Reviewerly/exHarmony huggingface dataset:
Projected fuzzy C-means with probabilistic neighbors

In recent years, graph optimization dimensionality reduction methods have become a research hotspot in machine learning. The main challenge of these methods is how to choose proper neighbors for graph construction. For high-dimensional data clustering tasks, most methods often conduct a dimensionality reduction method at first and then perform a clustering method in sequence. However, such a sequential strategy may not be optimal because the reduced data obtained in the first stage may not be suitable for clustering. In this article, a novel method called Projected Fuzzy c-means with Probabilistic Neighbors(PFCM), which unifies graph optimization and Fuzzy c-means, is proposed. Our model projects the data into an optimal subspace at first and then learns the sparse weights matrix by considering probabilistic neighbors and membership matrix together on the projected data. The above two steps run iteratively until the algorithm converges. Especially, L0-norm constraints are employed on the weights matrix to avoid the obstacles caused by outliers. An optimization procedure is designed to solve the proposed model effectively. We conducted numerous experiments on eight benchmark data sets. The experimental results show that the performance of the proposed method is better than some available dimensionality reduction algorithms for clustering tasks.
Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

Abstract Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.
A comparative study on false information governance in Chinese and American social media platforms

Abstract False information has become a profound issue of the Internet governance partly because of its serious impact on the political elections and public daily information flows. Different countries and social media platforms have adopted divergent approaches in handling false information. This study conducts a comparative study of the governance models of social media platforms in the United States and China over false Information from the perspectives of the balance of governance mechanism, that is, the balance between self‐regulation, external regulation and coregulation. It explores the laws, regulations and rules of falseinformation in two countries, exams social media platform's regulatory mechanism, focusing on comparison of Weibo and Facebook. The research shows that to increase transparency and accountability, both Weibo and Facebook have devoted certain governance power and responsibilities to internal or external intermediaries. This may induce two negative implications: accountability of the external actors and reduction of platform's supervisory obligations. The paper concludes by arguing that having a more effective, accountable and inclusive governance of false information in social media platforms, we not only need to find a balance between the functions and power of different stakeholders in governance but also need to seek a delicate balance between the three regulatory models of social media falseinformation governance.
Optimization of automatic extraction procedure for particles in asphalt mixture towards superior robustness and accuracy

Three-dimensional image reconstruction plays a pivotal role in microstructure information extraction and virtual mechanical experiments of asphalt mixture. However, the traditional pixel-based reconstruction algorithm tends to be powerless in processing three-dimensional (3-D) mesostructure. Therefore, this paper aims to develop a robust and accurate algorithm for high-quality aggregate particle extraction. To this end, the global thresholding and three-dimensional mathematical morphological methods were conducted directly based on voxel operation. Then the calculated weight-based gradation was utilized to develop the robustness and accuracy analysis map (RAAM), which assesses and compares the traditional and novel reconstruction algorithms. As a sequence, the voxel-based method had higher accuracy and lower perturbation sensitivity than the existing procedures. Additionally, the computed efficiency was improved dramatically by breaking tight bounds and conservatism. Finally, the inevitable algorithm defects caused by multiple morphological treatments in the traditional method have disappeared in the novel one.
Adult content image recognition by Boltzmann machine limited and deep learning

Abstract Adult content images have a detrimental effect on Internet users, a significant number of whom are minors. Therefore, it is essential to control and detecting adult content images using multimedia processing and computer vision techniques. Previous studies have typically focused on manual-engineered visual features that may be difficult to detect and analyze. This paper presents a new model that employs deep convolutional neural networks within a Gaussian-Bernoulli limited-time, for adult content image recognition of a wide variety in a precise and effective manner. There are various layers within Convolutional Neural Networks for feature extraction and classification. Gaussian-Bernoulli limited-time was used for feature extraction to describe the images, and these features were summarized using the Boltzmann machine limited in the feature summary phase. The benefit of such an approach is convenience in carrying out feature extraction. Additionally, when tested on the most modern criterion dataset, this finding is believed to be more precise compared to other state-of-the-art approaches. The results obtained prove that the proposed approach leads to a higher efficiency.
Part Decomposition and Refinement Network for Human Parsing

Dear Editor, This letter is concerned with human parsing based on part-wise semantic prediction. Human body can be regarded as a whole structure composed of different semantic parts, and the mainstream single human parser uses semantic segmentation pipeline to solve this problem. However, the differences between human parsing and semantic segmentation tasks bring some issues that are inevitable to avoid. In this paper, we propose a novel method called part decomposition and refinement network (PDRNet), which adopt part-wise mask prediction other than pixel-wise semantic prediction to tackle human parsing task. Specifically, we decompose the human body into different semantic parts and design a decomposition module to learn the central position of each part. The refinement module is proposed to obtain the mask of each human part by learning convolution kernel and convolved feature. In inference stage, the predicted human part masks are combined into a complete human parsing result. Through the decomposition, refinement and combination of human parts, PDRNet greatly reduces the confusion between the target human and the background human, and also significantly improves the semantic consistency of human part. Extensive experiments show that PDRNet performs favorably against state-of-the-art methods on several human parsing benchmarks, including LIP, CIHP and Pascal-Person-Part.
Integration in the C-band between quantum key distribution and the classical channel of 25 dBm launch power over multicore fiber media

