Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Grouped Federated Learning Scheme Against Malicious Clients

The existence of malicious clients in federated learning leads to the decline of model performance or even training failure. Considering the huge advantages of the blockchain in the decentralization, feasibility and high reliability, many schemes based on blockchain have been proposed. However, due to the large number and scattered distribution of clients, it is difficult to backtrack malicious clients. In this paper, to reduce the time to backtrack the malicious clients and improve the accuracy of model training in the federated learning, we pro- pose a Blockchain-based Grouped Federated Learning Scheme (BGFLS). Specifically, to quickly and accurately backtrack the malicious clients, we present a grouping federated learning architecture, which applies blockchain technology to each group. Moreover, we design an algorithm to backtrack the malicious clients and propose a block structure to support backtracking. Theoretical analysis shows that the BGFLS can quickly backtrack malicious clients and protect shared parameters. We evaluate the BGFLS on MNIST and Fashion-MNIST datasets. The experimental results demonstrate that the accuracy of the BGFLS is higher than that of the GeoMed scheme around 0.08 and is higher than that of the Krum scheme around 0.07 when the proportion of the malicious clients is 20%. Meanwhile, the backtracking efficiency of the BGFLS is higher than that of the original blockchain.
Model Selection - Knowledge Distillation Framework for Model Compression

The significant increase in the computation and parameter storage costs of CNNs promotes its development in various applications and restricts its deployment in edge devices as well. Therefore, many neural network pruning methods has been proposed for neural network compression and acceleration. However, there are two major limitations to these methods: First, prevailing methods usually design single pruning criteria for the primitive network and fail to consider the diversity of potential optimal sub-network structure. Second, these methods utilize traditional training method to train the sub-network, which is not enough to develop the expression ability of the sub-network under the current task. In this paper, we propose Model Selection - Knowledge Distillation (MS-KD) framework to solve the above problems. Specifically, we develop multiple pruning criteria for the primitive network, and the potential optimal structure is obtained through model selection. Furthermore, instead of traditional training methods, we use knowledge distillation to train the learned sub-network and make full use of the structure advantages of the sub-network.To validate our approach, we conduct extensive experiments on prevalent image classification datasets. The results demonstrate that our MS-KD framework outperforms the existing methods under a wide range of data sets, models, and inference costs.
DP-Face: Privacy-Preserving Face Recognition Using Siamese Network

With the rapid development of deep learning, face recognition technology based on deep learning has been widely developed in recent years. However, during the training of the deep learning model, there is a risk of privacy leakage. If an attacker obtains private data, such as tags of the training data, the face images may be restored, and private information is leaked. To pro-tect the private information of the face recognition model, we introduce differential privacy technology to propose a privacy - preserving face recognition scheme using the Siamese Network framework called DP-Face. Unlike other privacy-preserving face recognition methods, we can adjust the balance between privacy and utility through privacy budgets according to actual needs. Experimental results show that the effectiveness and privacy of the proposed DP-Face can be well guaranteed.
Research on student behavior characters from the campus big data

Investigating behavioral characteristics of college students is of great importance to grasp students' behaviors and assist administrators make efficient manage strategies. In this paper, our research focuses on the discovery of burst nature and memory effect of selecting behavior and repeating behavior, which are universal characteristics of human beings, from the campus big data. To this end, we firstly analysis the interevent time distributions of selecting canteens and entering the library, respectively. Results show that both of these behaviors significantly follow heavy-tail power-law distribution, which verify the preference of these behaviors. Then, burstiness parameter B and memory M are measured based on interevent time series, and we observe that the corresponding distributions approximately follow Gaussian forms. Furthermore, we analyze the relations between students' GPA and B or M. The main results indicate that GPA not only decays with the value of burst of entering the library, but also is directly proportional to the absolute value of memory of selecting canteens.
NeSyChair: Automatic Conference Scheduling Combining Neuro-Symbolic Representations and Constrained Clustering

