Reviewerly/exHarmony huggingface dataset:
A multi-graph neural group recommendation model with meta-learning and multi-teacher distillation

Group recommendation has garnered significant attention recently, aiming to suggest items of interest to groups. Most deep learning-based approaches for group recommendation focus on learning group representation through group–user interactions or by aggregating user preferences. However, these approaches face challenges due to the complexity of group representation learning and the limited availability of group–item interactions. To address these difficulties, we propose a multi-graph neural group recommendation model with meta-learning and multi-teacher distillation. Our model consists of three stages: multiple graphs representation learning (MGRL), meta-learning-based knowledge transfer (MLKT), and multi-teacher distillation (MTD). In the MGRL stage, we construct two bipartite graphs and an undirected weighted graph based on group–user interactions, group–item interactions, and similarity between groups, respectively. We then utilize a linear variational graph auto-encoder network to learn the semantic features of groups, users, and items. In the MLKT stage, we introduce a multilayer perceptron (MLP) network for the recommendation task and design a meta-learning-based knowledge transfer algorithm to train an initialization parameter that incorporates user preference knowledge. In the MTD stage, we use the trained parameters as initialization parameters for all teacher networks. We evenly divide the group–item interactions among the teachers and sequentially train the teacher networks. Finally, we employ a multi-teacher distillation approach to train student networks with superior performance. Furthermore, we conduct extensive experiments on six real-world datasets to evaluate the effectiveness of our proposed model. The experimental findings demonstrate that our model, in comparison to state-of-the-art approaches, achieves superior performance in terms of group recommendation.
EBOF: A fusion-domain color image watermarking scheme based on approximate Hessenberg factorization

With the rapid development of mobile networking devices and Internet technology, color images gradually occupy the dominant position of image applications, and the protection of color image copyright is also imminent. In this paper, a blind watermarking scheme for color images based on approximate Hessenberg factorization (AHF) in fusion domain is proposed. The formation rule of Hessenberg matrix coefficients is further researched based on the Householder transform, and the approximate values of Hessenberg matrix at position (1, 2), (2, 1), (2, 2) are directly obtained and used to hide the watermark information in the spatial domain. The proposed scheme not only consists of fractional order Chen’s chaotic system (FOCCS) to encrypt the watermark image, but also combines with Error correction coding application, B-R-G watermark embedding principle, Overflow processing of extreme pixel block and Firefly algorithm (EBOF). Moreover, the embedding position and the amount of embedding information can be also adaptively selected according to the image size. The simulation results show that the presented fusion-domain scheme outperforms state of the art schemes. On the premise of ensuring low time complexity, the presented watermarking scheme has strong robustness, high invisibility and high security.
Application of virtual human sign language translation based on speech recognition

For the application problem of speech recognition to sign language translation, we conducted a study in two parts: improving speech recognition's effectiveness and promoting the application of sign language translation. The mainstream frequency-domain feature has achieved great success in speech recognition. However, it fails to capture the instantaneous gap in speech, and the time-domain feature makes up for this deficiency. In order to combine the advantages of frequency and time domain features, an acoustic architecture with a joint time domain encoder and frequency domain encoder is proposed. A new time-domain feature based on SSM (State-Space-Model) is proposed in the time- domain encoder and encoded using the GRU model. A new model, ConFLASH, is proposed in the frequency domain encoder, which is a lightweight model combining CNN and FLASH (a variant of the Transformer model). It not only reduces the computational complexity of the Transformer model but also effectively integrates the global modeling advantages of the Transformer model and the local modeling advantages of CNN. The Transducer structure is used to decode speech after the encoders are joined. This acoustic model is named GRU-ConFLASH- Transducer. On the self-built dataset and open-source dataset speechocean, it achieves optimal WER (Word Error Rate) of 2.6% and 4.7%. In addition, to better realize the visual application of sign language translation, a 3D virtual human model is designed and developed.
Energy and carbon-aware initial VM placement in geographically distributed cloud data centers

Energy and carbon efficiency has become one of the most important research topics in the cloud computing research area because of the growing demand for cloud data center infrastructures. In addition to the environmental impact of emitting carbon, local authorities impose a carbon tax based on the amount of emitted carbon by the data centers. Therefore, this paper introduces the energy and carbon-aware initial VM placement (ECAIVMP), one of the most efficient strategies for minimizing the total energy consumption and carbon emission of cloud data centers without sacrificing the quality of service. Our proposed algorithm considers multiple geo-distributed cloud data centers and acknowledges both IT and non-IT power usage during the virtual machine placement process. An extensive simulation in the CloudSim Plus framework is conducted to evaluate the performance of our proposed algorithm using real-world datasets, including workloads, power model benchmarks, and outside temperature. The experimental results demonstrate that our algorithm is able to reduce energy consumption by 17% and carbon emissions by 6% on average compared to other competitor algorithms.
Multi-agent off-policy actor-critic algorithm for distributed multi-task reinforcement learning

