Reviewerly/exHarmony huggingface dataset:
Boosting Performance of Visual Servoing Using Deep Reinforcement Learning From Multiple Demonstrations

In this study, knowledge of multiple controllers was used and combined with deep reinforcement learning (RL) to train a visual servoing (VS) technique. Deep RL algorithms were successful in solving complicated control problems, however they generally require a large amount of data before they achieve an acceptable performance. We developed a method that generates online hyper-volume action bounds from demonstrations of multiple controllers (experts) to address the issue of insufficient data in RL. The agent then continues to explore the created bounds to find more optimized solutions and gain more rewards. By doing this, we cut out pointless agent explorations, which results in a reduction in training time as well as an improvement in performance of the trained policy. During the training process, we used domain randomization and domain adaptation to make the VS approach robust in the real world. As a result, we showed a 51% decrease in training time to achieve the desired level of performance, compared to the case when RL was used solely. The findings showed that the developed method outperformed other baseline VS methods (image-based VS, position-based VS, and hybrid-decoupled VS) in terms of VS error convergence speed and maintained higher manipulability.
ANS: Assimilating Near Similarity at High Accuracy for Significant Deduction of CNN Storage and Computation

Activation data size has been roaring with the development of convolutional neural networks, which accounts for the boosting storage requirements. Our insight indicates that non-zero values dominate activations, of which the patterns demonstrate near similarity. We propose ANS method to compress activations in real time during both training and inference. High compression ratio with less accuracy loss is achieved by our optimization strategies, including determination of selection box (SB) size according to the amount of zero values of layer, learning and calibrating threshold dynamically, using the mean value of similar SB as compression value. Over 49% of compression ratio is achieved with accuracy loss of less than 0.892%, as well as reduction of multiplications by more than 60%. Comparing to three state-of-art compressed methods under five mainstream CNN models, ANS provides compression ratio improvement of 3.2x over RLC5, 1.9x over GRLC and 1.7x over ZVC. The ANS compressor and decompressor are implemented in Verilog and synthesized in 28nm node, which indicates that ANS has less cost of performance and hardware overburden. ANS modules could be seamlessly attached at the interface or deeply coupled into DNN accelerator with changed data path in the MAC array, which achieve 38% and 56% reduction in energy consumption, respectively.
Opinion Mining Using Multi-Dimensional Analysis

This review addresses emerging literature in the field of Opinion Mining with a particular emphasis on user-generated textual content. The focus is on the various tasks involved in Opinion Mining, which satisfies the comprehension of the essential criteria and methodologies that should be considered prior to embraking on the task. The paper provides an in-depth analysis of benchmarked datasets, widely-used feature sets, algorithms, techniques, open-source tools, challenges, real-world applications, and insights into various dimensions of Opinion Mining. The findings have both theoretical and practical implications, as it highlights the significance of Opinion Mining of textual content in comprehensive society. This review covers both technical and real-world knowledge and offers a comprehensive understanding of available open-source tools that are used in real-time.
What You Don't Know Will Hurt You - Fighting the Privacy Paradox By Designing for Privacy and Enforcing Protective Technology

The current privacy laws are insufficient in protecting consumer privacy as they ignore the difficulties faced by consumers in managing their digital privacy, leading to the phenomenon of the privacy paradox. Companies processing personally identifiable information (PII) make it difficult for consumers to manage their privacy through confusing privacy policies and click-through agreements. The average consumer may also suffer from the non-belief in the law of large numbers (NBLLN) problem which often leads to privacy resignation. This article argues that consumer-oriented legislation mandating privacy by design principles through enforcing fair information practices and privacy engineering is needed to effectively protect the right to digital privacy. Additionally, increasing privacy awareness is crucial in fighting the paradox of privacy management.
Metadata Role to Retrieve E-Resources and Its Managmenet: a Research

Internet plays a crucial role in the modern digital period, government make official pronouncement online, politician campaign online, writers find audience online, teachers find students online, resource experts find researchers online, land protest movement find traction online and advocates finds judges online. Internet Archive Founder Brewster Kahle explore the role of libraries in preserving modern electronic resource and its management to digital environment, but this digital information ecosystem be unstable. The global government concentrate to change their role in this digital ecosystem on standard level through the adaption of new technology. Hence, Metadata is original description of data about data or information about information, provides information about other data originated by computer communities, that is used to classify, label, understand, organised, shortening, and searching for data much easier to serve the public on assurance, automatic and discovered. Without classification, cannot manage the huge amount of data and did not make easier to find relevant data which assist in identifying the nature and features of the data. Metadata creation may be done manually or by metadata generators that’s extract key information from object. Metadata harvested captured metadata record that created and shared cataloguing model and it’s embedded in electronic resources or stored files. In this digital revolution scenario, generally utilized the cataloguing and indexing for electronic information resources. The utilization of metadata increased day-by-day, that system support metadata creation and search engine. The electronic environment challenges may boost the search engine vendor better to taken advantage of creating metadata development and made aware of the importance of it. The purpose of the study is knowing the relevant information of three tier function may cataloguing, classifying, and indexing. It facilitates to discover fact relevant information, locate the specific resources, and organise the electronic resources for new arrivals of collection or data files to provide additional information needed to administer, manage collection, allow digital objects to be re-presented in new technical environment for search category.
Blockchain and Distributed Ledger Technologies for IoT Security: A Survey paper

