Reviewerly/exHarmony huggingface dataset:
An Interactive Generation Support System for Picture Books

In this paper, we propose an interactive support system for generating picture books. The proposed system consists of two parts: a sentence generation part and a picture book image generation part. The sentence generation part takes a topic and an opening sentence of a picture book as inputs, and generates sentences of the picture book in an interactive manner. The user can select the most appropriate sentence from the generated sentences to create a story for the picture book, and can modify specific parts of the sentences. The image generation part generates a background image of the picture book using the text as an input, and acquires the necessary image objects for the picture book from the web. Using the retrieved images, the user can paste the image objects into the background image to create a picture book image. With these two parts, a picture book can be created without the user having to directly write the text and pictures. Through evaluation experiments, we confirmed that the proposed system can generate a picture book that is comfortable as a story and that can arouse the reader’s interest.
A New Efficient Method for Refining the Reinforcement Learning Algorithm to Train Deep Q Agents for Reaching a Consensus in P2P Networks

The usage of distributed Peer-to-Peer (P2P) networks has been growing steadily for a reasonable period. Various applications rely on the infrastructure of P2P networks, where nodes communicate to accomplish a task without the need for a central authority. One of the significant challenges in P2P networks is the ability of the network nodes to reach a consensus on a shared item; the challenge increases as time passes. Thus, this work proposes a new effective method for tweaking the Deep Reinforcement Learning (DRL) algorithm to train Deep Q Network (DQN) learning agents to reach a consensus among the P2P nodes. We propose various hierarchies of deep agents to address this crucial challenge in P2P networks. DRL is utilized to build and train agents; more precisely, DQN learning agents are constructed and trained. Two scenarios are proposed and evaluated. In the first scenario, one DQN agent is trained to find the consensus between the network nodes. In the second scenario, three hierarchies with different numbers of layers of agents are proposed and evaluated. In both scenarios, the P2P network used is a blockchain network. The best result was obtained using the third hierarchy of the second scenario; the overall accuracy of the model is 87.8%.
Application of Decision Tree Algorithm in College Students' Mental Health Evaluation

With social economy, the accelerated pace of life and work, and the increasingly fierce social competition, people are under increasing social pressure in life, study and work. Especially college students, psychological problems are becoming more and more prominent, and psychological diseases and obstacles are increasing. If they can not be solved in time, they may cause serious social consequences. Therefore, developing a set of psychological evaluation system for students is becoming more and more important for colleges and universities. The decision tree algorithm is used to evaluate the status of college students. The purpose is accuracy and effectiveness of decision tree algorithm in predicting depression, anxiety and suicidal ideation of college students. The design of this study is descriptive. This means that variables are measured at the same time or at different times, but not at the same time. In addition, it is important that there are no confounding variables between the two variables (that is, if one variable changes, the other will change). The establish the compulsive symptom decision model, and the data mining related technologies and algorithms are used to synthesize the mining and feed them back to the psychological counselors, providing a theoretical basis for the evaluation and intervention.
Side-Channel Resistant 2048-bit RSA Implementation for Wireless Sensor Networks and Internet of Things

We present a practical realization of Rivest-Shamir-Adleman (RSA) with a 2048-bit key on MSP430, a widely used microcontroller in wireless sensor network and Internet of things applications, and show that 2048-bit RSA is feasible on a constrained microcontroller. We exploit several methods for acceleration, e.g. Montgomery modular multiplication, subtractive Karatsuba-Ofman and Chinese remainder theorem (CRT) based modular exponentiation, and achieve RSA encryption and decryption with a 2048-bit key on MSP430 in just 0.14 s and 7.56 s, respectively. Our implementation on the low-end MSP430 microcontroller achieves 2048-bit RSA significantly faster (×2.9 and ×2.4 for encryption and decryption) with respect to the existing implementation in the literature on the comparable ATmega128 microcontroller. While our implementation is secure against the brute force attack due to its 2048-bit key, and thus 112-bit security level, it also includes the necessary side-channel countermeasures, e. g. message and key blinding, to help mitigate implementation attacks such as simple power analysis and differential power analysis.
Differential Evolution without the Scale Factor and the Crossover Probability

