Reviewerly/exHarmony huggingface dataset:
Impact of Expectation of Higher Education and Achievement Motivation on Student’s Perception of Purpose in Life

The purpose of this research is to explore the impact of expectation of university study on student's perception of purpose in life by considering the role of achievement motivation.Based on classic measurements PIL, AMS and EUS, the empirical research method, including descriptive statistics analysis, regression analysis and mediation effect test, was conducted on a sample consisted by 412 undergraduate students coming from national wide universities.Four main findings were illustrated.First, student's perception of purpose in life is entirely low, with the mean value of 60.8 scores.Second, individual characteristics (sex, grade, major, household register, university type) are not related with PIL.Third, both the study expectation and the employment expectation are significantly related with PIL.Fourth, achievement motivation plays mediating role in the relations between expectations and PIL, with mediation effects of 6.75% and 7.87%, respectively.The findings comprehensively demonstrated that the expectations of study and future employment established at the very beginning of university education can determine student's perception of PIL significantly, and part of the impact is transferred by student's achievement motivation.
Analysis of Random Body Bias Application in FDSOI Cryptosystems as a Countermeasure to Leakage-Based Power Analysis Attacks

This paper analyses a novel countermeasure to Leakage Power Analysis Attacks based on the application of a random Body Bias voltage level at the beginning of the encryption process. The countermeasure effectiveness is established through the development of a theoretical model of the Pearson correlation coefficient in the presence of a varying body bias under both noiseless assumptions and in the presence of algorithmic noise, and through simulations on a partial cryptosystem implemented in 28 nm FDSOI technology. A study of the effect of averaging power measurements is also developed and contrasted against Monte Carlo simulations of the countermeasure scheme, effectively providing a floor for the increase in required measurements to identify the secret key.
Identification of Teachers and Students’ Readiness to E-Learning Implementation

The purpose of this study is to determine the readiness to implement e-learning in school, parents, and students at SD UMP, seen from the readiness of human resources, finance, and technology skills. The research method used is qualitative research with descriptive method. Participants in this study included a principal, 15 teachers, and 32 students, and the data were collected through observation, interviews, and documentation. This study shows that principals and teachers are ready for implementing e-learning from the side resources, finance, and technological skill. However, students and their parents have unprepared for it. They need more time to adapt the e-learning and the allocation their expenditure to it.
Classification Rule Discovery for Software Bug Severity using KNN with different distance metric

KNN is one of the most popular machine learning algorithms.There are different parameters on which the performance of an algorithm depends.The performance of KNN algorithm depends on value of K, and distance metric.So while implementing the KNN algorithm for the classification rule discovery, a proper care must be taken to choose the value of 'K'.Second parameter which is also very important for the KNN algorithm is the distance metric.As there are different types of the distance function which can be used with KNN, selection of distance function is also an effective task.In this paper value of 'K' is selected on the basis of mean error and it is found that best values of 'K' are 8, 12 and 15.Then using K=8, 12 and 15, KNN algorithm is implemented for bug severity classification.A comparative analysis of three distance metrics Euclidean, Manhattan and Hamming distance functions is done.
Web Based College Chatbot - SDABot

Previously, before the existence of chatbots and websites, for eliciting information like syllabus, fees related details, assessment or exam schedule, or tasks like fees payment, students had to be physically present at the respective place in college. This is a time consuming process. With technological advancement, necessary information is available on the college’s website. However, the process of acquiring information from the website turns out to be tedious when the end user is not able to find the information and therefore leaves the website. To do away with such problems chatbot can be developed to help visitors have a more human-like conversational experience thus easing access for users to required information. The SDA bot can be deployed on the web and integrated with the college’s website. To resolve their queries any end user can use this chatbot to acquire information related to college or student related information. This project is specifically designed to solve end users’ queries and give instant responses to them.
Deep Autoencoder-Based Image Compression using Multi-Layer Perceptrons

