Reviewerly/exHarmony huggingface dataset:
The problem of using English reading strategies perceived by Thai EFL students

It is recognized that reading ability is essential for Thai EFL students and English reading is perceived as a difficult task among them. To understand the difficulty experienced by the students while reading English text, this study hence aimed at investigating problems of using English reading strategies perceived by Thai EFL students as well as providing suggested reading instructions for Thai lecturers to cope with the emerging problems. This study employed a mixed-method design. The participants of the study were 412 Thai EFL students who, in this study, were also considered as Gen Z students at a large-size university in Bangkok, Thailand. The participants were asked to complete an online questionnaire on 4-point Likert’s scale, which was adapted from Aebersold and Field (1997). Moreover, 10 students were recruited for two focus group interviews. The results revealed that Thai EFL students experience difficulties in using English reading strategies when they have to skip unknown words during the first reading (mean = 2.67, SD = 1.01) followed by varying reading speed rates according to the type of the reading passage (mean = 2.66, SD = 0.97), and guessing the meanings of unknown words by using the context clues (mean = 2.59, SD = 0.92) respectively. Based on both quantitative and qualitative results and personality traits of Thai Gen Z, suggested reading instructions including guided reading, improving student’s vocabulary knowledge and reading comprehension, technology-blended classroom and critical reading were purposed.
Formally Verified Credentials Management for Industrial Control Systems

The field of industrial automation is experiencing growth in interconnectivity and digital interaction.This growth is slower than in a consumer segment due to often critical nature of industrial control systems.Security of such systems is an important aspect as malicious behaviors could lead to potential system malfunction, injuries or financial losses.As control networks are becoming more complex, having a robust credential management for system operators and users that could interact with the system components is an essential need.One way of assuring the robustness of the credential management is by using formal methods.In this paper we present a formally verified credential management system for use within industrial control systems.We demonstrate that the credential management can use centralized credential storage with secret passwords available only to system administrators.We use UPPAAL to formally analyze security properties based on requirements defined by our industrial partner and present the viability of formal verification to a real-world industrial case study.
Research on Data Asset Management of Electricity Industry Based on Blockchain

Electricity data, which is generated in electricity Internet of things (IOT), is the important asset for electricity enterprises to discover consumer values and raise economy benefits. Due to the commercial value of electricity data and the high demands of data management in electricity IOT, this paper provides a novel framework for management of electricity data. This framework is developed based on blockchain, according to the concept of data asset management. The proposed framework contains five blockchain based modules to provide electricity enterprises various kinds of data services and management. Our framework contributes to offer a novel solution for securely managing and efficiently utilizing electricity data.
TOPICAL ISSUES OF PROFESSIONAL AND PSYCHOLOGICAL SUITABILY IN THE SYSTEM OF THE MIA OF THE REPUBLIC OF TAJIKISTAN

This article discusses the main problems and issues of professional and psychological suitability in the system of the Ministry of Internal Affairs of the Republic of Tajikistan. The analysis of the existing scientific literature was carried out, and also the author's recommendations were formulated to improve the professional training and professional suitability of the employees of the Ministry of Internal Affairs. Along with the professional training of police officers, their psychological qualities also play a decisive role in ensuring efficiency in operational-search activities.&#x0D; The quality and effectiveness of the implementation of operational-search activities, then its level directly depends on the psychological characteristics of the operative and his professional training. It should be noted that scientifically grounded conclusions about the professional and psychological suitability of employees can only be achieved with the help of high-quality and correct psychological diagnostics, that is, using psychometric methods.&#x0D; The basis for the survey of professional and psychological suitability, the procedure, procedure, conditions and terms of the survey, cases of re-examination are determined by regulatory legal acts.&#x0D; Consequently, the research and scientifically grounded measures to determine professional suitability in the internal affairs bodies of the Republic of Tajikistan are relevant, and the scientifically grounded results obtained as a result of our research can be used to further improve the professional and psychological selection to the law enforcement agencies of the country.
Infinite arguments and semantics of dialectical proof procedures

We study the semantics of dialectical proof procedures. As dialectical proof procedures are in general sound but not complete wrt admissibility semantics, a natural question here is whether we could give a more precise semantical characterization of what they compute. Based on a new notion of infinite arguments representing (possibly infinite) loops, we introduce a stricter notion of admissibility, referred to as strict admissibility, and show that dialectical proof procedures are in general sound and complete wrt strict admissibility.
Designing and Deploying Shape-changing Artifacts in Everyday Settings Over Time: Extending Practices and Methodologies for Materiality in HCI

