Reviewerly/exHarmony huggingface dataset:
Analysis of the Functions of the Adults’ Lifelong Education Tutor's Support in Electronic Educational Environment

Due to digitalization of the economy students of higher educational organizations must possess digital competencies, both for the performance of professional duties and for learning in a digital environment. Adult learners are a heterogeneous audience, and have different levels of readiness to study in the electronic information educational environment (EIEE). Most of the adult learners need tutor support for their educational activities. The functions of a tutor are defined in standards, but they depend on the age, psychological, social, and professional characteristics of the students. The article aims to analyze the functions of tutoring support for lifelong education of adults in the electronic education environment. Difficulties in distance and blended learning are determined; the typical problems the adult students face with when completing additional professional programs are named. The typical functions of tutor support for different categories of adult learners are inductively identified and described. The experience of implementing the functions of tutors in the framework of the federal project “New Opportunities for Everyone” of the national project “Education” on the basis of the South Ural State University (National Research University) is systematized.
Aliasing Layer: A New Method for Removing Parallel Imaging Aliasing and EPI Ghosting Artifacts in CNN [Presidential Award Proceedings]

Purpose : Residual aliasing artifacts are often generated in MR images acquired using parallel imaging (PI) and/or echo-planar imaging (EPI). Existing denoising methods based on convolutional neural networks (CNNs) assume that an image has spatial locality. Since the artifacts do not satisfy the assumption of CNNs, denoising methods based on CNNs cannot remove artifacts efficiently. In this presentation, the author proposes a new method that can significantly reduce residual aliasing artifacts. The proposed method utilizes the locations of aliasing artifacts and/or N-half ghost artifacts, which can be analytically calculated.
Data Provenance in Vehicle Data Chains

With almost every new vehicle being connected, the importance of vehicle data is growing rapidly. Many mobility applications rely on the fusion of data coming from heterogeneous data sources, like vehicle and "smart-city" data or process data generated by systems out of their control. This external data determines much about the behaviour of the relying applications: it impacts the reliability, security and overall quality of the application's input data and ultimately of the application itself. Hence, knowledge about the provenance of that data is a critical component in any data-driven system. The secure traceability of the data handling along the entire processing chain, which passes through various distinct systems, is critical for the detection and avoidance of misuse and manipulation. In this paper, we introduce a mechanism for establishing secure data provenance in real time, demonstrating an exemplary use-case based on a machine learning model that detects dangerous driving situations. We show with our approach based on W3C decentralized identity standards that data provenance in closed data systems can be effectively achieved using technical standards designed for an open data approach.
Elliptic Curve Cryptography

The first proposed asymmetric encryption scheme was that of Rivest, Shamir, and Adleman, using exponentiation in the group of integers modulo the product of two large primes. Koblitz and Miller independently proposed the use of the groups of points on elliptic curves. In this chapter we cover the algorithm for using curves for cryptography both for encryption and for key exchange. Since the arithmetic to do point addition is expensive, we include the formulas for adding points efficiently. Finally, we include the Pohlig-Hellman attack, which should not be successful if the curves are chosen properly, and the Pollard rho attack, which is the current best attack on the elliptic curve discrete log problem.
Implementation of E-Learning in English for Islamic Education Learning at Islamic Education Study Program during the Covid-19 Pandemic

Covid-19 has been declared as a global pandemic by WHO. It affects all aspects of humans’ life, including their education. Teaching and learning processes during the pandemic of Covid-19 are done online or by implementing e-learning. This is to avoid the contact among people. This study aims to find out the media used to do e-learning, the consideration in picking the media, as well as the advantages and disadvantages found during the implementation of e-learning. This research involved 6 English lecturers teaching in some tourism vocational universities in Bali. Data of the research were gained through online questionnaires. Then the data were analysed by finding the percentages as well as describing them. It is found that (1) Google Classroom is used by most od the respondents to do e-learning, then followed by Zoom, Edmodo, WhatsApp, and YouTube, (2) accessibility, communication feature, media supported, the provided assessment types, design, and the pricing and budget are the consideration for respondents in picking the e-learning media, (3) the inequality of internet access is the biggest challenge faced by the lecturers in implementing e-learning, but they found that it provides a flexibility in terms of time and spaces.
Comparison of a novel dominance-based differential evolution method with the state-of-the-art methods for solving multi-objective real-valued optimization problems

