Reviewerly/exHarmony huggingface dataset:
Learning to Sample with Local and Global Contexts in Experience Replay Buffer

Experience replay, which enables the agents to remember and reuse experience from the past, has played a significant role in the success of off-policy reinforcement learning (RL). To utilize the experience replay efficiently, the existing sampling methods allow selecting out more meaningful experiences by imposing priorities on them based on certain metrics (e.g. TD-error). However, they may result in sampling highly biased, redundant transitions since they compute the sampling rate for each transition independently, without consideration of its importance in relation to other transitions. In this paper, we aim to address the issue by proposing a new learning-based sampling method that can compute the relative importance of transition. To this end, we design a novel permutation-equivariant neural architecture that takes contexts from not only features of each transition (local) but also those of others (global) as inputs. We validate our framework, which we refer to as Neural Experience Replay Sampler (NERS), on multiple benchmark tasks for both continuous and discrete control tasks and show that it can significantly improve the performance of various off-policy RL methods. Further analysis confirms that the improvements of the sample efficiency indeed are due to sampling diverse and meaningful transitions by NERS that considers both local and global contexts.
Visual Attendance Recording System for Classroom Using Facial Features

Student attendance in schools, colleges, or universities plays a significant role in facilitating the learning process in a variety of ways. As there are a large number of students in a classroom, the main emphasis is given to detect and recognize all faces that are streamed in the camera. This process runs in parallel with the class lecture so that no extra time is required for calling out the names or roll numbers of the students to record the attendance. Face detection is used to locate the face in a image. There are two phases in automating the attendance process; first, the system is trained on the images of all students of the class. After this step, the system takes the live video of the class as input; it automatically detects and recognizes the face of each student present in the video and marks the presence of those students in a class. Those who are not found in the video will be marked absent automatically. The results show that the system performs well and saves nearly 5–10 min of a lecture.
Reengineering the Library: Issues in Electronic Resources Management

"Reengineering the Library: Issues in Electronic Resources Management." Journal of Electronic Resources in Medical Libraries, 18(1), pp. 78–79
Big data technology in education

The article discusses the implementation of big data in the educational process of higher education. The authors, analyzing a large amount of data, referring to the types of services provided by e-government, indicate that there are many pressing problems, many services are not yet automated. In order to improve the professional training of teachers of Computer Science of the L.N. Gumilyov Eurasian National University, educational programs and courses have been developed 7M01514 — «Smart City technologies», «Big Data and cloud computing» and 7М01525 — «STEM-Education», «The Internet of Things and Intelligent Systems «on the theoretical and practical foundations of big data and introduced into the educational process. The arti-cle discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered, as well as hardware and software that collects, stores, sorts big data, well as the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment.
Analisis SWOT dan Media Pembelajaran di Politeknik dalam Masa Covid-19

The purpose of this study is to find out and analyze how the media and learning strategies carried out in the Covid-19 period of polytechnics using the SWOT analysis approach. The problem that occurs during Covid-19 in the learning process is not provided regulation about distance learnin, the selection of appropriate learning media and strategy to help learners more easily understand the material being studied.The unit of analysis in this study is students in Business Administration-PNJ, sample selection using random sampling, data collection techniques using literature studies, observations and questionnaires. The method used in this research is a qualitative descriptive. The results showed that the learning media used were zoom meetings, e-learning and WhatsApp, and there were no rules that could be used as guidelines regarding the model set at PNJ for use in the distance learning proces. The results of this research used SWOT analysis for learning strategy are: (S): transportation costs are more efficient, easier to manage time management, have a lot of time to do campus work, access to learning can be done from anywhere, learning motivation increases, more relaxed and comfortable in the learning process, completing assignments from lecturers have enough tim, more motivated, have their own planning work, increase, (W): not all lecturers use the zoom meeting or google classroom, learning process is not communicative, learning situation more different when the presentation uses online, (O) have learn more material from open source, live more healthy, no stress, more patient and happy learning arround the families, accustomed to online learning, accustomed to with information technology, (T) it takes time for discipline learning in the new normal era, lecturers must be more creative in teaching and learning, mastering of uisng information technology.
An Intelligent Sign Communication Machine for People Impaired with Hearing and Speaking Abilities

