Reviewerly/exHarmony huggingface dataset:
Quality Framework Performance in One Year of COVID-19 Restrictions in a Higher Education Institute

The entire academic year 2020-21 was completed under COVID-19 restrictions. This created challenges for students and staff, and to the quality frameworks that underpin the higher education (HE) being offered. Higher education (HE) quality frameworks were tested in a manner that had not been foreseen on their creation. This work examines the performance of one HE framework, which was created long before national lockdowns or social distancing, after an entire academic year of 2020-21. Critical evaluation will be offered by the authors, by external examiners, from student surveys and from an institutional quality review panel which took place in the academic year. The aim is to evaluate if the quality framework was sufficiently robust to offer our students a quality education experience, to evaluate the impact of some operational modifications and to reassure the public in terms of the standard of our graduates.
Enhanced RSA key encryption application for metering data in smart grid

Purpose Digitizing of the electrical power grid promotes the advantages of efficient energy management alongside the possibilities of major vulnerabilities. A typical inadequacy that needs critical attention to ensure the seamless operation of the smart grid system remains in the data transmission between consumer premises smart devices and the utility centres. Many researches aim at establishing security protocols to ensure secure and efficient energy management resulting in perfect demand–supply balance. Design/methodology/approach In this paper, the authentication of the smart meter data has been proposed with enhanced Rivest–Shamir–Adleman (RSA) key encryption using an efficient way of generating large prime numbers. The trapdoor one-way function applied in the RSA algorithm makes it almost impossible for the reverse engineering attempts of cracking the key pair. Findings The algorithm for generating prime numbers has been tested both with the convention method and with the enhanced method of including a low-level primality test with a first few hundred primes. The combination of low-level and high-level primality tests shows an improvement in execution time of the algorithm. Originality/value There is a considerable improvement in the time complexities when using the combination method. This efficient generation of prime numbers can be successfully applied to the smart meter systems, thereby increasing the strength and speed of the key encryption.
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning

In this paper, we investigate the Domain Generalization (DG) problem for supervised Paraphrase Identification (PI).We observe that the performance of existing PI models deteriorates dramatically when tested in an outof-distribution (OOD) domain.We conjecture that it is caused by shortcut learning, i.e., these models tend to utilize the cue words that are unique for a particular dataset or domain.To alleviate this issue and enhance the DG ability, we propose a PI framework based on Optimal Transport (OT).Our method forces the network to learn the necessary features for all the words in the input, which alleviates the shortcut learning problem.Experimental results show that our method improves the DG ability for the PI models.
Comparison of Three Proposal Methods in Steganography Encryption Secret Message using PVD and MapReduce

This paper will present a comparison between three proposed methods. All of these methods include hiding a secret message inside a video for the aim of transferring it to another party with high security and a high embedding rate in order to ensure that the secret message is not discovered by the attacker. In addition , facilitating deals with video frames as large data for the purpose of analyzing, dividing and controlling frames easily by the programmer, using the MapReduce method. This is done by dividing the video into a series of frames, and before the hiding process, the message is encrypted using the advance encryption standard (AES) algorithm. These basic processes are implemented in all three proposed methods, the rest of the details for each method are: The first method: used the pixel value difference (PVD) algorithm to hide the secret message in the video. In addition, the stego secret key was also used. This key is used for the purpose of deciding the locations of the pixels that will be employed to hide the secret message inside it. The second method: the MapReduce principle is used for the purpose of facilitating dealing with video frames. The chosen frame will enter the MapReduce stages. This is implemented by dividing the frame into three matrices red, green, blue (RGB). Each matrix represents a map. Moreover, the technique that is used for concealment is the least significant bit (LSB) technology which uses the stego secret key (x2) for the purpose of selecting sites that will be hidden by it. The third method: Also, the MapReduce principle is used, but this method is implemented by dividing the frame into four blocks. Each block represents a map. In one of the stages of the MapReduce, the hiding process will be done by using the (PVD) method which uses the stego secret key (n+15). Finally, the reducer, which is the last stage, will collect the results of each block to generate the stego-frame. The results of the three methods are efficiency, transparency, robustness and powerful in stego video. It is noticed that the second method has achieved the lowest capacity, thus achieving high security. As for the third method, it achieved the highest capacity and the highest execution time was the first method. Despite this, all the three methods have achieved high security. The attacker or unauthorized person cannot detect any suspicious differences in a stego video. These results are obtained through using many measurements: peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE), Entropy and correlation coefficient. Index Terms—steganography, PVD, LSB, MapReduce, Secret key.
Byzantine Resilient Non-Convex SCSG with Distributed Batch Gradient Computations

