Reviewerly/exHarmony huggingface dataset:
Blockchain Technology for Secure and Intelligent Industry Applications

This chapter explores the significance of blockchain technology in protecting data for intelligent applications across various industries. Blockchain is a distributed ledger that ensures the immutability and security of transactions. Given the increasing need for security measures in industries, understanding blockchain technology is crucial for preparing for its future applications.This chapter aims to examine the use of blockchain technology across industries and presents a compilation of existing and upcoming blockchain technologies for intelligent applications. The methodology involves reviewing research to understand the security needs of different industries and providing an overview of methods used to enhance multi-institutional and multidisciplinary research in areas like the financial system, smart grid, and transportation system.The findings highlight the benefits of blockchain networks in providing transparency, trust, and security for industries. The Responsible Sourcing Blockchain Network (RSBN) is an example that utilizes blockchain's decentralized ledger to track sustainable sourcing from mine to final product. This information can be shared with auditors, corporate governance organizations, and customers.The practical implications of this chapter are significant, serving as a valuable resource for industries concerned with identity privacy, traceability, immutability, transparency, auditability, and security. Understanding and implementing blockchain technology can address the growing need for secure and intelligent applications, ensuring data protection and enhancing trust in various sectors.
Detecting aggression in clinical treatment videos

Many clinical spaces are outfitted with centralized video recording systems to monitor patient–client interactions. Considering the increasing interest in video-based machine learning methods, the potential of using these clinical recordings to automate observational data collection is apparent. To explore this, seven patients had videos of their functional assessment and treatment sessions annotated by coders trained by our clinical team. Commonly used clinical software has inherent limitations aligning behavioral and video data, so a custom software tool was employed to address this functionality gap. After developing a Canvas-based coder training course for this tool, a team of six trained coders annotated 82.33 h of data. Two machine learning approaches were considered, where both used a convolutional neural network as a video feature extractor. The first approach used a recurrent network as the classifier on the extracted features and the second used a Transformer architecture. Both models produced promising metrics indicating that the capability of detecting aggression from clinical videos is possible and generalizable. Model performance is directly tied to the feature extractor’s performance on ImageNet, where ConvNeXtXL produced the best performing models. This has applications in automating patient incident response to improve patient and clinician safety and could be directly integrated into existing video management systems for real-time analysis.
Eagle-inspired manipulator with adaptive grasping and collapsible mechanism and modular DOF for UAV operations

This paper presents an eagle-inspired manipulator designed to enhance the efficiency of medium and large multi-rotor UAVs during contact operations such as picking, obstacle cleaning, and grasping. Drawing inspiration from the skeleton structure and motion law of an eagle’s leg, we propose a prototype design of a large load, wide range of movement, multiple DOF, and collapsible manipulator. The main structure of the manipulator consists of a planetary reducer and a coupled parallel linkage mechanism, which enables it to have greater payload capacity while also increasing its range of motion. The grasping mechanism is based on the motion law of an eagle’s claw, which effectively increases the contact area with targets. To further enhance the DOF of the grasping mechanism, we have designed an ankle mechanism that utilizes a pair of double helical telescopic mechanisms to constrain the DOF of the universal joint, connecting the grasping mechanism to the main structure of the manipulator. Regarding the comparison with existing techniques, our eagle-inspired manipulator has several advantages over previous designs. On one hand, the grasp planning strategy, which is inspired by the motion law of an eagle’s claw, can achieve more reliable and accurate grasping than existing techniques. On the other hand, the manipulator’s collapsible design facilitates easier transportation and storage while maintaining its wide range of motion. What’s more, the modular DOF design allows for greater flexibility in adapting to different payloads and operating environments when compared to existing technologies. We outline the development process of this manipulator, including kinematic modeling and control. The ability of the manipulator system is tested, and flight test experiments are conducted on a UAV carrying the manipulator to demonstrate its practicality and effectiveness.
GPU Acceleration of High-Precision Homomorphic Computation Utilizing Redundant Representation

