Reviewerly/exHarmony huggingface dataset:
Elicitation of Requirements for an AI-Enhanced Comment Moderation Support System for Non-tech Media Companies

Traditional (news) media companies are increasingly facing rising participation in their discussion sections and a simultaneous surge of abusive contributions. Legally required to prevent the dissemination of hate and threats, manual moderation is an increasingly daunting task for journalists and part-time community managers. Consequently, many comment sections are closed for economic reasons world-wide. While there is ongoing academic and practice research on machine learning (ML) systems to detect abusiveness or hate, the focus typically remains on this limited technical task. Integrations into systems for practical community management are still rare. Based on eleven semi-structured interviews with experts of four German newspapers of varying size (incl. an observation of their working patterns), complemented by insights from workshops on community management, we could identify five major functional requirements for creating such integrated systems. This range goes from the need for increased transparency and controllability to better support for team-based community management. In this paper, we outline each requirement’s origin and implications for the development of integrated, artificial intelligence (AI)-enhanced comment moderation support system (CMSS).
Temporal Consistency Loss for High Resolution Textured and Clothed 3D Human Reconstruction from Monocular Video

We present a novel method to learn temporally consistent 3D reconstruction of clothed people from a monocular video. Recent methods for 3D human reconstruction from monocular video using volumetric, implicit or parametric human shape models, produce per frame reconstructions giving temporally inconsistent output and limited performance when applied to video. In this paper we introduce an approach to learn temporally consistent features for textured reconstruction of clothed 3D human sequences from monocular video by proposing two advances: a novel temporal consistency loss function; and hybrid representation learning for implicit 3D reconstruction from 2D images and coarse 3D geometry. The proposed advances improve the temporal consistency and accuracy of both the 3D reconstruction and texture prediction from a monocular video. Comprehensive comparative performance evaluation on images of people demonstrates that the proposed method significantly outperforms the state-of-the-art learning-based single image 3D human shape estimation approaches achieving significant improvement of reconstruction accuracy, completeness, quality and temporal consistency.
Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning

Recent empirical works reveal that visual representation learned by deep neural networks can be successfully used as descriptors for image retrieval. A common technique is to leverage pre-trained models to learn visual descriptors by ranking losses and fine-tuning with labeled data. However, retrieval systems’ performance significantly decreases when querying images of lower resolution than the training images. This study considered a contrastive learning framework fine-tuned on features extracted from a pre-trained neural network encoder equipped with an attention mechanism to address the image retrieval task for low-resolution image retrieval. Our method is simple yet effective since the contrastive learning framework drives similar samples close to each other in feature space by manipulating variants of their augmentations. To benchmark the proposed framework, we conducted quantitative and qualitative analyses of CARS196 (mAP = 0.8804), CUB200-2011 (mAP = 0.9379), and Stanford Online Products datasets (mAP = 0.9141) and analyzed their performances.
A secure public key cryptosystem based medical records using non-commutative group

Abstract Tolerant records, including specialists’ conclusions of illnesses, hint of medicines and patients’ conditions, nursing activities, and assessment results from united wellbeing calling offices, are the main clinical records of patients in clinical frameworks. With understanding records, clinical staff can comprehend the whole clinical data of a patient so that, as indicated by the patient’s conditions, more exact analyses and more fitting top to bottom medicines can be given. By and by, in such a cutting-edge society with blasting data advances, customary paper-based understanding records have confronted a great deal of issues, for example, absence of uniform organizations, low information versatility, slow information move, indecipherable penmanship styles, tremendous and inadequate capacity space, trouble of preservation, being handily harmed, and low adaptability. In this article we propose new public key cryptosystem for securing medical record based on non-commutative group. The securities of our key exchange protocol based on hybrid of group root extraction problem and Conjugacy Search Problem in non-commutative group implement our key exchange protocol in special matrix group.
Evaluation of IoT-Setting Method Among Senior Citizens in Japan