Distributed learning is an important paradigm in the current machine learning algorithms with large datasets. In this paper, distributed stochastic optimization problem of minimizing a nonconvex function in an adversarial setting is considered. A robust variant of the stochastic variance-reduced algorithm is proposed. In the distributed setup, we assume that a fraction of worker nodes (WNs) can be Byzantines. We assume that the batch gradients are computed at the WNs and the stochastic gradients are computed at the central node (CN). We provide the convergence rate of the proposed algorithm which employs the design of a novel filtering rule that is independent of the problem dimension. Furthermore, we capture the effect of Byzantines present in the network on the convergence performance of the algorithm. We evaluate the performance of the proposed algorithm and present the simulation results using real world datasets, in addition to providing the theoretical guarantees.
RETRACTED ARTICLE: Real-time detection of aerobics posture based on strain sensor

Abstract The Internet of Things is a network that realizes the intelligent connection between things. It is another major industry after the computer industry and the Internet industry. It is also a collection of computer technology, communication technology, sensor technology, storage technology, and many other leading technologies in an integrated industry, the application industry is very wide. This article mainly introduces the real-time detection of aerobics posture based on strain sensors. This paper proposes a real-time detection method of motion posture based on strain sensors, using flexible rods to simulate the joints of aerobics athletes. Through the survey of the motion posture by the strain sensor, the relevant data and parameters are collected, and then the data are processed, and the data are finally transmitted to the terminal. Since the stress of a specific point on the surface of the flexible rod is proportional to the curve of that point, strain gauges can be used to detect curve changes at multiple points in real time and draw the change curve on the computer. The experimental structure of this paper shows that the use of BP neural network to process the measurement data improves the accuracy of real-time detection. The error distance between the sampling point and the actual value is less than 0.3 cm. In addition, through the real-time detection of the motion posture, it can be concluded that good Body posture is the basic requirement of aerobics, and it is of great significance to improve the effect of improving the quality of movement and artistic expression.
Application of Computer VR Technology in Physical Education Class

VR technology refers to virtual reality technology, which is widely used in many fields such as military and medicine. It has also achieved remarkable results. The application of VR technology to physical education classrooms will have a huge impact on traditional physical education concepts, teaching goals, teaching processes and teaching methods. This article combines the connotation and characteristics of VR technology to analyze its application in physical education classes, hoping to expand the way of physical education teaching and enhance the fun of teaching.
Large scale services for connecting and integrating hundreds of linked datasets

Michalis Mountantonakis is a Postdoctoral Researcher of the Information Systems Laboratory at FORTH-ICS (Greece) and a Visiting Lecturer in the Computer Science Department at University of Crete (CSD-UoC), Greece. He obtained his PhD degree from the CSD-UoC in 2020. His research interests fall in the areas of large-scale semantic data integration, linked data and semantic data management. The results of his research have been published in more than 20 research papers. For his dissertation, he awarded a) the prestigious SWSA Distinguished Dissertation Award 2020, which is given to the PhD dissertation from the previous year with the highest originality, significance, and impact in the area of semantic web, and b) the Maria Michael Manasaki Legacy's fellowship, which is awarded to the best graduate student of CSD-UoC, once a year. In his dissertation, supervised by Associate Professor Yannis Tzitzikas (Computer Science Department at University of Crete), Michalis Mountantonakis dealt with the problem of Linked Data Integration at large scale, which is a very big challenging problem. He factorized the integration process according to various dimensions, for better understanding the overall problem and for identifying the open challenges, and proposed novel indexes and algorithms for providing core services, which can be exploited for several tasks related to Data Integration, such as: for finding all the URIs and all the available information for an entity, for producing connectivity analytics, for discovering the most relevant datasets for a given task, for dataset enrichment, and many others.
Research on File Management Based on Block Chain Technology

research-article Research on File Management Based on Block Chain Technology Share on Author: Jiehua She Guangzhou Institute of Technology, China Guangzhou Institute of Technology, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2099–2105https://doi.org/10.1145/3482632.3484107Online:22 November 2021Publication History 0citation8DownloadsMetricsTotal Citations0Total Downloads8Last 12 Months8Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Formation of soft-skills in future teachers in the context of teaching practice in a pandemic

