Reviewerly/exHarmony huggingface dataset:
Mutual reconstruction-based linear discriminant hashing for image retrieval

Recently, hash learning has attracted much attention due to its ability to convert high-dimensional data into compact binary codes for efficient retrieval and storage. However, most classification-based supervised hashing methods neglect the original goal of increasing the similarity of similar samples while reducing the similarity of different samples in the learned hash code. In this paper, we combine the idea of linear discriminant analysis with hash learning by designing a new scatter matrix and propose a supervised hash learning framework to learn optimal discriminative hash codes for image retrieval. To solve the problem of reducing information loss during the learning process, we design a novel method via mutual reconstruction between binary hash codes and original features. Thus, a method named Mutual Reconstruction-based Linear Discriminant Hashing (RDAH) is obtained, which further improves the discriminative ability of the learned hash code. We test the performance of RDAH on three image datasets to show the superiority on large-scale image retrieval.
Automatic Chinese-English Translation Algorithm based on Out-of-vocabulary Words in the Context of Cross-cultural Communication

In the context of cross-cultural communication, translation between languages has become increasingly important. Based on automatic Chinese–English translation, this study examined the processing of out-of-vocabulary (OOV) words. First, this paper briefly introduces two basic translation models: seq2seq and Transformer. Second, we propose a semantic-based OOV processing method, which replaces OOV words with the most similar words by calculating the semantic similarity of word vectors and then uses the source-language sentences with the replaced words to train a translation model. Compared to the seq2seq model, the Bilingual Evaluation Understudy (BLEU) values of the Transformer model were higher (37.26 for the NIST06 dataset and 30.75 for the NIST08 dataset). After OOV processing, retaining low-frequency OOV words was conducive to the improvement of BLEU scores, which were increased by 0.63 and 0.09 for NIST06 and NIST08 for the Transformer model, respectively. This shows the effectiveness of the OOV processing method. The OOV processing method could be applied to automatic Chinese–English translation.
An Analysis of Verbal and Non-Verbal Signs in Rexona Clinical Protections Advertisement

Humans interact using verbal and nonverbal communication tools, such as messages, pictures, sounds, and gestures. This study is intended to examine the connotative and denotative meanings of verbal and nonverbal signs in the “Rexona Clinical Protection” advertisement. The study is designed with a qualitative approach. The observation method was used in collecting the data. The data was obtained from the website Rexona. There are three items of the data analysis, namely verbal and nonverbal signs analyzed based on Saussure’s semiotic theory, and the meaning of the verbal and nonverbal signs was analyzed using Barthes’s theory. Wierbicka’s color theory lends support to the denotative and connotative meanings. As a result, the writer discovered six verbal and six nonverbal signs in this advertisement. Advertisers highlight the products in this advertisement, attempting to persuade viewers that using “Rexona Clinical Protection” products is one of the most important things we can do to protect our bodies from body odor and excessive sweating. From this analysis, it can be concluded that the “Rexona Clinical Protection” advertisement uses meaningful sentences to attract the attention of its target consumers.
National Security Policies of Nigeria and Benin Republic as Strategies for Curbing Cross-border Crimes

This study interrogates the constantly changing national security policies of Nigeria and Benin Republic in response to the cross-border security threats that are currently ravaging the two West African countries. The common focus in extant literature is on the diverse transnational organised crimes, with little or no attention paid to the manner in which the security threats have substantially shaped the national security policies of the two states. The study is guided by critical human security theory. Data was collected from primary sources; 13 interviews were conducted in Nigeria and Benin Republic, which were complimented by direct observations at the Nigeria-Benin borders. Some data were also derived from secondary sources, and the analysis of data generated was essentially qualitative, using descriptive and content analyses. Findings show that the security policies of both countries are encapsulated in an array of different documents and that they are, essentially, reactions to the constantly evolving security threats. The study calls for the harmonisation of border security policies of the two neighbouring countries in order to successfully rein in on cross-border criminal cartels.
CREATING DIGITAL DICTIONARIES OF LOCAL LANGUAGES AND UPGRADING SCHOOL TEACHERS PATENT KNOWLEDGEIN KONAWE AND EAST KOLAKA REGENCIES