Fully homomorphic encryption (FHE) can perform computations on encrypted data, allowing us to analyze sensitive data without losing its security. The main issue for FHE is its lower performance, especially for high-precision computations, compared to calculations on plaintext data. Making FHE viable for practical use requires both algorithmic improvements and hardware acceleration. Recently, Klemsa and Ö nen (CODASPY'22) presented fast homomorphic algorithms for high-precision integers, including addition, multiplication and some fundamental functions, by utilizing a technique called redundant representation. Their algorithms were applied on TFHE, which was proposed by Chillotti et al. (Asiacrypt'16). In this paper, we further accelerate this method by extending their algorithms to multithreaded environments. The experimental results show that our approach performs 128-bit addition in 0.41 seconds, 32-bit multiplication in 4.3 seconds, and 128-bit Max and ReLU functions in 1.4 seconds using a Tesla V100S server.
Manajemen Risiko Teknologi Informasi Menggunakan Metode Fmea (Studi Kasus: Diskominfo Pemprov Riau)

Information Technology (IT) has an important role for an organization in supporting business activities. The implementation of IT supports the transformation of business processes, reduces operational costs (reduce costs), improves service quality to consumers, and ultimately supports organizational performance improvement. In line with the development of IT, currently IT is seen not only as a tool for business activities, more than that, IT has a strategic role in achieving the organization's business goals. In order to achieve this goal, it is necessary to support adequate IT management so that the existence of an IT infrastructure is needed. Of course, all forms of IT utilization and processing cannot be separated from threats to data integrity and network security. The Riau Provincial Government as part of the Unitary State of the Republic of Indonesia which is aware of the demands of the develop 1ent of ICT already has many Information Systems, especially in Diskominfo. In achieving the organization's business goals requires risk management for success or smooth achievement. According to Emmett Vaughan and Therese Vaughan, risk management is a scientific approach that aims to overcome all risks by anticipating losses that occur and implementing procedures that are able to minimize losses. There are various methods and tools for risk management analysis, one of which is quite popularly used is FMEA (Failure Mode &amp; Effect Analysis). The results of the analysis show that the greatest potential for failure is hardware damage due to irregular maintenance and improper use of RPN 144. Based on the analysis carried out, it can be seen what potential failures have the highest RPN value that must be prioritized first.
A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy

As machine learning (ML) has become pervasive throughout various fields (industry, healthcare, social networks), privacy concerns regarding the data used for its training have gained a critical importance. In settings where several parties wish to collaboratively train a common model without jeopardizing their sensitive data, the need for a private training protocol is particularly stringent and implies to protect the data against both the model's end-users and the other actors of the training phase. In this context of secure collaborative learning, Differential Privacy (DP) and Fully Homomorphic Encryption (FHE) are two complementary countermeasures of growing interest to thwart privacy attacks in ML systems. Central to many collaborative training protocols, in the line of PATE, is majority voting aggregation. Thus, in this paper, we design SHIELD, a probabilistic approximate majority voting operator which is faster when homomorphically executed than existing approaches based on exact argmax computation over an histogram of votes. As an additional benefit, the inaccuracy of SHIELD is used as a feature to provably enable DP guarantees. Although SHIELD may have other applications, we focus here on one setting and seamlessly integrate it in the SPEED collaborative training framework from [20] to improve its computational efficiency. After thoroughly describing the FHE implementation of our algorithm and its DP analysis, we present experimental results. To the best of our knowledge, it is the first work in which relaxing the accuracy of an algorithm is constructively usable as a degree of freedom to achieve better FHE performances.
ANXIETY IN JOHN GREEN’S NOVEL THE FAULT IN OUR STAR

This study aims to reveal the type of anxiety experienced by the protagonist and her defense mechanisms against the anxiety in John Green’s novel The Fault in Our Star. The causes of anxiety can be seen for the first time when Hazel is diagnosed with thyroid cancer. That situation results in a big impact on people around her, especially her parents' boyfriends. They get worried that she will hurt the people around her. This study uses the psychoanalytic theory of Sigmund Freud to explain the protagonist's type of anxiety and her defense mechanisms to get rid of the anxiety. In completing the analysis, this study applies the qualitative research method because the data collected in this study are in the form of words as quotations taken from the source. The results of this study indicate that the first protagonist's anxiety is caused by feeling guilty, overthinking, and worrying about keeping an eye out for the future. Therefore, two types of anxiety can be found in the novel. They are moral and realistic anxieties. Then, the protagonist commits two defense mechanisms to avoid her anxiety. They are rationalization and reaction formation. Finally, this data proves that the protagonist in the novel suffers from anxiety, and she tries hard to cure the disease.
The development of genially-based interactive learning multimedia for elementary school students

