Reviewerly/exHarmony huggingface dataset:
Combining Local and Global Features Into a Siamese Network for Sentence Similarity

Sentence similarity is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering.However, a variety of linguistic expressions and ambiguities of words in sentences make it difficult to measure sentence similarity.Many studies show that using local features or global features of a sentence will produce satisfactory sentence representations that can be utilized to measure sentence similarity.Local features reflect the relationships of adjacent words for each sentence and the sequence information of a sentence are usually expressed by global features.However, local features lack abilities to capture sequence information while a small amount of extracted global features is not enough to produce sentence representations with good qualities.In this paper, we propose A Hybrid Model combining Local and Global features into a Siamese Network (HM-LGSN) for sentence similarity calculation.We first propose a new convolution neural network architecture called group convolution neural network to extract the most representative local features (or word semantic features).Then we combine these new features with pre-trained embeddings of words as input to the Bidirectional Gated Recurrent Units to extract global features of sentences.Finally, we select the global features to form sentence representations and calculate sentence similarity through Manhattan distance.The experimental results on SICK, MSRVID, STS-B datasets show that the accuracy of our proposed model is significantly improved by combining local features and global features.
Rubik Gaussian-based patterns for dynamic texture classification

Illumination, noise, and changes of environments, scales negatively impact on encoding chaotic motions for dynamic texture (DT) representation. This paper proposes a new method to overcome those issues by addressing the following novel concepts. First, different Gaussian-based kernels are taken into account as an effective filtered pre-processing with low computational cost to point out robust and invariant features. Second, a discriminative operator, named Local Rubik-based Pattern (LRP), is introduced to adequately capture both shape and motion cues of DTs by proposing a new concept of complemented components together with an effective encoding method. In addition, it also addresses a novel thresholding to take into account rich spatio-temporal relationships extracted from a new model of neighborhood supporting region. Finally, an efficient framework for DT description is presented by exploiting operator LRP for encoding various instances of Gaussian-based volumes in order to form a robust descriptor against noise, changes of illumination, scale, and environment. Experiments for DT classification on benchmark datasets have authenticated the interest of our proposal.
The EDMM Modeling and Transformation System

Since deployment automation technologies are heterogeneous regarding their supported features and modeling languages, selecting a concrete technology is difficult and can result in a lock-in. Therefore, we presented the Essential Deployment Metamodel (EDMM) in previous work that abstracts from concrete technologies and provides a normalized metamodel for creating technology-independent deployment models. In this demonstration, we present tool support for EDMM in the form of the EDMM Modeling and Transformation System, which enables (i) creating EDMM models graphically and (ii) automatically transforming them into models supported by concrete deployment automation technologies.
A multi-scale representation model of polyline based on head/tail breaks

This paper proposes a model to quantify the multiscale representation of a polyline based on iterative head/tail breaks. A polyline is first transformed into a corresponding Fourier descriptor consisting of normalized Fourier-series-expansion coefficients. Then, the most significant finite components of the Fourier descriptor are selected and ranked to constitute the polyline constrained Fourier descriptor. Using Shannon’s information theory, information content of the constrained Fourier-descriptor components is defined. Next, head/tail breaks are introduced to iteratively divide the constrained Fourier descriptor into head and tail components according to the heavy-tailed distribution of information contents. Thus, simplified polylines are reconstructed using ordered heads generated from head/tail breaks. Finally, the radical law is introduced and applied to model multiscale polyline representation by quantifying the scale of each simplified polyline. Three experiments are designed and conducted to evaluate the proposed model. The results demonstrate that the proposed model is valid and efficient for quantifying multiscale polyline representation.
A New Approach to Detect Anomalous Behaviour in ATMs

An automated teller machine is an electronics telecommunications device which is utilized by people, mostly to withdraw money. In the present scenario, a fair amount of the population using an ATM machine to withdraw cash are facing a problem of robberies and theft due to lack of security guards. Surveillance cameras being used in the ATM cells, however monitoring capabilities of law enforcement agencies has not kept pace. So, in this system anomalous behavior is detected using CNN and LSTM on the surveillance videos. Accurate recognition of anomalous behavior at a point in time is the most challenging problem for systems. The anomaly as well as non anomaly dataset is fed to a machine and trained to identify abnormal behavior. Classifying the video is quite difficult as it contains spatial as well as temporal data. Remembering the temporal data is what is needed for the successful classification of videos. Previous research that had taken place were only on activity detection of humans and none were proposed for abnormal behaviors in ATMs. Therefore, this system has proposed a method to identify and classify whether it is abnormal behavior or not. As it is becoming very crucial for well beings of our society.
"No powers, man!": A Student Perspective on Designing University Smart Building Interactions

