Reviewerly/exHarmony huggingface dataset:
Predicting Online Student Effort with Accelerometer, Heart Rate Sensors, and Camera Using Random Forest Regression Model

In online education through web conference tools, teachers cannot grasp students' states by watching their behaviors like in an offline classroom.Each student also cannot be affected by others' good behavior.This paper proposes a prediction method of the student effort through acceleration sensors and a heart rate sensor worn on a student's body, and a local camera.The effort is expressed by the levels of concentration, excitation, and bodily action.A Random Forest regression model is used to predict each level from the sensor and camera data.Exhibiting the prediction result brings visibility of student states like offline.We verified the effectiveness of the prediction model through an experiment.We built the Random Forest regression prediction models from the sensors, camera, and student effort data obtained by actual lectures.In the case of building one prediction model for one lecture/one subject, the average R 2 values were 0.953, 0.925, and 0.930 in the concentration, excitation, and bodily action, respectively.The R 2 was -0.835 when one prediction model trained by one lecture's data is applied for another lecture's prediction.That was 0.285 when one model by 4 subjects' data is applied for prediction for the rest 1 subject.It means that the prediction model has high accuracy but is dependent on individual persons and lectures, which forces a burden to individual student to collect initial training data for individual lecture to build a prediction model.We also found that the acceleration data are the most important features.It implies the effectiveness of using acceleration sensors to predict student effort.
Quantumness’ Degree of Thermal Optics’ Approximations

We assess the degree of quantumness of the P, Q, and W quantum optics’ approximations in a thermal context governed by the canonical ensemble treatment. First, we remint the reader of the bridge connecting quantum optics with statistical mechanics using the abovementioned approximations at the temperature T. With the ensuing materials, we explore with some detail some features of the above bridge, related to the entropy and to thermal uncertainties. Some new relations concerning the degree of quantumness of the P, Q, and W are obtained by comparison between them and the exact and classical treatments.
Secure and Robust Internet of Things with High-Speed Implementation of PRESENT and GIFT Block Ciphers on GPU

With the advent of the Internet of Things (IoT) and cloud computing technologies, vast amounts of data are being created and communicated in IoT networks. Block ciphers are being used to protect these data from malicious attacks. Massive computation overheads introduced by bulk encryption using block ciphers can become a performance bottleneck of the server, requiring high throughput. As the need for high-speed encryption required for such communications has emerged, research is underway to utilize a graphics processor for encryption processing based on the high processing power of the GPU. Applying bit-slicing of lightweight ciphers was not covered in the previous implementation of lightweight ciphers on GPU architecture. In this paper, we implemented PRESENT and GIFT lightweight block ciphers GPU architectures. It minimizes the computation overhead caused by optimizing the algorithm by applying the bit-slicing technique. We performed practical analysis by testing practical use cases. We tested PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128 block ciphers in RTX3060 platforms. The throughput of the exhaustive search are 553.932 Gbps, 529.952 Gbps, 583.859 Gbps, and 214.284 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. For the case of data encryption, it achieved 24.264 Gbps, 24.522 Gbps, 85.283 Gbps, and 10.723 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. Specifically, the proposed implementation of a PRESENT block cipher is approximately 4× higher performance than the latest work that implements PRESENT block cipher. Lastly, the proposed implementation of a GIFT block cipher on GPU is the first implementation for the server environment.
Difference of Probability and Information Entropy for Skills Classification and Prediction in Student Learning

The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes’ theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
Highly Secure Cryptography Algorithm Method to Safeguard Audios and Visuals

Internet hacking has become common now a days and is increasing day by day. It is a high time to safeguard our data. There are several cryptographic methods and algorithms that are evolved and already exist. How about additional protection makes us stress free? In this paper, I present a unique design of cryptographic algorithm which is specifically designed for Auditory cryptography and visual cryptography to make the encryption and decryption technique stronger. The purpose is to make it very difficult to decode the file when an unauthorized user accesses the data. This algorithm is a combination of multiple techniques such as Ant Algorithm, Logical Gates Technique, Dual authorization PINs, Indexed Arrays. Combination of these techniques makes the algorithm unique and strong to secure the data. This research was implemented on audio files, images and video files. The study of the result shows effective way of masking the data as it is hard to decode without PINs. Also, performance of the algorithm is efficient during encryption and decryption process.
Neural Network -Based Prediction Model for Determining Student Expertise Authors