This research focuses on producing genially-based interactive multimedia learning for fifth-grade elementary/MI students. This research belongs to the type of research and development research and development (RND). The procedure for developing genially-based interactive learning multimedia uses a development model developed by Sugiyono with ten stages, namely potential and problems, data collection, product design, design validation, product revision, initial trial, product revision, usage trial, product revision, and mass production. However, in the current state of the COVID-19 pandemic and the limited time, conditions, and costs, researchers only carried out these stages, only to stage 9, namely product revision. The results of the validation by media experts with a score of 71 in the good category, material experts with a score of 86.6 in the very good category, and learning experts with a score of 80 in the very good category, so that genially-based interactive learning multimedia is feasible to use. The results of the responses of the SD Muhammadiyah Karangkajen II class teachers in the initial trial, namely by obtaining a score of 82.2 with a very good category, then the results of the responses of the students of SD Muhammadiyah Karangkajen II in the initial trial with the subject of 4 students with a score of 90 in the very good category, while the response of students in the use trial with the subject of 19 students with a score of 95.2 with a very good category. Then, the average value of student responses is 92.6. Based on the results of teacher and student responses, genially based interactive multimedia learning is feasible to use.
Analysis of Elementary Students' Reading Difficulties and its Relevance to Teacher and Parent Guidance

The existence of teacher guidance is one of the most significant factors. Teachers are the most important part of the teaching and learning process, both in formal and informal education channels. Therefore, in every effort to improve the quality of education in the country, it cannot be separated from various matters relating to the existence of the teacher himself. while parents are everyone who is responsible for family or household duties in daily life both in children's educational activities or others, because with parental attention, it can minimize the occurrence of learning difficulties in students. Early reading learning difficulties are reading learning difficulties in elementary school children in grade III. This study aims to describe the difies of beginning reading in students and find out how the guidance of teachers and parents in learning activities. This research uses a descriptive approach. Subjects were grade III students, teachers and parents. The results show that the difficulties in reading of grade III elementary school students are: (1) Not yet able to connect basic words into a sentence; (2) always wrong in spelling activities and pronouncing letters. (3) always mistaken in pronouncing basic words (4) difficulty spelling and combining first and last syllables; (5) spelling slowly and disjointedly and sometimes incorrectly; (6) not yet able to read a sentence consisting of three basic words; (7) very slow progress in reading skills.
Research on the Blockchain Technology Diffusion in China’s Supply-Chain Finance Industry

The high level of risk in supply-chain finance can be overcome by the technical features of blockchain. To smooth the promotion and application of blockchain technology in China's supply-chain finance industry, this research constructed a system dynamics model based on a technology–organization–environment (TOE) framework to simulate blockchain diffusion and adoption. The results indicated that in the initial stage, the motivation and obstacles of blockchain adoption were mostly related to external factors such as government policies, organizations' technology levels, industry standards, and managers' willingness. It is necessary to use government policies to build a good ecology for the growth of professional blockchain enterprises. However, in the development and maturation stages of blockchain diffusion, the influence of environmental and organizational factors was found to diminish. It is necessary to deepen scientific research, consolidate the comparative advantages of blockchain itself, and maintain the sustainable diffusion of this technology.
LinkEE: Linked List Based Event Extraction with Attention Mechanism for Overlapping Events

Event extraction (EE) is an essential task in natural language processing (NLP), which aims to extract information about the events contained in a sentence. Most of the existing works are not appropriate for Chinese overlapping event extraction, where a Chinese overlapping event sentence contains multiple event types, trigger words and arguments. In this paper, we propose LinkEE, an attention-based linked list model for event extraction, which organizes event types, trigger words and arguments as the nodes in the linked list, and leverages conditional fusion functions as edges to pass specific information. Bi-directional feature information of the input is learned through Bi-LSTM, while contextual information is captured by Hydra attention to enhance the text vector representation. Experimental results on the public dataset FewFC illustrate that the proposed model LinkEE is superior to previous methods.
A Novel Approach for Fake Review Detection Based on Reviewing Behavior and BERT Fused with Cosine Similarity

The pre-trained models such as BERT for fake review detection have received more attention. Most of research has overlooked the role of behavioral features. Additionally, the improvements of the pre-trained models have increased the computational overhead. To enhance BERT's ability to extract contextual features while minimizing computational overhead, we propose an end-to-end model for fake review detection that combines textual and behavioral feature information. Firstly, the review average cosine similarity and the review corpus are jointly fed into BERT to obtain textual feature vectors. Then, the underlying patterns in the behavioral information are extracted by CNN to construct behavioral feature vectors. Finally, the two feature vectors are concatenated for fake review detection. The entire model and each component were evaluated on YELP dataset. Compared with the original BERT model, the F1 score and AUC score of the BERT model fused cosine similarity are improved by 6.30% and 6.37%, respectively. Our model achieves a further improvement of 12.47% and 12.48% in F1 score and AUC score, and shows good performance in precision and recall. The experimental results show that the proposed model improves the effectiveness of BERT for detecting fake reviews, and is particularly suitable for scenarios where can capture behavioral features.
Trust Building in Cross-border E-Commerce: A Blockchain-Based Approach for B2B Platform Interoperability