The Artificial Neural Network is one of the heavily used alternatives for solving complex problems in machine learning and deep learning. In this research, a deep autoencoder-based multi-layer feed-forward neural network has been proposed to achieve image compression. The proposed neural network splits down a large image into small blocks and each block applies the normalization process as the preprocessing technique. Since this is an autoencoder-based neural network, each normalized block of pixels has been initialized as the input and the output of the neural network. The training process of the proposed network has been done for various block sizes and different saving percentages of various kinds of images by using the backpropagation algorithm. The output of the middle-hidden layer will be the compressed representation for each block of the image. The proposed model has been implemented using Python, Keras, and Tensor flow backend.
Enhancing the operational efficiency of quantum random number generators

Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.
COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation

Software comprehension helps us to maintain software better.The developers often use documentation and mutual communication to understand software.However, the incompletion of documentation usually causes some problems such that we cannot acquire the detailed information of software.So researchers are now using key classes as starting points for software understanding and propose several effective methods for key class identification.Such methods are mostly implemented using supervised and unsupervised learning methods; however, they may be subject to class imbalance, insufficient amount of data, and lack of accuracy.In this paper, we propose a novel approach, called COSPA (identifying key Classes in Object-Oriented Software using Preference Aggregation), to identify key classes in software.Our approach is inspired by the social choice theory and uses Kemeny-Young method to find a preference that minimizes the total difference between different pairs of sequences returned by three key class identification methods (i.e., a-index, InDeg, and OSE).Further, we rank the classes in descending order according to that preference set, and a small number of top-ranked classes are considered as key candidates.To better evaluate the performance of our approach, we apply it to six Java software systems and compare it with nine state-ofthe-art approaches.In the experiments, we use Friedman to evaluate all methods in the whole set of research subjects; results show that, though no single method works always best in all subject systems, our method indeed performs better than others.
Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

In this paper, we propose a robust real-time vehicle tracking and inter-vehicle distance estimation algorithm based on stereovision.Traffic images are captured by a stereoscopic system installed on the road, and then we detect moving vehicles with the YOLO V3 Deep Neural Network algorithm.Thus, the real-time video goes through an algorithm for stereoscopy-based measurement in order to estimate distance between detected vehicles.However, detecting the real-time objects have always been a challenging task because of occlusion, scale, illumination etc.Thus, many convolutional neural network models based on object detection were developed in recent years.But they cannot be used for real-time object analysis because of slow speed of recognition.The model which is performing excellent currently is the unified object detection model which is You Only Look Once (YOLO).But in our experiment, we have found that despite of having a very good detection precision, YOLO still has some limitations.YOLO processes every image separately even in a continuous video or frames.Because of this much important identification can be lost.So, after the vehicle detection and tracking, inter-vehicle distance estimation is done.
Design and Implementation of Embedded Real-Time English Speech Recognition System Based on Big Data Analysis

This article uses Field Programmable Gate Array (FPGA) as a carrier and uses IP core to form a System on Programmable Chip (SOPC) English speech recognition system. The SOPC system uses a modular hardware system design method. Except for the independent development of the hardware acceleration module and its control module, the other modules are implemented by software or IP provided by Xilinx development tools. Hardware acceleration IP adopts a top-down design method, provides parallel operation of multiple operation components, and uses pipeline technology, which speeds up data operation, so that only one operation cycle is required to obtain an operation result. In terms of recognition algorithm, a more effective training algorithm is proposed, Genetic Continuous Hidden Markov Model (GA_CHMM), which uses genetic algorithm to directly train CHMM model. It is to find the optimal model by encoding the parameter values of the CHMM and performing operations such as selection, crossover, and mutation according to the fitness function. The optimal parameter value after decoding corresponds to the CHMM model, and then the English speech recognition is performed through the CHMM algorithm. This algorithm can save a lot of training time, thereby improving the recognition rate and speed. This paper studies the optimization of embedded system software. By studying the fixed-point software algorithm and the optimization of system storage space, the real-time response speed of the system has been reduced from about 10 seconds to an average of 220 milliseconds. Through the optimization of the CHMM algorithm, the real-time performance of the system is improved again, and the average time to complete the recognition is significantly shortened. At the same time, the system can achieve a recognition rate of over 90% when the English speech vocabulary is less than 200.
Application of CSK Encryption Algorithm in Video Synergic Command Systems