People who are impaired with speaking and hearing abilities use sign language for communication between them, but it is a tough task for them to communicate with the outside world. Through this paper, we are proposing a system to convert Indian Sigh Language (ISL), American Sign Language (ISL) and British Sign Language (BSL) hand gestures to a textual format of the respective language as well as convert text in to their preferable Sign language. In this paper, we are capturing ISL, ASL, BSL gestures through a web camera. The streaming video of hand gestures is then sliced to distinct images to match the finger orientation to the corresponding alphabets. Finger orientations as features of the hand gestures in terms of angles made by fingers, numbers of fingers completely open, semi-open, fully closed, finger axis verticals or horizontal and recognition of each finger are prepossessed and required for gesture recognition. Implementation is done for alphabets uses single hand and results are explained. After prepossessing the hand part of the sliced frame in the form of masked image is projected to the extraction of features from the image frame. To classify different gestures we used SVM (Support Vector Machine), CNN (Convolutional Neural Network) for further testing the probable gesture and recording the accuracies of each algorithm. Implementation is done over our own regular ISL, BSL, ASL data-set made by us only, using the web camera of our laptops. Our Experimental results depict that our proposed work and methodology can work on different backgrounds like a background consist of different objects or may have some sort of color background etc. For text to sign conversion we create a video which tells respective text into sign language.
Electric IoT Perception Layer Data Privacy-preserving Using Multi-identity-based Fully Homomorphic Encryption

The edge perception layer of electric Internet of Things (eIoT) involves a large amount of user's data, including identity, location and other sensitive data, facing the risk of privacy disclosure. To solve this problem, we first established an eIoT model based on edge IoT agent, and propose a dynamic privacy preserving scheme using Multi-identity-based Fully Homomorphic Encryption (MIBFHE). Different from the exiting schemes, the system is no longer limited to the same secret key. Each user generating their own secret key on the basis of identity, and key management, revocation are much easier to achieve. In addition, our scheme is dynamically faulttolerant, and it can still be normal to calculate even if the device is added or failed. The experimental results verify the effectiveness and advantage in the cost of computational and communication compared to the existing schemes.
A Deep Dive into Adversarial Robustness in Zero-Shot Learning

Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In contrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes. In this paper, we present a study aimed on evaluating the adversarial robustness of ZSL and GZSL models. We leverage the well-established label embedding model and subject it to a set of established adversarial attacks and defenses across multiple datasets. In addition to creating possibly the first benchmark on adversarial robustness of ZSL models, we also present analyses on important points that require attention for better interpretation of ZSL robustness results. We hope these points, along with the benchmark, will help researchers establish a better understanding what challenges lie ahead and help guide their work.
Innovating Like an Optimist, Preparing Like a Pessimist: Ethical Speculation and the Legal Imagination

In this essay, I discuss the problem of unanticipated consequences in the design and regulation of emerging technology, pointing to the difficulty of foresight as an underlying cause. Next, I draw a line between the issue-spotting capabilities of “thinking like a lawyer” and cultivating foresight as a skill. Finally, I describe my own experiences with using science fiction and creative speculation in teaching ethics and policy, and argue for the usefulness of creative speculation as a tool for those who are designing, deploying, and regulating technology. I sometimes describe myself, in the context of technology, as an optimist who believes it is important to think like a pessimist. I believe that such tools can help us create the future that we want rather than the one that we fear.
Heat Exchanger Network Retrofit Using Particle Swarm Optimisation Algorithm

Heat exchanger network (HEN) retrofit is an effective way to reduce energy consumption and improve energy efficiency. A key criterion in industrial practice to evaluate the performance of a retrofit plan is the minimising total annualised cost. However, when pursuing the goal of minimising total annualised cost by graphical tools, sub-optimal solutions are inevitable because of the difficulty in finding the balance of utility cost and investment. In this study, a method based on constrained particle swarm optimisation (PSO) algorithm is developed for the annual cost minimisation when retrofitting HENs. The proposed method is applied based on an initial retrofit plan obtained by graphical tools. It can achieve the aim of total annualised cost reduction by adjusting the inlet and outlet temperatures of each heat exchanger through PSO with the consideration of different utility prices and investment. The results show that this method can find optimal temperature spans for heat exchangers to reduce the total annualised cost.
Aggregated Hold-Out