As the Internet of Things (IoT) continues to grow, the security concerns associated with this technology become increasingly important. Blockchain and Distributed Ledger Technologies (DLTs) are emerging as potential solutions for IoT security due to their decentralized and immutable nature. In this survey paper, we provide a comprehensive overview of the state-of-the-art research in the use of blockchain and DLTs for IoT security. We begin by introducing the basic concepts of blockchain and DLTs, followed by a discussion of their potential applications in IoT security. We then review the existing literature on the topic, including both academic and industrial research. We categorize the literature into different application areas, such as authentication, access control, and data integrity. Finally, we discuss the open research challenges and potential future directions for the use of blockchain and DLTs in IoT security. This survey paper provides a valuable resource for researchers and practitioners interested in understanding the current state-of-the-art research in this important area of IoT security.
Computation using shaped supercontinuum generation within a neural network

Optical computing may one day provide fast and efficient calculation, with complex nonlinear interactions occurring at the speed of light. Here we use supercontinuum generation as an optical computing block within a neural network. Supercontinuum generation is an interesting candidate for optical computing because it can be adjusted to operate over a large range of nonlinearity in a single device by adjusting laser pulse parameters. Here, the optical block accepts inputs in the form of actuator settings for a chirped fiber Bragg grating pulse shaper, and outputs a measured supercontinuum spectrum. A small neural network before the optical block translates the calculation data into actuator settings, and another small network after the optical block translates the spectrum into the desired output. In this architecture, the optical block acts like a static, fixed-weight neural network, while the two translation networks are trained to adapt the optical block to the particular computational problem. As first demonstrations, we train the system to perform hand-writing identification, and use the supercontinuum as the decoder in an autoencoder network. As temperature-controlled pulse shaping is slow, we first emulate the supercontinuum with a neural network trained on spectra measured at randomized heater settings. The emulation network is frozen and inserted in place of the optical block for training of the translation layers on computer. The training data is converted to spectra with the optical device for retraining of the output translator, compensating for emulation errors.
Blockchain, Information Security, Control, and Integrity: Who Is in Charge?

Blockchain technology has attracted substantial interest in recent years, most notably for its effect on global economics through the advent of cryptocurrency. Within the health care domain, blockchain technology has been actively explored as a tool for improving personal health data management, medical device security, and clinical trial management. Despite a strong demand for innovation and cutting-edge technology in plastic surgery, integration of blockchain technologies within plastic surgery is in its infancy. Recent advances and mainstream adoption of blockchain are gaining momentum and have shown significant promise for improving patient care and information management. In this article, the authors explain what defines a blockchain and discuss its history and potential applications in plastic surgery. Existing evidence suggests that blockchain can enable patient-centered data management, improve privacy, and provide additional safeguards against human error. Integration of blockchain technology into clinical practice requires further research and development to demonstrate its safety and efficacy for patients and providers.
Brightness and vividness lead to better viewing for UHD, HDR programming

Modern display appliances have increased picture quality by taking advantage of high-pixel resolution and High Dynamic Range (HDR) in new-age OLED-based products. This trend will become pervasive with the continued proliferation of underlying technologies. This paper helps point researchers to modeling those accounts for the increase in picture quality. The TV market has a growing number of HDR products, as listed by the popular consumer review website, RTINGS.com. They include many ultra-high definition (UHD), HDR televisions that exceed 1000 candela-per-meter-squared (cd/m<sup>2</sup>) peak luminance as reported by Babcock (2023). Also, the monitor and notebook HDR market is growing evident by the number of HDR display products certified by the VESA (Video Electronics Standards Association) DisplayHDR logo compliance program. Relying only on wattage to increase luminous intensity, however, is neither subtle nor cost effective. Sheer brightness in a typical ‘UHD/HDR’ television may be desirable but isn’t the only tool available to display makers. This talk reviews models that assess ‘UHD/HDR' displays for their clarity, brightness and most importantly their vividness. Quantum dot pixel-rendering technology has driven image vividness to new levels when matched with an OLED-emissive pixel (QD-OLED) according to supporting researched cited herein. Quantum dot advances that are in fact necessary for advancing the state-of-the-art in UHD imaging for televisions and computer monitors. This presentation will emphasize the importance of an augmented color appearance model for HDR technology that can reach beyond the DCI-P3 color space.
Characterization of a temporal-mode sorter using multiple-delay crossed-beam spectral interferometry

