Reviewerly/exHarmony huggingface dataset:
Helpfulness Prediction of Online Drug Reviews

Online drug reviews are increasingly available for drug users and medical professionals to collect first-hand patient experiences on drugs. The challenge is, however, to provide them with quick and accurate access to the helpful reviews mingled with an extensive amount of less helpful ones. One way to facilitate information access to helpful reviews is helpfulness prediction. By analyzing the features extracted from reviews, the helpfulness of a review can be objectively evaluated. However, the user sentiments and opinions contained in drug reviews, compared with the ones in other product reviews, are exceedingly complicated to be analyzed due to the differences in users' physical and mental conditions. Hence, in this paper, we proposed a mechanism that combines medical-domain features (medical word sentiments, proportion of medical words in reviews) into a helpfulness prediction model. Besides, we noticed that the number of useful counts received by a review cannot sufficiently validate its helpfulness since useful counts are significantly influenced by the length of duration it was exposed to readers and total read times. Hence, to address this selection bias issue, we proposed four methods and examined their performances. Our findings show 1) the added medical features significantly elevated the accuracy of helpfulness prediction results. 2) readability, proportion of medical words and proportion of nouns are the three features showing the highest importance in helpfulness prediction. 3) the method to select sample based on useful counts achieves the best performance possibly because the sample data generated by this method contains more unbiased feature characteristics of review helpfulness.
Facial Expression Recognition Based on Convolutional Neural Networks and Edge Computing

With edge devices playing an increasingly important role in our daily lives, edge computing and human-computer interaction, especially facial expression recognition, become research central issues in academia and industry. However, surprisingly, utilizing edge computing and neural networks for facial expression recognition has been neglected for many years, very few research can be found. To be focusing on such topics. In this paper, we improve Visual Geometry Group 19 with the idea of residual learning. To be specific, for each block of Visual Geometry Group 19, we add its input to its output. The result of the addition will be the input of the next block. Then, we minimize the size of our model by pruning and post-training quantization to achieve a higher efficiency and maintain the model's accuracy at the same time when deploying it on edge devices. The experiment result shows that our model has a 98.99% accuracy on the CK+ dataset. Besides, when deploying on edge devices, its inference time is less than many other popular neural networks that are designed for deploying on edge-devices.
WITHDRAWN: Dynamic and scalable virtual machine placement algorithm for mitigating side channel attacks in cloud computing

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Effective Skeleton-Based Action Recognition by combining Graph Convolutional Networks and Fisher Vector Encoding

Action recognition from skeleton data is an important research field of computer vision. Recently, the Graph Convolutional Network (GCN), which generalize CNN to more generic non-Euclidean structures, shows encouraging improvement in skeleton-based action recognition. However, the effect of the GCN architecture has not been fully explored. In this work, we propose a Fisher Vector (FV) encoding based GCN architecture (FV-GNN), which are potent to learn better action representations by integrating the GCN model with FV encoding. On two large-scale datasets for skeleton-based action recognition, Kinetics and NTU-RGBD, it achieves improvements over the recent approaches.
Exploring Neural Machine Translation for Sinhala-Tamil Languages Pair

In the face of rapid globalization, the concept of translation performs the most important role in continuing the existence of native languages. Most of the research on Natural Language Processing in Neural Machine Translation has achieved an impressive result through parallel corpus dataset. Low resourced languages confront low performance due to the lack of parallel corpus data. Creating parallel corpus for language pair is more expensive and needs the persons who are expert knowledge for both languages. In this research, we present the availability of developing the translator for Sinhala-Tamil languages pair using monolingual corpus dataset. In this paper, the Byte Pair Encoding (BPE) is applied for overcoming the Out-Of-Vocabulary (OOV) problem in both Sinhala and Tamil languages. Our first part of the research is using monolingual word embedding approach for developing the translation in between Sinhala-Tamil language pair only using monolingual corpora. The second part of the research we use both parallel and monolingual corpus data with transformer architecture. The BLEU score and the synonyms analysis are used to evaluate the approach we suggested.
Two New Families of Asymptotically Optimal Codebooks from Characters of Cyclic Groups

