Reviewerly/exHarmony huggingface dataset:
MGFNet: A Multi-granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID) aims to match the same pedestrian in different forms captured by the visible and infrared cameras. Existing works on retrieving pedestrians focus on mining the shared feature representations by the deep convolutional neural networks. However, there are limitations of single-granularity for identifying target pedestrians in complex VI-ReID tasks. In this study, we propose a new Multi-Granularity Feature Fusion and Mining Network (MGFNet) to fuse and mine the feature map information of the network. The network includes a Local Residual Spatial Attention (LRSA) module and a Multi-Granularity Feature Fusion and Mining (MGFM) module to jointly extract discriminative features. The LRSA module aims to guide the network to learn fine-grained features that are useful for discriminating and generating more robust feature maps. Then, the MGFM module is employed to extract and fuse pedestrian features at both global and local levels. Specifically, a new local feature fusion strategy is designed for the MGFM module to identify subtle differences between various pedestrian images. Extensive experiments on two mainstream datasets, SYSU-MM01 and RegDB, show that the MGFNet outperforms the existing techniques.
PEVLR: A New Privacy-Preserving and Efficient Approach for Vertical Logistic Regression

In our paper, we consider logistic regression in vertical federated learning. A new algorithm called PEVLR (Privacy-preserving and Efficient Vertical Logistic Regression) is proposed to efficiently solve vertical logistic regression with privacy preservation. To enhance the communication and computational efficiency, we design a novel local-update and global-update scheme for party $$\mathcal{A}$$ and party $$\mathcal{B}$$ , respectively. For the local update, we utilize hybrid SGD rather than vanilla SGD to mitigate the variance resulted from stochastic gradients. For the global update, full gradient is adopted to update the parameter of party $$\mathcal{B}$$ , which leads to faster convergence rate and fewer communication rounds. Furthermore, we design a simple but efficient plan to exchange intermediate information with privacy-preserving guarantee. Specifically, random matrix sketch and random selected permutations are utilized to ensure the security of original data, label information and parameters under honest-but-curious assumption. The experiment results show the advantages of PEVLR in terms of convergence rate, accuracy and efficiency, compared with other related models.
Human-to-Human Interaction Detection

Different from conventional human interaction recognition, which uses choreographed videos as inputs, neglects concurrent interactive groups, and performs detection and recognition in separate stages, we introduce a new task named human-to-human interaction detection (HID). HID devotes to detecting subjects, recognizing person-wise actions, and grouping people according to their interactive relations, in one model. First, based on the popular AVA dataset created for action detection, we establish a new HID benchmark, termed AVA-Interaction (AVA-I), by adding annotations on interactive relations in a frame-by-frame manner. AVA-I consists of 85,254 frames and 86,338 interactive groups, and each image includes up to 4 concurrent interactive groups. Second, we present a novel baseline approach SaMFormer for HID, containing a visual feature extractor, a split stage which leverages a Transformer-based model to decode action instances and interactive groups, and a merging stage which reconstructs the relationship between instances and groups. All SaMFormer components are jointly trained in an end-to-end manner. Extensive experiments on AVA-I validate the superiority of SaMFormer over representative methods.
Intelligent UAV Swarm Planning Based on Undirected Graph Model

The coordination of multiple drones for formation flight and collaborative task execution in the air, known as drone cluster control, has become a key research focus in recent years. Collision avoidance during formation maintenance remains a challenging aspect of cluster control, as traditional cluster control and path planning algorithms struggle to enable individual drones to independently avoid obstacles and maintain formation within the cluster. To address this issue, this paper proposes a cluster modeling method based on an undirected graph, which optimizes the entire model by adding constraints. A distributed system is also utilized to plan the entire cluster, improving the efficiency and robustness of the system and allowing individual drones to execute their flight tasks independently. Experimental verification was conducted in a ROS-based simulation environment, and the results demonstrate that our proposed algorithm effectively maintains the formation of drone clusters with high performance and stability.
S3ACH: Semi-Supervised Semantic Adaptive Cross-Modal Hashing

Hash learning has been a great success in large-scale data retrieval field because of its superior retrieval efficiency and storage consumption. However, labels for large-scale data are difficult to obtain, thus supervised learning-based hashing methods are no longer applicable. In this paper, we introduce a method called Semi-Supervised Semantic Adaptive Cross-modal Hashing (S3ACH), which improves performance of unsupervised hash retrieval by exploiting a small amount of available label information. Specifically, we first propose a higher-order dynamic weight public space collaborative computing method, which balances the contribution of different modalities in the common potential space by invoking adaptive higher-order dynamic variable. Then, less available label information is utilized to enhance the semantics of hash codes. Finally, we propose a discrete optimization strategy to solve the quantization error brought by the relaxation strategy and improve the accuracy of hash code production. The results show that S3ACH achieves better effects than current advanced unsupervised methods and provides more applicable while balancing performance compared with the existing cross-modal hashing.
Multi-view Stereo by Fusing Monocular and a Combination of Depth Representation Methods

