Reviewerly/exHarmony huggingface dataset:
Vocabulary development of EFL young learner after long peer interaction with video games

The development of online games nowadays is not only for the purpose of entertaining but it can also be for English learning purpose since English language is commonly used as a language of instruction there in the games. Many researchers have developed online games on language use, one of which is English that focuses on vocabulary development and communicative language functions. This present study aims to find out or investigate the interactions and vocabularies learned by EFL learner while playing video games. The design of this research was a case study with a high school student. The data was taken from observation, videotaping peer interactions and depth interviews. The result indicated that he interacted not only with games but also with friends or teachers in English classrooms in gaining vocabulary (incidental vocabulary learning). The interaction occurred incidentally where they learned new vocabulary from interaction with video games and expressively used them in realistic situations. The data analysis relieved different words EFL Students learned from video games interaction as good as vocabulary gains. It included vocabulary support and learning patterns, spontaneous vocabulary interaction, and vocabulary improvement in video games interaction skill. As a result, this research found a potential theory that was implemented unconsciously in the observation of activities. This research can be used as a way for both teacher and student to optimize vocabulary development.
Designing unlocked gramm application to promote students’ self-directed learning

The process of self-direction is viewed as essential for achieving worthwhile and significant educational achievements. It is related to setting up learning objectives, keeping one's intentions, and aiming for high-quality learning. In order for students to reach the ultimate educational goal of being continuous learners with the ability for future educational progress, learning interests, and possibilities for lifelong learning, self-direction is considered as crucial. The objective of this study was to create the Unlocked Gramm mobile application, which would promote students' self-directed learning to struggle the issue of students' low motivation for grammar study using Design and Development (D&amp;D) methodology as research design. The quantitative and qualitative analysis were used to examine the design, development, and evaluation of the media. The study was considered successful to the fact that the generated application satisfied the requirements for an appropriate media and was supported by statistical evidence.
Moves against cybercrime infrastructure have limits

Headline INT: Move against cybercrime infrastructure has limits
To be or not to be… on shelf: Demonstrating catalog and shelf integrity using interlibrary loan statistics

Libraries are places where patrons find resources that can help them accomplish their information needs. One of the key elements of this need is being able to find what they are looking for. While much has been written about information seeking behavior and library wayfinding, however, there is a more fundamental argument. That is merely finding the item when the Library Management System says it should be on the shelf. In these little moments, the library has an opportunity to build a sense of trust with the patron. The patron can trust that an item that the library says is there, is there. It is also an opportunity for a library to have a small victory in a positive patron library experience. However, if the item is not there that small victory is squandered. The challenge though is large scale shelf checks for shelving integrity is cost and time prohibitive. What is needed is a quick way to determine library and catalog health. This paper will demonstrate how this can be accomplished by looking at the ILL statistics. While this may seem like a simple answer demonstrating it with statistical relationships confirms what many libraries may view as a forgone assumption.
A Technical Comparison of YOLO-Based Chest Cancer Diagnosis Methods

Cancers have become one of the deadliest diseases in the world, and early diagnosis becomes vital for a patient's survival. As deep learning advances, YOLO has become an attractive tool as it supports real-time interactions. Thus, YOLO is expected to be applied in cancer diagnosis. A technical study of a YOLO-based computer aid diagnosis system for chest cancers is presented in the paper. Four kinds of the image in cancer diagnosis, histopathological images, mammograms, CTs, and Low-dose CTs, are introduced. Three issues of implementing a computer aid diagnosis system (CAD) are discussed and analyzed, including the usage of handcrafted features, the high false positive rate in clinical practice, and difficulty in detecting irregular nodules in spiral CTs. In discussion, the drawback of handcrafted features in the region of interest (ROI) extraction can be addressed by applying extra architectures like ResNet50 as extractors. A trained network can serve as a non-nodule filter to reduce the false positive rate in diagnosis. Image data can be categorized based on morphological features in data preprocessing to train a more sensitive model, then irregular-shape nodules can be detected by CAD.
Analysis of Wordle Game Mechanism Based on LSTM and MLP

This paper uses data analysis, deep learning and natural language processing techniques to study the difficulty and result distribution of word guessing based on the Wordle game mechanism. First, a three-layer LSTM model is established to predict the number of reported results, and the correlation between word factors and guessing difficulty is analyzed. Next, a multi-sequence LSTM model combined with NLP models is established to predict the result distribution of specific words at specific times. Finally, an MLP classification model is built to classify the difficulty of words.
How to Imagine the World with Text? From Text-to-image Generation View

