Reviewerly/exHarmony huggingface dataset:
Digital Transformation of Research Communications: Academic Journal as an Electronic

The article discusses characteristics and main stages of production of an electronic academic journal as one of the most important elements of contemporary research communication. The authors examine the advantages of electronic periodicals from the point of view of the development of scientific communications, as well as the role of an electronic scholarly journal in the life of academic community which is forming around digital platforms (primarily around research social networks). The conclusion is that most of the issues with the electronic format of a peer-reviewed publication have been resolved over the past decades,and remaining difficulties are most often associated with the degree of qualification of authors and editors which is needed to prepare well-structured metadata and full-text articles.
Fake Product Detection using Image Processing

Nowadays, the entire global has grown up every fasted similarly the technology also grown up. So, my project is basically fake product detection. The people can know how the product are fake or original in all the counterfeit product. In my project I ,have mainly taken the product is Bisleri bottles using the image processing with python language is used to identify the original or fake product using the key feature extraction with using of open cv module we can identify the product. In my project targeted on distance and key points of train image and inputted image. Computing those images we can easily find out the original and fake product.
Attention-based multi-modal fusion sarcasm detection

Sarcasm is a way to express the thoughts of a person. The intended meaning of the ideas expressed through sarcasm is often the opposite of the apparent meaning. Previous work on sarcasm detection mainly focused on the text. But nowadays most information is multi-modal, including text and images. Therefore, the task of targeting multi-modal sarcasm detection is becoming an increasingly hot research topic. In order to better detect the accurate meaning of multi-modal sarcasm information, this paper proposed a multi-modal fusion sarcasm detection model based on the attention mechanism, which introduced Vision Transformer (ViT) to extract image features and designed a Double-Layer Bi-Directional Gated Recurrent Unit (D-BiGRU) to extract text features. The features of the two modalities are fused into one feature vector and predicted after attention enhancement. The model presented in this paper gained significant experimental results on the baseline datasets, which are 0.71% and 0.38% higher than that of the best baseline model proposed on F1-score and accuracy respectively.
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion

This paper proposes a new "decompose-and-edit" paradigm for the text-based speech insertion task that facilitates arbitrary-length speech insertion and even full sentence generation. In the proposed paradigm, global and local factors in speech are explicitly decomposed and separately manipulated to achieve high speaker similarity and continuous prosody. Specifically, we proposed to represent the global factors by multiple tokens, which are extracted by cross-attention operation and then injected back by link-attention operation. Due to the rich representation of global factors, we manage to achieve high speaker similarity in a zero-shot manner. In addition, we introduce a prosody smoothing task to make the local prosody factor context-aware and therefore achieve satisfactory prosody continuity. We further achieve high voice quality with an adversarial training stage. In the subjective test, our method achieves state-of-the-art performance in both naturalness and similarity. Audio samples can be found at https://ydcustc.github.io/retrieverTTS-demo/.
Anomaly Matters: An Anomaly-Oriented Model for Medical Visual Question Answering

Medical images contain various abnormal regions, most of which are closely related to the lesions or diseases. The abnormality or lesion is one of the major concerns during clinical practice and therefore becomes the key in answering questions about medical images. However, the recent efforts still focus on constructing a generic Visual Question Answering framework for medical-domain tasks, which is not adequate for practical medical requirements and applications. In this paper, we present two novel medical-specific modules named multiplication anomaly sensitive module and residual anomaly sensitive module to utilize weakly supervised anomaly localization information in medical Visual Question Answering. Firstly, the proposed multiplication anomaly sensitive module designed for anomaly-related questions can mask the feature of the whole image according to the anomaly location map. Secondly, the residual anomaly sensitive module could learn a flexible anomaly feature while preserving the information of the original questioned image, which is more helpful in answering anomaly-unrelated questions. Thirdly, the transformer decoder and multi-task learning strategy are combined to further enhance the question-reasoning ability and the model generalization performance. Finally, qualitative and quantitative experiments on a variety of medical datasets exhibit the superiority of the proposed approaches compared to the state-of-the-art methods.
PAINT: Photo-realistic Fashion Design Synthesis

