Reviewerly/exHarmony huggingface dataset:
Design Empowerment: Participatory Design Towards Social Sustainability

Sustainability and sustainable design are more concerned with the physical environment than the social dimension. But design should give sufficient attention to social sustainability for balanced development. Although social sustainability’s definition is vague due to its excessively abundant connotation, power is always core. When it comes to shaping a fair and just distribution of power in society, empowerment is crucial to optimize power allocation. Through historical overview, conceptual analysis, and case studies, this paper argues that empowerment is the core criterion of participatory design because participatory design changes the power relationship among participants profoundly, then summarizes the key elements to achieve empowerment. While participatory design contributes to social sustainability, it also illustrates the inevitable responsibility of design.
A Secure Multiparty Quantum Homomorphic Encryption Scheme

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private data. In this paper, a novel secure multiparty quantum homomorphic encryption scheme is proposed, which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest server. Firstly, each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and key. Secondly, with the help of the almost dishonest server, the non-maximally entangled states are pre-shared between the client and the server to correct errors in the homomorphic evaluation of T gates, so as to realize universal quantum circuit evaluation on encrypted data. Thirdly, from the perspective of the application scenario of secure multi-party computation, this work is based on the probabilistic quantum homomorphic encryption scheme, allowing multiple parties to delegate the server to perform the secure homomorphic evaluation. The operation and the permission to access the data performed by the client and the server are clearly pointed out. Finally, a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.
Research on Phonetic Indexing Interface in Xinhua Dictionary Based on Differences of English and Chinese Letters

Xinhua Dictionary is an indispensable reference book in Chinese primary school teaching. However, through observation, it is found that due to the different pronunciations of English and Chinese letters, elementary school students who are beginners in language use the order of English capital letters as the phonetic index order. When using a Chinese character dictionary, phonetic confusion is easy to occur, which affects the retrieval efficiency. Based on the above questions, this paper combines the literature of linguistics and psychology to conduct an in-depth analysis around people, products and usage scenarios. A comparative study of letters and pronunciations that are easily confused between Chinese and English was conducted to explore the regularity of phonetic confusion. Experiment design was carried out on the preliminary summary of confusing phonetic sounds, to evaluate the prevalence of phonetic confusion problems, and to obtain pronunciations with higher confusion frequency. Integrate design elements and propose reasonable design solutions. At the same time, the redesign and usability test of the dictionary sequence retrieval interface are carried out. It is hoped that the research will shed light on the design improvement of user search interfaces in dictionaries and other reference books.
The Bonsai Hypothesis: An Efficient Network Pruning Technique

Machine learning technology has made it possible to solve a variety of previously unfeasible problems. Accordingly, the size of network models has been increasing. Thus, research on model compression by network pruning has been conducted. Network pruning is usually performed on already-trained network models. However, it is often difficult to remove a large number of weights from a network while maintaining accuracy. We suppose that the reason for this is that well-trained networks have many weight parameters with complicated correlations among them, and such parameters make pruning difficult. Based on this supposition, in this paper, we state the bonsai hypothesis: pruning can be more effective when starting from untrained models than already trained models. To support the hypothesis, we present a simple and efficient channel pruning algorithm. We performed pruning of untrained and trained models using the proposed algorithm for VGG16 on CIFAR-10 and CIFAR-100. As a result, we found that the untrained models tend to reduce more channels than the already-trained models.
Focus Estimation Using Associative Information to Support Understanding of Art

Research on text reading comprehension support has been conducted mainly through text leveling. Text simplification, for example, is a process of simplifying sentences or rephrasing difficult words into simple ones. However, there are some sentences for which simplification does not support reading comprehension. These include technical texts and texts that explain difficult concepts such as art and design. In this study, we visualize the points of interest that change throughout such texts. By doing so, the meaning of the original text is not lost, and the reader is given the opportunity to learn. Our approach uses time series, association, and word rarity to calculate a weighted average. We treat sentences as period-separated time-series information, and increase the weight of words in sentences that are newly occurring. We also extract the associative distance from an associative concept dictionary and treat the reciprocal as a weight. The rarity of a word is defined as the number of times it appears in Japanese Wikipedia. The higher the number of occurrences, the less rare the word is. Our experiments suggest that visualizing the points of interest in a text can support reading comprehension. In addition, similar concepts that do not appear in the text are output as points of interest, suggesting that they can support learning.
Research on data outlier detection method based on sample parameter selection LOF