Codebooks with small inner-product correlation have applied in direct spread code division multiple access communications, space-time codes and compressed sensing. In general, it is difficult to construct optimal codebooks achieving the Welch bound or the Levenstein bound. This paper focuses on constructing asymptotically optimal codebooks with characters of cyclic groups. Based on the proposed constructions, two classes of asymptotically optimal codebooks with respect to the Welch bound are presented. In addition, parameters of these codebooks are new.
A Security Qualification Matrix to Efficiently Measure Security in Cyber-Physical Systems

Implementations of Cyber-Physical Systems (CPS), like the Internet of Things, Smart Factories or Smart Grid gain more and more impact in their fields of application, as they extend the functionality and quality of the offered services significantly. However, the coupling of safety-critical embedded systems and services of the cyber-space domain introduce many new challenges for system engineers. Especially, the goal to achieve a high level of security throughout CPS presents a major challenge. However, it is necessary to develop and deploy secure CPS, as vulnerabilities and threats may lead to a non- or maliciously modified functionality of the CPS. This could ultimately cause harm to life of involved actors, or at least sensitive information can be leaked or lost. Therefore, it is essential that system engineers are aware of the level of security of the deployed CPS. For this purpose, security metrics and security evaluation frameworks can be utilized, as they are able to quantitatively express security, based on different measurements and rules. However, existing security scoring solutions may not be able to generate accurate security scores for CPS, as they insufficiently consider the typical CPS characteristics, like the communication of heterogeneous systems of physical- and cyber-space domain in an unpredictable manner. Therefore, we propose a security analysis framework, called Security Qualification Matrix (SQM). The SQM is capable to analyses multiple attacks on a System-of-Systems level simultaneously. With this approach, dependencies, potential side effects and the impact of mitigation concepts can quickly be identified and evaluated.
Stacked-Autoencoder Based Anomaly Detection with Industrial Control System

The Industrial Control System (ICS) is a system for controlling industrial systems. It is mainly a national infrastructure, and if it is shut down, it can have a huge impact on our lives. Therefore, ICS is mainly operated in a closed network to minimize security threats. However, ICS has also increased its Internet connection points as the IoT advances, which has increased security threats. Until now, it was difficult to secure a data set from an actual operating environment in ICS, so it was difficult to study effective security techniques. In this paper, we proposed a stacked-autoencoder (SAE), deep Support Vector Data Description (SVDD)-based data anomaly detection technique using an ICS dataset created based on a testbed similar to an actual operating environment, and derived detection accuracy for each threshold. In both models, the highest accuracy was derived when the threshold was 0.98, and the accuracy was 96.03% in the SAE model and 95.48% in the Deep SVDD model.
Machine Vision for As-Built Modeling of Complex Draped Composite Structures

The transition in the use of fiber composite structures from special applications to application in the mass market is accompanied by high demands in quality assurance. The consequential costs of unclear process design, unknown fiber orientations, and uncertainty regarding the effects of any fiber angle deviations can lead to market considerations (higher costs/time for development) in mass production that advise against the use of fiber composites, despite their superiority compared with conservative materials. Active monitoring of the deposited reinforcement layers and an evaluation of the real fiber orientation can form the basis of a robust industrial use of fiber composites by a first-time right production that is able to reduce the process variability. This paper describes the application of an image analysis system to provide both geometric topology and local reinforcement fiber orientation feedback to a finite-element (FE) model. The application during an industrial composite part production is described, and the possibilities of using it for the improvement of the lightweight character, the reduction of rejects, and the realization of a quality management system are shown. The determined component data are made directly available for use in numerical simulations and, thus, they serve as a non-destructive evaluation of the components under real conditions in which all production-dependent influences that affect the fiber orientation are incorporated.
Android Application Market Prediction Based on User Ratings Using KNN

Google play store is a digital distribution service operated and developed by Google. It provides various applications that can be downloaded directly on Android devices, allowing users to browse and download their desired applications. When they search for an app, users can see the list of applications with the name and its rating on its side. It is helping users to choose the best application based on the application rating. Developers use their strategy in naming their applications attractively to increase their rating. In this paper, we create a market prediction modeling based on user rating for Android apps on Google Play Store throughout 2019 using the KKN algorithm. After that, we perform a feature engineering and an analysis using exploratory data analysis to find information in the dataset. By doing k=1-20 iteration, we can determine the best k value in KNN. We used 5-fold cross-validation to evaluate model, which is created with 88.68% accurate result, a recall of 87.46%, a precision of 89.5%, and an RMSE of 0.213. The generated model has a better performance compared to another algorithm, with a 10% increased accuracy.
Intelligent Optimization Of Curriculum System Based On Washington Accord Graduate Attribute