In this article, we investigate a new problem of generating a variety of multi-view fashion designs conditioned on a human pose and texture examples of arbitrary sizes, which can replace the repetitive and low-level design work for fashion designers. To solve this challenging multi-modal image translation problem, we propose a novel Photo-reAlistic fashIon desigN synThesis (PAINT) framework, which decomposes the framework into three manageable stages. In the first stage, we employ a Layout Generative Network (LGN) to transform an input human pose into a series of person semantic layouts. In the second stage, we propose a Texture Synthesis Network (TSN) to synthesize textures on all transformed semantic layouts. Specifically, we design a novel attentive texture transfer mechanism for precisely expanding texture patches to the irregular clothing regions of the target fashion designs. In the third stage, we leverage an Appearance Flow Network (AFN) to generate the fashion design images of other viewpoints from a single-view observation by learning 2D multi-scale appearance flow fields. Experimental results demonstrate that our method is capable of generating diverse photo-realistic multi-view fashion design images with fine-grained appearance details conditioned on the provided multiple inputs. The source code and trained models are available at https://github.com/gxl-groups/PAINT .
The Semantic Framework of Library Intelligent Question Answering System Based on Exploratory Search Behavior

Analyze consultation question characteristics, identify users’ exploratory search behavior stage and library element demand, and integrate interview skills into intelligent question answering system, which improve the accuracy of library intelligent question answering. Analyze virtual consulting archives data of the National Library of China from 2011 to 2020, label characteristic vocabulary in users’ consultation questions, use SPSS software to test correlation between research elements, and design semantic framework of library intelligence question answering system based on hypothesis test results. Through data analysis, this research concludes that there is a relationship between library elements orientation in user consultation questions and cognitive stage, and target resources discovery plays a crucial role in users’ cognitive stage. Therefore, applying natural language processing technology to analyze user consultation questions, extracting characteristic vocabulary related to library elements, users’ cognitive stage and target resources, so as to generate personalized intelligent consultation answers. Accordingly, design a semantic framework for intelligent question answering system based on user exploratory search behavior, which will improve the answering accuracy of intelligent question answering system.
Face Detection Based on Dynamic Min Size and Dense Connection

Since the face detection technology is widely used, improving the detection speed and accuracy in face detection tasks has become a key challenge. Therefore, this paper takes the MTCNN model as the research object and makes improvements, the purpose of which is to optimize the detection speed and accuracy simultaneously. A dynamic min size algorithm is proposed. According to the size of the input image, it dynamically controls the minimum size of the face to be recognized by the model and reduces the number of iterations of the image pyramid, increasing the number of detection frames per second by 4[Formula: see text]fps. Then, the standard convolution structure in the P-Net and R-Net models is replaced by the depth-wise separable convolution, which effectively reduces the number of parameters and computation of the model. Meanwhile, an O-Net model with a densely connected structure is also developed. Our experiments with well-known public datasets have demonstrated that the proposed network structure can improve the detection frame rate. By reusing the features of different levels of the image, the recall rate and the precision rate of the MTCNN model on the validation set are increased by 2.39% and 1.65%, respectively.
Social media, personal branding, and the hypoesthesia of communication corruption

The increasing use and misuse of social media in personal branding have prompted the recent study to examine it from the perspective of communication corruption, namely the act of reducing the audience’s right to receive messages completely and correctly according to facts. This study uses a phenomenological approach by interviewing in-depth key informants from academics who have personal branding experience through social media. We found some communication corruption in the dimensions of the message (such as in visual touch-ups and exaggerating facts), media (abusing media utilities and manipulating media features), context (constructing fake situations and settings to create desired impressions), and behavior (deliberately manipulating and deceiving). The informants admitted to doing this because it is normal and common for netizens in the social media world. We call this phenomenon hypoesthesia of communication corruption, namely the banality of the immorality in interpreting corrupt actions and events in communication. This study is the first in the issue of communication corruption in personal branding through social media and the first to present the concept of hypoesthesia of communication corruption based on empirical insights.
Transformation of Higher Education System Using Blockchain Technology

