Reviewerly/exHarmony huggingface dataset:
Cross-Domain Person Re-Identification Based on Feature Fusion

Person re-identification (ReID) is one of the commonly used criminal investigation methods in reconnaissance. Although the current ReID has achieved robust results on single domains, the focus of researches has shifted to cross-domain in recent years, which is caused by domain bias between different datasets. Generative Adversarial Networks (GAN) is used to realize the image style transfer of different datasets to alleviate the effect of cross-domain. However, the existing GAN-based models ignore complete expressions and occlusion of pedestrian characteristics, resulting in low accuracy in feature extraction. To address these issues, we introduce a cross domain model based on feature fusion (FFGAN) to fuse global, local and semantic features to extract more delicate pedestrian features. Before extracting pedestrian features, we preprocess feature maps with a feature erasure block to solve an occlusion issue. Finally, FFGAN enables a more complete visual description of pedestrian characteristics, thereby improving the accuracy of FFGAN in identifying pedestrians. Experimental results show that the effect of FFGAN is significantly improved compared with some advanced cross-domain ReID algorithms.
Epistemic Awareness as the Main Concept

Having defined the notion of ‘awareness’Epistemic awareness as realizing one’s own informationInformation, this chapter aims to explore the applications of this concept to both EpistemologyEpistemology and Epistemic LogicEpistemic Logic (EL). Distinguishing between awareness ofAwareness of and awareness thatAwareness that will result in two types of awarenessAwareness, that classify the types of informationInformation the agentAgent (epistemic) bears in mind. Based on this distinction a new interpretation of the concept of awarenessAwareness will be provided and a different understanding of the notion of ‘explicit knowledge’Explicit Knowledge will be defined, given rise to the concept of Explicit Aware KnowledgeExplicit Aware Knowledge (EAK), core component of this research.
Research on Online Practice Platform of University Accounting Major Course in Big Data Context

With the development of information technology, the application of big data has been popularized to all sectors of social development. As a highly applied and practical discipline, accounting major courses in colleges and universities undertake the responsibility of training accounting talents for the society. The construction and application of the network practice platform of big data can enrich accounting teaching resources, improve the interest and innovation of the course, increase the school-enterprise alliance, and promote the employment of students. This paper explores the construction and application of college accounting network practice platform under the background of big data, proposes a new model of accounting practice teaching in colleges and universities, and provides new possibilities for the reform and innovation of accounting in colleges and universities.
Robot environment representation based on Quadtree organization of Fuzzy Signatures

This paper presents a novel approach to mobile robot environment representation to hold information on detected obstacles. The method is inspired by fuzzy signature-based formalism and is based on classical quadtrees as a data indexing structure. Each detected feature point is evaluated by a fuzzy-ruleset defining the presumed significance of each detected object. Feature points and their fuzzy-mapping are indexed in a classical quadtree-based fashion. During the reconstruction of the environment representation, inference is done by the traversal on the constructed tree using accumulated fuzzy-ruleset. Our goal is to use this representation format for further robotic tasks such as obstacle avoidance in a distributed computational environment.
Formation of health culture in the context of digitalization of education

The article studies the issues related to the use of digital technologies in organization of the educational process of distance learning and testing the quality of the digested educational material in the disciplines of Physical Culture and Sports. The advantages and disadvantages of distance learning organization are revealed. The problems of increasing students’ involvement into arranging independent wellness classes are analyzed. The developed electronic information and educational system is an integral part of the program for health culture formation in the context of digitalization of education. The components of the module allow you to distantly teach and test the quality of the digested educational material, as well as to ensure the control of students’ fitness shape. The use of modern tools of digital technologies helps to increase students’ interest in their classes.
Do You Care What Robots Think? – Embarrassing Encounters with Service Robots

Based on 20 personal in-depth interviews, we developed the Service Robot Embarrassment Perception Framework (SR-EPF). We identify different sources for embarrassment in the service encounter. Furthermore, the framework discusses the role of anonymity, discretion, privacy, feelings and mind and interpersonal for different types of AI and which coping customers apply reduce the feeling of embarrassment.
Big Data Brain Image Data Repair and Diagnosis Technology Based on Deep Learning

Abstract In this paper, we propose an automatic tumor detection algorithm in MRI brain images based on significance modeling based on directional features.Firstly, the MRI brain image is preprocessed to remove the interference of the skull region in the image.Then, directional-feature-based saliency detection is used to increase the contrast of the lesion area to achieve more accurate extraction of the tumor image area.A large number of experiments have been carried out on the brain image data sets, and compared with the mainstream automatic tumor detection methods, the effectiveness of the proposed algorithm has been proved.
Determinant Factors of Sellers’ Satisfaction in Urban “Marchés”