In this paper a new distributed multi-agent Actor-Critic algorithm for reinforcement learning is proposed for solving multi-agent multi-task optimization problems. The Critic algorithm is in the form of a Distributed Emphatic Temporal Difference DETD(λ) algorithm, while the Actor algorithm is proposed as a complementary consensus based policy gradient algorithm, derived from a global objective function having the role of a scalarizing function in multi-objective optimization. It is demonstrated that the Feller-Markov properties hold for the newly derived Actor algorithm. A proof of the weak convergence of the algorithm to the limit set of an attached ODE is derived under mild conditions, using a specific decomposition between the Critic and the Actor algorithms and additional two-time-scale stochastic approximation arguments. An experimental verification of the algorithm properties is given, showing that the algorithm can represent an efficient tool for practice.
Development and Effects of University Career Education Subject in the Age of Hyper-Uncertainty

The purpose of this study is to develop more adaptive career education subject in an era of uncertainty as it is now and to verify the effects on career decision-making self-efficacy and career adaptability of university students. For this purpose, the participants of this study was selected from 40 students who participated in a university in A city and participated in the class for one semester by developing subjects in consideration of alternative career theory(career construction, planned happenstance, career theory of chaos) and psychological factors (growth mindset, grit, resilience) in addition to the common theme of self-understanding, understanding of the job world, career planing, and career preparation through prior research, needs for university students, and opinion of experts. 41 students who did not take the class were selected as the control group and the difference in effect between the groups was analyzed through ANCOVA. As a result, it was confirmed that the career education subject developed in this study had a statistically significant effect on career decision-making self-efficacy and career adaptability of university students. The results of this study can be used as a useful basic data for establishing a career education plan that can increase the confidence and adaptability of college students preparing for career and employment in a situation where uncertainty is high.
Knowledge-that, Knowledge-how, consciousness and artificial intelligence

The article is devoted to the discussion about the relationship between knowledge and skill. This discussion arose within the critique of dualism. It is believed that dualism presupposes an intellectualism in which knowledge-how always derived from knowledge-that. The article proposes a method of distinguishing knowledgehow and knowledge-that through characteristic properties. This method allows us to draw a strict boundary between the two types of knowledge. This is followed by an explanation of the relationship between the two types of knowledge through the inclusion of knowledge-how as a necessary but not sufficient condition for knowledge-that. In the final part of the article, the principle of indistinguishability of knowledge-how among different agents is introduced, on the basis of which it is argued that the division into knowledge-how and knowledge-that works in the interests of dualism, and not vice versa, as supposed by Gilbert Ryle. This division makes sense in light of the Chinese room argument and the demonstration of the difference between humans and weak artificial intelligence.
Expert System to Improve and Develop Mathematic Teachers Skills

This work intends to improve the educational process through the development of teachers skills and raise the level of learning by reducing the problems and obstacles facing the teacher and providing many different learning methods. This is done through expert systems, which is one of the important fields of knowledge-based artificial intelligence, where the expert system acquires all the information acquired by the expert in the field of mathematics by transferring all his scientific experience and how the mathematics expert deals with it, so that the knowledge acquired by experts in the field of mathematics is collected. In order to prepare a knowledge base and deal with it through the expert system, as well as the possibility of making the expert system that the teacher deals with, ready to answer any inquiries or the possibility of explaining any part of the curriculum on demand.
An Xception Model Based Real-time Violence Detection

Nowadays the usage of CCTV surveillance is increasing. The number of cases of violence in public places like airports, railway stations, bus stops, shopping malls, restaurant, sports arenas and party halls have only been discovered after the fact. It is too late. Our objective be located to develop a complete structure capable of video analysis in real-time manner, which can identify the existence of violence from input video such as cctv and report the violence to concerned authorities such as higher authorities, police to take necessary action. In the proposed work using CNN for feature extraction and LSTM Interpreting features in chronological order. The distinct Xception system manner, we Achieving an effective solution for analysis video in real-time fashion through violent scenes obtained from the video allows us to legally monitor the situation with the relevant authorities. A mobile app, Telegram bot that can report the violent event Immediately to concern authorities.
Integration of Different Optimization Approaches for the Classification of Different Arabic and English Voice Commands