As cloud storage grows, companies are storing and handling vast volumes of data throughout this century. Large knowledge arrives from businesses, the Internet of Things (IoT), and unstructured channels. Nevertheless, the findings of an experiment are not relevant merely because large data and research methods are accessible. The idea that data offers valid information relies on the right data. Organizations who want to implement Blockchain in their organizations would then have to ensure that they have the appropriate big data and the highest standards because it can no longer be changed while on a Blockchain. Blockchain could be a catalyst for better data if done properly, leading to improved insights. The characteristics of blockchain extend to any sector and tackle questions regarding data quality and confidentiality, which hinder the usage and exchange of big data. Organization that wants to use big data and Blockchain will find new instruments that have been developed to support this, like BigchainDB built on MongoDB. The introduction 500of the features required for business growth, including scalability, database capability, and audit trails would allow Blockchain-based apps that follow organizational requirements simpler for companies to create. Finance is the first place to consider Blockchain applied to Big Data. All transactions in Bitcoin (BTC) are kept inside the Blockchain Network. The transaction data is not private though BTC provides a degree of anonymity. Blockchains have a lot of potential, especially when private data are involved. Blockchains are used in healthcare to help reduce the effects of cybercrime and data theft. Training of data attacks is sadly not that far behind financial and healthcare. In 2017, schooling witnessed the most data breach in all sectors, comprising 13% of all violations. Student data may seem worthless, even if it is a good commodity among cybercriminals since most small children and adolescents have limited financial knowledge. Because recording and processing information is simpler, Blockchain can be used to improve library and educational institution knowledge systems. Many institutions participate in the worldwide education market, and it becomes even more difficult to monitor the status and authenticity of the diplomas and use different approaches/procedures. Today higher education agencies are also offering grants to campuses in various cities and nations. Significant security concerns are therefore considered in securing data transactions, such as student profiles and certifications. The blockchain technology is a potential answer to this issue, as in many other fields.
Syrian Refugees' Acceptance and Use of Mobile Learning Tools During the Covid-19 Pandemic

Mobile learning, which is widely used in educational settings during the Covid-19 pandemic, will continue playing a critical role in learning environments in the future. Since the successful implementation of mobile learning in education is largely based on users’ acceptance of these technologies, it is essential to understand the factors affecting learners’ acceptance of mobile devices as learning tools. This study investigated Syrian adult refugees’ acceptance and use of mobile learning tools. The results revealed that Syrian adult refugees were positive about using mobile devices in learning Turkish as a second/foreign language, and there exists a concrete and significant correlation among all the constructs of the mobile learning tools acceptance like Perceived Ease of Use, Contribution to Foreign Language Learning, Negative Perceptions, and Voluntariness of Use. Factors affecting mobile learning acceptance was also investigated in the study, and the results indicated significant differences among the refugees regarding their characteristics such as age, gender, level of education. The results also revealed that while the refugees did better in the tests over time, mobile learning acceptance had no significant effect on foreign language achievement. Depending on these results, it can be suggested that mobile devices should be integrated into the education system as a component of the curriculum.
Smart Redundancy Schemes for ANNs Against Fault Attacks

Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%.
Continuing education as an important component of the modern education system

Introduction. Constant socio-economic and political transformations in the Russian Federation are reflected in all spheres of human society. Within the framework of universal competition, qualified labor based on modern scientific knowledge, concepts and achievements is the main resource and value. The relevance of this work lies in the fact that recently the designated problem has received increased attention, both from the state education system and from employers. The issue of developing a system of continuing professional education focused on training a specialist capable of ensuring progress in his industry becomes significant. The rapid development of the modern labor market requires a new type of the employee with versatile knowledge, excellent special training, active lifestyle, willingness to adapt to new situations, develop and improve their level. All this indicates that education should be continuous, focused on advanced training of his broad knowledge. Theoretical analysis. The article discusses theoretical approaches to the definition of continuing education, describes its current state, and also defines the role of this phenomenon in the life of a person of our time. Conclusions. In conclusion, the purpose of continuing education in modern conditions, its individualization and personification are presented.
Exploration of Educational Data Mining in Improving Teaching Quality