Considering the authoritative engineering education certification standards is of great significance to formulate a curriculum system that meets the actual needs of engineering talents. However, the existing research on intelligent optimization of curriculum system pays little attention to certification standards. In this paper, a mathematical model of course selection and balance problem is established with the optimization goal of the support degree of curriculum system based on Washington Accord Graduate Attribute and the balance of academic load between semesters, and a two-stage improved genetic firefly algorithm(GAFA) is proposed to solve the model. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified by taking the actual training requirements and course data as examples.
An Image Processing and Artificial Intelligence based Traffic Signal Control System of Dhaka

Traffic jam is one of the greatest problem of Bangladesh. It affects mostly on its capital city, Dhaka, where density of population is second highest among the world. One of the major reason for occurring traffic jam is inaccuracy of the use of traffic signal. This paper introduces an intelligent traffic control system for four nodes traffic system. This system is entirely controlled by the use of image processing and artificial intelligence techniques. Image processing leads for detecting the density of vehicles by using Haar Cascade method, whereas artificial intelligence helps to modify the timing of traffic signal accurately time by time. These process held automatically and police can monitor from police box all over the time by computer. Moreover, in case of emergency, a manual system is introduced, which can support traffic police to turn the system to manual and operate the timing manually. Finally, traffic data is collected from road and prove the effectiveness of proposed system. This system will support as an extremely effective, self-coordinated and self-organized traffic control appliance.
Comparative Performance Analysis of Underlying Network Topologies for Blockchain

Blockchain is a distributed ledger technology operating on top of the peer-to-peer (P2P) network, and the variety of Blockchain deployments have been observed in both public and private manners. In Blockchain, the information dissemination among Blockchain nodes relies on the one-to-all-the-others broadcast mechanism over the P2P network. The underlying network topology introduces a significant impact on the performance of information dissemination in the Blockchain P2P network as well as the effectiveness and stability of Blockchain operation as a consequence. When a private blockchain is deployed in a controlled environment, it is crucial to understand the effect of underlying network topology on Blockchain performance and operational effectiveness. This paper simulates various well-known network topologies, including Hypercube, Torus, Fat-Tree, and Scale-Free as an underlying network of the Blockchain, giving more focus on controlled environments such as Data Center Network (DCN). We comparatively evaluate the performance of each network topology based on both Blockchain and network aspects. The evaluation results show that Hypercube performs the best among the topologies being assessed, especially in information propagation delay and resilience against adverse situations.
Design of Home Care System For Rural Elderly Based on Artificial Intelligence

Abstract With the development of Internet of Things technology, cloud computing technology, big data technology, and artificial intelligence technology, the application of these new technologies to rural life can help rural elderly care at home. The home care system for the elderly in rural areas is based on artificial intelligence technology and uses the Internet of things technology to collect real-time environmental parameters at home and to optimize the living environment. It uses intelligent image recognition technology to judge the behavior of the elderly and interlopers and gives effective early warning and alarm. It uses wearable devices to measure the health of the elderly in real-time. It uses the man-machine dialogue program to solve the life needs of the elderly to improve the well-being of the elderly life. It integrates big data and cloud computing technology to process the collected information.
Fisheye Image Object Detection Based on an Improved YOLOv3 Algorithm

The accuracy and speed of object detection based on deep learning are much higher than that of human eyes, but the application of deep learning in object detection of fisheye image remains to be studied. Although the fisheye image has a wide field of vision, it has the problem of geometric distortion. In order to solve the problem that it is difficult to obtain features caused by fisheye image distortion, the 8-fold and 16-fold down-sampling feature images obtained by YOLOv3 for fisheye image detection were respective Maxpool to the same size as the 32-fold down-sampling feature images for fusion, so as to realize feature reuse and increase the detail features in the 32-fold down-sampling feature images. This paper constructs its own fisheye image data set and carries out experiments. The experimental results show the improved YOLOv3 algorithm has higher object detection accuracy for fisheye images in comparison to the original algorithm.
Out of Sight, Out of Mind: UI Design and the Inhibition of Mental Models of Security