This study uses a neural network algorithm approach with backpropagation method to predict the accuracy level of determining the student's area of expertise. Neural network algorithm is an artificial neural system or artificial neural network is a physical cellular system that can obtain, store and use knowledge gained from experience, to activate using sigmoid bipolar where the output value ring is between -1 to 1. This area of expertise or concentration will taken by students through the selection of subjects of expertise that are in the curriculum of the study program offered. Academic supervisor / academic guardian who will direct students in choosing subjects according to the study interests desired by students. However, this direction is not well known by students. As a result, there is a possibility that students do not choose their field of expertise properly and it will affect the final score of the subject of expertise and competency testing. The analysis technique in this study uses descriptive statistical analysis which is an analysis of the frequency distribution, the size of the concentration, and the size of the spread. Because this study uses a neural network algorithm approach, the frequency distribution, the size of the concentration and the size of the spread are the number of hidden layers, iterations, learning rate, MSE and the confusion matrix (accuracy), which is to know the level of accuracy. The data to be analyzed is maining data or data which is training data consisting of 12 criteria (inputs) and 1 output (consisting of 3 areas of expertise), this data will be integrated into the Alyuda Neurointelligence software , from this software the number of hidden layers is determined , iterations, learning rate , MSE. The results of this study obtained the level of prediction accuracy model, namely: 84.8484% (hidden layer 2, neuron input layer = 13, learning rate = 0.1 and iterations = 500), 91.9191% (hidden layer 2, neuron input layer = 13, learning rate = 0.1 and iterations = 1000), and 95.9596% (hidden layer 3, neuron input layer = 13, learning rate = 0.1 and iterations = 2000).
Movie Recommender System using Machine Learning

In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.
Understanding Transformer Memorization Recall Through Idioms

To produce accurate predictions, language models (LMs) must balance between generalization and memorization. Yet, little is known about the mechanism by which transformer LMs employ their memorization capacity. When does a model decide to output a memorized phrase, and how is this phrase then retrieved from memory? In this work, we offer the first methodological framework for probing and characterizing recall of memorized sequences in transformer LMs. First, we lay out criteria for detecting model inputs that trigger memory recall, and propose idioms as inputs that typically fulfill these criteria. Next, we construct a dataset of English idioms and use it to compare model behavior on memorized vs. non-memorized inputs. Specifically, we analyze the internal prediction construction process by interpreting the model's hidden representations as a gradual refinement of the output probability distribution. We find that across different model sizes and architectures, memorized predictions are a two-step process: early layers promote the predicted token to the top of the output distribution, and upper layers increase model confidence. This suggests that memorized information is stored and retrieved in the early layers of the network. Last, we demonstrate the utility of our methodology beyond idioms in memorized factual statements. Overall, our work makes a first step towards understanding memory recall, and provides a methodological basis for future studies of transformer memorization.
Direct Calculation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>Z</mml:mi><mml:mi>Z</mml:mi></mml:math> Interaction Rates in Multimode Circuit Quantum Electrodynamics

Hamiltonians of the superconducting qubits of Transmon type involve non-zero ZZ-interaction terms due to their finite and small anharmonicities. These terms might lead to the unwanted accumulation of spurious phases during the execution of the two-qubit gates. Exact calculation of the ZZ-interaction rates requires the full diagonalization of the circuit Hamiltonians which very quickly becomes computationally demanding as the number of the modes in the coupler circuit increases. Here we propose a direct analytical method for the accurate estimation of the ZZ-interaction rates between low-anharmonicity qubits in the dispersive limit of the multi-mode circuit-QED. We observe very good agreement between the predictions of our method and the measurement data collected from the multi-qubit devices. Our method being an extension of our previous work in [1] is a new addition to the toolbox of the quantum microwave engineers as it relates the ZZ-interaction rates directly to the entries of the impedance matrix defined between the qubit ports.
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years. However, existing pre-trained MLMs often output an anisotropic distribution of token representations that occupies a narrow subset of the entire representation space. Such token representations are not ideal, especially for tasks that demand discriminative semantic meanings of distinct tokens. In this work, we propose TaCL (Token-aware Contrastive Learning), a novel continual pre-training approach that encourages BERT to learn an isotropic and discriminative distribution of token representations. TaCL is fully unsupervised and requires no additional data. We extensively test our approach on a wide range of English and Chinese benchmarks. The results show that TaCL brings consistent and notable improvements over the original BERT model. Furthermore, we conduct detailed analysis to reveal the merits and inner-workings of our approach.
Associating Latent Representations With Cognitive Maps via Hyperspherical Space for Neural Population Spikes

Recently, there has been a focus on drawing progress on representation learning to obtain more identifiable and interpretable latent representations for spike trains, which helps analyze neural population activity and understand neural mechanisms. Most existing deep generative models adopt carefully designed constraints to capture meaningful latent representations. For neural data involving navigation in cognitive space, based on insights from studies on cognitive maps, we argue that the good representations should reflect such directional nature. Due to manifold mismatch, models utilizing the Euclidean space learn a distorted geometric structure that is difficult to interpret. In the present work, we explore capturing the directional nature in a simpler yet more efficient way by introducing hyperspherical neural latent variable models (SNLVM). SNLVM is an improved deep latent variable model modeling neural activity and behavioral variables simultaneously with hyperspherical latent space. It bridges cognitive maps and latent variable models. We conduct experiments on modeling a static unidirectional task. The results show that while SNLVM has competitive performance, a hyperspherical prior naturally provides more informative and significantly better latent structures that can be interpreted as spatial cognitive maps.
Resource- and Time-Efficient Computation Offloading in Vehicular Edge Computing: A Max-Min Fairness Oriented Approach