Community Service Activities by a Team of Lecturers (Lecturers) of the Faculty of Law, Lecturers of Faculty of Cultureand Lecturers of Informatics Engineering Faculty of Engineering Haluoleo University at SDN 2 Puosu and SDN 1 SendangMulyasari in Konawe and East Kolaka Regencies, Southeast Sulawesi Province. This activity was carried out for 6 months starting from observation, coordination and consolidation with SDN 2 Puosu and SDN 1 SendangMulyasari followed by preparation of teaching materials, IPR / Patent laws, provision of Digital Dictionary Applications and Information Systems, implementation of activities and preparation of reports.The purpose of this Community Service activity is to provide knowledge about IPR / Patent Laws for Teachers and Students of SDN 2 Puosu and SDN 1 SendangMulyasari in Konawe and East Kolaka Regencies to implement the Creation of Regional Language Digital Dictionaries that can be used for learning students and teachers through the knowledge transfer process. And campaigning for pirated IPR / Patent laws and the use of Regional Language Digital Dictionary Copyrights to create Digital Dictionary Applications and Information Systems that can be used for students and teachers to learn at the office and at home.Some important materials presented in this Community Service Activity are socializing IPR / patent laws, understanding patent laws, copyright use of digital dictionaries of local languages, making applications and digital dictionary information systems, and others.The results obtained from this activity are expected to be able to understand IPR / Patent Laws and Copyright Use of Regional Language Digital Dictionaries for making Digital Dictionary Applications and Information Systems, especially for Teachers and Students of SDN 2 Puosu and SDN 1 SendangMulyasari in Konawe and East Kolaka Regencies, as well as additional knowledge and insight into the world of information technology. And it is hoped that it can help spread IPR / Patent laws and Copyright Use of Digital Regional Language Dictionaries as a form of implementation and educate students and teachers regarding the preservation of regional languages.
Optimization of the Shortest Tsunami Evacuation Route Using Djikstra’s Algorithm in Benoa Village

Benoa Village has an area of approximately 2.38 km² and a population of 9,569 people in 2020 with a population density of 4,013 people/km2. This area is included in the list of tsunami-prone areas because the area is located on the edge of the Indian Ocean, which is known as an area with a high level of earthquake and volcanic activity. The 2004 tsunami that hit the coast of the Indian Ocean increased the potential for similar disasters to occur in the area. Determination of the shortest evacuation route in Benoa Village using Djikstra Algorithm. The result obtained is a path from the evacuation starting point vertex to the comfort zone node. Thisvertex represents places and road intersections arranged in the form of a weighted graph (distance) with a total of 51 vertexs, and an Adjacency Matrix is formed which is processed using the C++ Program. The Safe Zone vertex (Grand Hyatt Bali Temporary Meet Point (V50), Hattrick Futsal (V51)) are headed from the evacuation starting point of Serangan Beach (4.49km to V50, 6.94km to V51), Noanui Beach (3.95km to V50, 6.39km to V51), Samuh Beach (2.75km to V50, 5, 19km to V51), Nusa Dua Beach A (3.54km to V50, 5.48km to V51), Nusa Dua Beach B (3.47km to V50, 4.79km to V51), Peninsula Island (4.69km to V50, 4.80km to V51), Megiat Beach (4.81km to V50, 4.15km to V51) and Geger Beach (5.86km to V50, 5.09km to V51).
Peer acceptance of female students in private schools: study of differences in peer acceptance by gender