Words are an effective and convenient way to describe the world, but sometimes what the texts convey may be misunderstood by readers. The expression of pictures is more vivid, easy to understand and has no borders, but creating a painting often takes a long time. Text-to-image makes the two expressions complement each other: It makes every ordinary person a “painter”, so that they can feel the world, express themselves, and create more whimsy through many rich pictures. For this vision, technologists are trying their best to improve image generation models, which enables computers to generate high quality images with texts better. And they are solving some technical defects, for instance, sometimes the content of generated images is strange. In the future, text-to-image can be adapted to applications in AI such as computer-aided design, image editing, and be employed in the field of art such as movies and artworks, and then it may even make a big difference on people's life, enriching the public's spiritual world and conveying information by vivid images.
Administrative Empowerment and Its Impact on The Creativity of Workers in The Government Hospitals Operating in Yemeni Countryside A field study in government hospitals in Al-Hujaria districts- Taiz

The study aimed to know the level of administrative empowerment with its dimensions (Delegation of Authority, Training, Effective Communication, Workers’ Motivation, and Building Work Teams) and its impact on workers’ creativity in government hospitals operating in the Yemeni countryside. For achieving the study objectives, the descriptive-analytical approach was used, and the study population consisted of all the workers in three governmental hospitals operating in the Al-Hujaria districts in Taiz Governorate: Khalifa Hospital, Al Nashma Hospital, and Alshheed Adnan Hospital, with a total number of (378) individuals. By the comprehensive survey method, the study questionnaire was distributed to the available study population (208) individuals, due to the decrease in employment because of the siege and the security conditions that Taiz is going through. (150) of them responded. Only (145) questionnaires were valid for analysis, at a rate of (70%) of the total distributed questionnaires. By analyzing the data using descriptive analysis methods and simple and multiple regression analysis using the SPSS program, the results of the study concluded that there is a positive impact of administrative empowerment on the workers’ creativity in government hospitals understudy, and also that there is a positive impact of the four administrative empowerment’s dimensions: delegation of authority, training, effective communication, and workers’ motivation on the workers’ creativity; while there was no impact of the team-building on the workers’ creativity in the government hospitals understudy, due to the variation in the workers’ awareness level of administrative empowerment practices.
Laser-Visible Face Image Translation and Recognition Based on CycleGAN and Spectral Normalization

The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, it becomes important to convert laser images into visible images and then identify them. For image translation, we propose a laser-visible face image translation model combined with spectral normalization (SN-CycleGAN). We add spectral normalization layers to the discriminator to solve the problem of low image translation quality caused by the difficulty of training the generative adversarial network. The content reconstruction loss function based on the Y channel is added to reduce the error mapping. The face generated by the improved model on the self-built laser-visible face image dataset has better visual quality, which reduces the error mapping and basically retains the structural features of the target compared with other models. The FID value of evaluation index is 36.845, which is 16.902, 13.781, 10.056, 57.722, 62.598 and 0.761 lower than the CycleGAN, Pix2Pix, UNIT, UGATIT, StarGAN and DCLGAN models, respectively. For the face recognition of translated images, we propose a laser-visible face recognition model based on feature retention. The shallow feature maps with identity information are directly connected to the decoder to solve the problem of identity information loss in network transmission. The domain loss function based on triplet loss is added to constrain the style between domains. We use pre-trained FaceNet to recognize generated visible face images and obtain the recognition accuracy of Rank-1. The recognition accuracy of the images generated by the improved model reaches 76.9%, which is greatly improved compared with the above models and 19.2% higher than that of laser face recognition.
Investment and Firm Performance Variability