The LOF data anomaly detection method has some defects, such as the value of k has great influence on the accuracy of detection results, and the selection of k value usually adopts trial method, which consumes a lot of calculation time. Therefore, this paper proposes an anomaly detection method for LOF data based on sample parameter selection, Tagged according to the sample data set point of normal and abnormal point, the adaptive selection of k value and outlier detection, so as to improve the accuracy of data outlier detection and calculation speed, and through the example of meteorological data outliers detection showed that LOF abnormal data points based on sample parameter selection method in the detection accuracy and reliability are improved significantly.
Automatic Semi-quantitative Histological Assessment of Tissue Traits Using a Smart Web Application

A smart web application suitable for classifying goblet cell hyperplasia and level of mucus production in stained lung tissues from mice with experimentally induced allergic asthma. Multiple trainer-model approaches are investigated and proposed in this manuscript, based on machine learning techniques, which provide a technological evolution in the analysis of traits of biomedical imaging. Several schemes, which consist of pre-trained image classifiers on ImageNet, are analyzed and compared each other. Lung tissue images of mice with allergic asthma, depicting mucus-containing periodic acid-Schiff (PAS) positive bronchial cells, are fed as input datasets. The performance of each model is evaluated, based on a variety of metrics: accuracy, recall, precision, cross entropy, f1-score, confusion matrix. Such a web tool could contribute to biomedical research by providing an automated standardized way to determine phenotypic severity of histological traits based on a semi-quantitative scoring scale.
SAFA: A System for Assisting Fair Assessments in Team Collaboration

In teamwork, assessments are applied to examine individuals’ contributions and team performance, helping them conduct self-reflection. Nowadays, leader assessments are commonly used in teams, but they are influenced by subjective factors of leaders. Fair assessments enhance team members’ enthusiasm for work and their belongings to the team. In this paper, we proposed an approach integrating self-assessments, peer assessments, and cross-team assessments to assist leaders in making fair and comprehensive assessments. We developed a prototype system, SAFA. To test the system's effectiveness, we conducted an experiment with 32 participants. The participants were divided into eight teams, with one leader and three members each. We gave each experiment team the same projects and asked team leaders and members to assess their jobs using our system. The analysis showed that the fairness of the assessments has been proved and recognized by team members. The system can be used in project-driven teams in colleges or companies to build an equal working environment and promote team performance.
The Development Trend of Examination-oriented Education and Quality Education in China: Learning from British and America Educational System

The purpose of this study was to research the differences and connection between examination-oriented education and quality education which are two significant educational systems nowadays. Based on the concrete phenomenon in China, British and United States, offering some proposals on Chinese educational system, and combine advanced educational concepts and guaranteed accountability system in British and US to lead a new way for Chinese quality education. Psychological method is also a valid method for Chinese high schools so that the students can feel the advantages of quality education.
Video Coding using Learned Latent GAN Compression

We propose in this paper a new paradigm for facial video compression. We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intra and inter compression. Each frame is inverted in the latent space of StyleGAN, from which the optimal compression is learned. To do so, a diffeomorphic latent representation is learned using a normalizing flows model, where an entropy model can be optimized for image coding. In addition, we propose a new perceptual loss that is more efficient than other counterparts. Finally, an entropy model for video inter coding with residual is also learned in the previously constructed latent representation. Our method (SGANC) is simple, faster to train, and achieves better results for image and video coding compared to state-of-the-art codecs such as VTM, AV1, and recent deep learning techniques. In particular, it drastically minimizes perceptual distortion at low bit rates.
AI as an explanation agent and user-centered explanation interfaces for trust in AI-based systems

People think as computers do, and computers have become humanized. It has been a long time since the border between human and computer collapsed, and the interface as a communication channel between them has been diversified and its definitions have grown more complex. In this vein, it is necessary to pay attention to “Explainable Artificial Intelligence (XAI),” which can be referred to as the machine learning technology which constructs a more explainable model while maintaining high performance. Explainable, provable, and transparent AI is very important in establishing trust for computing technologies. In user-centered standpoints, XAI has very significant implications for interdisciplinary AI, as the discussion on the human–AI relationship and interaction deepens. The design of an explanation interface is relevant for effectively providing AI results in user-centered perspectives. It is very important to provide and emphasize information by considering the information selection, assignment, and forms. Explanation ways can be designed and applied according to the properties of AI-based systems.
Sign Language Hand Glove