Spectral interferometry is employed to characterize a temporal-mode sorter, also known as a quantum pulse gate, which is essential to ensure efficient information encoding and processing. We present and test a model to reconstruct transfer functions and propose a feasible experiment.
Day-Ahead Power Loss Minimization Based on Solar Irradiation Forecasting of Extreme Learning Machine

Power losses exist naturally and have to be cared for in the operation of electrical power systems.Many researchers have worked on various methods and approaches to reduce losses by incorporating distributed generators (DG), particularly from renewable sources.These studies are based on the maximum unit penetration of the DGs, which is rarely achieved, resulting in inaccurate calculations.This paper proposes an advanced solution for calculating power losses by incorporating an Extreme Learning Machine (ELM) method for forecasting the solar irradiation.The ELM algorithm was used to create a model for forecasting solar radiation in the Manokwari region and its surroundings.Daily solar radiation in the region has been predicted using the model.NASA's 8016 data on temperature and solar irradiation were used to train the ELM model.With an MAE value of around 0.6392 and a training time of 4.4375 seconds, the test results demonstrate that the built model has good accuracy.The operation of a 1000 kWp solar power plant based on the ELM data forecasting can reduce the power loss of the existing distribution network around the location from 1.5095 kW/hour to 0.9068 kW/hour.Furthermore, the power plant operation can minimize the power loss by 39.9249 percent, from 36.2280 kW to 21.7640 kW.
Risks in Parts Supply Chain and Their Impact on Supply Chains and Shipping Sector: Focusing on Automobile Industry

Of late, various risk factors, such as the closure of container ports due to the COVID-19 pandemic, decline in container fleets, and instability in final demand, have had a detrimental impact on the business environment. Such negative effects could prevent companies from achieving their goals. Therefore, it is necessary to identify the risk and implement appropriate risk management strategies based on the impact factors of the risk. This study analyzed the impact of supply chain risks on the domestic automobile production network and maritime car carrier market. We quantified supply chain risks that arise in domestic automobile production processes and employed the vector auto regression models to analyze the effects of these risks on the automobile supply chain and pure car carrier market. In addition, using impact reaction function, we analyzed the process through which the risk in the supply chain extends to automobile production and pure car carrier market. It is expected that this study will help set long-term and short-term targets for production plans and demarcate corporate strategies through effective response to market conditions.
Basic research to analyze the current status of children’s rights education programs: Focusing on data structuring

연구목적: 본 연구의 목적은 실질적으로 운영되고 있는 아동권리교육 프로그램의 운영현황을 파악하여 효과성 있는 아동권리교육 프로그램 운영에 관한 정책 수립 내지 연구를위한 기초 자료를 제공하고 향후 아동권리교육 프로그램 운영의 질 제고를 위한 개선방안을 모색하는 것이다. 방법: 아동권리교육 프로그램에 관한 현황 수집 데이터가 비정형 데이터임에 따라 데이터구조화 방법론에 의거하여 현황을 분석하고 세부 내용의 정형적 표준화, 유형적 세분화, 구조적 체계화를 시도하였다. 결과: 아동권리교육 프로그램은 교육 프로그램을 개발해서 타 기관에 제공하는 교육담당기관과 교육 프로그램을 실질적으로 운영하는 교육운영기관이 유기적으로 연계하여 교육프로그램을 운영하고 있었으며 이를 크게 교육제공자, 교육대상, 교육목적, 교육내용, 교육지역 및 교육장소, 교육회기 및 교육시간, 교육담당자 및 교육방식, 만족도 평가라는기본 항목을 중심으로 현황을 분류체계화 하였다. 또한 기본 항목별 세부 속성 내용을유형적으로 분류하여 체계적인 서식 항목으로 제시하였다. 결론: 본 연구를 통해 아동권리교육 프로그램의 구조화된 데이터 관리가 중요함을 확인하고 종합데이터베이스 구축의 필요성을 제시하였으며 각 분류체계를 바탕으로 한 질 관리가 긴요함을 제언하였다.Objectives: This study aims to identify the current status of child rights education programs that are actually being operated. In addition, based on this, we intend to provide basic data for policy establishment or research on the operation of effective child rights education programs. In addition, the study seeks improvement measures to improve the operational quality of child rights education programs in the future. Methods: As the current status of collected data on child rights education programs is that of unstructured data, the current status was analyzed based on a data structuring methodology, and formal standardization of details, typological subdivision, and structural systemization were undertaken. Results: Regarding child rights education programs, it was found that the educational training institution that develops and provides these educational programs to other institutions and the educational operating institution that actually operates the educational program operate the educational program via an organic connection. The children’s rights education program was classified and systematized based on the education provider, education target, educational purpose, educational contents, education area and place, education session and education times, education managers and education methods, and satisfaction evaluations. In addition, detailed property contents for each basic item were classified into types and presented as systematic format items. Conclusions: The results of this study suggest the importance of data management and the necessity of building a comprehensive database for analyzing the current status of child rights education programs as well as the importance of quality management based on each classification system.
Analog Electronic Circuit Synthesis Using Simulated Annealing and Geometric Circuit Evolution