Video command and dispatch systems have become essential communication safeguard measures in circumstances of emergency rescue, epidemic prevention, and control command as, data security has become especially important. After meeting the requirements of voice and video dispatch, this paper proposes an end-to-end encryption method of multimedia information that introduces a multiple protection mechanism including selective encryption and selective integrity protection. The method has a network access authentication and service encryption workflow, which implants startup authentication and key distribution into the information control signaling procedure. This method constitutes a key pool with the three-dimensional Lorenz System, the four-dimensional Cellular Neural Network (CNN) System and the four-dimensional Chen System where the key source system and initial conditions are decided by the plaintext video frame itself. Then, this method optimizes the chaotic sequences to further enhance system security.
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models

Commonsense reasoning benchmarks have been largely solved by fine-tuning language models.The downside is that fine-tuning may cause models to overfit to task-specific data and thereby forget their knowledge gained during pre-training.Recent works only propose lightweight model updates as models may already possess useful knowledge from past experience, but a challenge remains in understanding what parts and to what extent models should be refined for a given task.In this paper, we investigate what models learn from commonsense reasoning datasets.We measure the impact of three different adaptation methods on the generalization and accuracy of models.Our experiments with two models show that fine-tuning performs best, by learning both the content and the structure of the task, but suffers from overfitting and limited generalization to novel answers.We observe that alternative adaptation methods like prefixtuning have comparable accuracy, but generalize better to unseen answers and are more robust to adversarial splits.
Experimental Evaluation of Classical Multi Agent Path Finding Algorithms

Modern optimal multi-agent path finding (MAPF) algorithms can scale to solve problems with hundreds of agents. To facilitate comparison between these algorithms, a benchmark of MAPF problems was recently proposed. We report a comprehensive evaluation of a diverse set of state-of-the-art optimal MAPF algorithms over the entire benchmark. The results show that in terms of coverage, the recently proposed LazyCBS algorithm outperforms all others significantly, but it is usually not the fastest algorithm. This suggests algorithm selection methods can be beneficial. Then, we characterize different setups for algorithm selection in MAPF, and evaluate simple baselines for each setup. Finally, we propose an extension of the existing MAPF benchmark in the form of different ways to distribute the agents’ source and target locations.
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors

In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail.We first show that 5 to 10% of training data are enough for a BERT-based error detection method to achieve performance equivalent to what a nonlanguage model-based method can achieve with the full training data; recall improves much faster with respect to training data size in the BERT-based method than in the non-language model method.This suggests that (i) the BERTbased method should have a good knowledge of the grammar required to recognize certain types of error and that (ii) it can transform the knowledge into error detection rules by fine-tuning with few training samples, which explains its high generalization ability in grammatical error detection.We further show with pseudo error data that it actually exhibits such nice properties in learning rules for recognizing various types of error.Finally, based on these findings, we discuss a cost-effective method for detecting grammatical errors with feedback comments explaining relevant grammatical rules to learners.
Fast Corner Detection in Augmented Reality Learning Management of the Corpse

50 out of 170 senior high school students do not understand the material of the corpse management practicum. This study aims to make it easier for students to understand better and remember caring for corpses in the classroom. A learning application for managing bodies made using Augmented Reality (AR) technology. The algorithm used is FAST Corner Detection. This algorithm's sequence is: resizing the image, changing the image to grayscale, smoothing the gray color (histogram), and determining the threshold as a determining point for the point marker. This application contains material on the procedures for bathing, bathing, praying, and burying the body. The FAST Corner Detection algorithm's performance takes 0.41 seconds to detect an object and can be done at a distance of 1 meter. The application also meets 82.8% of the user side's usability level, which indicates that this application is beneficial for learning. Interface testing gets a final score of 90.7%. This result suggests that the application is in outstanding criteria.
An Attention Self-Supervised Contrastive Learning Based Three-Stage Model for Hand Shape Feature Representation in Cued Speech

