameter space and thus improve sample efficiency. We demonstrate that online extensions of sparse variational Gaussian process inference algorithms provide a scalable method for implementing active learning approaches for smoothed model checking.
IPL: From Lens of Data Science

Cricket is most lovable game in the world. It is played in many formats. One of the most popular format is IPL (Indian Premier League) which is hosted by BCCI (Board of Control for Cricket in India) every year. IPL is involved in business and money as the players across the globe are auctioned by the businessmen to form their teams. This paper presents the analytics of IPL match using the dataset of previous year matches i.e. from year 2008 to 2019. Various attributes like DLS method applied, venue, toss decision, toss winner and many more have been analyzed to check whether they contribute in predicting the winner or not. In this work, some graphs has also been plotted to visualize the performance of teams and players on the basis of different attributes.
Virtual Reality and Ergonomics: Making the Immersive Experience

The hardware requirements of Virtual Reality (VR) environments are more affordable each day. Users can be immersed in virtual environments by different technologies but the most affordable immersive technology with enough feature is Head-mounted display (HMD), a visual display that is more or less rigidly attached to the head. Devices like HTC Vive and Oculus Rift allow creating VR experience with reduced budget and high levels of interaction. The human experience is a multisensory process, which shifts according to changes in perception and individual experiences. Ergonomics emerges as an indispensable discipline for human interaction with the new technologies providing high usability (efficiency, learnability and satisfaction), proper functionality (easy navigation, easy interface condition) and achieving the most varied possible ends of them. To supply which aspects provides a better experience, recent research addressing technical and structural issues of technological devices will be considered, taking into account the immersion variables and the user’s comfort. It will be shown that since of the hand-held stereoscope by David Brewster among of the years, there is an incessant search for the creation of what is not presented, sharpening our imagination, capturing our attention and providing new interactions. Also, since the Renaissance technology has become an essential element in the perception of the gazer thanks to the development of optical devices and techniques for representing the perspective. This synthesis of current research will be helpful to VR Researchers, developers, designers, entrepreneurs, managers, marketers and users to select the appropriate features and hardware for each VR experience abstract should summarize the contents of the paper in short terms, i.e. 150–250 words.
FPGA Acceleration of Generative Adversarial Networks for Image Reconstruction

Accurate and efficient Machine Learning algorithms are of vital importance to many problems, especially on classification or clustering tasks. In recent years, a new class of Machine Learning has been introduced called Generative Adversarial Network (GAN) which relies on two neural networks: a generative network (generator) and a discriminative network (discriminator). These two networks compete with each other with aim to generate new data such as images. For example, a GAN is capable of reconstructing an image which is filled by noise or has some regions damaged. Image reconstruction has found its application in the field of computer vision, augmented reality, human computer interaction and animation as well as medical imaging. However, this type of algorithm requires many MAC (multiply-accumulate) operations and high power consumption to operate. In this work, we implement an Image reconstruction algorithm with GANs, specifically as a case study we train a model capable of restoring clothing images based on the fashion-MNIST dataset. Additionally, we implement and accelerate it on a Xilinx FPGA SoC which as platforms are proven to address these kind of problems very efficiently in terms of performance and power. The design also achieves better performance and power efficiency from CPU and GPU with 0.013 ms average reconstruction time per image and 43 db PSNR on the FPGA quantized configuration.
Semantic Relation from Biomedical Text Documents Using Machine Learning Algorithm

Semantic relations are underlying relation between the concepts present in sentences. Semantic relation plays an important role for building applications namely, information retrieval (IR), information extraction (IE), question answering (QA) and chatbots. This paper proposed a model to extract entities, and identifying the semantic relations exists between the entities. This paper is used to extract entities using novel feature extraction techniques with Naïve Bayes approach. Furthermore, it is used to identify the semantic relations using various machine learning approaches. This paper addresses eight semantic relations namely, prevent, no cure, disease only, side effect, vague, treatment only, cure and none. The model is evaluated by standard metrics and produced the result of 68.42% F-score for this data set.
Personality-oriented education as a factor of sustainable development of the higher education system

