Reviewerly/exHarmony huggingface dataset:
An adversarial learning approach for discovering social relations in human-centered information networks

Abstract The analytics on graph-structured data in cyber spaces has advanced many human-centered computing technologies. However, if only utilizing the structural properties, we might be prohibited from unraveling unknown social relations of nodes especially in the structureless networked systems. Up-to-date ways to unfold latent relationships from graph-structured data are network representation learning (NRL) techniques, but it is difficult for most existing ones to deal with the network-structureless situations due to the fact that they largely depend on the observed connections. With the ever-broader spectrum of human-centered networked systems, large quantities of textual information have been generated and collected from social and physical spaces, which may provide the clues of hidden social relations. In order to discover latent social relations from the accompanied text resources, this paper attempts to bridge the gap between text data and graph-structured data so that the textual information can be encoded to substitute for those incomplete structural information. Generative adversarial networks (GANs) are employed in the cross-modal framework to make the transformed data indistinguishable in graph-domain space and also capable of depicting structure-aware relationships with network homophily. Experiments conducted on three text-based network benchmarks demonstrate that our approach can reveal more realistic social relations from text-domain information compared against the state-of-the-art baselines.
A Hybrid Approach of Lexicon-based and Corpus-based Techniques for Arabic Book Aspect and Review Polarity Detection

The problem of Aspect Based Sentiment Analysis (ABSA) has been studied very well in English language, and there are many approaches have been suggested for ABSA.However, there are other languages did not find enough attention in this field.One of these languages: Arabic language that covers a large area of the world.ABSA contains several tasks.This paper addresses for both aspect polarity and review polarity detection.A hybrid approach proposed to treat polarity detection problem, which consists of corpus-based approach (review book corpus) and lexical-based approach (automatic lexicon) used for this work.The work starts by annotating dataset and cleaning it from a noisy data, after that automatically building the lexicon with sensible words with their polarity that hints to extract aspects with their polarity.Experimental results showed that the proposed system got an acceptable result.
Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting

We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions. Similar to other factor analysis methods, DSARF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensional latent variables that are inferred using stochastic variational inference. DSARF is different from the state-of-the-art techniques in that it parameterizes the weights in terms of a deep switching vector auto-regressive likelihood governed with a Markovian prior, which is able to capture the non-linear inter-dependencies among weights to characterize multimodal temporal dynamics. This results in a flexible hierarchical deep generative factor analysis model that can be extended to (i) provide a collection of potentially interpretable states abstracted from the process dynamics, and (ii) perform short- and long-term vector time series prediction in a complex multi-relational setting. Our extensive experiments, which include simulated data and real data from a wide range of applications such as climate change, weather forecasting, traffic, infectious disease spread and nonlinear physical systems attest the superior performance of DSARF in terms of long- and short-term prediction error, when compared with the state-of-the-art methods.
Test Case Understandability Model

Several automated test case generation techniques have been proposed to date, although the adoption of such techniques in the industry remains low.A key factor that has contributed to this low adoption rate is the difficulty experienced by the developer in terms of reading and understanding automatically generated test cases.For this reason, it is essential to construct a test case understandability model for improving the generated test case.In the present paper, we extracted 20 test case metrics, six developer related metrics and two understandability proxies from a white-box test case classification experiment.Based on these metrics, we employed classification and regression algorithms to build test case understandability model.From the experiment, we can conclude that combined metrics always exhibit better discriminatory performance in classification models as well as a higher correlation in regression models when compared to a model that involved only test case metrics or developer metrics.
DanceIt: Music-Inspired Dancing Video Synthesis