The design of plane-sweep deep MVS primarily relies on patch-similarity based matching. However, this approach becomes impractical when dealing with low-textured, similar-textured and reflective regions in the scene, resulting in inaccurate matching results. One of the methods to avoid this kind of error is incorporating semantic information in matching process. In this paper, we propose an end-to-end method that uses monocular depth estimation to add semantic information to deep MVS. Additionally, we analyze the advantages and disadvantages of two main depth representations and propose a collaborative method to alleviate their drawbacks. Finally, we introduce a novel filtering criterion named Distribution Consistency, which can effectively filter out outliers with poor probability distribution, such as uniform distribution, to further enhance the reconstruction quality.
Cybersecurity in the digital economy: Safeguarding your business

In the rapidly evolving digital economy, cybersecurity has emerged as a paramount concern for businesses across various sectors. The advent of the gig economy, characterized by the increasing reliance on independent contractors and freelancers, has introduced novel cybersecurity challenges. This shift necessitates a reevaluation of traditional security protocols to address the unique vulnerabilities posed by remote work environments and diversified access points to sensitive data. The study would elaborate on the heightened risks of data breaches and cyber-attacks due to the decentralized nature of the gig economy. It would emphasize the critical need for enhanced cybersecurity measures to safeguard sensitive information from unauthorized access and potential exploitation. The discussion would include the implications of gig workers using personal devices to access corporate networks, potentially bypassing established security safeguards and exposing business data to increased risks. Furthermore, the abstract would underscore the importance of proactive cybersecurity strategies, including the implementation of robust security protocols, regular software updates, and the use of advanced encryption and firewall technologies. It would stress the need for ongoing vigilance and adaptation to emerging threats in the digital landscape. The overall focus would be on the imperative of aligning cybersecurity strategies with the unique demands of the gig economy to ensure the protection of valuable data assets, maintain consumer trust, and uphold the integrity of the digital marketplace in an era of increasing cyber threats.
Health information needs of rural women in ilorin east local government area of Kwara State, Nigeria

The study assessed the health information needs of the rural women in Ilorin East Local Government Area of Kwara State, Nigeria. The study used primary data. The primary data were collected using structured questionnaire. Multistage sampling technique was use to select respondents for the study. A total number of eighty rural women were selected for the study. Data were analyzed using frequency, percentage, mean and Likert type rating scale. The result of the study revealed that the mean age of the respondents was 37.7 years, their average income was 27,637.50 Naira. The result revealed that majority (98.8 %) of respondents’ source of health information were radio/television (98.8%), Neighbors and friends (92.5%), drug hawkers (90.0%), family members (77.5%), local chemist shops (75.3%) and Doctors/Nurses (67.5%). The common health challenges in the study area are typhoid fever (40.0%), malaria (43.8%) and (28.8%) miscarriages. Also, the result identified that First aid treatment with a mean score (2.21), Drug/Pharmaceutical care information (2.20) and infant’s health care (2.10) were areas where the respondents highly needs health information. The result also reveals that Illiteracy (2.51) and low level of income (2.08), communication barrier (1.93), poor electricity (1.91) and poor attitude of health personnel (1.91) were some of problems preventing rural women from getting health information. The study concluded that there is information need among rural women in the study area. The study therefore recommends that for government to achieve its vision of good health for all, there is need for targeted policies in the areas of health information needs of rural women and there should be provision of health facilities in the rural areas by the government and donor agencies.
Conflict of Laws and Tokens in Swiss Private International Law

Switzerland figures among one of the first jurisdictions to include distributed ledger technologies (DLTs) in its legal framework through the recently adopted federal act commonly referred to as the DLT Act. In order to address the complex issue of determining the law applicable to tokens stored on a DLT, the DLT Act amended on 1 February 2021 the Swiss Private International Law Act (PILA), which now provides for conflict-of-laws rules that notably applies to tokens that embed a claim. This article aims to explain the context in which this amendment took place by providing an overview of the notion of tokens and presenting the main purposes of the DLT Act. It then presents and discusses the solutions chosen by the Swiss lawmaker, which follow the general principles that also generally prevail for other types of rights. As a result, the issuer of a token benefits from considerable freedom to determine the governing law of a tokenised claim. Absent a choice of law, the PILA sets forth subsidiary solutions based on the seat and the habitual residence of the issuer.
原子炉格納容器内部調査動画からのダイジェスト画像及び広域画像作成への試み