In this paper we make the case that UI design inhibits mental models of security by concealing most of the security-relevant aspects of software functionality. Users are frequently required to make decisions that have important security implications, that requires a mental model of software infrastructure to know what actions are ‘safe’ versus ‘unsafe’. People build internal causal models of what they experience that have explanatory and predictive power, and therefore form the basis of the decision-making faculty. By concealing security information, user interfaces hinder the user from building the kinds of models that will keep them safer, and only the small minority who are willing to go beyond the interface will acquire this knowledge. We suggest increasing the visibility of some essential information about the security-relevant aspects of software functionality in a way that ordinary users will be able to make sense of, so that through normal interactions with software everyone develops the kind of knowledge needed to better support security. We review the cognitive science and cybersecurity literature on mental models, present three ‘case studies’ which embody the security concealment problem, and present preliminary suggestions for how UI design might amend this problem.
SMartyModeling: an Environment for Engineering UML-based Software Product Lines

Software Product Line (SPL) has been successfully consolidated as an approach for systematic reuse. The adoption of the SPL approach aims at increasing the reuse of requirements and artifacts, thus reusing documents, source code and artifacts and ensuring better quality control to software production in a large-scale. One of the essential activities for SPL management is the modeling of variability. Variability modeling in UML-based SPL has been carried out mostly using the UML Profiling mechanism, in which new stereotypes and tagged values are created for such purpose. The available option in general-purpose UML tools for exporting UML models is through XMI files, standardized by OMG. This option is important to process XMI files in particular environments or tools, for example, managing variabilities, generating product configurations from an SPL, and even collecting metrics, and estimating SPLs. However, different versions, tool restrictions, and different file standards compromise operations involving XMI files. In this scenario, the industry has increasingly required the supporting tools for the SPL approach. However, the current support tools are mainly restricted to the problem space based on feature modeling and present problems with data integration with other tools. Therefore, we developed SMartyModeling, an environment for engineering UML-based SPLs in which variabilities are modeled as stereotypes using any UML compliant profile. This paper presents an overview of SMartyModeling, describing its motivation, main components, and available features.
Segmentation and Attention Network for Complicated X-Ray Images

X-ray security automatic screening has attracted a broad attention for providing auxiliary support to eliminate potential threat in public. However, recent automatic detection algorithms may not perform quite well when a great number of dangerous articles are overlapping and compact in the X-ray images. In order to deal with it, this paper takes the segmentation scheme and attention network into consideration. Based on Mask R-CNN, we propose a two-stage deep CNN model called SAN to solve the performance degradation caused by the dense distribution of objects in X-ray images. The model contains a semantic segmentation network block to generate soft attention masks for each category and an attention network to combine each of the attention masks with the ROIs. In the experiment, the proposed SAN achieves 86% AP on our dataset, and brings an obvious improvement compared with recent detector algorithms on complicated X-ray dataset.
Psychosocial determinants of healthcare personnel’s willingness to carry real-time locating system tags during daily inpatient care in hospital managing COVID-19 patients: insights from a mixed-methods analysis

Real-time locating systems (RTLS) enable contact tracing and hand hygiene reminders, to improve hospital safety. Successful implementation requires healthcare personnel (HCP) to carry RTLS tags continuously. We assessed for determinants of HCP's willingness to use RTLS tags during routine inpatient care, and evaluated concerns using mixed-methods analysis.We conducted a cross-sectional study in the 330-bed purpose-built National Centre for Infectious Diseases in Singapore, from January 15 through February 4, 2020. The anonymous survey comprised 24 questions based on constructs from behavioral models and an open-ended question. Principal component analysis was performed to derive the latent factor structure applied in the multivariable logistic regression analysis. Concerns were analyzed using thematic analysis.Of 260 HCP (nurses [40.8%], ancillary and administrative staff [23.1%], allied health professionals [18.5%], and physicians [17.7%]), 75% were willing to use the RTLS tag. After adjusting for age, gender, healthcare professional group, and duration of practice, the acceptance of the use of the RTLS tag (adjusted OR 11.28 [95% CI 4.39-29.00], P < .001) was highly associated with the willingness to use the RTLS tag. HCP who perceived the tag to be easy to use (adjusted OR 2.80 [95% CI 1.37-5.72], P = .005), were also more willing to use the tag. HCP were willing to carry the RTLS tag for the purpose of contact tracing despite privacy concerns.More communications on the intentions and data protection standards of the RTLS, and accessory enhancements for HCP's convenient and sustained use of the RTLS tag are crucial, to optimize RTLS's usefulness during the COVID-19 pandemic.