Close your eyes and listen to music, one can easily imagine an actor dancing rhythmically along with the music. These dance movements are usually made up of dance movements you have seen before. In this paper, we propose to reproduce such an inherent capability of the human-being within a computer vision system. The proposed system consists of three modules. To explore the relationship between music and dance movements, we propose a cross-modal alignment module that focuses on dancing video clips, accompanied by pre-designed music, to learn a system that can judge the consistency between the visual features of pose sequences and the acoustic features of music. The learned model is then used in the imagination module to select a pose sequence for the given music. Such pose sequence selected from the music, however, is usually discontinuous. To solve this problem, in the spatial-temporal alignment module we develop a spatial alignment algorithm based on the tendency and periodicity of dance movements to predict dance movements between discontinuous fragments. In addition, the selected pose sequence is often misaligned with the music beat. To solve this problem, we further develop a temporal alignment algorithm to align the rhythm of music and dance. Finally, the processed pose sequence is used to synthesize realistic dancing videos in the imagination module. The generated dancing videos match the content and rhythm of the music. Experimental results and subjective evaluations show that the proposed approach can perform the function of generating promising dancing videos by inputting music.
General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

We concentrate on the importance and future conceptual development of wearable devices as the major means of personalized healthcare. We discuss and address the role of wearables in the new era of healthcare in proactive medicine. This work addresses the behavioral, environmental, physiological, and psychological parameters as the most effective domains in personalized healthcare, and the wearables are categorized according to the range of measurements. The importance of multi-parameter, multi-domain monitoring and the respective interactions are further discussed and the generation of wearables based on the number of monitoring area(s) is consequently formulated.
Secure Attestation of Virtualized Environments

Securing the integrity of virtualized environments like clouds is challenging yet feasible. Operators have discovered the advantages of virtualization technology in terms of flexibility, scalability, cost-effectiveness, and availability. Applications range from network and embedded devices to big data centers and cloud computing. Trusted Computing technology can be employed to protect the integrity of a system by leveraging a Trusted Platform Module (TPM) and remote attestation.Existing research on remote attestation of virtualized environments differs in scalability, resource consumption, and provided security guarantees. While some approaches scale at large and use the TPM efficiently, they are way more intrusive, requiring changes to hypervisor and Virtual Machine (VMs). Others render entirely impractical with an increasing number of VMs, caused by the TPM being the bottleneck.In this paper we analyze existing work on remote attestation for virtualized environments and discuss benefits as well as shortcomings. We identify an approach that provides adequate security and is easy to implement but is prone to relay attacks. We improve that approach by developing countermeasures, while maintaining existing security guarantees. Our contribution requires only minimal changes to the hypervisor system, keeping existing attestation protocols intact. We implement and evaluate on production-grade hardware, and compare our improved attestation approach with the most sophisticated alternative approach.With performance measurements and further evaluations we show that our solution outperforms the other approach for a small number of VMs, as used in network devices and embedded systems.
An Effective Adversarial Attack on Person Re-Identification in Video Surveillance via Dispersion Reduction

Person re-identification across a network of cameras, with disjoint views, has been studied extensively due to its importance in wide-area video surveillance. This is a challenging task due to several reasons including changes in illumination and target appearance, and variations in camera viewpoint and camera intrinsic parameters. The approaches developed to re-identify a person across different camera views need to address these challenges. More recently, neural network-based methods have been proposed to solve the person re-identification problem across different camera views, achieving state-of-the-art performance. In this paper, we present an effective and generalizable attack model that generates adversarial images of people, and results in very significant drop in the performance of the existing state-of-the-art person re-identification models. The results demonstrate the extreme vulnerability of the existing models to adversarial examples, and draw attention to the potential security risks that might arise due to this in video surveillance. Our proposed attack is developed by decreasing the dispersion of the internal feature map of a neural network to degrade the performance of several different state-of-the-art person re-identification models. We also compare our proposed attack with other state-of-the-art attack models on different person re-identification approaches, and by using four different commonly used benchmark datasets. The experimental results show that our proposed attack outperforms the state-of-art attack models on the best performing person re-identification approaches by a large margin, and results in the most drop in the mean average precision values.
Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud

Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.
Unsupervised Monocular Depth Estimation Method Based on Uncertainty Analysis and Retinex Algorithm