Cued Speech (CS) is a communication system for deaf people or hearing impaired people, in which a speaker uses it to aid a lipreader in phonetic level by clarifying potentially ambiguous mouth movements with hand shape and positions. Feature extraction of multi-modal CS is a key step in CS recognition. Recent supervised deep learning based methods suffer from noisy CS data annotations especially for hand shape modality. In this work, we first propose a self-supervised contrastive learning method to learn the feature representation of image without using labels. Secondly, a small amount of manually annotated CS data are used to fine-tune the first module. Thirdly, we present a module, which combines Bi-LSTM and self-attention networks to further learn sequential features with temporal and contextual information. Besides, to enlarge the volume and the diversity of the current limited CS datasets, we build a new British English dataset containing 5 native CS speakers. Evaluation results on both French and British English datasets show that our model achieves over 90% accuracy in hand shape recognition. Significant improvements of 8.75% (for French) and 10.09% (for British English) are achieved in CS phoneme recognition correctness compared with the state-of-the-art.
Ceramic Production of the Kulai Culture in the Novosibirsk Ob Region: Based on Materials from the Kamenny Mys Burial Ground

Purpose. The Kulay Cultural-Historical Community was one of the largest formations in Western Siberia in the early Iron Age. The Kamenny Mys burial ground is located in the Kolyvan district of the Novosibirsk region. The study is devoted to the technical and technological analysis of 49 vessels of Kulay ceramics (Novosibirsk variant) of the Kamenny Mys burial ground. Results. It was determined that natural clays of medium plasticity, possibly pretreated, were used as the initial plastic raw material. The dominant recipe for the clay paste is clay + broken stone (68 %). The construction was carried out according to the tank-bottom program with the help of flaps. The hollow body was constructed using patchwork molding. Mainly mechanical smoothing processed the surfaces of the vessels. The firing of dishes could be carried out in fireplaces or hearths. Conclusion. Ceramic technology reflects the initial mixing of different pottery skills. Stability is retained by the substrate skills of the selection of the initial plastic raw material, the design of the beginning and the hollow body. Mixing is manifested at the level of adaptive skills – methods of composing molding materials and machining surfaces. Such a situation may be a consequence of the interaction of groups of the Kulay population with the carriers of the Bolsherechenskaya culture and the beginning of their mixing, which led to the fusion of technological pottery skills.
An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System

Liquid flow in a process industry is one of the significant factors which should be controlled to get the better quality and decrease the expense of generation.Customary methodology includes manual tuning of the input process parameter to obtain the required flow rate is tedious and exorbitant.Notwithstanding, estimation of a precise computational model for fluid stream control procedure can fill in as elective methodology.It is only a non-straight enhancement issue.As a contextual investigation, the WFT -20-I measure control arrangement for flow rate measurement and Control issue is thought of.In this work we proposes a hybrid improved particle swarm optimization (PSO-GWO) used to start the people's position, which can build the decent variety of the wolf pack, balance the global and neighborhood search capacity of the calculation and improve the intermingling pace of the calculation contrast with the Gray wolf enhancement (GWO) and Particle swarm advancement (PSO).Non linear models are improved utilizing those recently proposed streamlining strategies.Additionally all the utilized optimization techniques can anticipate the fluid stream rate with good exactness.The outcomes were investigated by utilizing the root mean square error (RMSE), exactness, and the different measures to evaluate the level of identification performance of the liquid flow contextual analysis model.The trustworthiness of the present models was compared with the past model for similar subsystems utilizing competitive intelligent methodologies.The measurable examination of the acquired outcomes produced the proposed HPSOGWO has most elevated generally speaking proficiency (i.e.99.96%) and it beat the others strategies for the majority of the instances of demonstrating for fluid stream control process.The outcomes of the present model show that the proposed approach gives prevalent demonstrating execution and outflanks its rivals.
A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering