Reviewerly/exHarmony huggingface dataset:
An optimization scheme for campus security prevention and control

With its unique charm for student education, campus security has recently attracted extensive attention from all walks of life.The scheme design and system management of campus security prevention and control has become a hot spot in related fields.In addition to the basic campus security prevention and control design, the current security prevention and control scheme design tends to achieve more perfect campus security prevention and control from multiple directions and angles.Firstly, this paper introduces the design concept and common measures of campus security prevention and control.Secondly, the paper analyzes the surrounding environment of the school, analyzes the factors that have not been considered in school security prevention and control, and puts forward an optimization scheme of campus security prevention and control.
An Investigative Analysis for IoT Based Supply Chain Coordination and Control Through Machine Learning

The use of the Internet of Things (IoT) has brought about radical changes in the construction and business sectors, and companies are now using technology to remain competitive, support the exploitation of competitive advantages and increase growth and profitability. The use of the next generation of computers facilitated industrial change in all areas, and IoT helped shape the production structure, build an efficient value chain and achieve economic growth points. It can be argued that the introduction of IoT has changed the way we create value in the supply chain, which creates better opportunities for companies to improve scalability, make faster decisions and achieve better profits and growth. Although there are few challenges for the company, such as optimizing resources, investing in IoT and related digital technology, changing the production process and supply chain, etc., these new problems tend to change the organization’s bases and change the traditional way of doing things. Business. digital environment for effective customer engagement.
Image Security Algorithms—Proposed Methods and Critical Performance Analysis for the Best Fit

In the modern age, most data transfers involve a variety of images. From social media to defense applications, the transfer of images plays a vital role. The characteristics such as the massive amount of data, high redundancy, and a strong correlation between every pixel in all directions demand image security, resulting in image encryption. The paper aims to improve the performance of AES by proposing a modified AES algorithm and improving the performance of ECC through a hybrid algorithm that combines ECC with an RGB pixel displacement algorithm. This paper critically analyzes the comparative performance of the proposed algorithms against the standard encryption algorithms such as RSA, ECC, AES, and PDA. The Root Mean Square Error, Peak Signal to Noise Ratio, Histogram analysis, Information entropy, rate of change of the number of pixels, Unified average intensity, correlation coefficient, Resistance to Noise attack analysis, and the encryption time analysis are the metrics used to analyze the performance of image encryption.
Support-Based High Utility Mining with Negative Utility Values

High utility itemset mining (HUIM) aims at knowledge discovery from the datasets by finding patterns that have high utility values. Most of the existing algorithms suffer from the drawback of generating huge number of results that overwhelm the decision-making process for industry applications. Also, the real-life datasets often consist of items that have both positive and negative utility values in order to represent the profit and losses, respectively. In this paper, we propose a novel mining algorithm that maps closely to the real-life applications by producing only a reasonable number of outputs based on a support measure, from the datasets that have both positive and negative utility values. Several experiments are undertaken to test the efficacy of the proposed approach. Empirical evaluation suggests that the proposed approach is highly efficient for dense datasets.
Results of implementation of gaming technology as a pedagogical means of forming the organizational and managerial competence of a future manager

The article presents the results of a study of the implementation of gaming technology as a pedagogical means of forming the organizational and managerial competence of a future manager at a university. The essence of the “means of learning” concept is revealed; the relevance of the use of pedagogical means in the process of vocational education is substantiated; the degree of development of the problem of using games in the process of training and professional development of managerial personnel at the university is shown. The competence-based and contextual approaches are presented as the methodological basis of the study; the features of the application of the competence-based process for the organization of the formation of the organizational and managerial competence of the future manager are disclosed; the concept, essence, structure and tools for assessing the organizational and managerial competence of a future manager are disclosed; the possibility of using the contextual approach for the formation of organizational and managerial competence of a manager in activities, including in the process of playing activities as a way of learning is shown; the principles of the implementation of games aimed at the formation of this competence are described - subjectivity, problematicity, professional orientation, dialogue, joint activity of game participants. The article presents a set of games implemented in the research process and includes the following game technologies: business game “Goal-setting as a basis for planning”, simulation game “Effective Communications”, role-playing game “Delegation of Authority”, business game “Effective Control”, “Organization of planning”, “Organization of control”. To confirm the effectiveness of the introduction of gaming technologies in the process of training student managers at the university, the results obtained in the control and experimental groups are shown, the results of processing the results based on the Mann-Whitney U-criterion are given; conclusions are drawn that the use of gaming technologies has proven its effectiveness.
An Efficient Self-embedding Fragile Watermarking Scheme Based on Neighborhood Relationship