The scientific and methodological basis of the modern concept of higher education is the synergetic unity of classical and modern psychological and pedagogical approaches. Modern higher education is focused on introducing a young person to fundamental knowledge, on developing practical skills in future professional activities and preparing them for lifelong learning. However, modern education is not focused on the student’s life and professional self-determination. The integrated use of personality-oriented technologies in the educational process helps to stimulate the development of personal potential, mental activity, “launches” the development of cognitive processes, and contributes to the formation of competencies necessary for a future specialist. Personaloriented education at the University will help to the painless entry of a young specialist into professional activities and professional community.
A Study of New Decade of Agile and Lean Methodologies for Development Software

Organizations adopt software development methodologies and practices that are often not properly evaluated, triggering risk in their projects. Therefore, the objective of this systematic review is to examine detailed information on the impact and efficiency of Agile and/or Lean practices in organizations, and in this context to provide a basis for creating an ontological model and suggest parameters to consider before adopting software development practices. The synthesised results were obtained from four search engines: ScienceDirect, IEEE, Google Scholar and Springer. The information was categorized according to the research questions covering the foundations of the emergence of Agile and Lean practices, its contributions, impact and efficiency proven through parameters for creating a base ontology model concerning the faults and critical points in the proper use of Software development methodologies and practices as Agile/Lean in organizations. Additionally, this systematic review will allow for the creation like as future work of an ontology that identifies the organizational guidelines to consider before adopting a Software development methodology.
NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task

This paper describes NAIST's system for the English-to-Japanese Simultaneous Text-totext Translation Task in IWSLT 2021 Evaluation Campaign.Our primary submission is based on wait-k neural machine translation with sequence-level knowledge distillation to encourage literal translation.
Developing and Validating a Set of Usability and Security Metrics for ATM Interfaces

In previous works, the authors evidenced the lack of specific metrics for UX improvement in the ATM domain, and the importance of the Usability and Security of ATM interfaces, principally because it is a channel where people interact with cash. According to this, covering the lack of specific metrics for the ATM domain is very important. For that, we proposed developing and validating a set of Usability and Security Metrics for the ATM domain. To do this, first, the Security and Usability metrics that were used by other banking domains were taken as input. Second, we reviewed the metrics that the ISO 25000 standard provided. With this input, we adapted the metrics to the specific characteristics of the domain. The result was a proposal of metrics for ATMs; divided metrics focused on quantifying aspects of Usability and metrics focused on measuring aspects of security in the interfaces. It was subjected to expert judgment to establish whether the proposed metrics had what was necessary to be validated later. We consulted experts in ATM interface design and domain experts from various banks in Peru. We adapted the proposed metrics to a survey format and asked each of the experts to place a score on four aspects. Finally, we prepared and conducted User Tests containing tasks related to the withdrawal operation for the validation scenario, and we prototyped the ATM interfaces of the four most iconic banks in Peru. A SUS questionnaire followed the User Tests. The results finally obtained were compared with the results obtained from SUS in order to validate if they both gave the same trend as output. We could conclude that the Metrics proposed were validated by expert judgment and by the validation scenario previously described.
Requirements for Electronic Educational Resources for Blended Learning in Topological Optimization Discipline

In a pandemic, many universities have switched to blended learning. This implies an increase in the volume of students' independent work and may lead to a decrease in the quality of education. In this regard, the article substantiates expedient content, as well as functional requirements for electronic educational resources for the realization of a blended learning model. The authors using the example of development of electronic educational resources for studying the discipline “Topological optimization” implement the stated requirements. The research novelty is determined by the fact that teachers can change the content of resources that has an interactive graphical interface that meets the requirements of safety and comfort, using simple computer programs Notepad, Paint and Word. The practical significance of the study is determined by the reduction of information inequality among students with the help of these resources in conditions of insufficient hardware and software of students.
DCQUIC: Flexible and Reliable Software-defined Data Center Transport

Numerous innovations based on the data center TCP support the rapid development of data center. However, with changes in topology, scale and traffic patterns, the new requirements of data center on transport protocol are more agile and more reliable. Improving transport performance by patching TCP seems to be facing a bottleneck. We explored the possibility of applying QUIC inside the datacenter networking. This paper proposes a new data center transport scheme based on QUIC, called data center QUIC (DCQUIC). We especially proposed an proactive connection migration mechanism suitable for datacenter networking. Like the efficiency of UDP and the reliability of TCP, DCQUIC exhibits exciting performance and scalability, and may become a potential transport technology to support the development and innovation of data centers in the future.
<b>Multivariate mapping for experienced users:</b> comparing extrinsic and intrinsic maps with univariate maps