To develop methods for supporting senior citizens is critical because the number of senior citizens is growing globally. Recently, the focus is on supporting senior citizens using information technology (IT), including the Internet of Things (IoT). In the IoT system, IoT tools such as cameras and sensors are connected and interacted by the Internet. Furthermore, we can customize their function easily using smartphones to modulate the function we want. Therefore, senior citizens should customize IoT products and help themselves. However, it is challenging to customize IoT product functions for senior citizens because they had fewer chances to use smartphones or computers than young people. Therefore, other customizing methods for IoT products are needed for senior citizens. In this study, we design a workshop to test, where the setting of the user interface (UI) is suitable for senior citizens. We develop three UIs, namely, voice, touch, and camera. To explore which method is suitable for senior citizens, we evaluate the setting time for each method. Consequently, the setting time of the voice interface is shorter than the others. Furthermore, the system usability scale (SUS) score was higher than 68, indicating that the setting method using the voice interface is acceptable for senior citizens. These results indicate that the voice interface is a suitable setting method for senior citizens.
QoE Assessment of Human Perception of Softness in Networked Haptic Virtual Environment

In this paper, we examine human perception of softness in a networked haptic virtual environment by Quality of Experience (QoE) assessment. In our assessment, each subject bursts two balloons with different values of softness as soft objects by pushing the top of each balloon with a haptic interface device in a 3D virtual space. Then, the subject answers which balloon is softer or harder than the other. Assessment results clarify the imperceptible range and allowable range of softness difference.
An Improved Approach for Devanagari Handwritten Characters Recognition System

It is a very complicated task to recognize the handwritten characters and scanned data/images in recent years. The different sizes and writing methods of the characters play a critical role in clearly identifying the handwritten characters. This script's massive prevalence must be taken care of by using advanced technologies to connect to the real world to a greater depth. Machine Learning is one of the most popular technologies that has attracted the recent research work of handwritten character recognition using A.I. techniques. Various new technologies have been developed to execute fast neural networks with little exhaustive knowledge requirements. Here, we operate using Keras and Python libraries for building our model. The main aim of CNN is to recognize the training data and fit that training data into models that should help human beings. In this paper, an attempt has been made to construct and evaluate a simple individual learning algorithm (like k-means and SVM) using Keras to recognize isolated Devanagari handwritten characters datasets and assess the impact of variations in parameters in the learning phase. The proposed methodology gives a better result. The accuracy is better than individual algorithm performance.
Improving Decision Making Using Semantic Web Technologies

With the rapid advance of technology, we are moving towards replacing humans in decision making–the employment of robotics and computerised systems for production and delivery and autonomous cars in the travel sector. The focus is placed on the use of techniques, such as machine learning and deep learning. However, despite advances in machine learning and deep learning, they are incapable of modelling the relationships that are present in the real world, which are necessary for making a decision. For example, automating sociotechnical systems requires an understanding of both human and technological aspects and how they influence one another. Using machine learning, we can not model the relationships of a sociotechnical systems. Semantic Web technologies, which is based on the concept of linked-data technology, can represent relationships in a more realistic way like in the real world, and be useful to make better decisions. The study looks at the use of Semantic Web technologies, namely ontologies and knowledge graphs to improve decision making process.
Generalized Nets. An Overview of the Main Results and Applications

Generalized Nets (GNs) are a powerful tool for discrete event simulation and parallel processes flow representation. The apparatus of GNs is equally well suited for modelling simple systems, as well as large, complex systems. The major strength of a discrete event simulation is its ability to model random events and to predict the effects of complex interactions between these events. GN-models could be used as a quick method for analysing and solving complex problems. This article presents a brief overview of the evolution of the GNs theory and its various fields of application. The results discussed here are based on years of research by scientists of the Bulgarian Academy of Sciences.
Principle and Design of A High Performance Analog Switch Circuit

A high speed double-pole double-throw analog switch circuit capable of transmitting negative signal is designed in this paper, which can transmits signal in both directions. The power supply current of the designed analog switch does not exceed 6.5μA, the leakage current is about 18.3nA, the on-resistance is 2.3Ω, the -3dB bandwidth is 10.53GHz, and the signal distortion is about 0.01%. The characteristics of the analog switches in the current market are analyzed, and the model of high speed analog switch structure is described. Then, the system structure of the designed analog switch circuit and the reasons for using the double-well process are analyzed. Next, the overall working principle of the circuit is explained. Finally, simulation and analysis are performed on the drawn layout.
Breast Cancer: Classification of Tumors Using Machine Learning Algorithms