As the technology is growing at a drastic rate, threats to confidential images have also increased. Various software have been developed with which anyone can easily manipulate the images having malevolent intents or passing some information by hiding in it. The motive of this paper is to propose an approach through which we can detect whether an image has been tampered or not. To detect tampering, we have used pixel-wise bit slicing approach. This approach is efficient in a way as it can locate the precise area that has been tampered; for this, we have used the last three least significant bits (LSBs) operated on first five most significant bits (MSBs). If there is any tampering, XOR operator is used on the bits of original image and tampered image to locate the area of distortion. We have shown the accuracy of this algorithm by testing this algorithm on various images under attack.
Storytime Surprise! Solving the Puzzle of Distracted Adults

It’s not hard to find library staff complaining about distracted adults at storytime. Still, as a person who began her library career before the invention of the personal computer, I can say that being distracted is nothing new. It has simply become more convenient and obvious. Regardless, I acknowledge the struggle. We make rules and announce them intently. This sometimes leads to confrontations or shaming. Public relations can suffer. Or perhaps we hear that we are to be more “engaging.” But what is this supposed to mean? Wear a funny hat? Slip on a banana peel? With all due respect, many of us don’t feel comfortable being clowns. And storytime should be about the stories, not us.
Practical Seed-Recovery of Fast Cryptographic Pseudo-Random Number Generators

Trifork is a family of pseudo-random number generators described in 2010 by Orue et al. It is based on Lagged Fibonacci Generators and has been claimed as cryptographically secure. In 2017 was presented a new family of lightweight pseudo-random number generators: Arrow. These generators are based on the same techniques as Trifork and designed to be light, fast and secure, so they can allow private communication between resource-constrained devices. The authors based their choices of parameters on NIST standards on lightweight cryptography and claimed these pseudo-random number generators were of cryptographic strength. We present practical implemented algorithms that reconstruct the internal states of the Arrow generators for different parameters given in the original article. These algorithms enable us to predict all the following outputs and recover the seed. These attacks are all based on a simple guess-and-determine approach which is efficient enough against these generators. We also present an implemented attack on Trifork, this time using lattice-based techniques. We show it cannot have more than 64 bits of security, hence it is not cryptographically secure.
DEVELOPMENT OF LEGAL LITERACY: AN EXPERIENCE OF CREATING GUIDANCE SUPPORT

The article discusses the correlation between the «functional competence» and «legal literacy» terms, analyzes approaches to defining the term «legal literacy»; also, it provides an example of developing interactive legal games designed to practically engage the students of the different education levels to learn legal material to form legal consciousness and legal awareness of a modern person and citizen.
SENTIMENT ANALYSIS BASED ON PROBABILISTIC CLASSIFIER TECHNIQUES IN VARIOUS INDONESIAN REVIEW DATA

Sentiment analysis is the field in data science to achieve a broader holistic view of users’ needs and expectations. Indonesian user opinions have the potential to manage to be valuable information using sentiment analysis tasks. One of the most supervised learning techniques used in Indonesian sentiment analysis is the Naïve Bayes classifier. The classifier can be optimized and tuned in various models to increase the sentiment analysis model performance. This research aims to examine the performance of various Naïve Bayes models in sentiment analysis, especially when implemented in small datasets to handle overfitting problems. Four different Naïve Bayes models used are Gaussian, Multinomial, Complement, and Bernoulli. We also analyse the effect of various pre-processing techniques on the models’ performance. Moreover, we build the first fashion dataset from the Indonesian marketplace which has a unique character compared to the datasets from other domains. Finally, we also use the various dataset in the experiment to test the Naïve Bayes models' performance. From the experiment result, Complement Naïve Bayes is superior to other models, especially in handling overfitting with F1-score of approximately 0.82.
An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming

As object detection has gained popularity in recent years, there are many object detection algorithms available in today's world. Yet the algorithm with better accuracy and better speed is considered vital for critical applications. Therefore, in this article, the use of the YOLOV4 object detection algorithm is combined with improved and efficient inference methods. The YOLOV4 state-of-the-art algorithm is 12% faster compared to its previous version, YOLOV3, and twice as faster compared to the EfficientDet algorithm in the Tesla V100 GPU. However, the algorithm has lacked performance on an average machine and on single-board machines like Jetson Nano and Jetson TX2. In this research, we examine the performance of inferencing in several frameworks and propose a framework that effectively uses hardware to optimize the network while consuming less than 30% of the hardware of other frameworks.
Library Automatic Borrowing System Based on STM32 and Java

