Reviewerly/exHarmony huggingface dataset:
Destructive Ability Analysis of Genetic Algorithm Operator When Solving Business Process Problem of Structural and Parametric Synthesis

The work analyzes the destructive ability of operators of a classical genetic algorithm when solving the problem of controlling the trajectory of a population during the search for solutions. In accordance with the hypothesis put forward, when solving the problem of structural-parametric synthesis of large discrete systems with a given behavior, it is advisable to change the settings of the functioning of the genetic algorithm operators to ensure: better convergence of the genetic algorithm, avoid attenuation, signal the need to restart the solution synthesis procedure, and also facilitate the derivation of the population from local extrema. It is proposed to use an artificial neural network as a control element (control add-on), which should implement control of the process of synthesis of simulation models of business processes based on a given behavior (the ability of a simulation model to convert a given input vector into a reference output), which is especially important when working with large systems. In accordance with the same hypothesis, an increase in the destructive ability of the crossing over and mutation operators allows the population to be dispersed across the solution space, which is advisable when attenuation occurs and is in a local extremum, and a decrease in the destructive ability contributes to a more thorough search for solutions in a certain area of the solution space. The paper provides examples of the work of operators and the behavior of the population when synthesizing simulation models of business processes based on the theory of Petri nets. To model the operation of a genetic algorithm and an artificial neural network, it is proposed to use the theory of Petri nets, which simplifies the process of managing the synthesis procedure and allows the use of parallel programming tools with distributed GPGPU computing on CUDA technology. As part of this study, an analysis of the behavior of the population when changing the operating settings of operators was carried out, which confirmed the hypothesis put forward.
Pengembangan media pembelajaran berbicara bahasa Prancis berbasis video pada siswa kelas XI SMAN 14 Jakarta

The purpose of this research is to develop French learning video media on speaking skills for grade XI. This research uses the research and development (R&amp;D) method with the ADDIE model (Mulyatiningsih, 2016) which consists of 5 stages, this research only carried out 3 stages, namely analysis, design and development. This is due to the limited time available in the research. The learning video media was made based on the results of the needs questionnaire of students of class XI IPS 1 at SMAN 14 Jakarta, the results of interviews with French teachers and the results of classroom observations. This research produces French learning videos for class XI high school that can be&#x0D; accessed easily through the youtube channel. In the process of making the video, the researcher used VN and Canva Pro applications. This learning video consists of 2 videos where in video 1 using inviter quelqu'un, accepter et refuser l'invitation material lasts 8 minutes and in video 2 with féliciter quelqu'un material lasts 5 minutes. In each video there are illustrations and also some small exercises to train students' speaking skills. In addition, in the description column there is an exercise link that is directly connected to learningapps. This learning video has been validated by material experts and media experts with very feasible results with a note of making some improvements and adding some components to the video. After making improvements and additions according to the experts suggestions, this learning video media is ready to be tested in the field.
3D Comform Radar System for Detection of Motion and Falls in Spatial Application

Enabling a long, self-determined life in old age contributes to increasing the quality of life of senior citizens. The prerequisite for this is the timely recognition of dangerous situations and the subsequent initiation of assistance measures. For reasons of personnel and finances, detection must be solved with the aid of technology. For reasons of acceptance, this technology should neither interfere with people's privacy nor be clearly visible in the room. A radar system for recording and evaluating movement profiles in a room was developed to detect hazardous situations. The analysis of the motion profiles is used to derive hazardous situations. The radar system, which consists of four radar modules, has a detection range of 360° with an angular resolution of 12°. This makes it possible to distinguish between people or objects in addition to detecting movements. Through the joint development of hardware and software, the system could be miniaturized and integrated into an overhead luminaire. It remains invisible to the user.
Sentiment analysis using various machine learning algorithms for disaster related tweets classification

Once a crisis arises, people use social media platforms (such as Twitter) to communicate real-time updates. This data is incredibly helpful to disaster relief and response organisations and may offer rapid notifications for prioritising requests. Text mining and machine learning algorithms can scan enormous amounts of unstructured data created by social media outlets like Twitter to recognise disaster-related content based on keywords and phrases. One of the difficulties that algorithms may confront is determining whether the tweet content discusses actual disasters or uses these keywords as metaphors. As a result, this research aims to apply natural language processing (NLP) and classification models to discriminate between authentic and bogus disaster tweets. This dataset from the Kaggle website includes tweets about genuine disasters and fictional disasters. Four machine learning classifier methods were used: KNN, SVM, XGBoostand, and Naive Bayes. KNN offers the highest accuracy.
CRYPTOCURRENCIES AND G7 CAPITAL MARKETS INTEGRATE IN PERIODS OF EXTREME VOLATILITY?