Breast cancer is currently one of the leading causes of death among women worldwide. Masses are considered significant signs of the existence of malignant lesions, as they occur in most breast cancer cases. However, their detection is challenging since masses have large variation in shape, margin, size and are often indistinguishable from surrounding tissue, making the radiologist's task tedious in the case where a significant number of mammograms require fast and accurate interpretation. For these reasons, computer-aided diagnosis (CAD) systems are being developed to make the diagnostic process easier for radiologists. In these systems, segmentation and classification of breast masses in mammograms are important steps. This work aims to evaluate the performance of machine learning techniques in classifying tumors into benign and malignant. The selected techniques were applied on 1663 mammograms from the Digital Database for Screening Mammography. Of the 1663 images, 769 images correspond to malignant cases, and 894 correspond to benign cases. The efficiency of each of the considered techniques was evaluated by using four metrics, namely, the false positive rate, sensitivity, specificity, and accuracy.
ALTCAI: Enabling the Use of Embodied Conversational Agents to Deliver Informal Health Advice during Wizard of Oz Studies

We present ALTCAI, a Wizard of Oz Embodied Conversational Agent that has been developed to explore the use of interactive agents as an effective and engaging tool for delivering health and well-being advice to expectant and nursing mothers in Nigeria. This paper briefly describes the motivation and context for its creation, ALTCAI’s various components, and presents a discussion on its adaptability and potential uses in other contexts, as well as on potential future work on extending its functionality.
Cancer Registry Coding via Hybrid Neural Symbolic Systems in the Cross-Hospital Setting

Cancer registries are critical databases for cancer research whose maintenance requires various types of domain knowledge with labor-intensive data curation.In order to facilitate the curation process with high quality in a timely manner, we developed a hybrid neural symbolic system for cancer registry coding.Unlike previous works which mainly worked on the dataset collected from one hospital or formulated the task as text classification problems, we collaborated with two medical centers in Taiwan to compile a cross-hospital corpus and applied neural networks to extract cancer registry variables described in unstructured pathology reports along with expert systems for generating registry coding.We conducted experiments to study the feasibility of the proposed hybrid for the task of cancer registry coding and compare its performance with state-of-the-art non-hybrid approaches.Furthermore, cross-hospital experiments were performed to study the advantages and limitations of transfer learning for processing reports from different sources.The experiment results demonstrated that the proposed hybrid neural symbolic system is a robust approach which works well across hospitals and outperformed classification-based baselines by F-scores of 0.13∼0.27.Compared to the baseline models, the F-scores of the proposed approaches are apparently higher when fewer training instances were used.All methods benefited from the transferred parameters learned from the source dataset, but the results suggest that it is a better strategy to transfer the learned knowledge through the concept recognition task followed by the symbolic expert system to address the task of cancer registry coding.
Vector-Borne Disease Outbreak Prediction Using Machine Learning Techniques

Vector-borne disease is a form of illness which is caused by parasites, viruses and bacteria. The infection is transferred through blood-feeding arthropods such as mosquitoes, fleas, ticks etc. Every year more than 700,000 deaths occur due to diseases such as yellow fever and malaria. These diseases are most common in tropical and subtropical areas and affect the underprivileged populations. Deep learning is an essential part of artificial intelligence that provides an uncanny power to systems to construct a complex network using layers of perceptrons which mimic the human neurons. This network combined with algorithms of machine learning may serve as one of the most powerful tools in healthcare to classify and analyse huge amount of medical data and predict future trends through supervised learning. This paper focused on effective prediction of vector-borne disease outbreak (multiclass classification) of three diseases (chikungunya, malaria, dengue) across the Indian subcontinent. We have examined and refined our model over data collected across India in 2013–2017. We have put forward an artificial neural network outbreak risk prediction algorithm using contrasting data. To our finest understanding, none of the previous works have centred on contrasting data in area of analysis of medical data. The prediction accuracy of our suggested ANN algorithm is 86%.
Towards the Determination of Safe Operating Envelopes for Autonomous UAS in Offshore Inspection Missions

A drive to reduce costs, carbon emissions, and the number of required personnel in the offshore energy industry has led to proposals for the increased use of autonomous/robotic systems for many maintenance tasks. There are questions over how such missions can be shown to be safe. A corollary exists in the manned aviation world for helicopter–ship operations where a test pilot attempts to operate from a ship under a range of wind conditions and provides subjective feedback on the level of difficulty encountered. This defines the ship–helicopter operating limit envelope (SHOL). Due to the cost of creating a SHOL there has been considerable research activity to demonstrate that much of this process can be performed virtually. Unmanned vehicles, however, have no test pilot to provide feedback. This paper therefore explores the possibility of adapting manned simulation techniques to the unmanned world to demonstrate that a mission is safe. Through flight modelling and simulation techniques it is shown that operating envelopes can be created for an oil rig inspection task and that, by using variable performance specifications, these can be tailored to suit the level of acceptable risk. The operating envelopes produced provide condensed and intelligible information regarding the environmental conditions under which the UAS can perform the task.
UAVs-based Small Object Detection and Tracking in Various Complex Scenarios