Temporal direct-selling shops in urban areas called “marchés” are drawing attention as a connecting place between urban consumers and producers in rural areas. However, little has been studied on sellers’ satisfaction, which is a crucial factor for the sustainability of the activity. Thus, this study investigated sellers’ satisfaction and identified the factors affecting their satisfaction by focusing on marchés in Tokyo. The data were collected through a questionnaire survey of sellers in “Taiyou no marché” held once a month in Kachidoki; 43 sellers responded. A binary logit model was employed to identify the factors that influence the sellers’ satisfaction. The results revealed that those who have regular customers, engagement with multiple persons, and higher academic background had higher satisfaction than those do not. Thus, marché organizers should consider these points for the sustainable management of marchés.
Consistent 3D Human Shape from Repeatable Action

We introduce a novel method for reconstructing the 3D human body from a video of a person in action. Our method recovers a single clothed body model that can explain all frames in the input. Our method builds on two key ideas: exploit the repeatability of human action and use the human body for camera calibration and anchoring. The input is a set of image sequences captured with a single camera at different viewpoints but of different instances of a repeatable action (e.g., batting). Detected 2D joints are used to calibrate the videos in space and time. The sparse viewpoints of the input videos are significantly increased by bone-anchored transformations into rest-pose. These virtually expanded calibrated camera views let us reconstruct surface points and free-form deform a mesh model to extract the frame-consistent personalized clothed body surface. In other words, we show how a casually taken video sequence can be converted into a calibrated dense multiview image set from which the 3D clothed body surface can be geometrically measured. We introduce two new datasets to validate the effectiveness of our method quantitatively and qualitatively and demonstrate free-viewpoint video playback.
Analysis of digital training platforms of universities: domestic and foreign experience

The purpose of the study . Assessment of the impact the COVID-19 pandemic on the quality of education based on a study of the implementation of e-learning technologies using digital platforms in both foreign and Kazakhstani universities. Methodology . Problems related to online learning and possible solutions were identified based on a retrospective and comparative analysis of foreign and Kazakhstan scientific research. Scientific literature search was carried out in large databases: Researchgate, Springer, ScienceDirect. During the search, 481 scientific articles were found that matched the entered search parameters. Given the inclusion and exclusion criteria, 465 papers were screened out, and the remaining 16 were sent for analysis. Originality / value of the research . Along with identifying barriers to the introduction of e-learning, the main factors during the COVID-19 pandemic were identified that influenced the effectiveness of the educational process. The study revealed that digital platforms create sustainable competitive advantages and are the main tool for modernizing the learning process. Findings . As a result of the study, the most significant factors related to technological knowledge management, as well as the level of readiness of universities, teachers and students to implement e-learning, were identified.
PHARN: A Probabilistic Graph Model Based Hierarchical Affective Reasoning Network for Conversational Sentiment Analysis

Conversational Sentiment analysis (CSA) is an import task in the text sentiment analysis filed, which aims to predict the sentiment sequence corresponding to the sentence sequence in the conversation. Recently, many researchers have proposed deep learning based models to learn both speaker-context and sentence-context information interactions. However, the existing models generally learn the sentiment-context interactions by simple non-linear function, which does not focus on affective reasoning and is lack of interpretability. To address those issues, we introduce probabilistic graph model (PGM). Inspired by the interaction matrix of the Coupled HMM, we propose a novel interaction matrix based improvement for output gating mechanism, which contains local 2-order sentiment-context interaction. Then we propose the interaction matrix Based Long Short-Term Memory Network (ConvLSTM), which captures local sentiment-context interactions. Next, inspired by the reasoning of PGM, we develop a novel PGM layer to model global high-order sentiment-context interaction for sentiment label sequence. Finally, we propose PGM Based Hierarchical Affective Reasoning Network (PHARN), which can learn both local and global sentiment-context interaction. Experiments on MELD and IEMOCAP datasets demonstrate the effectiveness of our model, which achieves excellent results on multiple benchmarks.
Efficacy of Indian Government Welfare Schemes Using Aspect-Based Sentimental Analysis