The impact of hearing and speech can cause people to become friends or feel lonely, adversely affecting their social and professional lives. Figuring out the meaning of a sign is not an easy task. Sign language is a well-structured gesture code where each gesture has a meaning assigned to it. Sign language is the only means of communication for the deaf. With the advancement of science and technology, many kinds of research and techniques have been born that not only reduce difficulties for deaf and dumb people but also can be deployed in different fields of work. Sign language is a language that instead of voice or sound patterns uses human communication and body language to convey the meaning. This involves mostly the mixing of shapes, orientation, and movement of the hands. Sign language is not only used by the deaf but also by those who can hear but cannot physically speak to any other people. Sign language is the language used by deaf and mute people that uses gestures instead of sound to convey or to express fluidly a speaker's thoughts. A gesture in sign language is a particular movement of the hands with a specific symbol made out of them. The main objective of this paper is to present a system capable of converting sign language gestures to auditory speech efficiently. A glove can help people who cannot speak. In glove sign language, we'll convert the sign into sentences so that we can easily communicate with this person. Several languages are spoken around the world. So this system aims to provide voice output in different regional languages
Covid-19, Digital Technologies, and Protest Movements

The pandemicpandemic lockdownslockdown(s)” enforcedDigital technologies (DTs) a momentprotest movements of isolation or rigorous social distancingsocial distancing but, as the previous chapters have shown, events also encouraged people to look outward.
Explaining Deep Neural Networks in medical imaging context

Deep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recognition. However, the decision-making processes of these models are generally not interpretable to users. In various domains, such as healthcare, finance, or law, it is critical to know the reasons behind a decision made by an artificial intelligence system. Therefore, several directions for explaining neural models have recently been explored.
Essay Rating System Using Machine Learning

Essay Rating is the process of assigning grades to the essays written for professional purposes. Automating this process implies the use of specified machine learning algorithms over the standard manual methods to evaluate the essays through Natural Language Processing. Essays can be termed as a large set of textual entities. Grading these essays involves distributing them into a fewer number of distinct categories based on certain constraints and standards. These categories are then used to assign the possible grades. Because of numerous factors like cost, time, biasness, and delay in results, there is a need to switch from manually checking the essays to automated grading. It is a very tedious task to evaluate a bulk of essays while probing every kind of error. Henceforth, this model of Automated Essay Grading System models seems quite assuring. It takes text input from the user and generates grades conforming to the pre-defined standards. After evaluating its useful attributes, an output is obtained in the form of a grade for the given essay. This project comprises of grammar-based Checking (Checking Unique Word count, Sentence Count, Grammatical Mistakes, Spelling Errors) and Context Based Checking (Application of NLP to categorize the essays).
Identification of Vehicle Dynamics Parameters Using Simulation-based Inference

Identifying tire and vehicle parameters is an essential step in designing control and planning algorithms for autonomous vehicles. This paper proposes a new method: Simulation-Based Inference (SBI), a modern interpretation of Approximate Bayesian Computation methods (ABC) for parameter identification. The simulation-based inference is an emerging method in the machine learning literature and has proven to yield accurate results for many parameter sets in complex problems. We demonstrate in this paper that it can handle the identification of highly nonlinear vehicle dynamics parameters and gives accurate estimates of the parameters for the governing equations.
Financial Application based on Virtual Reality

With the increasing influence of virtual reality and more and more devices supported, virtual reality has been widely applied in different areas. In this paper, we give a comprehensive introduction of the development of virtual reality as well as the prototype financial applications based on virtual reality. We also discuss the challenges when deploying these applications in the future.
Reconstruction of Capital Market Ecology Based on Blockchain Technology