The video of investigation inside the primary containment vessel (PCV) of the Unit 2 of the Fukushima Dai-1 number power plant in 2019 is too long with a lot of redundant information and has a lot of focus/lighting problems. This study, in order to enhance convenience for analyzing and utilizing the video inside the PCV, attempts to implement the following modules: 1) reduction of video time (time-shortening processing), 2) extraction of impressive scenes (digest image creation), and 3) generation of wide-area (panorama) images.
Technical Description of DLT for Conflicts Lawyers

This chapter explains the basics of Distributed Ledger Technologies (DLTs) as well as the typical use cases of DLTs. It first examines three key technical elements of blockchains: block and chain structure, peer-to-peer and consensus algorithm, and cryptography as well as the distinction between permissionless and permissioned blockchains. Next, some use cases of blockchain, such as Bitcoin, securities, NFTs, and Electronic B/Ls are briefly examined.
Blockchain Capabilities in Supply Chain Management: A Literature Review

Due to the unique characteristics of blockchain, such as transparency, tractability, decentralization, immutability, blockchain has been widely employed in multiple industries that request higher level of trust and security. Supply chain has a perfect match with blockchain, as both chains involve multi-party collaboration and coordination. The global Covid-19 pandemic in the past four years has accelerated the use of blockchain in supply chain. Prior researchers indicated that blockchain capability is increasingly becoming one of the important organizational IT capabilities, however, a thorough study or literature review on blockchain capability is rare in the literature. For the literature review purpose, this study uses Scopus as an academic searching engine to explore the past papers on blockchain capability in supply chain management, and summarizes the most influential 28 articles published in recent years. Based on resource-based view and appearance frequency in the literature, we develop a conceptual framework to investigate the internal and external capability dimensions of blockchain. The literature review results of this paper help researchers in the same research domain understand the components and measures of blockchain capabilities, and thus offer significant value in directing blockchain theory and practice in efficient supply chain.
Confidence Lipschitz classifiers: an instrument of guaranteed reliability

A new method of guaranteed solution for multiclass classification problem of stochastic objects is proposed. Within the framework of the proposed approach, the classification result is a finite set of class indices which with a predetermined confidence coefficient contains the index of the class to which the object being classified corresponds. In this case, the classification itself is realized on the basis of using a classifier of the new type which is called a confidence Lipschitz classifier. The definition of the confidence Lipschitz classifier is given and its main properties have been studied. Among them, the property of guaranteed reliability of the classification which is expressed in the construction of a confidence set of limited size containing the index of the true class with a predetermined coefficient of confidence, has been studied. The case of the assembly of Lipschitz classifiers, the properties of which are formalized in the form of a theorem, is considered. We consider a practically important example of using the proposed approach in the problems of compensation of the noise process dynamics in the channels of the fiber-optic monitoring system. The proposed approach is promising for use in those classification tasks in which the number of classes has an order higher than the second, including large-scale biometric identification systems as well as multi-channel systems for monitoring extended objects.
Method for testing NLP models with text adversarial examples

At present, the interpretability of Natural Language Processing (NLP) models is unsatisfactory due to the imperfection of the scientific and methodological apparatus for describing the functioning of both individual elements and models as a whole. One of the problems associated with poor interpretability is the low reliability of the functioning of neural networks that process natural language texts. Small perturbations in text data are known to affect the stability of neural networks. The paper presents a method for testing NLP models for the threat of evasion attacks. The method includes the following text adversarial examples generations: random text modification and modification generation network. Random text modification is made using homoglyphs, rearranging text, adding invisible characters and removing characters randomly. The modification generation network is based on a generative adversarial architecture of neural networks. The conducted experiments demonstrated the effectiveness of the testing method based on the network for generating text adversarial examples. The advantage of the developed method is, firstly, in the possibility of generating more natural and diverse adversarial examples, which have less restrictions, and, secondly, that multiple requests to the model under test are not required. This may be applicable in more complex test scenarios where interaction with the model is limited. The experiments showed that the developed method allowed achieving a relatively better balance of effectiveness and stealth of textual adversarial examples (e.g. GigaChat and YaGPT models tested). The results of the work showed the need to test for defects and vulnerabilities that can be exploited by attackers in order to reduce the quality of the functioning of NLP models. This indicates a lot of potential in terms of ensuring the reliability of machine learning models. A promising direction is the problem of restoring the level of security (confidentiality, availability and integrity) of NLP models.
EXPLORING THE PRACTICAL APPLICATION AND APPRAISAL OF THE 2013 ENGLISH CURRICULUM IN A STATE JUNIOR HIGH SCHOOL: A QUALITATIVE INVESTIGATION