The quantum-classical coexistence can be implemented based on wavelength division multiplexing (WDM), but due to Raman noise, the wavelength spacing between quantum and classical signals and launch power from classical channels are restricted. Space division multiplexing (SDM) can now be availably achieved by multicore fiber (MCF) to reduce Raman noise, thereby loosening the restriction for coexistence in the same band and obtaining a high communication capacity. In this paper, we realize the quantum-classical coexistence over a 7-core MCF. Based on the SDM, the highest launch power of 25 dBm is achieved which has been extended nearly 19 times in previous work. Moreover, both the quantum and classical channels are allocated in the C-band and the minimum wavelength spacing between them is only 1.6 nm. The coexistence system eliminates the need for adding a narrowband filter.
Assuring Anonymity and Privacy in Electronic Voting with Distributed Technologies Based on Blockchain

Anonymity and privacy in the electoral process are mandatory features found in any democratic society, and many authors consider these fundamental civil liberties and rights. During the election process, every voter must be identified as eligible, but after casting a vote, the voter must stay anonymous, assuring voter and vote unlinkability. Voter anonymity and privacy are the most critical issues and challenges of almost all electronic voting systems. However, vote immutability must be assured as well, which is a problem in many new democracies, and Blockchain as a distributed technology meets this data immutability requirement. Our paper analyzes current solutions in Blockchain and proposes a new approach through the combination of two different Blockchains to achieve privacy and anonymity. The first Blockchain will be used for key management, while the second will store anonymous votes. The encrypted vote is salted with a nonce, hashed, and finally digitally signed with the voter’s private key, and by mixing the timestamp of votes and shuffling the order of cast votes, the chances of linking the vote to the voter will be reduced. Adopting this approach with Blockchain technology will significantly transform the current voting process by guaranteeing anonymity and privacy.
Bedtime Monitoring for Fall Detection and Prevention in Older Adults

Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.
Employing Natural Language Processing as Artificial Intelligence for Analyzing Consumer Opinion Toward Advertisement

With the advent and integration of technology in business, marketers started investing in numerous media platforms to influence the consumer's sentiments. Artificial intelligence has been proved as one of the innovative tools of digitalization to change consumer's media habits. Owing to the growing trends of e-commerce, the traditional advertising model is insufficient. Therefore, advertisers are taking advantage of artificial intelligence technology to meet current requirements. Thus, a deeper understanding of product advertisement with reference to consumer sentiments and its implications need to be established. The current research depicts the contribution of artificial intelligence to analyze the consumers' attention, cognition, and emotion. The target product was Samsung Galaxy. Researcher of the current study has employed Think-aloud procedure for data analysis. Tweets dataset was divided into 2 categories. For international consumers' sentiments 30,877 tweets whereas for Pakistani consumers' sentiments tweets dataset was 26,834. For data analysis, authors used Nvivo for generating theme. The Nvivo produced word cloud. The word cloud generated with Pakistani tweets revealed that consumer attachment with Samsung product is based on emotional and attention and the preferred features of Samsung products are linked with emotional and attention. In contrary to that rest of the world tweets unfolded that emotion, attention, and cognition make consumer preferences while selecting Samsung products. This study is useful to the cellular companies for targeting across the world population. The consumer preference varies while selecting cell phones. This study will provide a better idea to cell phone companies for manufacturing consumer oriented cell phones to get better results. Moreover, future research should add more countries separate data and generate a comparative study between developed countries consumer and developing countries consumer preferences. In addition to companies with better insights of consumer can highlight the most attractive features of cell phone in their advertisements.
Discrimination of Automatically Generated Questions Used as Formative Practice

Advances in artificial intelligence and automatic question generation (AQG) have made it possible to generate the volume of formative practice questions needed to engage students in learning by doing. These automatically generated (AG) questions can be integrated with textbook content in a courseware environment so that students can practice as they read. Scaling this learn by doing method is a valuable pursuit, as it is proven to cause better learning outcomes (i.e., the doer effect). However, it is also necessary to ensure these AG questions perform equally as well as human-authored (HA) questions. In previous studies, it was found that AG and HA questions were essentially equivalent with respect to student engagement, difficulty, and persistence. While these question performance metrics expanded existing AQG research, this paper further extends this research by evaluating question discrimination using student data from a university Neuroscience course. It is found that the AG questions also perform as well as HA questions with respect to discrimination.
Classification of Human Motion Activities using Mobile Phone Sensors and Deep Learning Model