In recent years, the trust relationship of financial reports in the capital market has been severely challenged. The reliability, relevance and timeliness of the accounting information provided by the company's financial reports are obviously insufficient, and the efficiency of capital market accounting supervision has been greatly reduced. In the digital economy era, traditional double-entry accounting and financial accounting reports are no longer sufficient to serve the new economy, and it is imperative to reconstruct the accounting information disclosure system. This article attempts to implement the nine-character policy of capital market reform (system building, nonintervention, zero tolerance). Firstly, it is necessary to reconstruct the accounting information disclosure system based on blockchain technology; and under the background of the system with less government intervention, various entities automatically realize the maintenance of distributed ledger and the disclosure of financial accounting information, based on the principle of self-interest. Finally, in addition to legal restrictions, it is more difficult for untrustworthy persons to participate in the maintenance of distributed ledger, in order to increase their economic costs and achieve zero tolerance for them.
User Context Semantic Acquisition System Based on Multidimensional Tags in O2O Recommendation

Acquiring context semantics effectively is the premise and key of building context model. However, the existing context acquisition mainly relies on physical sensing devices, and the obtained context information is low-level and incomplete, which is difficult to support complex context semantic modeling and computing. Aiming at the needs of information recommendation in O2O applications, a user context semantic acquisition system based on multidimensional tags is proposed. Mobile users are guided to actively share their current situation through social multidimensional tags, so that rich and high-level user context information is obtain by the system. The implementation process of this system is as follows: firstly, the user's perception of the current situation is obtained through the multidimensional context tag system; secondly, the context tag is transformed into a formal context concept; finally, according to the predefined context ontology schema, an instantiated user context semantic model is established by using the context concept.
Content-aware Topic Recognition of Inquiry Letter

Facing the problem of match between the label and the text content of the current inquiry letter, this paper proposes a content-aware topic recognition of inquiry letter model. Firstly, the text data of the inquiry letter of Shanghai Stock Exchange are collected, and the text content features are extracted by using word vectorization (TF-IDF). The t-SNE dimension reduction algorithm is used to reduce the text vector data to 2-dimensional space, and then the K-means algorithm is used to cluster the inquiry feature data, and the inquiry letter are labeled according to the clustering results. Finally, the deep forest classification algorithm is used to train and classify the inquiry letter data. The application results show that the recall, precision and F-measure of the content-aware topic recognition based on the inquiry letter contents are improved compared with the traditional methods, which indicates that the proposed method based on the inquiry letter content is effective.
Research on the Quality of Regional Financial Development Based on Principal Component Analysis

Financial development is an important impetus for regional financial development. The more developed the financial industry is, the stronger the financial activities will penetrate into the regional economy, and the faster the regional economy will grow. During the forty years of China's reform and opening up, the development of the financial industry has played an important role in China's opening up and economic growth. This paper establishes a multi-index comprehensive evaluation system of regional financial development level, uses principal component analysis to empirically analyze the changes of financial development level of 31 provinces, municipalities and autonomous regions in China from 2014 to 2018, and obtains the changes of inter-provincial financial development score and ranking. Finally, the paper analyzes the reasons for the changes in the financial development level of typical regions and provides suggestions for the structural optimization and coordinated development of China's regional finance.
Accelerate the innovative development of blockchain technology and industry

With the continuous improvement of China's macropolicies in the field of blockchain and the increasing industrial investment, the research and application of blockchain technology are changing with each passing day and develop vigorously, and the integrated application of blockchain technology plays an important role in the new technological innovation and industrial reform. The characteristics of blockchain, such as decentralization, tamper proof and traceability, enable it to play a vital role in many areas, such as finance, intelligent manufacturing, Internet of Things, supplychain management, digital asset trading, social governance and the people's livelihood services. Accelerating the innovative development of blockchain technology and industry is conducive to expanding the application fields and development prospects of blockchain technology, helping China to achieve a leading edge in global technological competition and promoting the high-quality development of China's economy and society.
Discussion on the Application of Computer in Psychological Research

With the continuous development of the times, Chinese computer technology has been widely used in various fields of society and has made outstanding achievements, which has laid a solid foundation for the rapid development of various fields of society. The emergence of computer technology can replace brain activity, which effectively collects and analyzes massive amounts of data and information. It has greatly promoted the development of society towards intelligence and automation. Psychological research has always been a key issue of concern in China, and the emergence of computers has opened up a new path and direction for research activities in the field of psychology. Computer has become an indispensable key factor in the field of psychology. Based on this, this article is based on the important promotion of computers, expounds in detail the important application methods of computers in modern psychology research, and hopes to give relevant researchers different thinking directions.