Depth estimation of a single image presents a classic problem for computer vision, and is important for the 3D reconstruction of scenes, augmented reality, and object detection. At present, most researchers are beginning to focus on unsupervised monocular depth estimation. This paper proposes solutions to the current depth estimation problem. These solutions include a monocular depth estimation method based on uncertainty analysis, which solves the problem in which a neural network has strong expressive ability but cannot evaluate the reliability of an output result. In addition, this paper proposes a photometric loss function based on the Retinex algorithm, which solves the problem of pulling around pixels due to the presence of moving objects. We objectively compare our method to current mainstream monocular depth estimation methods and obtain satisfactory results.
SOCIAL MEDIA AND ITS INFLUENCE ON VOCABULARY AND LANGUAGE LEARNING: A CASE STUDY

Social media is a prominent medium of communication and used by all generations. Besides a tool of communication, institutions have integrated social media such as Twitter, Facebook, and YouTube as learning tools to deliver new information and connect with students. The popularity social media gained over the years has become a debate whether social media platforms are effective teaching and learning tool. This study attempts to seek the influence of social media on English vocabulary development among students in public and private universities in Malaysia. This study also aims to explore the influence of social media on interest in language learning skill. Besides, this study investigates how these platforms cause positive and negative influence language learning. Data and responses for this study made use of questionnaires. The questionnaire data was then analyzed quantitatively using SPSS. The findings revealed that social media sparks language learning interest among English learners. &#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0710/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Phishing Detection using Machine Learning Techniques

A phishing email is legal-looking email which may be planned with trap the beneficiary under trusting that same as certifiable email, Furthermore Possibly uncovers delicate data or downloads pernicious injecting codes through clicking ahead pernicious joins held in the particular figure of the email. There would various provisions receptive to phishing ID number. However, Dissimilar to predicting spam there need aid exactly couple of focuses that ponder machine Taking in routines to anticipating phishing. In this paper an information set is used to arrange those phishing identification those display dataset employments choice tree to predicting phishing messages. We would be setting off should investigate consideration of extra variables of the data set, which might enhance the predictive correctness of classifiers. For example, analysing email headers need demonstrated will move forward the prediction ability What's more diminishing those misclassification rate about classifiers.
Machine Learning and Feature Selection Approaches for Categorizing Arabic Text: Analysis, Comparison, and Proposal

This work adopts some classification approaches for categorizing Arabic text. The approaches are operated on two datasets as test-beds. A comparative study is done to evaluate the performance of the adopted classifiers. Some feature selection methods are also analyzed, investigated, and evaluated. Selecting the most significant features is important because the huge number of features may cause performance degradation for text classification. A comparative study is done among the adopted feature selection methods for classifying Arabic documents. Moreover, a modification is done on the feature selection approaches by doing amalgamation for the chosen methods. A novel method is also proposed for selecting the most appropriate features. The method is based on the semantic fusion and multiple-words (SF-MW) for constructing the features. A comparison is done among the adopted feature selection methods and the proposed one. The experimental results show that the best performance was for the SVM classifier compared to the KNN and NB classifiers. The combination among the adopted feature selection methods presents better results compared to the individual adopted ones. The proposed feature selection method (SF-MW) is promising as it reduced the features and achieved higher classification accuracy. The accuracy improvement was about 22% for the two chosen Arabic test-beds which contain 1246 and 1500 documents respectively. The proposed method is expected to be also efficient for other Arabic and English datasets.
Multi-Camera-Based Universal Measurement Method for 6-DOF of Rigid Bodies in World Coordinate System