While the HCI community has developed many unique shape-changing artifacts for supporting novel interactions and experiences, little research has investigated long-term organic experiences of living with these objects. In parallel, Wiberg has proposed the notion of the materiality of interaction as a new ontology for tangible computing. However, cases of designing for materiality are still sparse. The overarching goal of this doctorial study is to fill in the gap between materiality and shape change. To do so, I plan to adopt design-oriented HCI approaches to frame my design implementations and field deployments. Fabricating shape-changing artifacts may extend practices of designing for materiality. Reflecting on design processes and accumulating empirical data of these devices may enrich the understanding of materiality in interaction design and HCI fields.
Resolving Ambiguity in Hedge Detection by Automatic Generation of Linguistic Rules

An understanding of natural language is key in order to robustly extract the linguistic features indicative of deceptive speech. Hedging is a key indicator of deceptive speech as it can indicate a speaker's lack of commitment in a conversation. Hedging is characterised by words and phrases that display a sense of vagueness or that lack precision, such as suppose, about. The identification of hedging terms in speech is a challenging task, due to the ambiguity of natural language, as a phrase can have multiple meanings. This paper proposes to automate the process of generating rules for hedge detection in transcripts produced by an automatic speech recognition system using explainable decision tree models trained on syntactic features. We have extracted syntactic features through dependency parsing to capture the grammatical relationship between hedging terms and their surrounding words based on meaning and context. We tested the effectiveness of our model on a dataset of conversational speech, for 75 different hedging terms, and achieved an F1 score of 0.88. The result of our automated process is comparable to existing solutions for hedge detection.
Position and Velocity Fusion Using Multiple Monostatic Radar Sensors for Automotive Applications

In order to protect vulnerable road users (VRU), it is essential to detect and track them reliably and to predict their direction of motion i.e., their velocity vector relative to a vehicle's driving direction. A fusion approach for monostatic radar sensors, which computes target positions and the assigned two-dimensional instantaneous velocities, is proposed to realize these functionalities. These fused detections are used to cluster extended objects. Afterwards their position, velocity, and covariance matrices are estimated. A converted measurement Kalman filter is utilized to track these detected objects. Both velocity and position can be very accurately measured with the proposed fusion algorithm. Tracking by two separated radar stations greatly outperforms a single sensor setup, as verified by test data.
Maple as a tool to understand marketing game for up to four companies

Abstract In business environment, advertisement is very important. Companies spent a lot of money to advertise their products. When there are several players which have the same product to market, the competition will be quite tough since the market size is finite. The amount of the money spent for advertisement will affect the sale of the product. The profit for each company will depend on the amount of money spent for advertisement. However, this will also depend on the money spent by other companies, making the determination of the optimum profit become difficult. For two and three companies, exact value of the optimum profit for each company can be found. However, the analysis for more than three companies is quite complicated so we must resort to numerical method. Examples for the cases of two, three and four companies are given. A company with the smallest effectiveness factor and gross profit will have the smallest optimum profit. Maple has been widely used to derive formulas and to solve the case studies in this paper.
An Improved Fully Convolutional Network for Semantic Segmentation

Abstract A novel loss function based on category distribution for semantic segmentation is proposed in this paper. The new loss function is composed of two parts: (1) Pixel-wise cross-entropy. (2) Category distribution loss proposed. Most of existing semantic segmentation networks adopt a single pixel-wise cross-entropy loss function, which guides the network to independently predict the class each pixel belongs to. The downside is that ignoring global information of the image -- the distribution of all kinds of objects in the image, resulting in an unsatisfactory segmentation result. Category distribution loss proposed in this paper obtins category distribution information of the whole image by calculating the percentage of pixels to each class of objets. Acquired category distribution information can be represented as a feature vector, the distance of two vectors between prediction and ground truth forms category distribution loss. The new loss function measures the difference between pixels as well as the difference between global information. We apply the new loss to several classical networks, its pixel accuracy and mIoU accuracy on two benchmark datasets CamVid and Pascal VOC2012 are improved compared to using only pixel-wise cross-entropy. In addition, we also introduced the attention mechanism, which proved to be able to improve segmentation accuracy of the networks as well.
Automated Short Video Caption Generation Using Video Features and Audio Content