Several hyperparameters represent major sensitive factors for deep learning models. For this, different hyperparameter optimization approaches are proposed to accelerate the convergence towards the optimal configurations and to support the calculations during the long learning times, and thus to give improved performance. These approaches include Bayesian Optimization (BO), Hyperband and Tree Parzen Estimator (TPE), and they are proposed for optimization task in this paper. Also, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are suggested as classifiers and Mel frequency cepstrum coefficients (MFCC) and Mel as features. Experiments showed that the best results in terms of evaluated performances (Precision = 94.96%, Recall = 94.85%, F1 = 94.85%) were obtained with the combination of LSTM and MFCC (LSTM-MFCC) with BO on English voice command database compared to those obtained with other combinations of features and classifiers with different optimization approaches. Moreover, there is evidence that BO converged faster than TPE and HB, and converged to better configurations.
State Feedback Control of Interval- Valued Fuzzy Descriptor System Subject to Random Occurred Actuator Failures

In this paper, we present a new fault tolerant control (FTC) strategy for a general class of non-linear systems that involve algebraic equations, time delay, and stochastic actuator faults. The interval type-2 IT-2 fuzzy model, which incorporates lower and upper membership functions to characterize the dynamics of the considered systems, is adopted to deal with the uncertainty. The goal of the control strategy is to build an IT-2 fuzzy state feedback controller with mismatched membership functions. A novel LMIs-based criterion is developed to guarantee the admissibility of the closed-loop system in the mean-square sense, which assures that the scheme can be used. The effectiveness of the proposed control is illustrated using a numerical example.
Violence Detection Based on Multisource Deep CNN with Handcraft Features

Today's latest video surveillance technology has recently been used to monitor human interactions in automated processing systems. They play an important part in security matters. There are many contests in unique between violent and non-violent. Supernatural activities Like crowded atmospheres and camera view. In this article, we proposition a deep novel violence against this structural system Built on specific features derivative from craft methods and through which violence is detected. These features are related to the representation of the image, the appearance of the image, and their motion speed, and are fed as input to a neural network (CNN), which transforms them into spatial, temporal feature, and feature streams, trained a network through this spatial stream to recognize contextual patterns in each frame of video. This temporal stream consisted of three consecutive frames for learning each dynamic pattern of violent behavior. Differential measurement of optical flow. Furthermore, we familiarized a discriminatory feature with a new kinetic energy image to signify violent acts distinct from others in a spatio-temporal stream. The approach incorporates dissimilar features of violent actions by combining the significances of these streams. CNN is also called violence and trained label. They include hockey, movie and VP datasets that are crowded and not. These tentative results determine that the proposed in rapports of accurateness and dispensation time, the violence detection technique outperformed the prior study's results.
DWT Denoising Signal Implementation Based on FPGA Target

In this paper, we implemented the Discrete Wavelet Transform (DWT) for signal noise reduction on a Field Programmable Gate Array (FPGA) device, and combined the DWT module with the audio codec on the Cyclone-II EP2C35F72C6 FPGA on the Altera DE2 board connected to the chip. The signal was denoised by applying the thresholding only to the detail part of the DWT result. The results reveal the efficiency of our system design
Service-Level Agreement Management with Blockchain-based Smart Contract to Improve the Quality of IT Service Management.

Information Technology Service Management is managing information technology services that focus on customer satisfaction and ensure meeting Service Level Agreements (SLA). The organization used in this study is a university, and there are service providers, namely the information technology directorate center, and the service users are several sections in the university. SLA management is currently conducted by emailing SLA documents to each section. The current SLA management is challenging to manage contract recording because documents are in separate emails, and there needs to be a monitoring process for SLA. This study proposes a service-level agreement management system using blockchain-based smart contracts and uses a private blockchain network for implementing smart contracts. The results of this study show the process of managing SLA using a smart contract in a more structured to improve the quality of IT service management.
Improvement for Large-Scale Image Data using Fuzzy Rough C-Mean Based Unsupervised CNN Clustering: An Empirical Study on designbyhumans.com