Changing the requirements for a modern teacher naturally leads to a change in expected results, targets, approaches and procedures for the development of professional competence of teachers. Innovative transformations of almost all components of the educational system, including the professional training of future specialists, are today the basis for its modernization. The purpose of the study is the peculiarities of formation of soft-skills in future teachers in the context of teaching practice in a pandemic. For this, a number of methods were applied that make up the research methodology, such as systems analysis and synthesis, induction and deduction, comparison, classification, generalization and systematization, idealization and abstraction Professional and pedagogical competence in the field of teaching practice is determined by the totality of the teacher's skills in a special way to structure scientific and practical knowledge in order to effectively solve educational problems. Personal knowledge in this case is the result of developed abilities and orientation, influencing the perception of pedagogical phenomena, situations, as well as ways of transforming them from a scientific standpoint. As a result of the analysis, the main aspects and features of the formation of soft-skills in future teachers in the context of teaching practice in a pandemic were identified.
Exploring the promise of quantum computing

ACM Computing Prize recipient Scott Aaronson discusses his work in quantum complexity.
Error rate control for classification rules in multiclass mixture models

In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate at some nominal level. It is first shown that finding an optimal classification rule boils down to searching an optimal region in the observation space where to apply the classical Maximum A Posteriori (MAP) rule. Depending on the misclassification rate to be controlled, the shape of the optimal region is provided, along with a heuristic to compute the optimal classification rule in practice. In particular, a multiclass FDR-like optimal rule is defined and compared to the thresholded MAP rules that is used in most applications. It is shown on both simulated and real datasets that the FDR-like optimal rule may be significantly less conservative than the thresholded MAP rule.
Systematic Response to Campus Network Public Opinion in the Era of We-media

Entering the we-media era, there are more and more negative network public opinions in colleges and universities, and many colleges and universities have problems in dealing with network public opinions, especially lack of systematic thinking. Systematic response to the network public opinion of colleges and universities needs to proceed from four aspects, two of which are particularly important: one is based on the symbiosis theory, using the Logistic equation to demonstrate the effectiveness of multiagent monitoring of campus network public opinion. The second is to establish the index system and weights of campus network public opinion, and calculate the campus network public opinion into four levels through the calculation of the model, so as to improve the ability of colleges and universities in monitoring and early warning and quickly handling negative public opinions.
Estimating signal-dependent noise (SDN)-based motion variations to enhance gesture recognition

Gesture recognition is an important manoeuvre that uses known data patterns for comparison and/or training the recognition algorithm. Previously learned gestures can be recognized irrespective of the environment and position. However, even if the same gesture is repeated, variations are inevitably generated because of unconscious muscle movements. Such variations in motion can adversely affect the accuracy of gesture recognition algorithms. Extensive training data is conventionally used to reduce this effect. In this paper, we propose predicting such variations in gesture motions from a single training data-set. We first generate muscle activation data from the measured gesture trajectory using musculoskeletal simulation. Signal-dependent noise is then superimposed on the muscle activation data to generate 100 predictions containing noise variations. Back-calculation is performed on the predicted muscle activation. The final output is acceleration data in the X and Y axes, which are segmented to calculate the segment-wise variation. The inverse of variation is named as reliability unique to each segment. A 26% increase in the F-measure was observed when we compared the best-case scenarios of the proposed algorithm with conventional dynamic time warping gesture recognition without segmentation and consideration of motion variation.
A Generation-based Text Steganography by Maintaining Consistency of Probability Distribution

