Reviewerly/exHarmony huggingface dataset:
The Case against Commercial Antivirus Software: Risk Homeostasis and Information Problems in Cybersecurity

New cybersecurity technologies, such as commercial antivirus software (AV), sometimes fail to deliver on their promised benefits. This article develops and tests a revised version of risk homeostasis theory, which suggests that new cybersecurity technologies can sometimes have ill effects on security outcomes in the short run and little-to-no effect over the long run. It tests the preliminary plausibility of four predictions from the revised risk homeostasis theory using new survey data from 1,072 respondents. The estimations suggest the plausible operation of a number of risk homeostasis dynamics: (1) commercial AV users are significantly more likely to self-report a cybersecurity event in the past year than nonusers, even after correcting for potential reverse causality and informational mechanisms; (2) nonusers become somewhat less likely to self-report a cybersecurity event as the perceived riskiness of various e-mail-based behaviors increases, while commercial AV users do not; (3) the negative short-run effect of commercial AV use on cybersecurity outcomes fade over time at a predicted rate of about 7.03 percentage points per year of use; and (4) after five years of use, commercial AV users are statistically indistinguishable from nonusers in terms of their probability of self-reporting a cybersecurity event as perceptions of risky e-mail-based behaviors increase.
English vocabulary online teaching based on machine learning recognition and target visual detection

Online education has become an important way of learning English at present, and English vocabulary teaching can improve the efficiency of English vocabulary teaching through target visual detection. However, from the existing research, it can be seen that there are still some shortcomings in English vocabulary recognition. In order to improve the English vocabulary recognition effect, based on machine learning recognition technology, this study combines English vocabulary recognition needs of online education to construct an English vocabulary detection model based on convolutional neural network. The model takes the word’s overall feature as the feature extraction principle and adopts the analysis and extraction of the joint segment feature. Moreover, it discards the complicated process of first dividing a single letter and then performing feature extraction and recognition. In addition, this study design example tests to perform algorithm performance analysis. The experimental results show that the proposed algorithm model has certain effects, and it can be used as an auxiliary algorithm for online English vocabulary teaching.
QFD embedded Agile approach on IT startups project management

Given the significant role of startups in the wellbeing of the economy and considering that more than half of the existing startups either work or have activities related to the IT field, in the current paper we have proposed a model meant to aid IT startups in their development process and in achieving customer satisfaction while ensuring the quality of deliverables. The proposed model has been applied and tested on IT startups and the results have been disseminated in the course of the paper. The need for the proposed model arose from the observations regarding the issues that IT startups are faced with in regard to customer satisfaction, timely delivery of products, reaching a level of quality for the offered products and team management.
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning

This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worker in L-FGADMM periodically communicates with two neighbors, in which the periods are separately adjusted for different layers of its deep neural network. A constrained optimization problem for this setting is formulated and solved using the stochastic version of GADMM proposed in our prior work. Numerical evaluations show that by less frequently exchanging the largest layer, L-FGADMM can significantly reduce the communication cost, without compromising the convergence speed. Surprisingly, despite less exchanged information and decentralized operations, intermittently skipping the largest layer consensus in L-FGADMM creates a regularizing effect, thereby achieving the test accuracy as high as federated learning (FL), a baseline method with the entire layer consensus by the aid of a central entity.
Cloud Computing Security Challenges and Threats

In today’s sophisticated life, technology covers all aspects of our daily life, and that creates an environment for the attackers to access and attack a targeted organisation easily with the lowest cost. However, it is difficult for the organisation to detect it easily. Cloud computing, users can store data across the network in an online environment. Moreover, the data can be accessed everywhere across the world. Most people, especially users who do not know enough about the increasing number of attacks and the development of technology, are concerned about keeping their data in clouds because security is a critical issue. In the study of this paper, some methods and techniques are provided to protect data in an online environment. These methods and techniques have the lowest costs and can be employed by every user as an easy way to defend themselves against attackers.
Enhancing Team Interaction and Cross-platform Access in Web-based Collaborative Virtual Environments