Differential evolution has made great achievements in various fields such as computational sciences, engineering optimization, and operations management in the past decades. It is well known that the control parameter setting plays a very important role in terms of the performance improvement of differential evolution. In this paper, a differential evolution without the scale factor and the crossover probability is presented, which eliminates almost all control parameters except for the population size. The proposed algorithm looks upon each individual as a charged particle to decide on the shift of the individual in the direction of the difference based on the attraction-repulsion mechanism in Coulomb’s Law. Moreover, Taguchi’s parameter design method with the two-level orthogonal array is merged into the crossover operation in order to obtain better individuals in the next generation by means of better combination of factor levels. What is more, a new ratio of the signal-to-noise is proposed for the purpose of fair comparison of the numerical experiment for the tested functions which have an optimal value with 0. Numerical experiments show that the proposed algorithm outperforms the other 5 compared algorithms for the 10 benchmark functions.
Web archive analytics: Blind spots and silences in distant readings of the archived web

Abstract In this article, we discuss epistemological and methodological aspects of web archive analytics, a recent development towards more data-centred access to web archives. More specifically, we suggest understanding both the process of archiving and subsequent steps of analysis at scale as acts of observation that can be questioned for their epistemological priori. Therefore, we propose the concepts of ‘blind spots’ (features of the live web not included upon creation in the archive) and ‘silences’ (latent features present in the archive but requiring a particular method to be made articulate). In particular, we address two forms of silences playing a structural role in web archive analytics, crucial to both historians and social scientists alike: abundance (or scale) and time. We trace epistemological implications of web archive analytics across an exemplary case study workflow and suggest methodological answers to the issues raised in this process. On the data extraction side, we introduce warc2corpus (w2c), a new tool for extracting granular, structured data, especially temporal information related to the creation, modification, and publication specifically of webpages. For data analysis, we demonstrate how distant reading techniques—more specifically structural topic modelling (STM)—can contribute to providing a rich, temporally structured representation of textual web archive content that in turn can be subjected to scholarly inquiry, interpretation, and re-contextualization.
Information Literacy Skills Among Higher Education Institution Students in Malaysia

The emergence of the Covid-19 pandemic at the beginning of the year 2020 has affected people’s lives worldwide. Every nook and cranny must adapt to a new normal, which was unprecedented, including the education sector. In Malaysia, Open and Distance Learning (ODL) is implemented in the education field, especially at the tertiary level. The Ministry of Higher Education of Malaysia (MOHE) has instructed higher education institutions (HEIs) to adopt ODL since March 2020 for all academic programs. At present, for the second consecutive semester, ODL is being adopted.
“JE Connect” an Educational Purpose Android App for Tracking Students Attendance, Performance with Parents Feedback System

The project aims to enhance the management of student information, including attendance and academic progress, by introducing mobility and automation to the existing process. Traditional methods of communication, such as bulletin boards and verbal messages, are no longer sufficient in today's fast-paced world. Hence, the project proposes the use of Android technology to develop a mobile application that enables faster and easier communication between students, teachers, and parents. The Android-based app serves as a centralized hub that stores and provides access to various academic information, making it more efficient and accessible to everyone. The app provides a user-friendly interface for students to access and submit assignments, view attendance and grades, and interact with teachers. Parents can also keep track of their child's academic progress, attendance records, and communicate with teachers. This project addresses the limitations of previous systems, where information was stored either on hard files or on websites that were difficult to access and time-consuming to navigate. The proposed Android-based system offers an easier, safer, and more efficient way of managing student information.
Research of natural language processing based on dynamic search corpus in cultural translation and emotional analysis