The purpose of this study is to examine the synchronism between the US capital markets (DJ, S&amp;P 500), the United Kingdom (FTSE 100), Canada (S&amp;P/TSX), Germany (DAX 30), France (CAC 40), Japan (Nikkei 225), Italy (Italy Ds Market and major cryptocurrencies such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and the Crypto 10 index, from February 2018 to November 2021. Based on the findings, we found that BTC and ETH cryptocurrencies drastically reduced their level of integration with their peers over the 2020 worldwide pandemic era, whereas LTC maintained. We also discovered that the Dow Jones, S&amp;P 500, and DAX 30 stock indexes lowered their level of integration when compared to the pre-covid subperiod. For the UK capital market (FTSE 100), Canada (S&amp;P/TSX), Japan (Nikkei 225), France (CAC 40), and Italy (Italy Ds Market) the level of integration increased significantly. These findings support, in part, our research question, that during periods of stress and uncertainty in the global economy capital markets tend towards integration, thus calling into question the hypothesis of efficient portfolio diversification.
Problems for future geniuses

We consider key problems, the solution of which can change not only the scientific space, but also the current reality. Already today it is time to reflect on the tasks of tomorrow, which require fundamentally new approaches.
An Anti-Occlusion Matching Algorithm of Multimodal Images Based on Graph Structure

Occlusion is a common difficulty in application of multimodal image matching, which is essential for navigation and computer vision. However, recent multimodal image matching algorithms based on traditional methods lack research on anti-occlusion issues. To improve the matching probability of multimodal images with occluded target, in this paper, we proposed an anti-occlusion matching algorithm based on the graph structure by utilizing the consistency of the graph structure in the un-occluded areas. Firstly, we extracted corner point features from images and constructed them to graphs. Secondly, we proposed an improved graph matching algorithm to find corresponding points by measuring the similarity between local graph structures. Finally, we took advantage of a global mismatch elimination algorithm to obtain more robust results. The comparative experimental results on visible-infrared datasets demonstrated that the algorithm we proposed had improved matching probability, matching accuracy and had certain anti-occlusion ability.
FMRI-Guided Time-Symmetric Joint Model for Visual Attention Prediction

Visual attention prediction is linked to brain activity, cognition, and behavior. Despite the availability of brain activity features, previous studies have not fully utilized them, resulting in saliency maps predicted by models primarily based on image features that do not accurately reflect visual attention in the human brain. This inspires us to use functional Magnetic Resonance Imaging (fMRI) signals as a "brain observer" to supervise the training of developing models that integrate top-down image attention-dependent cues and supervise information from saliency maps generated from gaze movement patterns under natural stimuli. Hence, this paper presents an FMRI-Guided Time-Symmetric Joint Model to predict saliency maps from movie clips, which captures the dynamic aspects of human brain cognition and attention, enabling the combination of image features with brain features. Furthermore, we generalize the model to the MS-COCO challenge, evaluating its performance on non-movie data. Our model outperforms other brain-feature-free methods in focusing on visual attention regions of humans in both movie and non-movie datasets. Additionally, incorporating brain features improves model performance, indicating their ability to bridge the semantic gap between human cognition and visual images, allowing for more accurate capture of visual attention regions.
Non-parametric Cumulants Approach for Outlier Detection of Multivariate Financial Data

In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n-circle as a concave minimization problem. Then, we show that the CGF maximization approach can be interpreted as an extension of the standard principal component technique. Therefore, for validation and testing, we provide a thorough comparison of our methodology with two other projection-based approaches both on artificial and real-world financial data. Finally, we apply our method as an early detector for financial crises.
Specific-Modal Spatial Guidance and Feature Enhancement for Multi-modal Brain Tumor Segmentation