Purpose - The study analyzed 90 online firms worldwise and observed them for ten years to investigate their investments and firm performance variabilities. This study attemped to verify the existence of agency problems in online firms. Through this, the paper intends to expand the scope of research in the fields of investment and firm value both empirically and in theory. This study also attempted to supplement the insufficient logic of previous studies by analyzing the relationship between investment and profitability.&#x0D; Design/methodology - In this study, the investment is subdivided into over-, under-, and neutral investments, and an empirical analysis of the firm performance was conducted. As investment generally has long-term effects, the impact of a firm's investment on future firm performance and variabilities in firm performance was considered over the short-and medium-term period.&#x0D; Findings - It was found that there was a negative relationship between firms with an overinvestment and future firm performance. Underinvestment has no clear statistically significant results on firm performance. This implies that overinvestment causes more reduction in future firm performance than underinvestment. It was also found that underinvestment and overinvestment significantly increased the variability of firm performance. A positive significance was found between under- and over- investment with a variability of 3 years and overinvestment with a variability of 4 years in the future. A negative relationship was found between neutral investment propensity and future performance variabilities. Neutral investment has less effect on the future performance variability of a firm than a firm’s overinvestment and underinvestment. For online firms, underinvestment and overinvestment have a greater effect on the firm's future performance variability than neutral investment.&#x0D; Originality/value - The agency theory predicts that information asymmetry and adverse selection problems exacerbate conflicts of interest among stakeholders, thus firm performance. The study contributed to accumulating research on online firms that are currently underexplored by analyzing the investment behavior of major firms in the online industry.
The Effect of Container Shipping Supply Chain Integration on Operational Performance: The Mediating Role of Information Sharing

Purpose - Conventional supply chains (SCs) must identify facilitating roles, such as port or terminal operators, shipping companies, and freight forwarders in executing the various logistical activities that support the organizational strategies of shippers. Meanwhile, supply chain collaborative practices can engender a willingness to share relevant and mutually helpful information. To this end, this study seeks to identify the impact of supply chain integration (SCI) on information sharing (IS) and the operational performance (OP) of Korea’s container-shipping industry from the perspective of social capital.&#x0D; Design/methodology - We established the research model for this study based on previous studies. In administering the survey, we obtained 149 valid responses from employees working in liner-shipping companies and freight forwarders in Korea. With the collected questionnaires, the study’s hypotheses were tested using SPSS 26.0 and AMOS 26.0 Findings - The results indicate the existence of a mediated relationship where the impact of SCI on OP is mediated by IS. The effect of external integration (EI) on OP is fully mediated by information quality (IQ) and information-sharing content (ISC). Furthermore, EI, IQ and ISC partially mediate the relationship between internal integration (II) and OP.&#x0D; Originality/value - This study expands SCI contexts, where ISC and IQ respectively serve as bridges between EI and OP which has crucial implications for container-shipping companies in terms of improving their performance.
Scientific ethics in the context of digital transformation

This study analyzes the definitions associated with the term digital transformation. Through this study, we aim to contribute to academic research and public discussion of the impact of digital transformation on scientific research. We focus on data infrastructures and their implications for research within and across disciplinary and national boundaries. The study aims to reflect and share our experiences with digital tools in the digital humanities, using approaches from research in science and technology, the history and philosophy of science. In addition, the ambivalent effects of digital transformation on the development of science are considered. The characteristics of digital transformation are analyzed. Issues between digital transformation and scientific ethics were considered. Key words: digitalization, digital transformation, scientific ethics, technology, democratic openness, access, open use, digital infrastructure, plagiarism, Internet space, scientific space.
A Feasibility Study of a System for Generating Dynamically Electoral Rolls

Clean electoral rolls plays vital role in free and fair elections from the bedrock of an Indian democracy. An electoral roll is an official list of inhabitants who are eligible to cast their vote in an election in their residential area. An Inhabitant (citizen) can be registered as a voter in a place where they are ordinarily resident. However, the seasonal migration and relentless urbanization can end up being on more than one electoral roll. A variety of reasons relentless urbanization and inter-and intra-city movements pose a challenge to having an updated and accurate electoral roll in urban areas. Bogus voting is another seriously considerable issue which is duplication owing to their enrolment in electoral rolls in several places made by hyperactive political parties. The use of proposed System for Generating Dynamically Electoral Rolls (SGDER) is to identify eligible voters and not to just make the enrolment process simpler; it will also help to avoid the duplication of voter names across constituencies. This system is to exploit for meeting effectively and efficiently the electoral roll registration, update requirements of ever increasing inhabitants and it maximizes accuracy and minimizes the process time, discrepancy and maintains uniqueness. The proposed system proves to be better than existing door-to-door campaign in India.
A Study on the Generalization of Form and Meaning of verb classifier constructions