The constant enhancement of Indonesia's educational curriculum is aimed at improving the overall quality of education. This research specifically investigates the application of the 2013 English Curriculum at a state yunior high school in Palembang, South Sumatra, Indonesia, utilizing a qualitative evaluative approach. The study, conducted at SMP Negeri 1 Palembang, involved two English teachers and eighth-grade students. The findings revealed that the English teachers adhered to standard content and government syllabi when developing lesson plans. Teaching methods and assessments also aligned with government policies. Additionally, factors such as teacher quality, resource accessibility, and community involvement were identified as potential influencers on curriculum effectiveness. Although the curriculum has evolved, evaluating the 2013 curriculum can provide valuable insights for future improvements in Indonesia's educational curriculum.
DIGILANG: ENHANCING ENGLISH PROFICIENCY IN COMMUNITY-BASES TOURISM THROUGH TAILORED E-LEARNING

This study presents DigiLang, a customized e-learning platform, specifically developed to enhance English for Tourism proficiency among Community-based tourism members in Bogor Regency. The DigiLang platform was tailored to meet the specific purposes of language needs in the tourism sector. Through a comprehensive needs assessment, DigiLang provides a variety of focused resources, including personalized content, interactive modules, and practical exercises designed to foster proficient communication in the context of tourism. Further, the participants involved in this study were 30 villagers from three community-based tourism members who were randomly chosen from three tourism sites located in Bogor Regency. The participants were actively engaged in using DigiLang for English language learning within the tourism context. Their experiences and feedback were collected and analyzed to evaluate the effectiveness and suitability of the DigiLang materials in enhancing community-based tourism members' English proficiency. The results of the survey reveal learners’ positive attitudes towards (1) the user interface, (2) the language content and learning material, (3) the localized learning material. Participants also noted effective ways of online learning using DigiLang platform. Drawing from the outcomes of this developmental research, the DigiLang platform fulfilled the criteria for its adoption as a medium for online language learning, particularly in the English for Tourism context. s
DEVELOPING READING ASSESSMENT INSTRUMENT FOR INTERMEDIATE EFL LEARNERS: AZWAR MODEL

The study was inspired by the fact that teachers of an intermediate English reading course employed a pre-made reading instrument particularly tests obtained from the internet, the TOEFL, or a textbook without consulting the learning objectives. Therefore, they cannot meet the demand of the teaching and learning objectives and the classroom-based assessment that should be tailored to the unique circumstances of the course. The objective of this study is to create intermediate reading comprehension test items for EFL students that adhere to the "good test" criterion generated utilizing the Azwar Model (1996). Second-semester students of English Language department in one of institutions in Kediri, Indonesia were meant to take a reading test using this instrument. The results of the try-out from 75 EFL students from the same level demonstrates that the test met the requirements of being valid, reliable, and practical, had moderate difficulty level, good discrimination levels as the most, and 73 functional distractors of the total. Meanwhile, five-invalid test items are not going to be included to be utilized in assessing EFL students’ reading comprehension. Intermediate reading course lecturers could then utilize the 35 items which are considered as valid, reliable, and moderately difficult to measure their students’ reading achievement.
“I GET BLANK WHEN START TALKING”: AN EXPLORATION TOWARDS SOURCES AND STRATEGIES OF NON-ENGLISH FRESHMEN’S ANXIETY IN ESP SPEAKING PROGRAM

Foreign language anxiety has been recognized as one of the foremost challenges for language learners. This study intends to explore the sources of non-English freshmen’s anxiety in ESP speaking program and their ways of dealing with it. Similarly, ESP instructors’ solutions to mitigate their freshmen’s anxiety were also discovered. A qualitative approach with an exploratory case study design was conducted to enrich insight and better understand the phenomenon. Data collection involves classroom observations and semi-structured interviews with eight non-English freshmen and two ESP instructors enrolled in ESP speaking program at a private university in Malang, East Java. The data was then analyzed thematically using the interactive model of Miles et al., (2014). It was found that speaking anxiety commonly felt by non-English freshmen was caused by some sources from freshmen’s personal mentality, linguistic capability, and classroom activity. The finding also provides several strategies adopted by freshmen to overcome their anxiety. Furthermore, various approaches implemented by ESP instructors to decrease freshmen’s speaking anxiety were offered. It should be emphasized that anxiety among students will always linger upon learning a foreign language. Still, such learning roadblocks could be diminished. Instructors possess a crucial duty to lessen the worries of learners.
Chaotic Image Encryption: State-of-the-Art, Ecosystem, and the Promise of Quantum-Inspired AI

Recently, many researchers have been interested in the application of chaos in cryptography. Specifically, numerous research works have been focusing on chaotic image encryption. A comprehensive survey can highlight existing trends and shed light on less-studied topics in the area of chaotic image encryption. In addition to such a survey, this chapter studies the main challenges in this field, establishes an ecosystem for chaotic image encryption, and develops a future roadmap for further research in this area.