Multi-modal information plays a pivotal role in the segmentation of brain tumors. However, previous studies have largely overlooked the distinctive characteristics of individual modalities, which are correlated with the target tumor region due to distinct imaging principles. In this paper, we harness the distinctive traits of individual modalities and introduce a brain tumor segmentation model called specific modality guided brain tumor segmentation model (SMG-BTS). Our SMG-BTS adopts a three-branch encoder-decoder architecture. The main branch utilizes full modalities fused at input-level, while the two affiliated branches operate in parallel to provide guidance to the main branch in acquiring a robust representation. We propose a specific modality spatial guidance (SMSG) module to guide the process of feature extraction. Spatial information is obtained from selected modalities and utilized to enhance features extracted from the main branch. A shared-specific feature enhancement(SSFE) module is proposed to enhance the shared features across modalities and utilizes modality-specific features to further supplement specific information of modalities. Experimental results on the BraTS2021 benchmark dataset demonstrate the effectiveness of our proposed SMG-BTS over state-of-the-art brain tumor segmentation methods.
Methods of forming information culture in students

This article explains information culture and its specific features in the modern era of information culture among students. The scientific article presents an analysis of studies conducted on the culture of using the Internet and social networks among students studying in secondary specialized educational institutions. The information culture of students studying in secondary specialized Islamic educational colleges has been developed and explained extensively. The social factors that determine the lack of a critical approach to the selection of necessary information among students of a special Islamic educational college have been studied. Based on the fact that the acquisition of information culture is a vital necessity for future imams, the author made several proposals and recommendations in this regard. The main factors determining the level of formation of the information culture of students of secondary specialized Islamic education are explained. In the XXI century, every student, every teenager must meet the basic criteria of information culture; the problems of its use in the educational process and, of course, improving the culture of using the Internet are highlighted. Keywords: Communication; digital technologies; information; internet; social networks.
EFEKTIVITAS MEDIA GURITA KATA DALAM PEMBELAJARAN MENULIS PUISI BAGI SISWA SEKOLAH DASAR

Poetry writing material is one of the important contents of learning the Indonesian language and literature in elementary school. However, in its implementation, students needed help understanding the learning material for writing poetry. To overcome this problem, teachers can use learning media. One medium that can be used in teaching poetry writing is the visual octopus word learning media. This media is in the form of an imitation octopus with poetry-writing material on each tentacle. This study was carried out to examine the effectiveness of the word octopus in poetry writing material. This study used a quasi-experimental method with a one-group pretest-posttest design at Public Elementary School (SDN) 1 Kekeri. Preliminary and final test questions regarding poetry writing material were used as the main instruments for data collection. Data collection underwent three stages: an initial test, providing treatment by applying octopus word learning media, and a post-test. Data analysis was carried out in two phases: prerequisites and testing media effectiveness. The study results show that the Octopus Kata learning media has high effectiveness. It can be seen from the significant difference in the average initial and final test results. This study concludes that the octopus word learning medium effectively increases elementary school students' understanding of learning to write poetry.
Proceedings of the 30th Telecommunications Forum (TELFOR), Belgrade, Serbia, 15-16 November 2022

Nowadays, Learning Arabic is perceived as drab by students, with 2.4% finding it very drab, 25.3% finding it drab, and 34.9% finding it quite drab. This research is a development of the MLATS Arabic for easy Arabic language learning. The functional testing showed a 100% success rate for all 7 test cases, and the application performed well on five different computer browsers. The user satisfaction survey yielded a 95% average satisfaction rate for each indicator.
Two-Stage Solution for Ancilla-Assisted Quantum Process Tomography: Error Analysis and Optimal Design

Quantum process tomography (QPT) is a fundamental task to characterize the dynamics of quantum systems. In contrast to standard QPT, ancilla-assisted process tomography (AAPT) framework introduces an extra ancilla system such that a single input state is needed. In this paper, we extend the two-stage solution, a method originally designed for standard QPT, to perform AAPT. Our algorithm has <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(Md_{A}^{2}d_{B}^{2})$</tex> computational complexity where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> is the type number of the measurement operators, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d_{A}$</tex> is the dimension of the quantum system of interest, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d_{B}$</tex> is the dimension of the ancilla system. Then we establish an error upper bound and further discuss the optimal design on the input state in AAPT. A numerical example on a phase damping process demonstrates the effectiveness of the optimal design and illustrates the theoretical error analysis.
Exploring the Impact of IoT Adoption on Elderly Non-Communicable Disease Patients: Attitudes, Subjective Norms, and Perceived Behavioral Control in Malaysia