This paper analyzes the form and meaning characteristics of the verb classifier construction ‘X+Num+VCL’, and takes ‘X’ and the verb classifiers as the center to examines the productivity, schematicity, and compositionality of the verb classifiers constructions, and identifies the generalized content, degree and application scope of of the verb classifiers constructions. In addition, the theoretical basis for the generalization of the constructions is presented, in which there are two types of inheritance relations, ‘IS’ and ‘IM’, in the development of the verb classifier constructions, and various influencing factors. According to the results of the study, atypical constructions ‘A/N+Num+VCL’ exist in all constructions, and the expansion of the form and semantic function of such constructions shows an inconsistent degree in various constructions, but the fact of expansion itself and the direction of expansion shows a consistent orthodromic character in the constructions of verb classifiers. It can be seen that the generalization of the verb classifier constructions is universal (or at least has the directionality of universal development of constructions) and does not occur only for particular high-frequency verb classifier constructions. The expansion of verb classifier constructions is a cyclic system, as the language is constantly changing and the above sentence expansions will continue to evolve according to the use of the language users.
GenPhys: From Physical Processes to Generative Models

Since diffusion models (DM) and the more recent Poisson flow generative models (PFGM) are inspired by physical processes, it is reasonable to ask: Can physical processes offer additional new generative models? We show that the answer is yes. We introduce a general family, Generative Models from Physical Processes (GenPhys), where we translate partial differential equations (PDEs) describing physical processes to generative models. We show that generative models can be constructed from s-generative PDEs (s for smooth). GenPhys subsume the two existing generative models (DM and PFGM) and even give rise to new families of generative models, e.g., "Yukawa Generative Models" inspired from weak interactions. On the other hand, some physical processes by default do not belong to the GenPhys family, e.g., the wave equation and the Schr\"{o}dinger equation, but could be made into the GenPhys family with some modifications. Our goal with GenPhys is to explore and expand the design space of generative models.
Seq-SVF: An Unsupervised Data-Driven Method for Automatically Identifying Hidden Governing Equations

In this work, an unsupervised data-driven method based on sequential singular value filtering (Seq-SVF) is proposed to simultaneously identify multiple partial differential equations from observed data considering potential noises. This method is aimed to extend the Sparse Identification of Nonlinear Dynamics (SINDy) to the identification of general nonlinear partial differential equations by transforming the paradigm based on regression to an unsupervised paradigm. To discover the complex coupled equations of vector or tensor forms without prior knowledge, the techniques of singular value decomposition (SVD) and strong rank-revealing QR factorization (sRRQR) are applied to the data matrix, which ensures that the method can automatically identify the number and the corresponding linearly independent terms as the left-hand terms of the governing equations. To balance the complexity and the precision of modeling, a strategy for filtering singular values is designed to determine the sparse structure of governing equations from a large number of nonlinear basis functions. We show the success of the method to extract explicit and succinct models from many complex linear, nonlinear, and multiphysics mechanical systems, and the examples show more accuracy compared with using traditional sparse learning methods.
An Improved Adaptive Genetic Algorithm for Robot Path Planning

To solve the problems of slow convergence speed and easy to fall into local optimum when adaptive genetic algorithm is applied to robot path planning, an improved adaptive genetic path planning method is proposed. In this method, turning angle evaluation index is introduced to improve the practicability of the path planning objective function; The adaptive adjustment strategy for optimizing the crossover probability and mutation probability, and the disaster operation and reversal operation are added, effectively improving the global optimization ability and convergence speed of path planning. The simulation results show that compared with the traditional adaptive genetic algorithm, this algorithm has stronger global optimization ability, requires fewer iterations, and its path planning performance is better than other improved algorithms.
Ontology model of inspection record of low-voltage equipment in distribution Internet of things based on semantic segmentation

In the power system, distribution is an important part, which is an important link between users and power supply departments. The economic, safe and stable operation of the distribution network plays a very important role in the entire power system, and effective inspection is the most basic and important task to ensure the safety and stability of the distribution system, so as to grasp the operation of distribution equipment and possible threats in real time. Aiming at the problems of the traditional power distribution patrol inspection personnel not being in place in time, often using paper records, and low efficiency, combined with the characteristics and advantages of the power distribution patrol inspection system itself, and fully considering the industry standards and specifications, a semantic segmentation based inspection record ontology model for low-voltage equipment in the power distribution Internet of Things is proposed. The experimental results show that the proposed method can effectively process the inspection record text, and improve the intelligent level of electrical equipment management and equipment status evaluation.