This study aims to determine the difference in peer acceptance based on gender of private junior high school students in the city of Padang, West Sumatra Province, Indonesia. The number of subjects involved in this study were 146 private junior high school students consisting of 73 male and 73 female students. The sampling technique in this study used cluster sampling which was taken based on the sub-districts in the city of Padang. Peer acceptance of students is measured using a peer acceptance scale. The results of this study are that there are differences in peer acceptance between male students and female students which indicates that female students have a higher level of peer acceptance than male students. Female and male students had the highest peer acceptance aspect scores on validation and caring aspects (mean=23.59 and mean=21.53). The lowest aspect score for female students was intimate exchange (mean=20.51), while the lowest aspect for male students was conflict and betrayal (mean=18.12).Penelitian ini bertujuan untuk mengetahui perbedaan peer acceptance siswa SMP Swasta kota Padang berdasarkan jenis kelamin. Jumlah subjek yang terlibat pada penelitian ini adalah sejumlah 146 siswa SMP Swasta yang terdiri dari laki-laki 73 orang dan perempuan 73 orang. Teknik pengambilan sampel dalam penelitian ini menggunakan cluster sampling yang diambil berdasarkan kecamatan yang ada di kota Padang. Peer acceptance siswa diukur dengan menggunakan peer acceptance scale. Hasil penelitian ini menunjukkan bahwa terdapat perbedaan peer acceptance antara siswa laki-laki dan siswa perempuan Sekolah Menengah Pertama Swasta di kota Padang. Siswa perempuan memiliki tinggat peer acceptance yang lebih tinggi daripada siswa laki-laki. Siswa perempuan dan laki-laki memiliki skor aspek peer acceptance tertinggi pada aspek validation and caring (mean=23,59 dan mean=21,53), skor aspek terendah pada siswa perempuan adalah intimate exchange (mean=20,51), dan siswa laki-laki memiliki aspek terendah pada conflict and betrayal (mean=18,12).
Effectiveness of contact tracing for COVID-19 in Belgium

Contact tracing and investigation can prevent infectious disease transmission when carried out promptly. Data obtained from contact tracing also provides unique information on transmission dynamics that can be useful for policy making. The proportion of secondary infections arising from index cases that were previously identified as a close contact, hence “known index cases”, has been indicated by ECDC as a key performance indicator for contact tracing. Few empirical studies have been published on the effectiveness of contact tracing and investigation during the COVID-19 pandemic.&#x0D; Using data from the Belgian COVID-19 contact tracing (Sept 2020 – Dec 2021), we investigated the impact of contact tracing on onward transmission. Known index cases were compared to those not previously identified as close contact. More specifically, we compared their traced contacts and secondary cases as well as the serial interval, representing the time from illness onset in the primary case to illness onset in the secondary case. In addition, we investigated contact and transmission characteristics by age.&#x0D; Known index cases, previously informed about their exposure, were linked to fewer high-risk contacts and secondary infections. In addition, they presented a lower secondary attack rate compared to those not previously identified by contact investigation, suggesting behavioural changes. Also, a shorter serial interval was observed for known index cases. In addition to breaking transmission chains, contact tracing provides important information on transmission characteristics. When exploring age-specific data, we observed increased transmission rates from children to adults during the circulation of Delta and Omicron variants without an increase in contact rates between these age groups.&#x0D; The lower secondary attack rates for known index cases suggest that contact tracing in Belgium has been effective in reducing onward transmission. Their lower number of traced contacts and shorter serial interval are indicative that individuals aware of their exposure to SARS-CoV-2 were more reserved in their social contact behaviour. This reflects the mandatory quarantine for high-risk contacts. Our study also shows that in times of high burden on the healthcare system, key performance indicators of contact tracing should be interpreted with caution in light of changing intervention strategies. Many external factors influence the effectiveness of contact tracing like testing policy and compliance, the cooperation of index cases, and the imposed and adopted quarantine and isolation policies.&#x0D;
SECURITY OF PUBLIC KEY ENCRYPTION USING DICKSON POLYNOMIALS OVER FINITE FIELD WITH 2k