This research examines the impact of IoT adoption on elderly non-communicable disease (NCD) patients in Malaysia, focusing on their attitudes, subjective norms, perceived behavioral control, perceived innovativeness, and intentions towards IoT technologies. Through primary data collection using a quantitative approach, survey questionnaires were administered to a representative sample of elderly NCD patients. SmartPLS will examine the links between the key variables and the behavioural intention to use IoT among elderly NCD patients. For examining IoT adoption intention, this study developed a framework based on the theory of planned behaviour (TPB). This research will help older adults and healthcare practitioners understand the significance of IoT in healthcare. The findings of this study are expected to produce meaningful insights that could raise awareness and educating patients about the benefits of IoT adoption, involving social networks for support and encouragement, enhancing self-efficacy and technological skills, integrating IoT devices into existing healthcare systems, and implementing supportive policies.
On Decoder-Only Architecture For Speech-to-Text and Large Language Model Integration

Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. However, the seamless integration of speech signals into LLMs has not been explored well. The “decoder-only“ architecture has also not been well studied for speech processing tasks. In this research, we introduce Speech-LLaMA, a novel approach that effectively incorporates acoustic information into text-based large language models. Our method leverages Connectionist Temporal Classification and a simple audio encoder to map the compressed acoustic features to the continuous semantic space of the LLM. In addition, we further probe the decoder-only architecture for speech-to-text tasks by training a smaller scale randomly initialized speech-LLaMA model from speech-text paired data alone. We conduct experiments on multilingual speech-to-text translation tasks and demonstrate a significant improvement over strong baselines, highlighting the potential advantages of decoder-only models for speech-to-text conversion.
Research on Physical Education Teaching Mode in Colleges and Universities Based on VR Technology

Abstract This paper utilizes VR technology to construct a virtual sports training situation and proposes a sports training feedback teaching mode based on VR technology. Aiming at the VR high-resolution scene generation technology, it analyzes the image super-resolution recovery algorithm based on multi-frames, introduces the regularization solution method, and, on this basis, proposes a generalized entropy variational image super-resolution recovery algorithm to realize better VR panoramic visual teaching. Taking Taekwondo movement training in K-school as the research content, we compared the learning attitude change, the influence of special physical quality, and the technical mastery ability of Taekwondo movement between the experimental group and the control group. The evaluation results found that the technical score of movement completion ability after the experiment was 94.98±2.36 in the experimental group and 85.79±5.32 in the control group. The quality of physical education teaching can be improved by using VR technology in teaching mode.
Application of Blockchain Based on Fabric Consensus Network Model in Secure Encryption of Educational Information

Abstract This paper combines blockchain technology with an information encryption algorithm to design a blockchain-based educational information protection model. It focuses on the role of Fabric architecture and blockchain data structure for information security protection. A chaotic cipher is used to encrypt the information, combined with the iteration of nonlinear functions to generate pseudo-random numbers to realize the unpredictability of the information. Using symbolic dynamics to obtain the control parameter or initial value of the chaotic mapping, combining the sequence of symbols to solve for another value, and finally presenting it in the form of ciphertext in the client for the data accessor to query and access the data. The effectiveness and practicality of educational information security encryption are examined by applying the model of constructed information protection. The results show that the response time of the blockchain module changes depending on the information, and the response time is basically maintained between 2.2 seconds and 2.4 seconds, which is in a reasonable range. In educational information security defense, the information protection model is able to effectively defend the network against attacks, with the highest number of attacks against the attack named external, with the highest number close to 50 million.
Analysis of the Path of Improving English Listening and Speaking Ability Based on Big Data Technology

Abstract The training of college students’ English listening and speaking skills needs to be individualized scientifically and strategically in order to get a qualitative improvement. In this paper, we first analyze the correlation between students’ listening and speaking skill levels by using the improved maximum information coefficient (MIC) with the aid of an online platform. The students’ listening ability is assessed by the plain Bayesian algorithm, trained by the neural network to reduce the error, and the similarity between the feature vector and each pattern is calculated so that the element that obtains the highest value is used as the candidate mapping. Next, the schema mapping of data distribution (SMDD) method is combined to obtain the best matching schema. Finally, the effectiveness of English listening and speaking training with the Smart Teaching Platform was investigated through controlled experiments. The results show that in the post-test speech scores, the experimental group’s increase in speech scores was 2.322, while the control group’s increase was only 0.652. The effectiveness of the English listening and speaking ability training strategy is evident due to the obvious difference. This study proposes rational suggestions for improving college students’ English listening and speaking abilities.