Utilization of web-based Collaborative Virtual Environments (CVE) enhances user's interaction over large geographical distances. Deploying a variety of VR technologies increases demands to develop VR applications which would run on various systems and devices. Otherwise, user's access is limited by the supported operating system. Nowadays, concerning the quality and safety of web technologies, it is possible to provide Global Collaborative Virtual Environments (G-CVE) with cross-platform support. This paper introduces the LIRKIS G-CVE web-based system, built on top of the Networked-Aframe framework. The architecture of LIRKIS G-CVE is presented, together with is innovative Smart-client interface (SCI) component, which utilizes smart devices, such as smartphones and tablets, as input devices. The advantage of SCI over the standard input component of the Networked-Aframe is demonstrated via a series of experiments.
Specific emitter identification of radar based on one dimensional convolution neural network

Abstract Specific Emitter Identification (SEI) of radar has become a focus point of research and difficulty in the field of electronic reconnaissance, and the application of Automatic Dependent Surveillance Broadcast (ADS-B) signal in Identification Friend or Foe (IFF) system is beginning to raise increasing concern. At present, the research of deep neural network in the field of target recognition mostly focuses on the modulation pattern recognition of signal, but there are few applications for the specific emitter identification. In order to overcome the shortcoming that the effect of the traditional method is not excellent, a radar emitter target recognition method based on one-dimensional(1D) convolutional neural network is proposed for ADS-B signal in this paper. Taking the sequence data of ADS-B pulse signals as the training and testing samples of emitter target identification, the frequency domain features of ADS-B signal are obtained by Fast Fourier Transform (FFT), and the target identification is achieved by combining the convolution neural network with the fine feature extraction. The result of simulation demonstrates that the specific emitter identification based on frequency domain and one-dimensional(1D) convolutional neural network has an excellent recognition performance, which effectively resolves the shortcomings of the traditional method of feature extraction and low classification accuracy.
Describing and Processing Topology and Quality of Service Parameters of Applications in the Cloud

Abstract Typical cloud applications require high-level policy driven orchestration to achieve efficient resource utilisation and robust security to support different types of users and user scenarios. However, the efficient and secure utilisation of cloud resources to run applications is not trivial. Although there have been several efforts to support the coordinated deployment, and to a smaller extent the run-time orchestration of applications in the Cloud, no comprehensive solution has emerged until now that successfully leverages applications in an efficient, secure and seamless way. One of the major challenges is how to specify and manage Quality of Service (QoS) properties governing cloud applications. The solution to address these challenges could be a generic and pluggable framework that supports the optimal and secure deployment and run-time orchestration of applications in the Cloud. A specific aspect of such a cloud orchestration framework is the need to describe complex applications incorporating several services. These application descriptions must specify both the structure of the application and its QoS parameters, such as desired performance, economic viability and security. This paper proposes a cloud technology agnostic approach to application descriptions based on existing standards and describes how these application descriptions can be processed to manage applications in the Cloud.
An inverse-free Zhang neural dynamic for time-varying convex optimization problems with equality and affine inequality constraints

Time-varying convex optimization problems have attracted a great deal of attention in many fields due to its widespread application. Particularly, the approach to time-varying convex optimization problems with equality and affine inequality constraints simultaneously is a comprehensive but complicated problem at present. In this paper, three types of inverse-free Zhang neural dynamic (ZND) including two noise-tolerance ZND models are proposed and investigated for solving time-varying convex optimization problems with equality and affine inequality constraints. It is noted that the proposed noise-tolerance ZND models own the ability to suppress noise and one of those even achieves finite-time convergency. Compared with previous work, the proposed inverse-free ZND models effectively reduce the computation complexity by simplifying the model structure and conquer the problem of solving real-time matrix inverse during the computation process, which is more appropriate for a wider practical application.
Secure E-Auction System Using Blockchain: UAE Case Study

The emerging e-commerce systems open the way for several applications to be viable from off-line to online system. E-Auction is an effective ecommerce system that allows bidders and sellers to interact through online platforms. However, providing completely secure e-auction system that satisfies security conditions for all players in these systems requires very complex efforts in the traditional design. Blockchain and smart contract, as a revolutionary technology, has attracted the interest of different industries including the designing of e-auction systems. In this paper, our aim is to provide a prototype of secure blockchain e-auction system that lowering the uncertainties about identities of long-distance complex trade in an e-auction system that can be implemented in UAE services, especially, UAE Auction. In our implementation, we use smart contract in order to guarantee the necessary security requirements. The smart contract contains important information about the transaction details such as auctioneer data, the start time and the deadline of auction, the current winner data, and the current highest price.
Steering nonclassicality of Gaussian states