Over the past decade there has been a rapid growth in higher education system.A lot of new institutions have come up both from public and private sector offering variety of courses for under graduating and post graduating students.The rates of enrolments for higher education has also increased but not as much as the number of higher institutions are increasing. It is a concern for today's education system and this gap has to be identified and properly addressed to the learning community. Hence it has become important to understand the requirement of students and their academic progression. Educational Data Mining helps in a big way to answer the issues of predictions and profiling of not only students but other stake holders of education sectors. This paper discusses the application of various Data Mining tools and techniques that can be effectively used in answering the issues of predictions of student's performance and their profiling. This study explores multiple factors theoretically assumed to affect students' performance in higher education, and finds a qualitative model which best classifies and predicts the students' performance based on related personal and social factors. Educational Data Mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people' learning activities in educational settings.
Large-Scale IP Usage Identification via Deep Ensemble Learning (Student Abstract)

Understanding users' behavior via IP addresses is essential towards numerous practical IP-based applications such as online content delivery, fraud prevention, and many others. Among which profiling IP address has been extensively studied, such as IP geolocation and anomaly detection. However, less is known about the scenario of an IP address, e.g., dedicated enterprise network or home broadband. In this work, we initiate the first attempt to address a large-scale IP scenario prediction problem. Specifically, we collect IP scenario data from four regions and propose a novel deep ensemble learning-based model to learn IP assignment rules and complex feature interactions. Extensive experiments support that our method can make accurate IP scenario identification and generalize from data in one region to another.
Analyzing the Performance of Object Detection and Tracking Techniques

Objects are detected in computer perspective widely in many real-world applications. In case of video processing, detection and tracking of objects should be very proper and effective. Objects are detected and tracked by traditional methods such as background subtraction, optical flow, and frame differencing method. Convolution neural network, which is a deep learning-based approach, is recently adopted by many developers to identify the object. In this paper, methods of object detection are implemented, analyzed, compared, and discussed. Out of which a robust method has been suggested which satisfies the parameters of precision, recall, and accuracy, also the visualized parameters like object localization, classification, and forms a bounding box to the object are observed and analyzed. It is observed that convolution neural networks detect all relevant objects more accurately than traditional methods. CNN locates the identified object in a video frame using a bounding box that extracts the feature and trains the image for classification. Here, CNN is considered the most promising method for object detection and tracking and can be used in further study where complex work to be handled based on object detection like video inpainting or video restoration.
Searching for a New and Better Future of Work

Search engines were one of the first intelligent cloud-based applications that people used to get things done, and they have since become an extremely important productivity tool. This is in part because much of what a person is doing when they search is thinking. Search engines do not merely support that thinking, however, but can also actually shape it. For example, some search results are more likely to spur learning than others and influence a person's future queries [1]. This means that as information retrieval researchers the new approaches that we develop can actively shape the future of work.
Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction

Deep neural networks (DNNs) have been a key technique for click-through rate (CTR) estimation, yet existing DNNs-based CTR models neglect the inconsistency between their optimization objectives (e.g., Binary Cross Entropy, BCE) and CTR ranking metrics (e.g., Area Under the ROC Curve, AUC). It is noteworthy that directly optimizing AUC by gradient-descent methods is difficult due to the non-differentiable Heaviside function built-in AUC. To this end, we propose a smooth approximation of AUC, called smooth-AUC (SAUC), towards the rank-based CTR prediction. Specifically, SAUC relaxes the Heaviside function via sigmoid with a temperature coefficient (aiming at controlling the function sharpness) in order to facilitate the gradient-based optimization. Furthermore, SAUC is a plug-and-play objective that can be used in any DNNs-based CTR model. Experimental results on two real-world datasets demonstrate that SAUC consistently improves the recommendation accuracy of current DNNs-based CTR models.
Condition monitoring and anomaly detection in cyber-physical systems

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98% (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63%—the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.