The purpose of this paper is to promote the effective use of information resources, strengthen the deep processing of information resources, improve the performance and ease of use of the retrieval system, and realize the intelligence of information retrieval and the automation of information management.
CLD² Language Documentation Meets Natural Language Processing for Revitalising Endangered Languages

Language revitalisation should not be understood as a direct outcome of language documentation, which is mainly focused on the creation of language repositories.Natural language processing (NLP) offers the potential to complement and exploit these repositories through the development of language technologies that may contribute to improving the vitality status of endangered languages.In this paper, we discuss the current state of the interaction between language documentation and computational linguistics, present a diagnosis of how the outputs of recent documentation projects for endangered languages are underutilised for the NLP community, and discuss how the situation could change from both the documentary linguistics and NLP perspectives.All this is introduced as a bridging paradigm dubbed as Computational Language Documentation and Development (CLD²).CLD² calls for (1) the inclusion of NLP-friendly annotated data as a deliverable of future language documentation projects; and (2) the exploitation of language documentation databases by the NLP community to promote the computerization of endangered languages, as one way to contribute to their revitalization.
Verifying Autonomous Systems

This paper focuses on the work of the Autonomy and Verification Network ( https://autonomy-and-verification.github.io ). In particular it will look at the use of model-checking to verify the choices made by a cognitive agent in control of decision making within an autonomous system. It will consider the assumptions that need to be made about the environment in which the agent operates in order to perform that verification and how those assumptions can be validated via runtime monitoring. Lastly it will consider how compositional techniques can be used to combine the agent verification with verification of other components within the autonomous system.
Pixie: Preference in Implicit and Explicit Comparisons

We present Pixie, a manually annotated dataset for preference classification comprising 8,890 sentences drawn from app reviews.Unlike previous studies on preference classification, Pixie contains implicit (omitting an entity being compared), and indirect (lacking comparative linguistic cues) comparisons.We find that transformer-based pretrained models, finetuned on Pixie, achieve a weighted average F1 score of 83.34% and outperform the existing state-of-the-art for preference classification model (73.99%).
Partially Relaxed Masks for Knowledge Transfer Without Forgetting in Continual Learning

The existing research on continual learning (CL) has focused mainly on preventing catastrophic forgetting. In the task-incremental learning setting of CL, several approaches have achieved excellent results, with almost no forgetting. The goal of this work is to endow such systems with the additional ability to transfer knowledge when the tasks are similar and have shared knowledge to achieve higher accuracy. Since the existing system HAT is one of most effective task-incremental learning algorithms, this paper extends HAT with the aim of both objectives, i.e., overcoming catastrophic forgetting and transferring knowledge among tasks without introducing additional mechanisms into the architecture of HAT. The current study finds that task similarity, which indicates knowledge sharing and transfer, can be computed via the clustering of task embeddings optimized by HAT. Thus, we propose a new approach, named “partially relaxed masks” (PRM), to exploit HAT’s masks to not only keep some parameters from being modified in learning subsequent tasks as much as possible to prevent forgetting but also enable remaining parameters to be updated to facilitate knowledge transfer. Extensive experiments demonstrate that PRM performs competitively compared with the latest baselines while also requiring much less computation time.
Stress and strain created around miniscrews during en masse retraction - A finite element method

Background: En masse retraction of the anterior teeth may create anchorage problems. The effective means of obtaining anchorage in en masse retraction is by the usage of temporary anchorage devices. Aims and Objectives: The purpose of this study is focused on stress and strain created around miniscrew during en masse retraction using the cone beam computed tomography (CBCT) data obtained at two intervals and by the finite element analysis done using the CBCT data obtained. This will show us the stress and strain created around miniscrews and the withstanding capacity of miniscrews. Conclusion: Stress and strain was higher in TADs as compared to cortical and alveolar bone.
Your Social Circle Affects Your Interests: Social Influence Enhanced Session-Based Recommendation