One of the simplest methods to understand people's thoughts using images or text is commonly given as sentiment analysis. Sentiment analysis is used mostly in products advertisement and promotion depends on the user’s opinion. The process is based on the aspect-based sentiment analysis and it is used to understand and find out what someone is speaking about, and likeness and dislikeness. One of the real-world models of the perfect realm of this subject is the huge number of available Indian welfare plans like Swachh Bharat Abhiyan and Jan Dhan Yojna. In this paper, labeled data is used on the basis of polarity. Tweets are preprocessed and unigram features are then extracted. In the initial steps, tokenization process, stop word removal process, and stemming process are performed as preprocessing to remove duplicate data. The unigram features and labels trained by support vector machine (SVM), K-nearest neighbor (KNN), and a combination of SVM, KNN, and random forest as a proposed model are used in the presented work. Implementation of experimental proposed approach demonstrates that better results in accuracy and precision than SVM and KNN.
City manager’s resources and optimization of the online education system

This paper attempts to assess the resources of the City Manager to optimize the system of online education. The need to develop a Russian platform for the implementation of online education is justified by highlighting the main problems: technical, organizational, and pedagogical. It has been established that the problems described above are related primarily to the platforms on which the online education process is implemented. The authors evaluate the functionality of popular services used by Russian universities and conclude that their tools do not meet the goals of online education and do not allow realizing its potential. A pilot concept of an online-education platform, which is supposed to be created within the framework of public-private partnership, is presented. It is recommended to realize large projects, which require significant investments, as a part of cooperation of authorities, business community and science, and the emphasis is made on this aspect in the process of project concept creation. The main forms of interaction between the actors involved and the areas of their responsibility are considered, recommendations for the selection of the most appropriate procedure for the development of the platform for online education that meets the needs of students and teachers are given. It is concluded that such an initiative will help to overcome the main obstacles facing online education and bring it to a new level by combining the efforts of government and business.
An Event-Based Framework for Virtual Libraries

Virtual libraries have become essential parts of remote learning and working. This paper proposes an event-based framework, called eLIB, for virtual libraries with a focus on the connection between the reality and its virtualization where book placement in the virtual world directly reflects its recent location in the real world. The proposed framework has a presentation layer for users to browse through floors, bookshelves and books. A simple 2D and event-based mechanism uses real images of bookshelves and books instead of creating 3D virtual and augmented reality. This approach can provide adequate virtual user experience without overloading the system and networks compared with typical virtual and augmented reality systems. Each click by users generates an event which is handled by a corresponding Event Handler. The services in eLIB are provided as responses to various events or chains of events. The framework also has a unit to match a book spine image to its content description in the library catalog.
Explore the Relationship between Authentication Factor Multiplicity and Composite Vulnerability Exposures

With the continued expansion and adoption of multi-factor authentication technologies within organizations across industries, there exists a need to determine the relationship between composite vulnerability exposures and the multiplicity of authentication factors. To meet this need, we propose a power curve formula for the relationship between these variables, demonstrating the wellness of fit to generalized data sets. We provide an extension to the Common Vulnerability Scoring System (CVSS) v3 calculator, allowing the characterization of combined Common Vulnerability Exposure (CVE) in an objective and repeatable means, demonstrating the capabilities of that extent. This paper explores the potential for future work derived from the proposed mathematical formula representation of the relationship between authentication factor multiplicity and composite vulnerability scores.
Application Analysis of Automated Production Technology Based on Industrial Robots

Abstract With the all-round development of China’s economy and society, the equipment manufacturing industry has shifted from Chinese manufacturing to creation in the past. The domestic production rate of many advanced manufacturing equipment is getting higher and higher, which has made a huge contribution to economic and social development. In recent years, with the rise of industrial robot technology, more and more industrial robots have replaced traditional manual operations, and they have created greater benefits for enterprises with their precision, speed, technology, and efficiency.
IPS: Unified Profile Management for Ubiquitous Online Recommendations

ByteDance offers several massively popular products such as TikTok, Jinri Toutiao and Douyin for creating, sharing and discovering a variety of content, in which recommendation plays an indispensable role for helping billions of users to interact with highly personalized content. The personalized experience in products largely comes from the ability of sophisticated machine learning models to make accurate predictions based on users' interests and one key component in such systems is the user profile service.In this paper, we introduce Instance Profile Service (IPS), a large scale distributed system for managing unstructured profile data as well as serving various feature computations at ByteDance. Different products leverage IPS in many different ways and place various demands on the system, in terms of complex computation logic and latency requirements. One major challenge in the design of a large scale user profile system is how to strike the right balance among efficiency, scalability, reliability and versatility. With deliberated choices made on its design and implementation, we demonstrate IPS can provide a simple yet flexible solution to all these products while meeting the targeted high availability and performance goals. At ByteDance, IPS has successfully replaced many legacy profile systems and runs on thousands of machines. One of our largest production instances can process a hundred million feature queries and tens of millions writes per second.
Single Point Incremental Fourier Transform on 2D Data Streams