Aggregated hold-out (Agghoo) is a method which averages learning rules selected by hold-out (that is, cross-validation with a single split). We provide the first theoretical guarantees on Agghoo, ensuring that it can be used safely: Agghoo performs at worst like the hold-out when the risk is convex. The same holds true in classification with the 0-1 risk, with an additional constant factor. For the hold-out, oracle inequalities are known for bounded losses, as in binary classification. We show that similar results can be proved, under appropriate assumptions, for other risk-minimization problems. In particular, we obtain an oracle inequality for regularized kernel regression with a Lip-schitz loss, without requiring that the Y variable or the regressors be bounded. Numerical experiments show that aggregation brings a significant improvement over the hold-out and that Agghoo is competitive with cross-validation.
SpotifyGraph: Visualisation of User’s Preferences in Music

Many music streaming portals recommend lists of songs to the users. These recommendations are often results of black-box algorithms (from the user’s perspective). However, irrelevant recommendations without the proper justification may considerably hinder the user’s trust. Moreover, user profiles in music streaming services tend to be very large, consisting of hundreds of artists and thousands of tracks. So, not only the recommendation procedure details are hidden for the user, but he/she often lacks a sufficient knowledge about the source data the recommendations are derived from. In order to cope with these challenges, we propose SpotifyGraph application. The application aims on a comprehensible visualization of the relations within the Spotify user’s profile and therefore improve understandability of provided recommendations.
Entropy solutions for an adaptive fourth-order nonlinear degenerate problem for noise removal

<abstract> Noise is regarded as an unavoidable component of digital image acquisition. Hence, noise removal has been considered as one of the fundamental tasks in the field of image processing. Accordingly, excellent results have been achieved by using second-order models. However, these outcomes are affected by the staircase effect. To eliminate this anomaly and maintaining the balance of removing noise and preserving edges, a fourth-order model is proposed. The existence and uniqueness of the entropy solution for this model are established. Besides, to to verify the effectiveness of the model in noise removal, we carried out numerical experiment and presented our results. Indeed, the experimental results show that our model is superior to PM model and ROF model in terms of removing noise and preserving edges. </abstract>
Efficient Public Key Cryptography Scheme with Equality Test for Heterogeneous Systems in IIoT

Industrial Internet of Things (IIoT) incorporates various varieties of smart devices and communication technologies that allow organizations to move from conventional industries to smart industries. IIoT provides cost-saving and collaboration since it relies on the availability, flexibility, and powerful processing capabilities of the cloud server. Considering the cloud’s untrusted nature, it is essential to protect the IIoT data before uploading it to the server. However, encryption yields a further critical issue. Due to the “All-or-Nothing” decryption characteristic, it is impractical for the users of another public-key cryptosystem to download all the data stored on the server to obtain their needs. In resolving this issue, this paper proposes Efficient Public-Key Cryptography with Equality Test scheme for Heterogeneous Systems (PKC-ET-HS). The PKC-ET-HS scheme incorporates the notions of Certificateless Public-Key Encryption with Equality Test (CL-PKE-ET) as well as the Identity-Based Encryption with Equality Test (IBE-ET). The scheme enables the cloud server to check whether two ciphertexts having their encryptions performed under a heterogeneous systems contain identical messages. Moreover, in the Random Oracle Model (ROM), the modified Bilinear Diffie-Hellman Intractable (mDBHI) assumption is stated to construct the security of the suggested scheme. Ultimately, the performance evaluation authenticates that the suggested scheme is practicable and convenient for IIoT environments.
7P OF ENLIGHTENMENT

The authors propose an original classifi cation of the enlightenment phenomenon on the basis of the results of a largescale all-Russia study, which was carried out by the asset of the All-Russia Public and State Enlightenment Organisation he Russian «Knowledge» Society» (in 2017), as well as on the basis of the data of investigations of the Russian Public Opinion Research Centre (2016-2018), in terms of the concept of marketing and the prevalent 7P (marketing mix model) marketing instruments in the present article. The identifi cation of the nature and level of awareness of nlightenment projects among different target audiences, the identifi cation of interest in the subject matter and format of enlightenment activities, the motivation for periodic training, and the determination of the capacity of the population to pay for enlightenment products were among the tasks of the above-mentioned investigations. In addition, the most successful management practices in the fi eld of promotion of cultural, mass and socially signifi cant projects in both towns and megacities were summarised on the basis of expert interviews, carried out within the framework of the study. The reduced classifi cation of the enlightenment product, the simplifi cation of which is related to the factors of digitalisation of modern society and information space, taking into account the factors of complication of the social communication process and the permanently increasing requirements for personal and professional qualities of the person (especially with regard to skills and competences of the future), is given. Diverse social technologies for promoting enlightenment activities are proposed on the basis of the formulated provisions of the mix-marketing enlightenment complex, and also the conditions for their successful implementation are revealed. According to the authors of the article, many means and techniques of forming the internal and the external environment of enlightenment organisations, which have long been widely used in practice, are not actually described in both pedagogic and marketing scientifi c literature. The more complete scientifi c detailed and systemic analysis, comparison with traditional marketing concepts will make their practical application more focused and effective.
DCT based reversible watermarking technique for Medical Images with improved quality of watermarked image