Nonclassicality according to the singularity or negativity of the Glauber $P$-function is a powerful resource in quantum information, with relevant implications in quantum optics. In a Gaussian setting, and for a system of two modes, we explore how $P$-nonclassicality may be conditionally generated or influenced on one mode by Gaussian measurements on the other mode. Starting from the class of two-mode squeezed thermal states (TMST), we introduce the notion of nonclassical steering (NS) and the graphical tool of Gaussian triangoloids. In particular, we derive a necessary and sufficient condition for a TMST to be nonclassically steerable and show that entanglement is necessary. We also apply our criterion to noisy propagation of a twin-beam state and evaluate the time after which NS is no longer achievable. We then generalize the notion of NS to the full set of Gaussian states of two modes and recognize that it may occur in a weak form, which does not imply entanglement, and in a strong form, which implies EPR steerability and, a fortiori, also entanglement. These two types of NS coincide exactly for TMSTs, and they merge with the previously known notion of EPR steering. By the same token, we recognize another operational interpretation of $P$-nonclassicality: It is the distinctive property that allows one-party entanglement verification on TMSTs.
Enhancing Data Security in Cloud Using Blockchain

Daily lots of data is exchanged and loaded on a cloud into different sectors one of which is the health sector. Data exchanged between the patient and doctors need to be secured to gain patients' trust. Blockchain is a mechanism invented to secure data in a more advanced way. Blockchain stores data into chunks that make it hard to decode, which will help provide an extra layer of security. Hash chain is the most reliable part of the blockchain that will help keep the data unreadable. This data can be secured by using a blockchain mechanism at the backend of any hospital website to store the reports of the patients and maintain a two-way authentication for doctor's access to the reports. Using the concepts of dividing data into chunks and establishing an inter-link between each chunk is one of the aspects of blockchain which is implemented on the hospital generated data to inherit blockchain mechanism. In this paper, we have discussed the benefits of using this mechanism to secure patients' reports and how it increases trust in the stored data.
A Hauntology of Participatory Speculation

In this paper I conduct a hauntological analysis of participatory speculation, within the context of a study into understanding the potential for increasing recognition of LGBT+ young people's experiences of hate crime and hate incidents. Hauntology provides a means to further situate accounts of speculation in Participatory Design by sensitising us to the interplay of the virtual and the actual that enables us to expand our sense of the possible. Through understanding how participatory speculation is shaped by absent presences, this paper contributes to the discussion of post-solutionist practices in PD that foster care and responsibility across multiple sites and forms of participation in the face of issues that resist resolution. I conclude by considering by translating speculation into shared spaces of wonder, Participatory Design can foster ethical commitments that stay with the trouble.
Predicting Sentiments in Image Advertisements using Semantic Relations among Sentiment Labels

Understanding the sentiments evoked by advertisements is crucial in serving them appropriately to consumers. Advertisements often use images to evoke sentiments. An image can convey multiple sentiments of different nature. Automatically predicting these multiple sentiments can help serve better advertisements to consumers, especially in an online scenario at scale. In this paper, we present a neural network model based on graph convolution to predict such sentiments, which exploits the semantic relationship among the sentiment labels. We use it to predict multiple sentiment labels using an annotated dataset of 30,340 image-based advertisements. We also find a distance metric that best represents the distribution of sentiments in the dataset and utilizes it in a loss function that separates applicable sentiments from the non-applicable ones. We report an improvement in mean average precision and overall F1 score over a multi-modal multi-task state-of-the-art model.
Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.

Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this study, we use UK primary care electronic health records (EHR) from the CALIBER resource to identify and characterize clinically-meaningful clusters patients using unsupervised learning approaches of MCA and K-means. We discovered and characterized five clusters with different profiles (mental health, non-typical AD, typical AD, CVD and men with cancer). The mental health cluster had faster rate of progression than all the other clusters making it a target for future research and intervention. Our results demonstrate that unsupervised learning approaches can be utilized on EHR to identify subtypes of heterogeneous conditions.
We Need More Power to Stand Up

Stigma, a critical challenge for social justice, has not received much attention in ICTD literature. Most existing designs that aim to combat stigma draw on an 'information and awareness' approach that is often inadequate to address stigma's deeper roots. To address this gap, we have conducted an interview and design study in five special needs schools1 in Dhaka, Bangladesh, involving twenty-nine parents and nine teachers. Based on our study, we present how the primary caretakers of children with autism face the stigma associated with autism spectrum disorder (ASD) and how misogyny, economic apprehension, and misinformation generate this stigma. Drawing from a range of scholarly work in sociology and psychology, we demonstrate how those factors are rooted in the colonial history and contemporary social hierarchy of Bangladesh. Based on our participatory design sessions, we introduce and analyze potential design directions and connect our findings to the politics of inclusion and social justice in the context of the developing world.
DeepFaceAR: Deep Face Recognition and Displaying Personal Information via Augmented Reality

Biometric recognition is a popular topic in machine vision. Deep Neural Networks have been recently used in several applications, especially in biometric recognition. In this paper, we combine a Deep Neural Network alongside Augmented Reality to produce a system capable of recognizing the faces of individuals and displaying some information about the individual as an Augmented Reality. We used a dataset containing 1200 face images of 100 faculty members of the Shahrood University of Technology. After training the proposed Deep Network, it reached the recognition accuracy of 99.45%. We also provided some graphical targets for each person that contains his information. When a person is identified by the deep network, the target image provided for augmented reality is aligned with the angle and dimensions of the detected face and displayed on top of it.
Robust Multi-View Subspace Clustering Via Weighted Multi-Kernel Learning and Co-Regularization

Using multi-kernel learning to deal with the non-linear relationship of data has become a new research topic in the field of multi-view subspace clustering. However, the existing methods have the following three defects: 1) the simple consensus kernel weighting strategy cannot give full play to the advantages of multiple kernels; 2) they are sensitive to non-Gaussian noise and their learning affinity matrices cannot meet the block diagonal properties required by clustering, resulting in low clustering performance; 3) the complementary feature information between the data of each view cannot be fully mined. In this paper, a novel robust multi-view subspace clustering method is proposed based on weighted multi-kernel learning and co-regularization (WMKMSC). Based on the self-expression learning framework, block diagonal regularizer (BDR), multi-kernel learning strategy and co-regularization are integrated into the proposed model. Especially, as a robust learning method, the mixture correntropy is used to construct a robust multi-kernel weighting strategy, which is helpful to learn the best consensus kernel. Our method is more effective and robust than several of the most advanced methods on five commonly used datasets.
JomImage SnapFudo: Control Your Food in a Snap

Tracking calorie of food consumed is becoming harder when eating outside. With many choices of fast food available, the consumption of fast food is high among Malaysian. In 2014, WHO also has reported that, “Malaysia is the fattest country in South East Asia”. Nowadays, there are many calorie tracking applications available in store to help on weight loss. However, these applications may not be sustainable, as it requires many inputs for food logging. This study has proposed to improve the healthy eating habit and promote weight loss through mobile applications for android. This study will involve image recognition of food to minimize the food logging. From the survey resulted, most respondents are aware about their number of BMI but find a difficulty to track their calorie intake when dining out. The opinion concluded from interview, it is hard to know each ingredient and how much the chef put in their meals served. Thus, mobile application come out with the solution of image recognition and minimal design to ease the burden of inputs requirements that most calorie tracking application are struggling now. As the result, the implemented application can give knowledge to user on how to lose weight healthily and motivate them to achieve their weight goals.
Context aware access control for home voice assistant in multi-occupant homes

Home Voice Assistants (HVAs) like the Amazon Echo or the Google Home are increasingly being used to control smart devices in a smarthome. A HVA device takes in a spoken command (“Turn off the living-room lights and television”), and converts it into commands for respective devices. This paper presents CANVAS system, an Context AwareNess for Voice ASsistants system designed for multi-occupant smarthomes. CANVAS is designed to be run on a HVA, and makes use of the sounds in the home to provide additional context information to decide whether to execute the command, prompt for confirmation, or reject the command entirely. CANVAS also includes a pictorial utility to help non-expert users configure these rules. Our experiments indicate that CANVAS has a low computational overhead, making it suitable to be executed in HVAs. Our experiments Mechanical Turk suggest that the pictorial configuration utility is also intuitive.