In radio astronomy, antennas monitor portions of the sky to collect radio signals. The antennas produce data streams that are of high volume and velocity (~2.5 GB/s) and the inverse Fourier transform is used to convert the collected signals into sky images that astrophysicists use to conduct their research. Applying the inverse Fourier transform in a streaming setting, however, is not ideal since its computational complexity is quadratic in the size of the image.In this article, we propose the Single Point Incremental Fourier Transform (SPIFT), a novel incremental algorithm to produce sequences of sky images. SPIFT computes the Fourier transform for a new signal in a linear number of complex multiplications by exploiting twiddle factors, multiplicative constant coefficients. We prove that twiddle factors are periodic and show how circular shifts can be exploited to reuse multiplication results. The cost of the additive operations can be curbed by exploiting the embarrassingly parallel nature of the additions, which modern big data streaming frameworks can leverage to compute slices of the image in parallel. Our experiments suggest that SPIFT can efficiently generate sequences of sky images: it computes the complex multiplications 4 to 12x faster than the Discrete Fourier Transform, and its parallelisation of the additive operations shows linear speedup.
First Experiment to Human Behavior Modeling

A situation has been modeled from its components, activities of high semantic level, and these through events of actions of low semantic level. The following describes the modeling of more complex situations to model and is that in this type of modeling, the participation of the fotograma is observed with greater clarity. The proposal is to develop a tool that allows modeling human behavior. The results that may be possible to create a visual tool that can obtain valid conclusions of human behavior.
A Concrete Way to Develop Clinical Research in a Fair Way to the Users/Patients Using Blockchain Technology

Blockchain is being explored as a potential solution to many problems in areas other than the one created initially: cryptocurrency. Blockchain technology allows the authenticity of data, security in transactions, and privacy without the need for a third party. For that main reason, one of the growing interests concerns its application in healthcare, namely in clinical research. Multiple pain points of clinical research might benefit from the implementation of blockchain technology. This chapter shows some examples in which this technology is already implemented, identifying the advantages of its use. One of those advantages is clinical research, with the possibility of the patients managing their own clinical data and being properly rewarded for that. Research about clinical data monetization for patients is currently limited, and this chapter also proposes a hypothetical scenario of health data monetization workflow.
Information Design and the Hermeneutic Process of Knowledge

In this article, we start from a literature review on the concept of Information Design and analyze how and why the hermeneutic process of knowledge construction can be applied to visual information systems. We present several typological approaches associated with Information Design, such as Diagrammatic Design, Schematic Design, Infographic Design, and Wayfinding Design, sometimes with common or different graphic characteristics, the greater or lesser complexity associated with it and how the elements that constitute the language and visual grammar contribute to the organization, systematization, and understanding of the information produced in this context.
A CNN/FPGA Co-Exploration Method Based on Hourglass Network

Field Programming Gate Array (FPGA) has become a popular choice for neural network inference for its great potential of parallel computing and abundant on-chip buffer resource. Both software and hardware researchers are trying to explore the design space to obtain the best network model and the best accelerator design. Different from the existing sequential exploration method, we propose a CNN/FPGA co-exploration method to search for the best network model and hardware design simultaneously. We applied our method to Hourglass Network, a widely used model for pose estimation, and the top 5 experimental results show that our method can achieve an average of 22.44% faster in computation, 16.48% faster in memory access, and 17.81% faster in total, with a minor accuracy loss of 0.75%. The whole search and training process only cost 75 GPU hours.
Poetry Machines: Eliciting Designs for Interactive Writing Tools from Poets

Improvements in natural language processing and generation have made possible new and powerful creativity support tools for creative writers. However, it remains unclear how professional writers themselves might want to integrate technology into their existing writing practices. In this work we ran an elicitation study, asking 14 professional poets to consider how they would make use of computation in the context of a custom, interactive writing interface or "Poetry Machine." We found that the poets desired a wide range of functions, from presenting auditory responses to deleting random words. We also found that many poets did not simply report what their ideal interface would do but rather contextualized their designs by describing why they are artistically meaningful, sometimes with respect to specific literary influences and traditions. We present an initial analysis of the elicitation study and observe some differences between the Poetry Machine designs and existing creativity support tools. This study lays the groundwork for a second phase where we will build a selection of the machines and study how the poets use them over time.