Text steganography combined with natural language generation has become increasingly popular.The existing methods usually embed secret information in the generated word by controlling the sampling in the process of text generation.A candidate pool will be constructed by greedy strategy, and only the words with high probability will be encoded, which damages the statistical law of the texts and seriously affects the security of steganography.In order to reduce the influence of the candidate pool on the statistical imperceptibility of steganography, we propose a steganography method based on a new sampling strategy.Instead of just consisting of words with high probability, we select words with relatively small difference from the actual sample of the language model to build a candidate pool, thus keeping consistency with the probability distribution of the language model.What's more, we encode the candidate words according to their probability similarity with the target word, which can further maintain the probability distribution.Experimental results show that the proposed method can outperform the state-of-the-art steganographic methods in terms of security performance.
Detail-preserving depth estimation from a single image based on modified fully convolutional residual network and gradient network

Abstract Predicting a convincing depth map from a monocular single image is a daunting task in the field of computer vision. In this paper, we propose a novel detail-preserving depth estimation (DPDE) algorithm based on a modified fully convolutional residual network and gradient network. Specifically, we first introduce a new deep network that combines the fully convolutional residual network (FCRN) and a U-shaped architecture to generate the global depth map. Meanwhile, an efficient feature similarity-based loss term is introduced for training this network better. Then, we devise a gradient network to generate the local details of the scene based on gradient information. Finally, an optimization-based fusion scheme is proposed to integrate the depth and depth gradients to generate a reliable depth map with better details. Three benchmark RGBD datasets are evaluated from the perspective of qualitative and quantitative, the experimental results show that the designed depth prediction algorithm is superior to several classic depth prediction approaches and can reconstruct plausible depth maps.
READINESS TO CHANGE OF HOME TAILORED ENTREPRENEURS IN TECHNOLOGY-BASED PERSPECTIVE

Flexibility to face changes in line with digital development and business challenges in market turbulence, exchange rates, and pandemic must be held by an entrepreneur to survive today. Entrepreneurs should manage tangible assets such as information technology (IT) and intangible assets such as knowledge inherent in human capital. Both processes will build resilience and foundation toovercome changes as complex as any future business challenges. IT as a knowledge infrastructureneedsactivities support in processing, protecting, and distributing within organizational entities such as knowledge-sharing (KS). The existence of robustKS in the organization atmosphere encourages entities to be more cohesive in organizational learning, builds individual capability and creativity, and solves many work-life problems, which is related to the readiness of entrepreneurs to face change. The purpose of this research is to bridge inconclusive problem gaps between the effect of ITon readiness to change (RTC) by offering KS mediation. It is confirmatory research with a quantitative Structural Equation Modeling approach by taking 151 samples. The respondent ishome tailor entrepreneurs in the SMEs area. This study empirically proves that KS can be a partial mediator to leverage the role of IT on RTC.Flexibility to face changes in line with digital development and business challenges in market turbulence, exchange rates, and pandemic must be held by an entrepreneur to survive today. Entrepreneurs should manage tangible assets such as information technology (IT) and intangible assets such as knowledge inherent in human capital. Both processes will build resilience and foundation toovercome changes as complex as any future business challenges. IT as a knowledge infrastructureneedsactivities support in processing, protecting, and distributing within organizational entities such as knowledge-sharing (KS). The existence of robustKS in the organization atmosphere encourages entities to be more cohesive in organizational learning, builds individual capability and creativity, and solves many work-life problems, which is related to the readiness of entrepreneurs to face change. The purpose of this research is to bridge inconclusive problem gaps between the effect of ITon readiness to change (RTC) by offering KS mediation. It is confirmatory research with a quantitative Structural Equation Modeling approach by taking 151 samples. The respondent ishome tailor entrepreneurs in the SMEs area. This study empirically proves that KS can be a partial mediator to leverage the role of IT on RTC.
Enterprise Service Bus (ESB) and Business Process Management for System Development

This study aims to proposed system development using Enterprise Service Bus (ESB) and Business Process Management (BPM). It will construct based on specific EA documents such as business architecture. In this study, we design and build simulation using Enterprise Service Bus (ESB) tools and Business Process Management (BPM) tools also. The research sample is a business architecture document from an organization. The results of the study is mechanism to construct system development using ESB and BPM tools.