Creating the schedule for an academic conference is a time-consuming task. A typical conference schedule consists of sessions containing papers addressing the same research topic. To construct a schedule, conference papers must be grouped according to their research topic, and the obtained groups should fit the assigned time slots. This paper proposes an approach to automating the schedule-creation process. We use multilingual, neuro-symbolic paper representations and novel constrained clustering to group papers into clusters of predetermined size with the same topic fitting the schedule structure. In the process, we combine machine-learning, natural language processing, network analysis, and combinatorial optimization. We tested the components of the proposed approach on a newly created database of papers from six machine learning conferences, which were manually labeled by their research topics. The entire system was tested on two real-world conferences in a multilingual setting. The developed methodology is incorporated into an interactive automatic conference-scheduling system NeSyChair (Neuro-Symbolic Conference Chair), which can be used to create and improve conference schedules.
Unsupervised Learning Approach for Clustering Source Code based on Functionalities

The source codes of a software system are one of the most valuable resources in software development. A developer spends more time and money on development if they start coding from fresh for each similar functionality project. Topic modelling is crucial for software reuse since it can be difficult for developers to remove obsolete source code from large software systems with a lot of code. Topic modelling techniques has been used to evaluate and model source codes in several ways. Several studies on this domain have been used to extract topics from source codes using various statistical approaches and methodologies. These topic extraction methods are interconnected, and if software best practices aren't followed in older systems, it may ensue unreliability of the outcome. In response to these findings, the author conducted a study in which he extracted source code using a java parser library and predicted the source code functionality name using an unsupervised learning approach such as K-mean algorithm and Expectation Maximization (EM) clustering approach. This is the first effort to develop a clustering model to predict the semantic function name of source code using an unsupervised learning approach and compare the algorithms to get the optimal model.
Towards Open-Set Scene Graph Generation With Unknown Objects

Scene graph generation (SGG) aims to detect objects and their relationships in an image, thereby enabling a detailed understanding of a complex scene for various real-world applications. In SGG applications such as robot vision, it is important to correctly detect all objects without recognizing any object as another kind of object or ignoring it. However, previous studies on SGG do not consider unknown objects whose classes are unseen in training. Consequently, current SGG methods wrongly classify them as known object classes or overlook them. In this paper, we propose a new problem named “open-set SGG” with unknown objects, focusing on detecting even unknown objects and their relationships. Specifically, we formally define this new problem and propose an evaluation protocol, including an extended dataset with unknown objects and novel evaluation metrics designed for the open-set setting. We also build baseline methods by employing and extending existing SGG methods and compare them through experiments to establish the current baseline performance of open-set SGG. Finally, we discuss the limitations of the current SGG methodology in the open-set setting and point out future research directions.
Illustrating Poe’s Detection

Abstract It is not surprising that the texts of Edgar Allan Poe, that most visual of literary artists, have inspired so many images and that such a wide array of illustrators has taken on the challenges of rendering his highly imaginative scenes on paper. With a map to decipher, a treasure chest to locate, bags of gold to haul away, and a Black servant to serve as (often racist) comic relief, “The Gold-Bug” (1843) has provided artists with a plethora of subjects, making it Poe’s most frequently illustrated text. Although “The Murders in the Rue Morgue” (1841), regarded as the first modern detective story, does not offer such a range of options, illustrators have nevertheless emphasized its various aspects. Many of them have chosen not to portray the protagonist C. Auguste Dupin at all, while others have come up with creative ways to represent his mental powers and/or to associate him with vigorous action. No doubt Poe, with his detailed knowledge of and strong opinions about illustrations, would have approved of such inventiveness.
Hotel Management System Using Python Tkinter GUI

Hotel Management System is a desktop application software system where the management of entire hotel is computerized. The hotel management system is using Python. As the rich TKinter GUI for front end and SQL server as the secured backend database. The Tkinter is the gui library of python which is designed to help python developers create system program with a user interface. The hotel management system is rich gui user interface system.
DIGITALIZATION IN HIGHER EDUCATION - THE NEED FOR QUALITY TRAINING AND PROFESSIONAL REALIZATION OF STUDENTS

Technologies related to the digital environment are already entering all spheres of life. That is why they become obligatory for higher education as well. Digitalization in higher education aims to build a modern and sustainable education system in higher education, based on innovative and modern models of teaching and administrative services, by providing digital transformation, digitalization of programs and multidisciplinarity. The report identifies the need and benefits of digitalization in higher education to ensure quality training of students, adequate to the requirements of the labor market, and highlights the main directions of its application.
Towards Real-time 3D Visualization with Multiview RGB Camera Array