In order to enable students to directly face empirical data, summarize translation rules and learn translation skills, this paper studies the basis, motivation and methods of applying research dynamics in translation and teaching. Presenting data in class is the main method of dynamically searching corpora, which enables learners to face enough bilingual data that are easy to choose, and makes translation skills and teaching of translation of selected language items relatively focused. In recent years, the emotional analysis text has attracted academic scientists, and the professionals involved in the research, the use of research methods, and the cultural background related to language have become more and more extensive. In this paper, natural language processing is used to analyze emotions contained in translated texts. Natural language processing not only helps to manage the huge ability of data to efficiently translate text, but also helps to extract the hidden emotions in text translation. It only takes half the effort to achieve the multiplier effect. The multi label classification in natural language processing can reflect the information contained in emotion. The translated text is more detailed, which is helpful for further research.
Assessment of general and professional competences formation level by bachelors studying of specialty 101 Ecology

Purpose. To identify the gaps and weak points of the Education-and-professional programme for the Specialty 101 Ecology based on students' self-assessment of the formation of competencies to improve the training of bachelor's degree specialists. Methods. The study is based on a statistically reliable survey among students. Analytical-synthetic method, graphic and logical generalization methods are used. Results. The study compared the level of competencies in students after the third and the forth years of study. The categories "excellent", "good", "satisfactory" and "not mastered" were used for evaluation. It is established that the general competencies of graduates, in contrast to the third-year students are considered to be "good" and "excellent", which was influenced by industrial practice. There are three groups of professional competencies by level of change and general formation. The first group is a block of theoretical competencies that are formed during first three years of study and the level of knowledge in both groups of respondents is almost the same. The second group-competencies are rated by graduated lower than by the third-year students. The reason is the internship, during which students realized the low level of preparation for the practical work. The third one is the competencies that raised self-esteem after the internship. Ways to solve each problem situation are suggested. Conclusions. The formation of general competencies in students of both years fo study is good, except for competencies related to learning a foreign language, which can be changed by increasing motivation to learn languages. Different formation of professional competencies encourages changes in the Education-and-professional programme and the addition of work programs with topics that focus on future professional work, as well as stimulating extracurricular activities.
Instance-Level Few-Shot Learning With Class Hierarchy Mining

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base classes to effectively classify novel objects. These features are extracted from abundant data of base classes, which could be utilized to reasonably describe classes with scarce data. Specifically, we propose a novel superclass approach that automatically creates a hierarchy considering base and novel classes as fine-grained classes for few-shot instance segmentation (FSIS). Based on the hierarchical information, we design a novel framework called Soft Multiple Superclass (SMS) to extract relevant features or characteristics of classes in the same superclass. A new class assigned to the superclass is easier to classify by leveraging these relevant features. Besides, in order to effectively train the hierarchy-based-detector in FSIS, we apply the label refinement to further describe the associations between fine-grained classes. The extensive experiments demonstrate the effectiveness of our method on FSIS benchmarks. The source code is available here: https://github.com/nvakhoa/superclass-FSIS.
An Information-Theoretic Method to Automatic Shortcut Avoidance and Domain Generalization for Dense Prediction Tasks

Deep convolutional neural networks for dense prediction tasks are commonly optimized using synthetic data, as generating pixel-wise annotations for real-world data is laborious. However, the synthetically trained models do not generalize well to real-world environments. This poor "synthetic to real" (S2R) generalization we address through the lens of shortcut learning. We demonstrate that the learning of feature representations in deep convolutional networks is heavily influenced by synthetic data artifacts (shortcut attributes). To mitigate this issue, we propose an Information-Theoretic Shortcut Avoidance (ITSA) approach to automatically restrict shortcut-related information from being encoded into the feature representations. Specifically, our proposed method minimizes the sensitivity of latent features to input variations: to regularize the learning of robust and shortcut-invariant features in synthetically trained models. To avoid the prohibitive computational cost of direct input sensitivity optimization, we propose a practical yet feasible algorithm to achieve robustness. Our results show that the proposed method can effectively improve S2R generalization in multiple distinct dense prediction tasks, such as stereo matching, optical flow, and semantic segmentation. Importantly, the proposed method enhances the robustness of the synthetically trained networks and outperforms their fine-tuned counterparts (on real data) for challenging out-of-domain applications.
Dynamics investigation of the quantum-control-assisted multipartite uncertainty relation in heisenberg model with dzyaloshinski-moriya interaction