This article presents the SANN-GCE algorithm, a spice simulation driven meta-heuristic to design general discrete analog electronic circuits automatically, both circuit topology and component sizing.We introduce an encoding scheme called Geometric Circuit Evolution (GCE) that works associated with the Simulated Annealing algorithm and uses categorized degrees of freedom, that allows distinct characteristics of a circuit to change with different probabilities according to its type during the circuit evolution.We show through a series of seven active test circuits that SANN-GCE, compared to a benchmark, present a median fitness 15.88 times better, with a median standard deviation 6.72 times lower between runs.The median runtime found was 14.17 times lower.
Improved Arbitrary Graphical Password Authentication for Web Application Safety

These days passwords have a crucial role in the security of the system. so, these passwords must be unique and well-secured from Unauthorized access. Currently, passwords are generalized on each webpage. In today’s digital world ifit is a strong password then it makes our information more secure but this traditional method of text-based passwords which is a mix of both letters and numbers in lowercase and uppercase is very hard to remember So Graphical Passwords came into the picture. This Graphical Password Authentication is another approach to text-based Alphanumeric Passwords. In this user tiies to select the images that are available in the grid to log in to the system. The validation of Graphical Passwords is based on various authentication techniques like Recognition, Recall, and Cued Recall Techniques. Graphical Signature Password Authentication can help in overcoming many of the disadvantages faced by traditional Alphanumeiic Passwords. In this research paper, the survey on Graphical Password Authentication and providing a possible theory that can be used to authenticate ourselves and log in to the system or websites, which allows the user to set a password in the form of a graphical representation. our proposed solution deals with the disadvantages of existing systems that contain known attacks like shoulder surfing, Brute force, Guessing, and Dictionary. Graphical Password Authentication can help in overcoming many of the disadvantages faced by traditional Alphanumeric Passwords. Here as demonstrated in this research paper, is a result of our observation on Graphical Password Authentication and providing a possible theory that can be used to authenticate ourselves and login to the system or websites, which permits the user to select the pattern password in a graphical representation. our proposed solution deals with the disadvantages of existing systems which contain known attacks like shoulder surfing, Brute force, Guessing, and Dictionary.
Dynamic Grouping of Players and Analysis for Regional Tournaments

In this research work, the effective integration of the current management system in local tournaments, the construction of a general mode, and the design and implementation of tools for local tournaments to realize the management system are explained in detail. In this paper, the problems of the current tournament system are considered by analyzing the technology of the tournament management system and the situation of local tournaments. Therefore, this research study proposes a system for local tournament pairings with the features of addition, deletion, and updating of participant information, as well as several pairing features to ease the process of organizing the tournament. Single elimination is great when the organizer wants to get only one winner. Every match was exciting in terms of competitiveness. Match-Up Tournament pairing system will be an easy-to-use platform that aims to reduce the time-consuming process of manually pairing of candidates with pen & paper every time for each match and for every round. Currently, there is no solution that can solve the problem of manual pairing of participants and organize a match between them. In the proposed system, an organizer will have the option of either manual pairing or automatic pairing of candidates as per the requirements.
Activity Scheduling Method for Cloud-based Data Centers with Quality Performance Employing References Queues

The existing network architecture is enhanced by novel techniques like the cloud computing network because the network technology is advancing rapidly. The cloud computing system is made up of multiple processors and memory, faster networking, and different programming activities that enable it to offer people a variety of facilities through the Internet. But, at the same time, most services look for ideal service nodes, and the workloads on every node may not be evenly distributed. The Fitness Scheme Queues Select Instruments and Orientation Queue established Cloud Facility Architecture are designed to support more activities, reduce the time span, and enhance performances. Additionally, work might be split up more equitably to prevent overload.