A real-time 3D visualization (RT3DV) system using a multiview RGB camera array is presented. RT3DV can process multiple synchronized video streams to produce a stereo video of a dynamic scene from a chosen view angle. Its design objective is to facilitate 3D visualization at the video frame rate with good viewing quality. To facilitate 3D vision, RT3DV estimates and updates a surface mesh model formed directly from a set of sparse key points. The 3D coordinates of these key points are estimated from matching 2D key points across multiview video streams with the aid of epipolar geometry and trifocal tensor. To capture the scene dynamics, 2D key points in individual video streams are tracked between successive frames. We implemented a proof of concept RT3DV system tasked to process five synchronous video streams acquired by an RGB camera array. It achieves a processing speed of 44 milliseconds per frame and a peak signal to noise ratio (PSNR) of 15.9 dB from a viewpoint coinciding with a reference view. As a comparison, an image-based MVS algorithm utilizing a dense point cloud model and frame by frame feature detection and matching will require 7 seconds to render a frame and yield a reference view PSNR of 16.3 dB.
On Strategic Interactions in Blockchain Markets: A Three-stage Stackelberg Game Approach

Blockchain technology is a promising approach for solving the security and personal privacy problems in Internet applications. The successful commercial deployment of Blockchain markets relies on a comprehensive understanding of the economic and strategic interactions among different entities involved. In this paper, we focus on a blockchain market consisting of a blockchain platform (BP), multiple miners, and blockchain users (BUs), and formulate their interactions as a three-stage Stackelberg game. In Stage I, the BP strategizes the rewards granted to the miners, so as to attract the miners to contribute more computing power used for improving the security and privacy of the blockchain. In Stage II, each miner strategizes its computing power individually for winning the mining compe-tition, which is modeled as a non-cooperative game. In Stage III, the BUs strategize the transaction fee to acquire a corresponding service experience. With the objective of utility maximization, we develop a theoretical framework to analyze the hierarchical interactive behaviors among the entities in a backward inductive way. By solving the Stackelberg equilibrium, we determine the optimal strategies of entities in closed-form. Numerical results are provided to demonstrate the performance of the strategic interactions in the blockchain market.
The Effect of Organizational Culture on Financial Performance: Based on Cameron and Quinn Model (CVF)

The research objective is to investigate the impact of organizational culture (OC) based on the Cameron and Quinn model (The Competing Value Framework) on financial performance (FP) in Egyptian hotels. The research used five dimensions (occupancy percentage, average room rate, RevPAR, average market share, and average revenue) to measure FP in hotels. Quantitative methods were used to achieve the research objective through the questionnaire. A total of 416 questionnaires were provided to general, executive, HR, and quality managers working in four five-star hotels in Sharm El Sheikh city 104 hotel in Egypt. The research used three main data analysis techniques: descriptive analysis, correlation, and multi-regression by SPSS V25. Empirical findings showed that OC positively affected the FP of hotels. The research recommends that hotels in Egypt to attention to the consolidation of OC types because of their positive impact on enhancing the FP of hotels. Hotels should also promote organizational values such as fairness, equality, teamwork, and employee cooperation and, in addition, enhance the interest in organizational beliefs such as the human element and job satisfaction to increase rewards and incentives.
Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis

This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space’s characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.
Building and Improving Artificial Neural Network Classifier

Deep learning is a spectrum of machine learning which uses advanced neural networks to solve the various machine learning problems. Its working is very similar to the working of a human brain where the models take decision based on various input parameters. There are multiple open source libraries which implement neural networks, like Tensorflow, Theano, PyTorch, Keras etc. In this paper we have proposed a generic architecture that can be used for any type of classification problems with binary output or classification output using Deep Learning model: Artificial Neural Network (ANN). In the architectural model after Data preprocessing we first build the ANN classifier using Keras library with Tensorflow backends, second step we have apply Cross-validation method for better accuracy. Then we perform Dropout Regularization method for preventing from overfitting and at last we have applied grid search technique for parameter tuning that basically will test several combinations of Hyperparameter values and will eventually return the best selection choice with K-Fold cross validation. And the experimental results shows in higher accuracy with ours proposed architecture and in our proposed architecture results we remove the randomness from the model. In the proposed architecture we can again rebuild developing our model using Keras Callback function by using this feature in our model it does not create any major difference in terms of accuracy. But as we know the accuracy will vary with parameter tuning. The main advantage of using Keras Callback function method is it’s saves a lot of time for building model and it is easy for debugging the model.
A Shapley Value-Based Incentive Mechanism in Collaborative Edge Computing