Nowadays, computation offloading has become a research focus since it has the potential to solve the challenges faced when dealing with computation-intensive applications in the Internet of Vehicles (IoVs), especially in the 5G or future network environment. However, major issues still exist and the performance of main metrics can be improved to better adapt to the practical scenarios. This paper focuses on achieving resource- and time-efficient computation offloading in IoVs by boosting the cooperation efficiency of vehicles. Firstly, a fuzzy logic-based pricing strategy is designed to evaluate the cooperation tendency and capability of each vehicle from multiple aspects. Vehicles are encouraged to participate in the offloading process even if they are in a disadvantageous position compared to other vehicles. Secondly, a Max-Min fairness-oriented approach is proposed to find the most suitable offloading decision, and vehicles with poor cooperation capabilities are guaranteed to be treated equally in the offloading. Finally, two heuristic algorithms are presented to solve the problem with applicable complexity and to suit the practical IoV environment. Extensive simulation results prove that the proposed approach achieves remarkable performance improvements in terms of delay, service cost and the resource utilization ratios of vehicles.
Design of Updating Encryption Algorithm for Privacy Big Data Based on Consortium Blockchain Technology

The existing privacy big data encryption algorithm cannot achieve real-time update of big data and repeat more big data, resulting in low attack resistance and more malicious attack data. An updating encryption algorithm for privacy big data based on consortium blockchain technology is proposed in this paper. The redundancy of privacy big data is obtained, the deduplication technology is designed on this basis, and the big data preprocessing is completed. The source coding sequence of privacy big data is obtained, the security of network route query and identity is verified, and the update and extraction of user data access authority are realized. The data structure of encrypted block is generated by using consortium blockchain technology and updating homomorphic encryption technology to realize the updating encryption algorithm of privacy big data. Experimental results show that the proposed algorithm improves the ability of resisting attacks and the amount of malicious data intercepted. The encryption complexity is lower, the time consumption is shorter, and the error of privacy large data encryption is smaller.
Educational trends 2022: essence and innovation potential

The paper aims to analyze the educational trends of the year 2022 and determine whether they are relevant in the future, whether they are a response to the challenges of the present. Also, attention is paid to the method of SWOT-analysis, with the help of which the strengths and vulnerabilities of distance learning are identified. The results analyze the future of distance education, in particular, special attention is paid to the experience of implementing hybrid education as a likely promising direction of further learning. Another aspect is the introduction of STEM education, media education (as a counteraction to intentional propaganda), and Education for Sustainable Development as important elements of the modern learning process in the United States and European countries. In conclusion, it is concluded that these areas of educational activities will be trends in the next decade in the recurrence of the crisis. The scientific novelty of the work consists in the fact that for the first time at the synthetic level modern educational trends were studied, their prospects and risks of use were characterized. The abstract in English does not match the abstract in Spanish.
Continuous Gesture Sequences Recognition Based on Few-Shot Learning

A large number of demands for space on-orbit services to ensure the on-orbit system completes its specified tasks are foreseeable, and the efficiency and the security are the most significant factors when we carry out an on-orbit mission. And it can improve human-computer interaction efficiency in operations with proper gesture recognition solutions. In actual situations, the operations are complex and changeable, so the gestures used in interaction are also difficult to predict in advance due to the compounding of multiple consecutive gestures. To recognize such gestures based on computer vision (CV) requires complex models trained by a large amount of datasets, it is often unable to obtain enough gesture samples for training a complex model in real tasks, and the cost of labeling the collected gesture samples is quite expensive. Aiming at the problems mentioned above, we propose a few-shot continuous gesture recognition scheme based on RGB video. The scheme uses Mediapipe to detect the key points of each frame in the video stream, decomposes the basic components of gesture features based on certain human palm structure, and then extracts and combines the above basic gesture features by a lightweight autoencoder network. Our scheme can achieve 89.73% recognition accuracy on the 5-way 1-shot gesture recognition task which randomly selected 142 gesture instances of 5 categories from the RWTH German fingerspelling dataset.
An Approach for Vigilance Assessment using Hidden Markov Model

Purpose: To construct a hidden Markov model (HMM) for vigilance assessment to improve the real-time performance and accuracy of current vigilance measurement. Methods: ECG signal was collected by sensors, while the noise and baseline drift was eliminated from the original ECG signal. 10 volunteers were randomly selected. Their heart rate variability (HRV) were measured and trained parameters of the modified Hidden Markov model for vigilance assessment. Then, these data were collected to optimize using the Baum-Welch algorithm and obtained the state transition probability matrix A ̂ and the observation probability matrix B ̂. Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and used the Viterbi algorithm to find the optimal state, which compared with the actual state. Results: The constructed vigilance assessment model had a high accuracy rate the accuracy rate of data prediction for these three volunteers exceeded 80%. Conclusion: The Hidden Markov model for vigilance assessment can accurately predict the vigilance level and indicate broad application prospects.