Abstract Recently, Zheng constructs a quantum-control-assisted multipartite variance-based uncertainty relation, which successfully extends the conditional uncertainty relation to the multipartite case [Annalen der physik, 533, 2100014 (2021)]. We here investigate the dynamics of the new uncertainty relation in the Heisenberg system with the Dzyaloshinski-Moriya interaction. It is found that, different from entanglement, the mixedness of the system has an interesting single-valued relationship with the tightness and lower bound of the uncertainty relation. This single-valued relationship indicates that the tightness and lower bound of the uncertainty relation can be written as the functional form of the mixedness. Moreover, the single-valued relationship with the mixedness is the common nature of conditional uncertainty relations, and has no relationship with the form of the uncertainty relations. Also, the comparison between the new conditional variance-based uncertainty relation and the existing entropic one has been made.
Multilingual Support for Medicine Management Android Application

The demand for multilingual support in mobile applications has increased significantly in recent years. The ability to use an application in a preferred language enhances the user experience and increases the accessibility of the application to a wider audience. In this paper, we propose a multilingual sup- port feature for an Android application that provides medicine reminders to users. The feature includes a language selection option that allows users to choose their preferred language, and the application will display content in that language. Our approach ensures that the application is accessible and usable to a wider audience, regardless of their language proficiency
Read and Listen Library

There is a great saying that a good book is a good friend. Reading books can help human to improve one’s intelligence and is also a good exercise to the brain. So reading book habits have many advantages. The traditional method of reading books has its own advantages, but it consumes not only a lot of time and money but also a lot of space. They require more time to complete a book. Unlike the traditional methods, there are also audio podcasts available in the market which take only a few hours to complete a book but are also very distracting. The users of audio podcasts can be easily distracted by anything and hence they may lose track of the actual message in the book. Thus, our idea is to provide users with a new era of digital library where multiple options are available to them. They can choose to read a book, listen to its audio, or select the read and listen option where our system reads the book for them with highlighted text, so they pay full attention to the book and the audio playing. The users can read a book as a pdf or listen to the audio of the books or can even select an option such as read and listen where our system reads the book for the user with highlighted text so that the user pay full attention on the book and the audio playing. This requires users to pay full attention to the book, and keeps them concentrated. The chances of distraction in this type of reading are very few, and hence users can get the full benefit from the book. This method can also help users complete more books in less time. Our goal is to provide every book lover with a new learning experience and give them a satisfactory experience in reading a book.
Semantic web-based collaborative design system in cloud computing environment

The integration and remoteness of mobile terminals, as well as local area network-based office issues, have basically been solved, that is, a solution established on the basis of a cloud service collaborative platform, which can be implemented between various service systems. Each place enters the office system at all times to process related business, which improves the overall efficiency of our company's collaboration, and each information management system is no longer an information island. Technically, this article provides the possibility for the implementation of the system, and analyzes in detail the business requirements, performance requirements and functional modules of the system. In addition, it briefly introduces the requirements for the key technical means used. The system designed in this paper can effectively process a large amount of data and improve the operation efficiency of the system by separating the front-end management and back-end management. More importantly, the collaborative office platform designed by this system increases the sharing degree of information to the maximum, which can effectively reduce useless work, maximize work efficiency, and stimulate the enthusiasm of workers. At the same time, the operating costs of the system are reduced. It realizes the integration of information processing of data and efficient management of data.