The application of Dickson polynomial in public key cryptography is observed due to its permutation behaviors and semi-group property under composition. Here we have mostly concentrated on checking the one-wayness and semantic security of our scheme. The proposed scheme is based on Dickson polynomial over a finite field with 2k, whose security depends on the Integer Factorization Problem(IFP) and the Discrete Dickson Problem(DDP), which is as difficult as solving discrete logarithmic Problem (DLP). Our proposed cryptosystem is computationally secured with one wayness and semantic security, it also reduces the complexity of many other proposed schemes.
Investigation of Modality Selection and the Point of Switching: Focused on Voice and Touch Modalities

Objective: This study aimed to identify the optimal point of switching between voice and touch modalities for modality selection, regarding the number of characters and hierarchy-based menu structures.BRBackground: Technological advances have enabled the development of multimodal interaction in modern smart devices. Selecting the most adequate modality during interaction with devices is crucial since the interface is regarded as a significant element that can enhance usability and overall satisfaction.BRMethod: To achieve the research objective, the experiment was carried out by developing a modality selection task program through JavaScript. The experiment included three distinct tasks: Text entry, Simple keyboard touch, and Hierarchy menu. During the modality selection task, participants were given the option to select the more efficient modality between voice and touch modalities based on their preference.BRResults: The Text entry task evaluated the point of switching modalities based on the number of characters, but no significant difference was found in the probability of voice usage between the number of characters. Therefore, the syllable per touch ratio was adopted as an alternative measure in the Simple keyboard touch and Hierarchy menu tasks. A significant difference was found in the predicted probability of voice usage between the syllable per touch, identifying the optimal point of switching modalities, reaching up to two and five syllables per touch, respectively.BRConclusion: The results indicate that the point at which users switch between voice and touch modalities depends on the menu structure, and the syllable per touch ratio is a critical factor to consider in modality selection.BRApplication: The findings of this study provide significant insights for developers aiming to design multimodal interactions for various menu structures.
ROLE OF DISRUPTIVE TECHNOLOGY IN MANAGERIAL DECISION MAKING: A QUANTITATIVE INVESTIGATION

Disruptive technology or disruption has become a popular term in the social area and in the business industry. Disruptive technology is a new technology that has low cost and whose performance is measured by conventional criteria. It has high supplementary performance. Primarily, disruptive technology is described as “innovation that results in poor performance of a product that has been brought to the market at a different value proposition. The products related to disruptive technology are usually cheap and low cost, small, simple, and easy to use. The popularity and frequency of disruptive technology, its products, and business model innovation are increasing the process of digitalization, which is ultimately increasing the development and usage of disruptive technology and innovation that provides various businessmen and entrepreneurs with various opportunities. Disruptive technology has become so popular that it has turned into a secret solution in almost every management talk, startup pitches, as well as political speeches.
Sequential Representation of Sparse Heterogeneous Data for Diabetes Risk Prediction

Type 2 diabetes (T2D) is a major public health problem, and opportunistic screening to detect T2D at an early stage can help initiate interventions that delay or prevent the disease and its complications. In this study, we use electronic health records (EHR) and concepts extracted from clinical notes to predict future T2D risk. Our deep neural network-based model captures the temporal sequence of patient visits. We use explainable AI algorithms to assess the model decisions and observe alignment with the domain knowledge of clinical experts.
An Industrial Control System Risk Assessment Method

With the rapid development of information technology, the combination of industrial control system and network communication technology is widely used in energy, manufacturing, military, aerospace and transportation. Attacks against industrial control system every year are lower than those against the Internet, but they have a more significant impact on society. Therefore, risk assessment of industrial control system is the first task to prevent attacks. In this paper, we propose a risk assessment method for industrial control system, starting from the idea of game theory, combining the assessment index assignment methods of subjective hierarchy process analytic hierarchy process (AHP) and objective entropy weight method (EWM), determining the target matrix according to the weights of the two, and utilizing the approximation of the ideal solution. The final evaluation results are obtained by using the technique for order preference by similarity to ideal solution (TOPSIS). The results show that the combination of subjective and objective methods can make the weighting of assessment indicators more reasonable and effective.
MC-GAN: An Adversarial Sample Defense Algorithm