Cross-border E-commerce (CBEC) currently faces a significant trust problem among merchants. Existing research in this field mainly focuses on addressing intra-platform trust issues, while little attention has been given to resolving inter-platform trust challenges. The advent of blockchain technology, with its features of decentralization, transparency, autonomy, and security, offers promising potential for the advancement of cross-border e-commerce while also presenting its own set of challenges. To address this research gap, this paper proposes a trustworthy blockchain-based cross-border B2B e-commerce platform (referred to as BCBEP) based on blockchain technology. Firstly, various sources of distrust are analyzed to gain a comprehensive understanding of the problem. Building on this analysis, blockchain-enabled smart contracts are employed to address the decentralized benefit distribution issue. Secondly, a credit-based incentive mechanism is introduced to attract merchant interest and facilitate timely connections between merchants and their target customers. Thirdly, several key mechanisms are developed to showcase the effectiveness of the proposed platform. To demonstrate the practicality and feasibility of BCBEP, we conduct a case study evaluating an existing cross-border B2B e-commerce platform, "TradeIndia." The findings from this study provide valuable insights into the potential benefits and applicability of the proposed BCBEP platform in addressing inter-platform trust issues and enhancing cross-border e-commerce operations.
End-to-End Aspect-Based Sentiment Analysis Based on IDCNN-BLSA Feature Fusion

The existing end-to-end Aspect-Based Sentiment Analysis (ABSA) algorithms focus on feature extraction by a single model, which leads to the loss of the important local or global information. In order to capture both local and global information of sentences, an end-to-end ABSA method based on features fusion is proposed. Firstly, the pre-trained model BERT is applied to obtain word vectors; secondly, Iterated Dilated Convolutions Neural Networks (IDCNN) and Bi-directional Long Short-Term Memory (BiLSTM) with Self-Attention mechanism (BLSA) are adopted to capture local and global features of sentences, and the generated local and context dependency vectors are fused to yield feature vectors. Finally, Conditional Random Fields (CRF) is applied to predict aspect words and sentiment polarity simultaneously. On Laptop14 and Restaurant datasets, our model's F1 scores increased by 0.51%, 3.11% respectively compared with the best model in the comparison experiment, and 0.74%, 0.78% respectively compared with the single model with the best effect in the ablation experiment. We removed each important module in turn in subsequent experiments and compared it with our model. The experimental results demonstrate the effectiveness of this method in aspect word recognition and its better generalization ability.
The Construction Method of Knowledge Graph on Photovoltaic Industry Chain

As a strategic emerging industry supported by the state, the photovoltaic (PV) industry is supported by the national industrial policy and highly valued by local governments. With the continuous development of information technology, the introduction of knowledge graph technology into the research of PV industry chain can significantly improve the efficiency of representation, retrieval, and analysis of the PV industry chain, provide a deeper understanding of the structure and development dynamics of the PV industry chain, and provide powerful support for industrial research and decision-making. Therefore, this paper builds a preliminary PV industry chain knowledge graph through a comprehensive analysis of the industries, enterprises, and products involved in the PV industry chain, and realizes data storage and visualisation using the Neo4j graph database. This paper combines the knowledge graph with the PV industry to fully explore the industry chain information, which helps to grasp the overall situation and development trend of the industry timely identify the bottlenecks and risks in the industry chain, formulate more effective risk management and countermeasures, continuously optimize the industry chain structure and promote the sustainable development of the PV industry.
Artificial Intelligence

The potential for artificial intelligence (AI) still attracts polarising views. There are clear benefits and dangers. The key is being aware of what these are and understanding that it is possible to utilise AI in psychiatry safely. There has to be to a will to do so as it is far more expensive to develop AI applications safely than it is to develop them at all. As shown however, there are hidden costs that make this a false economy. Ultimately, transparency about the inner workings of AI and machine learning tools makes increasing their safety realistic. It reduces the sense of magic but that is a small price to pay.