Smart buildings offer an opportunity for better performance and enhanced experience by contextualising services and interactions to the needs and practices of occupants. Yet, this vision is limited by established approaches to building management, delivered top-down through professional facilities management teams, opening up an interaction-gap between occupants and the spaces they inhabit. To address the challenge of how smart buildings might be more inclusively managed, we present the results of a qualitative study with student occupants of a smart building, with design workshops including building walks and speculative futuring. We develop new understandings of how student occupants conceptualise and evaluate spaces as they experience them, and of how building management practices might evolve with new sociotechnical systems that better leverage occupant agency. Our findings point to important directions for HCI research in this nascent area, including the need for HBI (Human-Building Interaction) design to challenge entrenched roles in building management.
The Disruptive Blockchain Security Threats and Threat Categorization

From the last decade, Blockchain is not only disrupting Banking and Financial sectors but also disrupting Automotive, Entertainment, Financial Services, Food & Beverages, Insurance, Oil & Gas, Pharmaceutical, Retail, Social Media and Technology sectors. Blockchain is going to drive the next era of technology wheel, as a de-facto standard of trust to become the most widely adopted technology after the Internet. Blockchain is still young and evolving. Past security threats in Blockchain Platforms raise serious security concerns on the adoption of Blockchain. These security threats are disrupting the adoption of Blockchain. This research article presents a systematic review of the disruptive security threats in Blockchain 1.0 and Blockchain 2.0 based Platforms. This research could be a significant contribution to the literature to formulate a more secure and reliable security framework to boost Blockchain adoption.
Developing the ‘best practices’ of virtual reality design: industry standards at the frontier of emerging media

In this article, the author analyzes the ways in which the practices of virtual reality design are being standardized, focusing specifically on the Oculus Best Practices Guide (OBPG). Instructional writings like the OBPG are fruitful documents from which theories of practice can be extracted and, for companies like Oculus, they serve as alternative mission statements, articulating what Oculus wants and needs the standards and practices of its nascent product-medium to be. The author argues that the OBPG serves to create standards and practices that emphasize and maintain virtual reality (VR) user immersion in order to mitigate the weaknesses in the technology and better conform with VR’s idealized, hypothetical presentation in fiction and marketing rhetoric. The Guide plays a key role in Oculus’s larger attempts to mitigate market risk through the standardization of content across its distribution platforms in order to shape an inchoate technological object into a stable and lucrative entertainment medium. More broadly, the OBPG serves as an example of the specific ways in which market forces act on the development of new media practices, turning ‘standards’ into ‘industry standards’.
A Catalogue of Agile Smells for Agility Assessment

Background: The Manifesto for Agile Development has already inspired many software development methods such as Scrum, XP, and Crystal Reports. However, being “agile” is not trivial and only a few companies are capable of mastering so-called agile practices. Failure to apply the agile approach properly can do more harm than good and may jeopardize the benefits of an agile method. Thus, evaluating an organization's ability to apply agile practices using an agility assessment tool is critical. Aims: In this paper, we extend the metaphor of code smell and introduce the term agile smell to denote the issues and practices that may impair the adoption of the agile approach. The focus of the paper is defining and validating a catalogue of agile smells that can support agility assessment. Method: A literature review and a survey were conducted to identify and confirm the characterization of agile smells. Once identified, the agile smells were organized in a structured catalogue. Results: The literature review found 2376 references published between 2001 and 2018. We selected 55 papers for full consideration and identified 20 agile smells. The survey consulted 20 participants to determine the relevance of the selected agile smells. Conclusion: We have identified a set of 20 agile smells that were ranked according to their relevance. For each smell, we proposed at least one strategy to identify the smell's presence in real projects. The catalogue can be used by companies to support the assessment of their agility ability.
Job Recommendation through Progression of Job Selection

The task of job recommendation has been invariably solved using either a filter-based technique or through recommender systems where categorical features associated with jobs and candidates are used to generate recommendations. Through this paper, we are introducing a novel machine learning model which uses the candidates' job preference over time to incorporate the dynamics associated with highly volatile job market. In addition to that, our approach comprises several other smaller recommendations that contribute to problems of a) generating serendipitous recommendations b) solving the cold-start problem for new jobs and new candidates. We have used skills as embedded features to derive latent competencies from them, thereby expanding the skills of jobs and candidate to achieve more coverage in the skill domain. Our model has been developed and deployed in a real-world job recommender system and the best performance of the click-through rate metric has been achieved through a blend of machine learning and non-machine learning recommendations. The best results have been achieved through Bidirectional Long Short Term Memory Networks (Bi-LSTM) with Attention for recommending jobs through machine learning that forms a major part of our recommendation.
Personality analysis through handwriting recognition