We have witnessed drastic progress in object detection in recent years due to the development of neural networks. Most mainstream object detectors are inclined to detect objects of regular scale because their detection depends on deep convolutional feature maps. Our study focused on UAVs-based small object detection at a high altitude, i.e., 100 meters. We constructed a pipeline by integrating the foreground segmentation algorithm, the image classification algorithm, the boosted cascaded classifier, and the tracker together that can detect and track the small object progressively in a cascaded manner. We performed the qualitative and quantitative evaluation of our pipeline's performance under various complex conditions. The comparison study confirmed its superiority in small object detection and strong robustness against various influential nuisances. Based on our constructed pipeline, we developed a real-time UAVs-based small object detection and tracking system. The system architecture and the general steps taken by the UAVs to realize small object detection were also presented. Finally, we qualitatively and quantitatively evaluated 8 popular trackers based on relevant image attributes. The most suitable tracker can be determined in response to a given condition. Our study testified that by taking advantage of each algorithm germane to a given task, the implementation performance can be improved.
A Qualitative Research on the Impact and Challenges of Cybercrimes

Abstract We are in the digital era where we cannot imagine our lives without the Internet. Technology has become an integral part of our daily routine, and with the significant rise in technology, we see huge growth in cybercrimes. The use of information is increasing every day with the advent of more social media platforms that utilize millions of data per second globally. These data include sensitive information such as trade secrets, privacy, and security issues. The number of crimes is increasing day by day, and these cyber crimes can impact an individual, an organization, or even a whole nation. The research study aims to spread awareness about cybercrimes by offering a detailed analysis of the awareness, impact, and challenges to anyone vulnerable to cybercrime. The methodology adopted to study and analyze the impact and challenges caused due to cybercrimes is qualitative research. On a concluding note, the study will present suggestions to overcome the challenges and talk about the research’s future scope.
From Web Scraping to Web Crawling

The World Wide Web is the largest database comprising information in various forms from text to audio/video and in many other designs. However, most of the data published on the Web is in unstructured and hard-to-handle format, and hence, difficult to extract and use for further text processing applications such as trend detection, sentiment analysis, e-commerce market monitoring, and many others. Technologies like Web scraping and Web crawling cater to the need of extracting a huge amount of information available on the Web in an automated way. This paper starts with a basic explanation of Web scraping and the four methodologies—DOM tree parsing, semantic–syntactic framework, string matching, and computer vision/machine learning-based methodology—developed over time based on which scraping solutions and tools are formulated. The paper also explains the term Web crawling, an extension of Web scraping and introduces Scrapy, a Web crawling framework written in Python. The paper describes the workflow behind a Web crawling process initiated by Scrapy and provides with the basic understanding on each component involved in a Web crawling project, built using Scrapy. Further, the paper dives into the implementation of a Web crawler, namely confSpider that is dedicated to extract information related to upcoming conferences and summits from the Internet and may be used by educational institutions to promote student awareness and participation in multi-disciplinary conferences.
Why do master’s students of humanities and social sciences publish papers in Chinese-language predatory journals? A qualitative study based on Grounded Theory

In China, master's students in humanities and social sciences (HSS) are becoming the main target of Chinese-language predatory journals. Existing research has not paid enough attention to why these students publish papers in Chinese-language predatory journals. This research interviewed 30 HSS master's students with different majors using semi-structured interviews and Grounded Theory to analyze the data; it found that research discrimination, research context, self-awareness, and individual demand are the main reasons why students publish papers in Chinese-language predatory journals. This study provides the following suggestions in an effort to solve the problem of Chinese-language predatory journals. First, the Chinese government should draw up a blacklist of Chinese-language predatory journals. Second, the research evaluation departments of Chinese universities and research institutions should evaluate the research results of HSS master's students based on this list. Third, Chinese universities or scientific research institutions should strengthen the training of HSS master tutors and increase their awareness of Chinese-language predatory journals. And finally, Chinese HSS master's students should be taught about the hazards of Chinese-language predatory journals in research integrity and ethics courses, and refuse to publish papers in Chinese-language predatory journals.