Session-based recommendation aims at predicting the next item given a series of historical items a user interacts with in a session. Many works try to make use of social network to achieve a better recommendation performance. However, existing works treat the weights of user edges as the same and thus neglect the differences of social influences among users in a social network, for each user’s social circle differs widely. In this work, we try to utilize an explicit way to describe the impact of social influence in recommender system. Specially, we build a heterogeneous graph, which is composed of users and items nodes. We argue that the fewer neighbors users have, the more likely users may be influenced by neighbors, and different neighbors may have various influences on users. Hence weights of user edges are computed to characterize different influences of social circles on users in a recommendation simulation. Moreover, based on the number of followers and PageRank score of each user, we introduce various computing methods for weights of user edges from a comprehensive perspective. Extensive experiments performed on three public datasets demonstrate the effectiveness of our proposed approach.
Automatic Labeling of Human Actions by Skeleton Clustering and Fuzzy Similarity

Nowadays, human action recognition (HAR) has been applied in multiple fields with the rapid growth of artificial intelligence and machine learning. Applying HAR onto industrial production lines can help on visualizing and analyzing the correlation between human operators and machine utilization to improve overall productivity. However, to train HAR model, the manual labeling of certain actions in a large amount of the collected video data is required and very costly. How to label a large amount of video automatically is an emerging practical problem in HAR research domain. This research proposed an automatic labeling framework by integrating Dynamic Time Warping (DTW), human skeleton clustering, and Fuzzy similarity to assign the labels based on the pre-defined human actions. First, the skeleton estimation method such as OpenPose was used to jointly detect key points of the human operator’s skeleton. Then, the skeleton data was converted to spatial-temporal data for calculating the DTW distance between skeletons. The groups of human skeletons can be clustered based on DTW distance among skeletons. Within a group of skeletons, the undefined skeletons will be compared with the pre-defined skeletons, considered as the references, and the labels are assigned according to the similarity against the references. The experimental dataset was created by simulating the human actions of manual drilling operations. By comparing with the manual labeled data, the results show that all of accuracy, precision, recall, and F1 of the proposed labeling model can achieve up to 95% with 40% saving time.
Privacy-Preserving Neural Networks with Decentralized Multi-client Functional Encryption

Emerging machine learning methods have become a powerful driving force to revolutionize many industries nowadays, such as banking, healthcare services, retail, manufacturing, transportation. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. Functional encryption is a new type of encryption primitive in which a secret functional-key allows one to compute a specific function of plaintext from the ciphertext, making it very suitable for privacy protection machine learning scenarios. In this paper, we apply the concepts of decentralized multi-client function encryption to explore a new solution to the privacy-preserving convolutional neural network. The results of the experiment show that our scheme is feasible, and the accuracy of the final model on the test set is 92.1%, which is close to 93.2% of the convolution network connected to plain text.
Analysis of the Relationship Between the Family Environment, Learning Styles, and Peers with Learning Outcomes

This study aims to determine the relationship between the family environment, learning styles, and peers with learning outcomes in Office Administration Education Students, State University of Jakarta Class of 2019. This research uses questionnaire data, with a systematic random sampling data collection technique, namely systematic random sampling. This study took population data, namely 76 and the samples used were 64 samples. The data in this study used the Partial Least Square system, which is a statistical research testing system carried out using Structural Equation Model (PLS) software, to explain the existence of a relationship or influence between variables, and measure data or samples with some less than 100 data. In this study, the results were found, namely, (1) There was a significant relationship between the family environment and learning outcomes, (2) There was a significant relationship between learning styles and learning outcomes, and (3) There was a significant relationship between peers and learning outcomes, (4) There was no significant relationship between family environment variables and learning style variables on learning outcome variables, (5) There is a significant relationship between learning style variables and peer variables to learning outcomes variables, (6) There is no relationship or correlation between family environment variables and learning style variables but has a relationship or correlation with peer variables with learning outcome variables.
Practice on Blended Teaching under STEM Education Concept and Case Teaching: The “Applied Chemistry Experiment” Case

We integrated STEM (Science, Technology, Engineering, Mathematics) teaching concepts into SPOC (Small Private Online Course) blended teaching of applied chemistry experiments, and adopted the case teaching method for different experimental projects.Students completed online previews of experimental contexts and videos before class and discussed and analyzed the cases in groups during class.With teacher guides, the students provided solutions and conducted experimental operations.Effective STEM evaluation was used after class.Practicing the above teaching methods organically integrated online teaching resources and offline experimental operation, promoted students' autonomous learning, and cultivated students' scientific spirit and cooperative innovation ability.