AbstractAbstractThe uniqueness of an individual can be identified according to one's behavior, qualities, skills, characteristics, social connectivity, knowledge which all resemble to a single term called, "Personality." There are 'n' number of ways to identify personality of an individual like judging the face gesture, way of speaking or communication, dressing style, questionnaire format, public behavior, etc. To identify what kind of a personality an individual has, traits can be classified based on individual's handwriting. And this can be identified using the field 'Graphology'. Graphological tests are the most effective method to measure (a candidate's) 'fit' or match, for a position being applied for. According to research, it is proven that person's brain is only 15-20% active while communicating whereas the same brain is active around 85-90% when writing. Hence, analysis of handwriting stands at par to describe and analyze the personality traits of individuals. The paper showcases a landscape of handwriting recognition strategies deployed for personality analysis, some of which involve the use of traditional neural networks like Convolution Neural Network and Recurrent Neural Network. The results presented in this paper obtained by implementing the neural networks such as a Long Short-Term Memory or a Fuzzy Logic along with possible classifier sets are significant enough in terms of higher accuracy and recognition.Subject Classification: Primary 68T10Secondary 62H30Keywords: Convolutional Neural NetworkLong Short-Term MemoryFuzzy LogicMulti-ModelingDeep LearningOptical Character Recognition
Efficient Resource Allocation with Score for Reliable Task Scheduling in Cloud Computing Systems

Parallel task execution in a heterogeneous cloud computing system emerges as NP-complete problem and in the literature, many heuristics behave differently when deployed in various environments. Efficient resource allocation improves reliability and leads to the completion of jobs and minimization of delays. In general, static task scheduling algorithms consider the earliest finish time (EFT) of the task to minimize the makespan. In this work, we propose a new heuristic called Efficient Resource Allocation with Score (ERAS) for reliable task scheduling in cloud computing systems, which considers temporal operational availability of Virtual Machines (VM) by considering various types of delays and EFT to assign a normalized score to the processor for scheduling tasks. Here, the allocation of VMs to tasks is based on a score given to each VM, by considering multiple criteria. The results show that ERAS algorithm gives better performance with increased reliability when compared to existing algorithms that consider only EFT for allocation.
Speed Up Image Encryption by Using RSA Algorithm

Since the existing RSA Algorithm needs long key sizes, requires big memory spaces and also a long execution time, it seems necessary to make some changes or taking some necessary precautions during the implementation of RSA in order to provide maximum security level. In this paper, three experiments were developed in order to examine the execution time of the processes of encryption and decryption and comparing the results together to elicit the improved points and discussing them. Moreover, some programming techniques will be used in order to speed up the process of encryption or decryption. The results indicate that the execution time of encryption process was improved by 14% in experiment 3 by using some techniques to speed up the process, the decryption process was improved as well by 22%.
Global Spatio-Temporal Attention for Action Recognition Based on 3D Human Skeleton Data

The human skeleton joints captured by RGB-D camera are widely used in action recognition for its robust and comprehensive 3D information. Presently, most action recognition methods based on skeleton joints treat all skeletal joints with the same importance spatially and temporally. However, the contributions of skeletal joints vary significantly. Hence, a GL-LSTM+Diff model is proposed to improve the recognition of human actions. A global spatial attention (GSA) model is proposed to express the different weights for different skeletal joints to provide precise spatial information for human action recognition. The accumulative learning curve (ALC) model is introduced to highlight which frames contribute most to the final decision making by giving varying temporal weights to each intermediate accumulated learning results. By integrating the proposed GSA (for spatial information) and ALC (for temporal processing) models into the LSTM framework and taking the human skeletal joints as inputs, a global spatio-temporal action recognition framework (GL-LSTM) is constructed to recognize human actions. Diff is introduced as the preprocessing method to enhance the dynamic of the features, thus to get distinguishable features in deep learning. Rigorous experiments on the largest dataset NTU RGB+D and the common small dataset SBU show that the algorithm proposed in this paper outperforms other state-of-the-art methods.
Visual attention-aware quality estimation framework for omnidirectional video using spherical Voronoi diagram