This paper presents a method to automatically generate caption for short videos using video features and audio content. Main aim is to generate one line captions for videos in natural language (i.e., English) which will describe the video according to its content. To accomplish this task first, audio content is extracted from video so that both the works can be performed separately. Firstly, captions are generated on video features, by creating a fixed length vector to represent the features extracted from each video frame using convolution neural network (CNN), and then it is decoded using long short-term memory (LSTM) model into a sequence of words composing the description of the video. Secondly, audio content is considered for generating captions. Audio content from the video file is extracted transcribed, and used as an input to sequence-to-sequence model, with an added attention layer. Once the captions are generated from both the video as well as audio content, then they are merged to generate final caption. The proposed method had been compared with existing approaches and found to be the best with a bilingual evaluation understudy (BLEU) score of 77.0.
Blockchanging Money

Blockchain technology is changing the world incentive system, making programmable money. This kind of money is only fruitful and democratically livable in a transparent political environment. Otherwise, instead of unleashing innovation and collective action with the market's visible hand of qualified money, the new internet of value will deliver a digital money with the same algorithmic fate that social media met on the previous internet. The latter allows digitizing users' data and has been used to manipulate consumers and public opinion (possibly in the last two U.S. Presidential elections). Similarly, the former will let states and corporatocracy cross-reference social media and digital money's data, hurting privacy even more. As blockchains disseminate, having the crucial economic advantage of reducing transaction costs, only free-market competition between private and public blockchains guarantee transparency and democracy. Blockchain technology is the real McCoy, and decentralizing digital money is the free world's best shot, especially in the new normal triggered by COVID-19.
Research on Sentiment Dictionary Based on Sentiment Analysis in News Domain

The known Chinese sentiment dictionary has universal applicability, lacks the expansion of proprietary domains, and lacks support for network words. This paper proposes a new approach and applies it to the Internet news sentiment analysis text provided by the CCF Big Data & Computing Intelligence Contest. First, the corpus is segmented in Chinese to obtain candidate sentiment words that have not appeared in the sentiment dictionary. The candidate sentiment words are calculated in different sentimental texts. The proportion of appearance is obtained, and the emotional tendency of candidate emotional words is obtained, and then it is merged with the emotional vocabulary ontology database to obtain the expanded emotional dictionary. Experiments show that the expanded sentiment dictionary is better than the known Chinese sentiment vocabulary ontology database in the above corpus.
Addressing Computer Vision Challenges Using an Active Learning Framework

Computer vision has introduced new successful opportunities in everyday life. Recently, there has been a lot of progress, particularly in face recognition and object detection systems. These systems require a large amount of data to be trained with, in order to perform satisfyingly. Active learning addresses this challenge by leveraging a small amount of manually labelled data. This paper builds on state-of-the-art face recognition and object detection models, by implementing optimization techniques that enhance the recognition accuracy. Further training is being introduced by making use of a robust active learning framework that results in creating extended data sets. Finally, the paper proposes an integrated system, which involves efficient techniques of associating face and object identification information, in order to extract (in real-time) as much knowledge as possible from a video streaming.
Using Learned Health Indicators and Deep Sequence Models to Predict Industrial Machine Health

In this paper, we describe a machine learning approach for predicting machine health indicators with a large time horizon into the future. The approach uses state-of-the-art neural network architectures for sequence modelling and can incorporate numerical-sensor and categorical data using entity embeddings. Moreover, we describe an unsupervised labelling approach where classes are generated using continuous sensor values in the training data and a clustering algorithm. To validate our approach, we performed an ablation study to verify the effectiveness of each of our model’s components. In this context, we show that entity embeddings can be used to generate effective features from categorical inputs, that state-of-the-art models, while originally developed for a different set of problems, can nonetheless be transferred to perform industrial asset health classification and provide a performance boost over simpler networks that have been traditionally used, such as relatively shallow recurrent or convolutional networks. Taken together, we present a machine health monitoring system that can accurately generate asset health predictions. This system can incorporate both numerical and categorical information, the current state-of-the-art for sequence modelling, and generate labels in an unsupervised fashion when explicit labels are unavailable.
Yoyo trick on type‐II generalised Feistel networks