The current adversarial sample defense algorithms are plagued by issues such as poor defense effectiveness and high training costs. In order to enhance sample classification accuracy while reducing training costs, we have introduced a novel defense algorithm based on model compression within the framework of Generative Adversarial Networks (GANs). This algorithm integrates GANs with model compression techniques, and the results of testing on public datasets have demonstrated an improved accuracy in defending against adversarial samples along with reduced training costs.
Homotopy-Informed LSTM for Efficient Spectrum Prediction in Uav Communications

As wireless communication and unmanned aerial vehicles (UAVs) continue to advance steadily, the use of UAVs has become increasingly widespread. However, the scarcity of spectrum resources available for UAV communication poses a significant challenge. Spectrum prediction technology emerges as a key solution to alleviate this problem. Traditional spectrum prediction methods struggle to accurately forecast the spectrum at the next location due to the fast flight speed of UAVs and the dynamic spectrum environment. Therefore, this paper introduces a novel approach based on homotopy theory and the Long Short-Term Memory (LSTM) net-work model concept. Through experimental validation with actual measurement data, we confirmed that utilizing homotopy theory can streamline the network construction process and reliably yield good predictive results even with reduced training time.
Analysis of Strategic Choice of Hydropower Enterprise Based on Neural Network

This paper aims to study the strategic choice analysis of hydropower enterprises based on neural network, and explore how to use neural network technology to optimize the strategic decision of hydropower enterprises, improve the development efficiency and competitiveness. As a clean and renewable form of energy, hydropower plays an increasingly important role in the global energy structure. However, hydropower enterprises are faced with various challenges and opportunities in the process of development. How to plan strategy scientifically and rationally allocate resources has become an urgent problem to be solved. First, we review the background and significance of hydropower energy. As an inexhaustible king of energy, hydropower has unique advantages in the stability of energy supply and environmental protection. In the context of global energy transition and environmental protection, hydropower energy plays an important role in sustainable development. Secondly, we discuss the application of neural network in strategy analysis. As a bionic learning algorithm, neural network has shown strong computing power and intelligence level in data processing, pattern recognition, decision analysis and so on. In the strategic analysis of hydropower enterprises, neural networks can be used for multiple tasks such as data prediction, resource optimization, environmental risk assessment and market demand prediction, providing strong support for enterprise decision-making. Then, we put forward the research objective and content of this paper. Aiming at the problems in the strategic choice of hydropower enterprises, this paper aims to optimize the strategic choice of hydropower enterprises by using neural network technology to improve the efficiency of hydropower generation and the level of environmental protection. Based on the neural network, we will analyze the development and operation data of hydropower enterprises, explore the optimal strategic plan, and verify the proposed method and model through empirical analysis.
A Collaborative Cross-Attention Drug Recommendation Model Based on Patient and Medical Relationship Representations

The purpose of drug recommendation is to predict the effective and safe drug combinations required for the current visit based on the historical medical data of patients. How to better mine the hidden relationship in the medical data and effectively improve the accuracy of drug recommendation are research hotspots in the medical field. This paper proposes a Collaborative Cross-attention Drug Recommendation model (CCDR) based on patient and medical relationship representations, which mines medical data from two aspects to enhance the representation ability of the model. CCDR obtains patient representation vectors by modeling the patients’ historical sequence data using Bidirectional Gated Recurrent Unit. Meanwhile, CCDR designs a medical graph structure data learning method based on relationship division to better capture the complex association relationships among diagnoses, procedures, and drugs. Finally, the representation capability of the model is enhanced by introducing a collaborative cross-attention mechanism to fuse the information obtained from both medical sequence and graph structure data. The experimental results show that the CCDR model can effectively improve the performance of drug recommendation.