Omnidirectional video (ODV) enables viewers to look at every direction from a fixed point and provides a much more immersive experience than traditional 2D video. Assessing the video quality is important for delivering ODV to the end-user with the best possible quality. For this goal, two aspects of ODV should be considered. The first is the spherical nature of ODV and the related projection distortions when the ODV is stored in a planar format. The second is the interactive look-around consumption nature of ODV. Related to this aspect, visual attention, that identifies the regions that attract the viewer’s attention, is important for ODV quality assessment. Considering these aspects, in this paper, we study in particular objective full-reference quality assessment for ODV. To this end, we propose a quality assessment framework based on the spherical Voronoi diagram and visual attention. In this framework, a given ODV is subdivided into multiple planar patches with low projection distortions using the spherical Voronoi diagram. Afterwards, each planar patch is analyzed separately by a quality metric for traditional 2D video, obtaining a quality score for each patch. Then, the patch scores are combined based on visual attention into a final quality score. To validate the proposed framework, we create a dataset of ODVs with scaling and compression distortions, and conduct subjective experiments in order to gather the subjective quality scores and the visual attention data for our ODV dataset. The evaluation of the proposed framework based on our dataset shows that both the use of the spherical Voronoi diagram and visual attention are crucial for achieving state-of-the-art performance.
Efficient confidentiality-preserving data analytics over symmetrically encrypted datasets

In the past decade, cloud computing has emerged as an economical and practical alternative to in-house datacenters. But due to security concerns, many enterprises are still averse to adopting third party clouds. To mitigate these concerns, several authors have proposed to use partially homomorphic encryption (PHE) to achieve practical levels of confidentiality while enabling computations in the cloud. However, these approaches are either not performant or not versatile enough. We present two novel PHE schemes, an additive and a multiplicative homomorphic encryption scheme, which, unlike previous schemes, are symmetric. We prove the security of our schemes and show they are more efficient than state-of-the-art asymmetric PHE schemes, without compromising the expressiveness of homomorphic operations they support. The main intuition behind our schemes is to trade strict ciphertext compactness for good "relative" compactness in practice, while in turn reaping improved performance. We build a prototype system called Symmetria that uses our proposed schemes and demonstrate its performance improvements over previous work. Symmetria achieves up to 7× average speedups on standard benchmarks compared to asymmetric PHE-based systems.
The Role of Health Concerns in Phishing Susceptibility: Survey Design Study

Phishing is a cybercrime in which the attackers usually impersonate a trusted source. The attackers usually send an email that contains a link that allows them to steal the receiver's personal information. In the United States, phishing is the number one cybercrime by victim count according to the Federal Bureau of Investigation's 2019 internet crime report. Several studies investigated ways to increase awareness and improve employees' resistance to phishing attacks. However, in 2019, successful phishing attacks continued to rise at a high rate.The objective of this study was to investigate the influence of personality-based antecedents on phishing susceptibility in a health care context.Survey data were collected from participants through Amazon Mechanical Turk to test a proposed conceptual model using structural equation modeling.A total of 200 participants took part. Health concerns, disposition to trust, and risk-taking propensity yielded higher phishing susceptibility. This highlights the important of personality-based factors in phishing attacks. In addition, females had a higher phishing susceptibility than male participants.While previous studies used health concerns as a motivator for contexts such as sharing personal health records with providers, this study shed light on the danger of higher health concerns in enabling the number one cybercrime.
Simulate Deutsch-Jozsa algorithm with metamaterials

During the past few years, a lot of efforts have been devoted in studying optical analog computing with artificial structures. Up to now, much of them are primarily focused on classical mathematical operations. How to use artificial structures to simulate quantum algorithm is still to be explored. In this work, an all-dielectric metamaterial-based model is proposed and realized to demonstrate the quantum Deutsch-Jozsa algorithm. The model is comprised of two cascaded functional metamaterial subblocks. The oracle subblock encodes the detecting functions (constant or balanced), onto the phase distribution of the incident wave. Then, the original Hadamard transformation is performed with a graded-index subblock. Both the numerical and experimental results indicate that the proposed metamaterials are able to simulate the Deutsch-Jozsa problem with one round operation and a single measurement of the output eletric field, where the zero (maximum) intensity at the central position results from the destructive (constructive) interference accompanying with the balance (constant) function marked by the oracle subblock. The proposed computational metamaterial is miniaturized and easy-integration for potential applications in communication, wave-based analog computing, and signal processing systems.