Differential Evolution algorithm (DE) is a well-known nature-inspired method in evolutionary computations scope. This paper adds some new features to DE algorithm and proposes a novel method focusing on ranking technique. The proposed method is named as Dominance-Based Differential Evolution, called DBDE from this point on, which is the improved version of the standard DE algorithm. The suggested DBDE applies some changes on the selection operator of the Differential Evolution (DE) algorithm and modifies the crossover and initialization phases to improve the performance of DE. The dominance ranks are used in the selection phase of DBDE to be capable of selecting higher quality solutions. A dominance-rank for solution X is the number of solutions dominating X. Moreover, some vectors called target vectors are used through the selection process. Effectiveness and performance of the proposed DBDE method is experimentally evaluated using six well-known benchmarks, provided by CEC2009, plus two additional test problems namely Kursawe and Fonseca &amp; Fleming. The evaluation process emphasizes on specific bi-objective real-valued optimization problems reported in literature.&#x0D; Likewise, the Inverted Generational Distance (IGD) metric is calculated for the obtained results to measure the performance of algorithms. To follow up the evaluation rules obeyed by all state-of-the-art methods, the fitness evaluation function is called 300.000 times and 30 independent runs of DBDE is carried out. Analysis of the obtained results indicates that the performance of the proposed algorithm (DBDE) in terms of convergence and robustness outperforms the majority of state-of-the-art methods reported in the literature
Data Hiding and Retrieval Method Using LSB Substitution Algorithm

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier ?le formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exists a large variety of steganography techniques some are more complex than others and all of them have respective strong and weak points. Different applications have different requirements of the steganography technique used. For example, some applications may require absolute invisibility of the secret information, while others require a larger secret message to be hidden. This project hides the message within the image. This project uses LSB Substitution method to hide the secret message inside the image. Sender select the cover image with the secret message ?le (which can be of any format) and hide it into the image, it help to generate the secure stego image. The stego image is sent to the destination with the help of private or public communication network. On the other side the receiver downloads the stego image and using the software retrieve the secret text hidden in the stego image.
The Standardization Status and Standard Sets Construction of Beidou Satellite Navigation System

In this Paper, we summarize the current status of relevant standard setting and revision of Beidou satellite navigation system, and systematically state the demonstration and construction progress of the Beidou satellite navigation system standard system. Besides, we show that the standards at all levels play an important role in the construction and operation of the Beidou satellite navigation system. In addition, with the demonstration progress of the national comprehensive PNT system, we tend to state the development priorities and main tasks of the standardization work in the future, and try to put forward the research and demonstration ideas for the national comprehensive PNT standard system.
Deep Deterministic Policy Gradient Algorithm Based on Convolutional Block Attention for Autonomous Driving

The research on autonomous driving based on deep reinforcement learning algorithms is a research hotspot. Traditional autonomous driving requires human involvement, and the autonomous driving algorithms based on supervised learning must be trained in advance using human experience. To deal with autonomous driving problems, this paper proposes an improved end-to-end deep deterministic policy gradient (DDPG) algorithm based on the convolutional block attention mechanism, and it is called multi-input attention prioritized deep deterministic policy gradient algorithm (MAPDDPG). Both the actor network and the critic network of the model have the same structure with symmetry. Meanwhile, the attention mechanism is introduced to help the vehicles focus on useful environmental information. The experiments are conducted in the open racing car simulator (TORCS)and the results of five experiment runs on the test tracks are averaged to obtain the final result. Compared with the state-of-the-art algorithm, the maximum reward increases from 62,207 to 116,347, and the average speed increases from 135 km/h to 193 km/h, while the number of success episodes to complete a circle increases from 96 to 147. Also, the variance of the distance from the vehicle to the center of the road is compared, and the result indicates that the variance of the DDPG is 0.6 m while that of the MAPDDPG is only 0.2 m. The above results indicate that the proposed MAPDDPG achieves excellent performance.
Querying Big Graphs in Data Flow Language