Abstract: Clustering analysis, specifically for extensive image data, is increasingly being applied in various fields such as finance, risk management, prediction, etc., and has been a fascinating subject in many scientific discussions. Deep learning, a widely used approach, and classical methods address complex classification problems stemming from real-world cases. In this study, we took various approaches to classification problems and measured their effectiveness by combining different techniques using the results of different scenarios. Many approaches have been proposed to solve the clustering problem; complex clustering methods such as hierarchical, density-based, centroid-based, and graph theoretical have been submitted. However, when it comes to real-world applications, they exposed significant drawbacks when the dataset introduced immeasurable vagueness, uncertainty, or overlapping samples that made it impossible to predict and classify. Several attempts have been made to improve the clustering method's performance, including joint CNN clustering models. Still, many of them carry the cons of the complicated clustering method, which limits the capability of CNN. The combined CNN clustering method is designed to address the problem with those deterministic CNN clustering models and was evaluated on a dataset we collected from the website designbyhumans.com, with enough features to represent a non-synthetic dataset. This research aims to improve upon the established model by using estimation techniques in determining model parameters and graphing plots to justify those choices and give insights into how the model performs on a non-synthetic dataset like ours. We concluded that the model significantly improved compared with a popular complex clustering method, which has been evaluated by computational time, using different metrics to represent how better separated each cluster was. Based on conducted experiments and the future development of the method, we discussed and addressed some of the drawbacks of this approach.
Improving the Efficiency of Link Prediction on Handling Incomplete Knowledge Graph Using Clustering

A knowledge graph (KG) is used to store knowledge in the form of connected facts. Facts in KG are represented in the form of a triple (subject, predicate, object) or (head, relation, tail). KG is widely used in question answering, information retrieval, classification, recommender systems, and so on. However, a common problem with KG is incomplete KG. A KG is called incomplete if there is a missing relationship between two entities. An incomplete KG can have an impact on decreasing the accuracy of a task that uses the KG. One solution to the incomplete KG is to use link prediction. Link prediction aims to predict the missing relationship between two entities in a KG. Another problem is that the size of KG is large, consisting of hundreds or millions of entities and relationships. Handling large KG also needs to be considered. Therefore, link prediction on large KG also needs to be considered so that the link prediction process is more efficient. This paper discusses link prediction using embedding to overcome the incomplete KG problem. In addition, it is proposed to use clustering to increase the efficiency of the link prediction process. Clustering is used to group the embedding results. After the embedding results are grouped, scoring and loss function calculations to predict missing links are carried out in groups that are considered appropriate. It is expected that with this grouping, the time of link prediction process can be more efficient because there is no need to check all the vectors in the embedding space.
Goal Driven Code Generation for Smart Contract Assemblies

We are currently witnessing the proliferation of blockchain environments to support a wide spectrum of corporate applications through the use of smart contracts. It is of no surprise that smart contract programming language technology constantly evolves to include not only specialized languages such as Solidity, but also general purpose languages such as GoLang and JavaScript. Furthermore, blockchain technology imposes unique challenges related to the monetary cost of deploying smart contracts, and handling roll-back issues when a smart contract fails. It is therefore evident that the complexity of systems involving smart contracts will only increase over time thus making the maintenance and evolution of such systems a very challenging task. One solution to these problems is to approach the implementation and deployment of such systems in a disciplined and automated way. In this paper, we propose a model-driven approach where the structure and inter-dependencies of smart contract, as well as stakeholder objectives, are denoted by extended goal models which can then be transformed to yield Solidity code that conforms with those models. More specifically, we present first a Domain Specific Language (DSL) to denote extended goal models and second, a transformation process which allows for the Abstract Syntax Trees of such a DSL program to be transformed into Solidity smart contact source code. The transformation process ensures that the generated smart contract skeleton code yields a system that is conformant with the model, which serves as a specification of said system so that subsequent analysis, understanding, and maintenance will be easier to achieve.
The Use of Dynamic n-Gram to Enhance TF-IDF Features Extraction for Bahasa Indonesia Cyberbullying Classification

Cyberbullying detection in a sentence or utterance has challenges due to syntactic and meaning variations (lexical). Term Frequency-Inverse Document Frequency (TF-IDF) carries out textual feature extraction to produce candidates thematically based on word occurrence statistics. However, these candidates are generated without considering a term relationship between constituent elements in the parsing language syntax. This study discusses a TF-IDF feature extraction model using the n-Gram approach to produce candidate feature selection based on a specified term relationship. Thresholding applications for the formation of dynamic n-Gram segmentation were also discussed. Furthermore, the dynamic n-Gram model in TF-IDF feature extraction can be used in cyberbullying classification to overcome variations in syntax and meaning of sentences/speech from Bahasa Indonesia.