These days, it is extremely easy to duplicate and distribute digital media, and because of this the protection of intellectual property is a very important issue. In case of medical images, the patients information and details can be securely hidden in the cover image. But being a special case the used data hiding technique should be properly selected. Being Medical Images it is necessary to retrieve the exact medical image after removal of the watermark. In this paper a simple reversible watermarking technique based on DCT is implemented. After embedding watermark, it is necessary to maintain quality of the medical image. To improve the quality of the watermarked medical image, the blocks in which the watermark is embedded, is selected based on variance of the blocks. The algorithm is tested for various medical image modalities. Being a simple algorithm it is fast and efficient.
Education and Health: Evidence of Threshold Effects on Economic Growth

This work analyzes the effect of education and health on economic growth using a fixed-effect panel threshold model for 86 economies from 1960 to 2010. In accordance with non-linearities literature, the work verifies the existence of two income thresholds, after which health and education affect growth differently. Although the magnitude of the effect of health on economic growth is variable, the relationship between them is always positive. Nonetheless, education has only a positive effect on growth after the second income threshold. The intuition is that a healthier population implies a better economic performance, regardless of the development stage. Differently, a higher education should have favorable effects only from a minimal level of income compatible with a minimal physical capital.
Understanding the Challenges of Blockchain Technology Adoption: Evidence from China’s Developing Carbon Markets

Blockchain technology promises disruptive change to industry and society.Although academic researchers and practitioners have engaged with blockchain for years, actual adoption rates of this technology significantly lag predictions.This paper explores present barriers to blockchain adoption in an industry for which blockchain might be assumed to offer an obvious solution to a globally distributed challenge: the integration of carbon markets.Using technological framing theory as a focusing lens, we investigate the challenges of blockchain adoption in China's developing carbon markets from three expert groups: carbon market experts with blockchain knowledge only, carbon market experts with blockchain projects, and IT experts in carbon markets.Our findings suggest that different expert groups present distinctive and incongruent framing challenges where technical challenges are not the most significant.In exploring this we make contributions to the introduction of a socio-technical perspective in blockchain adoption research and provide practical suggestions to policymakers and practitioners.
Deep Deterministic Policy Gradients for Optimizing Simulated PoA Blockchain Networks Based on Healthcare Data Characteristics

Blockchain technology has proven to be the best solution for digital data storage today, which is decentralized and interconnected via cryptography.Many consensus algorithms can be options for implementation.One of them is the PoA consensus algorithm, which is proven to provide high performance and fault tolerance.Blockchain has been implemented in many sectors, including the healthcare sector that has different characteristics of larger and more diverse record sizes.Implementing blockchain costs a lot of money.We used a blockchain network simulator as the best alternative in our research.The main problems with blockchain implementation are having a dynamic characteristic network and providing a blockchain system that is adaptive to network characteristics.Therefore, we propose a method to optimize the simulated PoA blockchain networks using Deep Deterministic Policy Gradients by adjusting the block size and block interval.The simulation results show an increase in effective transaction throughput of up to 9 TPS for AIH and 5 TPS for the APAC data models, and without affecting other important aspects of the blockchain.
Review of Face recognition algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. In this paper, we summarized the procedure of face recognition and some common methods, including the face recognition based on Hidden Markov Model, geometrical features, and template matching. Hidden Markov Model mainly helps to discover the statistic relationship between each state to help with the face recognition; geometric feature of face images provides the information of the different parts on faces to help the identification process; and the face recognition based on template matching uses templates of different face regions to help with the face matching process.