Recently, human activity recognition (HAR) has gained a lot of importance due to its wide range of applications in virtual reality, healthcare, surveillance, security, automated control systems, etc. Latest mobile phones have advanced computational capabilities along with several embedded MEMS sensors, which enable us to detect various physical activities unobtrusively. Incorporating personalized motion samples can improve the accuracy of motion detection by mobile devices or wearable devices that are tailored to the individual. Recent works have demonstrated that the use of machine learning and statistical techniques can detect human activities more accurately. In this paper, we have classified two different physical activities, viz., walking and brisk-walking using a deep neural network (DNN). The personalized data has been collected using multiple sensors of the mobile phone with the two kinds of physical activity. Using mobile phone sensors like accelerometer, gyroscope, magnetometer, etc, data has been collected, examined, and used for training and testing the DNN model. Out of multiple sensors on the phone, we have identified the sensors which are more appropriate for the given motion/activities. Finally, we have achieved a classification accuracy of 96.5%.
Mobile phone use among Ukrainian and US students: a library perspective

Purpose This study aims to determine college/university students’ mobile phone practices to understand key user preferences and set a baseline understanding for the development of prospective library informational mobile services. Design/methodology/approach This study is based on the results of an online-survey method. The authors recruited students majoring in the Information Science program in School of Information at Pratt Institute (USA) (74 participants) and in Kyiv National University of Cultural and Arts (Ukraine) (89 participants). Findings The general trends in the use of mobile technologies by American and Ukrainian students were identified. Key components of library mobile services are offered. Such services may include information and reference service via messengers; development of library mobile applications of audio and video content; access to educational, scientific, popular science literature and fiction; and supplementing online services with library chatbots. Research limitations/implications This study was limited to only students majoring in information science at university; however, the data obtained helps outline several general trends common to student youth who are actively working with information. Practical implications The obtained results will be useful in the practical activities of libraries and other information institutions for the development of a comprehensive information service based on mobile technology. Originality/value This study contributes to understanding how to improve library informational service considering users habits and preferences. Libraries management and librarians can use the findings as a basis for prioritizing the improvement of library informational mobile service.
HedgePeer

Uncertainty detection from text is essential in many applications in information retrieval (IR). Detecting textual uncertainties helps extract factual information instead of uncertain or non-factual information. To avoid overprecise commitment, people use linguistic devices like hedges (uncertain words or phrases). In peer reviews, reviewers often use hedges wherever they are unsure about their opinion or when facts do not back their opinions. Usage of hedges or uncertain words in writing can also indicate the reviewer's confidence or measure of conviction in their reviews. Reviewer confidence is important in the peer review process (especially to the editors or chairs) to judge the quality of evaluation of the paper under review. However, the self-annotated reviewer confidence score is often miscalibrated or biased and not an accurate representation of the reviewer's conviction of their judgment on the merit of the paper. Less confident reviewers sometimes speculate their observations. Here in this paper, we introduce HedgePeer, a new uncertainty detection dataset of peer review comments, which is more than five times larger than the existing datasets on hedge detection in other domains. We curate our dataset from the open-access reviews available in the open review platform and annotate the review comments in terms of the hedge cues and hedge spans. We also provide several baseline approaches, including a multitask learning model with sentiment intensity and parts-of-speech as scaffold tasks to predict hedge cues and spans. We make our dataset and baseline codes available at https://github.com/Tirthankar-Ghosal/HedgePeer-Dataset. Our dataset is motivated towards computationally estimating the reviewer's conviction from their review texts.
The application of blockchain technology in smart sustainable energy business model

Blockchain technology is considered to be a disruptive technology that has real potential for change after steam engines, electricity, and the Internet. However, in terms of power generation, the current level of hydropower development in my country is only 39%. Compared with developed countries, there is still a big gap and a high development space. This article proposes a research on the application of blockchain technology in smart sustainable energy business models, studies the conversion path of sustainable energy systems, introduces integrated energy service blockchain technology, and builds a sustainable energy transition model. The selection of practical dimensions requires both Quantitative indicators also require cultural and behavioral qualitative indicators; finally, through the analysis of social development trends and government policies, the external variable landscape signals are parameterized. Sustainable energy rather than fossil energy is the energy of the future. Weak landscape signals have significantly weakened the development of the sustainable energy niche: the proportion of coal consumption has declined slowly. Although it has transformed from a system to a niche, it has always been higher than that of other energy types. Only with long-term landscape pressure, positive changes to sustainable energy technologies and infrastructure, and continuous changes in consumer preferences, can sustainable energy ultimately dominate.
Pseudo-haptics and self-haptics for freehand mid-air text entry in VR

Despite the lack of haptic feedback, two-finger typing remains as a common freehand mid-air text entry method in VR. To compensate for this limitation, we investigated the effect of pseudo-haptics and thumb-to-finger pinch self-haptics. Three types of virtual keyboards were developed for comparison: a normal virtual keyboard without any haptic features, a pseudo-haptic keyboard providing a haptic illusion by adding pseudo-haptic features to the normal virtual keyboard, and a pinch keyboard providing passive self-haptic feedback when the index finger touches the thumb to type bubble-shaped keys. A follow-up user study was conducted on the three keyboards to measure typing performance, perceived workload, and user experience. The results showed that the pseudo-haptic and pinch keyboards were comparable to the normal one in typing performance and workload, but beneficial in user experience and preference. The findings suggest that pseudo-haptics and self-haptics could achieve experiential benefits in VR text entry without obtrusive extra devices.