Big graph processing is an important and challenging Big Data problem. Big graphs are used in various fields especially where relationships between objects are important. Nowadays not only programmers work with Big Data. Therefore, for analysts, who often are non-programmers, a graph system will only gain acceptance, if there is a comprehensible way to work with it. Nevertheless, currently, only graph databases support respective languages, but not by distributed graph processing systems. This paper considers the possibility of using the data flow language Pig Latin and compiler Piglet to enable graph processing on a large scale. Currently, they do not support graph processing, the work implements graph algorithms and defines which operators for the Pig Latin language and the Piglet compiler are required to support the processing of the big graphs.
Evaluating the Impact of Research-Based Updates to an Adaptive Learning System

ALEKS is an adaptive learning system covering subjects such as math, statistics, and chemistry. Several recent studies have looked in detail at various aspects of student knowledge retention and forgetting within the system. Based on these studies, various enhancements were recently made to the ALEKS system with the underlying goal of helping students learn more and advance further. In this work, we describe how the enhancements were informed by these previous research studies, as well as the process of turning the research findings into practical updates to the system. We conclude by analyzing the potential impact of these changes; in particular, after controlling for several variables, we estimate that students using the updated system learned 9% more on average.
Providing Designers with Automated Decision-Making within SysML Models to Promote Efficient Model-Based Systems Design

Systems of Systems (SoS) design is a complex process that involves, among other activities, the specification of system structure and requirements, the analysis of behavior and performance, and the exploration of the most appropriate system design solutions. Integration of these activities is advocated by Model-Based Systems Design (MBSD), where a core system model can be enriched with additional capabilities, such as performance analysis or decision-making. The Systems Modeling Language (SysML) is a standard language to utilize model-based design of SoS and create system models, specifying requirements and system constraints. In this work, we focus on integrating decision-making capabilities into SysML to enable system designers to explore alternative solutions that fit the requirements described in SysML. The system model is transformed to a decision model, whose results are automatically incorporated back into the system model. The proposed approach runs iteratively on any typical SysML model and facilitates the designer to explore alternative design solutions, minimizing the manual effort needed to achieve them. As a case study, the approach is applied in the design of a remote patient monitoring system, namely the Remote Elderly Monitoring System (REMS), where the designer decides on a system configuration that covers the needs of the patients.
An Auto-Contouring Method for Kidney based on SVM

Abstract In adaptive radiotherapy planning, the contour map of the treatment target area is still the most difficult problem. Usually this work is done by a professional radiologist oncologist, which is very time-consuming and labor-intensive. In order to solve this problem, this paper proposes an automatic contour method based on support vector machine. Experiments were conducted on the lower abdomen MR data set of eight patients. In the experiment, the simulated data was only used to train the classifier, and the treatment day images were used to evaluate the performance of the classifier. DSI was used to compare the manual contour and automatic contour of the kidney. The experiment showed that, The automatic contour method based on support vector machine has better classification performance than most classification algorithms. Among the eight sets of results, the DSI value of the six sets of results is 1, and the smallest DSI value is also greater than 9.423.
A Cognitive Model Based Framework and Multi-layer Storage Architecture for Associative Memory

Memory is the foundation of intelligence. KID model covers multiple human cognitive processes such as learning and memory. This paper refines its memory process, especially focusing on associative memory of long-term memory. An associative memory framework with novel neural network storage architectures is presented to simulate human-like associative memory ability for machine intelligence. The presented framework involves an associative memory repository and two abstract functions, Assimilation() for knowledge encoding and storage, and Instantiation() for knowledge recall and application. The proposed novel storage architecture has two storage structures which both includes three kinds of layers: input layer, competitive layer and associative memory layer. Its design integrates multiple associative memory related neuroscience theories. It is characterized by chaotic feature, self-organization, self-adjustment, self-growth and associative recall. With the encapsulation of presented associative memory framework and novel storage architecture, the KID model can incorporate associative memory and be applied to various fields like intelligent information and knowledge management systems, personized products development and robotic intelligence.
Algebraic Logic and Knowledge Bases

Knowledge bases theory provides an important example of the field where applications of universal algebra and algebraic logic look very natural, and their interaction with practical problems arising in computer science might be very productive. In this paper we study the equivalence problem for knowledge bases. Our interest is to find out how the informational equivalence is related to the logical description of knowledge. The main objectives of this paper are logically-geometrically equivalent and LG-isotypic knowledge bases. We will see that these notions give us a good characterization of knowledge bases.
Quantum Voting in Reach for Ethereum and Algorand