The measurement of six-degrees-of-freedom (6-DOF) of rigid bodies plays an important role in many industries, but it often requires the use of professional instruments and software, or has limitations on the shape of measured objects. In this paper, a 6-DOF measurement method based on multi-camera is proposed, which is accomplished using at least two ordinary cameras and is made available for most morphological rigid bodies. First, multi-camera calibration based on Zhang Zhengyou’s calibration method is introduced. In addition to the intrinsic and extrinsic parameters of cameras, the pose relationship between the camera coordinate system and the world coordinate system can also be obtained. Secondly, the 6-DOF calculation model of proposed method is gradually analyzed by the matrix analysis method. With the help of control points arranged on the rigid body, the 6-DOF of the rigid body can be calculated by the least square method. Finally, the Phantom 3D high-speed photogrammetry system (P3HPS) with an accuracy of 0.1 mm/m was used to evaluate this method. The experiment results show that the average error of the rotational degrees of freedom (DOF) measurement is less than 1.1 deg, and the average error of the movement DOF measurement is less than 0.007 m. In conclusion, the accuracy of the proposed method meets the requirements.
ODO: Design of Multimodal Chatbot for an Experiential Media System

This paper presents the design of a multimodal chatbot for use in an interactive theater performance. This chatbot has an architecture consisting of vision and natural language processing capabilities, as well as embodiment in a non-anthropomorphic movable LED array set in a stage. Designed for interaction with up to five users at a time, the system can perform tasks including face detection and emotion classification, tracking of crowd movement through mobile phones, and real-time conversation to guide users through a nonlinear story and interactive games. The final prototype, named ODO, is a tangible embodiment of a distributed multimedia system that solves several technical challenges to provide users with a unique experience through novel interaction.
The Politics of Materiality: Exploring Participatory Design Methods, Tools and Practices

As participatory design approaches (PD) become incorporated into mainstream organizational practice, it becomes increasingly important for designers to consider how can participatory design tools, methods and practices be made more accessible, understandable and engaging for a broader audience.This paper presents a descriptive analytical framework that explores the materiality of artefacts used in PD, through various studies of the interaction of material and relational considerations and the implications of these interactions on design outcomes.We develop this framework by drawing lessons from (1) existing frameworks on materiality and PD tools; and(2) a series of empirical studies exploring materiality through a suite of artefacts, across different contexts and studies.We highlight the utility of this framework as a tool to reduce barriers for participation in design activities and increase participant engagement.
Case Method in Vocational Training for Future Specialists of Culture and Art

Today, the priority task of the higher education institution in the artistic specialty is to prepare a future specialist in the Folk-Art Culture, capable of carrying out creative and innovative activities at a high professional level.One of the effective methods of teaching the humanities is the case study method, but its applicability in the training of cultural and art workers requires adequate research.The study is aimed at evaluating the effectiveness of the case method when training in the Folk-Art Culture specialty.The study used a complex of theoretical (analysis of scientific literature and pedagogical experience in related fields of pedagogy) and empirical (pedagogical experiment) research methods.The article considers the application of the case method in the preparation of specialists in arts and culture and describes several cases applied in the process of mastering folk culture.Results of the research on the influence of the case method on the educational process allow the authors to say that the case method, aimed at independent knowledge acquisition by students, provides for the education of comprehensively developed, responsible, and thinking persons.The main task of a modern educator is to select educational technology adequate to the specialty, forming students' analytical skills, as well as their ability to independently acquire knowledge, apply it in problem-solving, and make a choice in a non-standard situation.The main conclusion of the research is that due to its versatility, the case method can be adapted to the educational process in arts and culture universities at different educational stages.
Pathological Airway Segmentation with Cascaded Neural Networks for Bronchoscopic Navigation

Robotic bronchoscopic intervention requires detailed 3D airway maps for both localisation and enhanced visualisation, especially at peripheral airways. Patient-specific airway maps can be generated from preoperative chest CT scans. Due to pathological abnormalities and anatomical variations, automatically delineating the airway tree with distal branches is a challenging task. In the paper, we propose a cascaded 2D+3D model that has been tailored for airway segmentation from pathological CT scans. A novel 2D neural network is developed to generate the initial predictions where the peripheral airways are refined by a 3D adversarial training model. A sampling strategy based on a sequence of morphological operations is employed for the concatenation of the 2D and 3D models. The method has been validated on 20 pathological CT scans with results demonstrating improved segmentation accuracy and consistency, especially in peripheral airways.