Abstract Multivariate mapping is a technique in which multivariate data are encoded into a single map. A variety of design solutions for multivariate mapping refers to the number of phenomena mapped, the map type, and the visual variables applied. Unlike other authors who have mainly evaluated bivariate maps, in our empirical study we compared three solutions when mapping four variables: two types of multivariate maps (intrinsic and extrinsic) and a simple univariate alternative (serving as a baseline). We analysed usability performance metrics (answer time, answer accuracy, subjective rating of task difficulty) and eye-tracking data. The results suggested that experts used all the tested maps with similar results for answer time and accuracy, even when using four-variable intrinsic maps, which is considered to be a challenging solution. However, eye-tracking data provided more nuances in relation to the difference in cognitive effort evoked by the tested maps across task types.
Main Characteristics of Digital Tools and Their Application in Training

The following work is devoted to the description of various digital tools which could transform the traditional classroom into in innovative one successfully. The tools are suitable for implementation in the learning process (both in the traditional form of learning and in distance learning), as a prerequisite for increasing the knowledge, skills and competencies of students. For teachers, they are another additional instrument for including the interactivity in the learning process, for attracting students' attention and increasing their motivation. The presented technologies are consistent with many of the recommendations set out in the European Digital Competence Framework (DigComp) and in Framework for 21st Century Learning. The included tools are related to the development of communication and collaboration skills, the creation of digital content, the development of critical thinking and problem solving, and the development of creativity and innovative thinking. Teachers who have passed the qualification course apply them regularly in the learning process and notice their positive effect on students.
What Does the HCI Design Industry Expect from an Entrant? Findings from Interviews with Indian UX Design Team Leads

India is the world’s largest exporter of HCI products. However, limited research is available on the HCI design processes adopted in the Indian industry. This paper reports findings from in-depth interviews with 25 Indian HCI design team leads on the design processes followed in the industry and the competencies expected from design professionals. One of the key findings from the interviews suggests that the industry expects a graduate to have an understanding of the business value of the design processes adopted for a given problem. It is also expected that entrants have knowledge of different types of HCI design processes relevant to specific organizations like agile sprints, lean processes, etc. Further, for each of the different design processes adopted by the industry, the specific core skills are expected to be present in the new professional in order to work seamlessly in the industry. Skills like stakeholder engagement ability, sprint usability testing, time management, user research techniques, usage of appropriate terminologies in design communication, co-creation ability with stakeholders of different expertise, etc., are what the industry expects from new HCI professionals. The findings of this study have curricular, pedagogical, and strategic relevance for both HCI academics and industry.
An efficient transfer learning-based Super-Resolution model for Medical Ultrasound Image

Abstract There are many imaging modalities for clinical diagnosis, but we heavily use ultrasound imaging; some of the ultrasound images are low resolution because of the body’s internal weakness. This paper presents a novel unsupervised super-resolution framework for ultrasound image enhancement without considering any large sets of training samples which is the major concern in many single image super-resolution techniques. Here, the most powerful nonlinear mapping is introduced within convolutional neural networks to restore potentially useful and prominent spatial information generated from the model sets considered for image enhancements. Here, two techniques are used: dilated convolution and residual learning to increase the convergence and reconstruction accuracy in terms of quality measures. The potential metrics of dilated convolution in extract spatial information and residual learning are used to narrow the convergence time by learning only the difference between the test input and distorted input image. By evaluating the proposed framework on real ultrasound image sets, the performance metrics are validated. The proposed model outperformed the other competitive image enhancement models in the USSR field.
Research Data Management in an Academic Library

Academic libraries need to store, preserve, and manage scholars' intellectual output, hence the importance of research data management in academic libraries. This chapter focuses on research data management in academic libraries, and it aims at examining the concept of research data, which is referred to as the evidence used to inform or support research conclusions, while data management, on the other hand involves planning for and creating data, organizing, structuring, and documenting data, backing up and storing data, and preparing data for analysis to share with others or to preserve for the long-term.