This work presents a structural attack against the type-II generalised Feistel network (GFN) with secret internal functions. First, equivalent structures of the 7-round type-II GFN are provided, which helps reduce the first guess of the secret round functions. Then, two yoyo game distinguishers are simultaneously employed for these structures to reduce the data complexity by half. Based on these two distinguishers, it is found that the original yoyo game algorithm, proposed to attack the 5-round Feistel structure, is not suitable for these structures, owing to the characteristics of the yoyo game cycle. To solve this problem, the partial look-up table recycling technique is presented, which can utilise collision cycles with insufficient information. This technique performs better as the width of each branch ‘n’ grows. For yoyo game attacks, this study systematically investigates its cycle characteristics to determine the reason for the short collision cycle. For 7-round type-II GFNs, this work presents the first decomposition thus far, which can be executed within a time complexity of O(n24n + 3) and a data complexity of O(23n + 2). We believe this work enriches the yoyo game attack and the application of type-II GFNs.
A Concurrent Optimization Schemes for Exercise Health Prediction Platform

In this paper, the concurrent optimization scheme of the exercise health prediction platform based on C/S architecture is reported. First, a cluster of servers is deployed to handle concurrent user requests. Second, based on Nginx, the front-end server was built as the user access port. And third the Spring Boot framework is adopted to deploy the human motion simulation model on the back-end application server. At same time, some optimization schemes about server are designed and implemented to enhance the concurrent processing capability of the cluster. Experimental results show that the concurrent optimization scheme presented in this paper has better performance. The processing efficiency of concurrent simulation prediction request is improved.
Cat's Eye: Media Insights Analyzer

Different media are used to report news happening all around the world to the public. The news from all over the world is a big data source on the Internet. Besides, the news media have their own writing style and stands towards the news which may cause great influence towards readers’ opinion. It is valuable to analyze the insights of various media. The existing works on sentiment analysis for the news data do not have a user-friendly interface to present their work and the news data are not retrieved regularly for continuous data analysis. To address the above problems, we propose Cat's Eye Project which aims to identify the stands of different news media through the usage of the words of those media, forecast the trend of some social issues, find out whether there is any indication before a new social issue formed and present our findings by using a user-friendly interface. We build our system on Google Cloud and use the built-in machine learning algorithms on Google Cloud to develop our learning model. By using cloud technology, the system implementation period is highly reduced, and it also guarantees the scalability of our system. The output finding can be used as interesting insights. Our pilot study showed that the media can affect the readers’ opinion towards social issues.
Research on the Application of Big Data Technology in Network Security Analysis

Abstract China’s information technology development and scientific and technological undertakings have entered a period of rapid development, and big data technology, as an important technical support and management tool, plays an increasingly prominent role in ensuring network security analysis. Faced with various deficiencies in network security analysis and data management at this stage, we must optimize network security analysis by applying network security data analysis platforms and big data core technologies. In particular, the multi-functional file management and technical integration in the data analysis process are conducive to the continuous improvement of file management, statistical efficiency and platform quality. Through scientific distribution and optimization of structure distribution, the efficiency of data and information storage management can be improved, and network security prevention capabilities can be improved to a large extent. This article first briefly explains the importance and necessity of big data technology, and analyzes the status quo of the problems in the process of data collection and analysis. The author further proposed the strategy of optimizing big data technology and promoting file management management to effectively guarantee the development quality and economic benefits of online statistical data and offline analysis technology.
Using Open Source Licensing to Regulate the Assembly of LAWS: A Preliminary Analysis

Lethal autonomous weapons (LAWS) are an emerging technology capable of automatically targeting and exercising lethal force. Many scholars and advocates have petitioned to ban the technology internationally for a myriad of reasons. However, there are practical challenges to implementing a ban. One such challenge is posed by the “intangible” nature of the software that LAWS depends on, which is incompatible with implementation mechanisms such as export control. Given the dual-use nature of software, and the fact that software is developed by teams of individuals, a number of soft governance mechanisms have been proposed to regulate this technology. In this paper, we investigate the feasibility of one particular approach: leveraging open source licenses as a means to prohibit the use of certain software in LAWS. This approach is largely motivated by the fact that open source software underpins all of technology, especially AI. Through a review of the recent tech activism and open source activism, we evaluate whether open source licenses can feasibly limit the use of open source software to only non-LAWS applications. We distill the current challenges facing “ethics-driven” open source licensing efforts into three main obstacles: the need for clarity of licensing language, the lack of enforceability of licenses, and the lack of cohesiveness of the open source community. We propose that addressing these factors are also success criteria for future anti-LAWS open source initiatives. We find that open source licenses provide more theoretical than practical promise in regulating LAWS, and conclude that cohesion in the open source community is the key to their potential practical success in the future.