This White Paper introduces and contributes the first implementation of the Decentralized Voting Algorithm. First, Part I provides an overview for the software structures relevant to this work. Second, Part II introduces a decentralized voting algorithm for transferring value on blockchain networks. Third, Part III explains the voting algorithmâ€™s implementation in reach, including the backend architecture, web deployment, and quantum integration. Perhaps most significantly, this paper solves the Decentralized Voting Problem with a new quantum consensus system.
Secured Library Access Through Face Recognition Integrated with RFID Technology

Securing resources in the library is one of the prime concerns by preventing the entry of unauthorized users into the library. Saving time of users at entry/exit and circulation points is the next key concern. To access the library, the member is expected to swipe or scan his smart or IDentity (ID) card at the entry/exit point, and he/she is expected to wait at circulation points for the issue of books. In this system, members cannot access the library if they forgot to bring or miss their cards. Unauthorized persons can also easily enter into the library with stolen or duplicate ID cards. In order to secure and automate the library, a new system of accessing the library is proposed with deep learning and Radio-Frequency IDentification (RFID) technology. The proposed system focuses on automating the authentication of user with face recognition at the entry/exit point (turnstile) and providing them good user experience and automating the issue of books (RFID tagged) by using RFID detectors at the exit point. This system yields good results with our test datasets. Activation of this system at library premises ensures security and also facilitates auto checks-in and out, and thus saves users' valuable time.
Overview of Memory Security Technologies

Recently, a concept of confidential computing has been introduced in cloud computing [1] . One of its key feature is protection of "Data in use". "Data in use" means the data on the main memory while the data is being processed. Memory encryption capability equipped with current main stream processors contributes above objective. Few cloud vendors started confidential computing services utilizing memory encryption feature with AMD SEV (Secure Encrypted Virtualization) [2] , [3] . In this presentation, overview of threats and mitigating technologies about main memory security is introduced.
Social Recommendation based on Graph Neural Networks

Social recommendation has become a hot research topic in recommendation applications. However, current studies have not well simultaneously portrayed the static and dynamic features of users and items. Especially, they do not combine time information, social relations, and users' previous rating information which are of vital importance in learning the feature representations of users and items during modeling. Besides, graph neural networks that integrate social relations into the feature representation of users and items have also achieved superior performance. Thus, we jointly consider all the above information and thereby propose a dynamic social recommendation model named DGARec-R by adopting attention-based graph convolutional networks and recurrent neural networks. Experimental results on two realworld datasets show that our model not only outperforms baseline methods and its variations, but also is good at dealing with cold-start users.
Breast Density Classification in Mammogram Images

Breast cancer is the common type of cancer in the world, which is most common among women. It is found that there is a correlation between the breast cancer and breast density, hence there is a need for developing a method that identifies the breast density so as to reduce morbidity and mortality. The aim of this work is to develop a Computer Aided Design (CAD) system that classifies the breast density according to the Breast Imaging-Reporting and Data System (BI-RADS) standard with the help of the digital mammographic images. To this end, we propose an effective feature descriptor, namely Locally Encoded Transform feature histogram (LETRIST) for capturing the essential characteristics that discriminates across the density categories. Proposed descriptor evaluated using Support Vector Machine (SVM) on the Mammographic Image Analysis Society (MIAS) database demonstrates the efficiency of proposed system.
Application of Pneumatic Soft Actuators as End-effectors on a Humanoid Torso Playing Percussion Instrument

Art and music are explored a lot in robotics as people strive to bridge the gap between how a human performs compared to a robot. The variations a person uses to produce different types of sounds are not easily achievable by robots. Various aspects, such as time of contact to note, the force exerted, and contact angle, cause such variations. This paper proposes a pneumatic actuator system as an end-effector that helps achieve variation in the sounds made by a robot while playing percussion instruments. The proposed method consists of an end-effector that has a mechanism to be inflated. This method focuses primarily on the force exerted while playing a beat by varying the amount of pressure in the inflatable system while playing a beat, resulting in different amplitudes of sounds produced when the end-effector strikes the percussion instrument. By using this mechanism, we get closer to the goal of having robots play instruments with the same amount of variations as a human. The field of art and music brings such challenges for variation, which can act as applications in various other areas due to the level of precision the field demands.