In recent years, with the rapid proliferation of smart devices, Mobile Edge Computing (MEC) has been regarded as a promising technique that provides computing services in proximity to end-users. To improve the performance of MEC systems, Collaborative Edge Computing (CEC) is proposed to balance the load among cooperative edge servers. In practice, however, edge servers belong to different MEC service providers (SPs) and they have no incentive to help others. To encourage the cooperation between self-interested SPs, in this paper, we propose a profit-sharing incentive mechanism based on the Shapley value. In addition to the desirable properties such as efficiency and fairness, we also proved that our mechanism induces optimal offloading strategies and provides every SP an incentive to join the coalition. To protect the private information of SPs, we defined an aggregate profit function for each SP and showed that revealing this function is sufficient to calculate the profit allocation. Simulation results demonstrate that the system performance and SPs' revenue are substantially improved under cooperation.
Biased Hypothesis Formation From Projection Pursuit

The effect of bias on hypothesis formation is characterized for an automated data-driven projection pursuit neural network to extract and select features for binary classification of data streams.This intelligent exploratory process partitions a complete vector state space into disjoint subspaces to create working hypotheses quantified by similarities and differences observed between two groups of labeled data streams.Data streams are typically time sequenced, and may exhibit complex spatio-temporal patterns.For example, given atomic trajectories from molecular dynamics simulation, the machine's task is to quantify dynamical mechanisms that promote function by comparing protein mutants, some known to function while others are nonfunctional.Utilizing synthetic two-dimensional molecules that mimic the dynamics of functional and nonfunctional proteins, biases are identified and controlled in both the machine learning model and selected training data under different contexts.The refinement of a working hypothesis converges to a statistically robust multivariate perception of the data based on a context-dependent perspective.Including diverse perspectives during data exploration enhances interpretability of the multivariate characterization of similarities and differences.
ANALYSIS AND EVALUATION OF ACADEMIC INFORMATION SYSTEM SECURITY USING NIST SP 800-26 FRAMEWORK

Along with the development of technology and information that is growing rapidly, currently the competition between educational institutions is getting stronger. If an institution is not able to keep up with the progress of information technology which is developing very quickly, it is certain that the institution will be left very far behind from all sides. However, there are things that really need to be considered due to the development of information technology, namely the consideration of the security of information systems owned by the Institution. For that we need an analysis and evaluation of the information system used to identify security in the information system. If the analysis and evaluation is not carried out, problems will arise related to the security of an information system such as data that is vulnerable to threats such as damaged and lost data so that the data becomes invalid. If the data is not valid, it is certain that the information generated will also not be reliable. Evaluation of information system security can be done with the framework. NIST is a framework that can be used to evaluate and identify security and risks in information systems. The information system security evaluation process is carried out by distributing questionnaires to the academic community in accordance with the NIST SP 800-26 framework and the data is managed to obtain the final result. The results of the academic information system security evaluation have an overall final score of 91.6%. The total value is obtained from the results of the number of calculations based on 3 components of the criteria tested, namely management control, operational control, and technical control. And from the 3 assessment criteria there are 17 sub-criteria that exist in each criterion. Based on this data, the security of the academic information system at AMIK Tunas Bangsa is included in the level 2 category, namely Documented Procedures.
Scientific and communicative interaction in HEIs during pandemic

This article aims to identify the benefits of the online platform for scientific and communicative interaction in HEIs during the pandemic. Pedagogical experiment during the academic year 2020/2021 was chosen as the main method of research, which included the study of the Mentimeter's effectiveness (online platform) and a students' survey at the Kyiv National University of Culture and Arts (KNUCaA). Results. The study showed that the use of the Mentimeter platform resources significantly increases the level of the student audience interest in the course material and during the study as a whole. This platform was involved by the teachers to work with students during the lecture or for educational purposes. Also, it was aimed to get feedback from students about the subject course content or the lecturer's methodological developments. Online surveys, created with the platform's help, allow establishing synchronous communication with the students during the lecture session remotely. Students' evaluation of the communication component of distance education under such conditions has higher rates. Communicative practices, which implementation into educational process contributes to the formation of open, communicative educational environment in modern institution of higher education, under long-distance education are of great importance not only for learning.