Reviewerly/exHarmony huggingface dataset:
Multi‐tier authentication schemes for fog computing: Architecture, security perspective, and challenges

Summary Fog computing has revolutionized the computing domain by enabling resource sharing, such as online storage, and providing applications and software as services in near vicinity to the edge nodes through the Internet. Small‐ to large‐sized companies, like Amazon, Google, Facebook, Twitter, and LinkedIn, have started switching to fog‐computing–enabled infrastructures. Fog computing being distributed in nature and in near vicinity gives rises to security and privacy issues. Although mostly now a days, user identification is adopted via single sign‐in process, such as simple password‐based authentications, which is not a secure process. Several multi‐tier authentication techniques are proposed to overcome single sign‐in process limitations. In this article, we go through state‐of‐the‐art schemes proposed over the period of 2011‐2018 for multi‐tier authentication, their weaknesses and security issues, and finally their solutions for fog‐computing environment. We performed the comparison of available multi‐tier authentication techniques based on three factors, ie, level of security, cost of deployment, and usability. Multi‐tier authentication techniques are classified into categories in accordance with the aspects that are concerned with the authentication process. We are optimistic that this work will provide useful information to the researchers about the architectures of fog enabled systems and the underlying authentication models in a consolidated form.
PathMiner: A Library for Mining of Path-Based Representations of Code

One recent, significant advance in modeling source code for machine learning algorithms has been the introduction of path-based representation -an approach consisting in representing a snippet of code as a collection of paths from its syntax tree.Such representation efficiently captures the structure of code, which, in turn, carries its semantics and other information.Building the path-based representation involves parsing the code and extracting the paths from its syntax tree; these steps build up to a substantial technical job.With no common reusable toolkit existing for this task, the burden of mining diverts the focus of researchers from the essential work and hinders newcomers in the field of machine learning on code.In this paper, we present PathMiner -an open-source library for mining path-based representations of code.Path-Miner is fast, flexible, well-tested, and easily extensible to support input code in any common programming language.
You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection

We propose a novel way of using videos to obtain high precision object proposals for weakly-supervised object detection. Existing weakly-supervised detection approaches use off-the-shelf proposal methods like edge boxes or selective search to obtain candidate boxes. These methods provide high recall but at the expense of thousands of noisy proposals. Thus, the entire burden of finding the few relevant object regions is left to the ensuing object mining step. To mitigate this issue, we focus instead on improving the precision of the initial candidate object proposals. Since we cannot rely on localization annotations, we turn to video and leverage motion cues to automatically estimate the extent of objects to train a Weakly-supervised Region Proposal Network (W-RPN). We use the W-RPN to generate high precision object proposals, which are in turn used to re-rank high recall proposals like edge boxes or selective search according to their spatial overlap. Our W-RPN proposals lead to significant improvement in performance for state-of-the-art weakly-supervised object detection approaches on PASCAL VOC 2007 and 2012.
We Should Not Get Rid of Incivility Online

Incivility and toxicity have become concepts du jour in research about social media. The clear normative implication in much of this research is that incivility is bad and should be eliminated. Extensive research—including some that we’ve authored—has been dedicated to finding ways to reduce or eliminate incivility from online discussion spaces. In our work as part of the Civic Signals Initiative, we’ve been thinking carefully about what metrics should be adopted by social media platforms eager to create better spaces for their users. When we tell people about this project, removing incivility from the platforms frequently comes up as a suggested metric. In thinking about incivility, however, we’ve become less convinced that it is desirable, or even possible, for social media platforms to remove all uncivil content. In this short essay, we discuss research on incivility, our rationale for a more complicated normative stance regarding incivility, and what other orientations may be more useful. We conclude with a post mortem arguing that we should not abandon research on incivility altogether, but we should recognize the limitations of a concept that is difficult to universalize.
Scented since the beginning: On the diffuseness of test smells in automatically generated test code

Software testing represents a key software engineering practice to ensure source code quality and reliability. To support developers in this activity and reduce testing effort, several automated unit test generation tools have been proposed. Most of these approaches have the main goal of covering as more branches as possible. While these approaches have good performance, little is still known on the maintainability of the test code they produce, i.e.,whether the generated tests have a good code quality and if they do not possibly introduce issues threatening their effectiveness. To bridge this gap, in this paper we study to what extent existing automated test case generation tools produce potentially problematic test code. We consider seven test smells, i.e.,suboptimal design choices applied by programmers during the development of test cases, as measure of code quality of the generated tests, and evaluate their diffuseness in the unit test classes automatically generated by three state-of-the-art tools such as Randoop, JTExpert, and Evosuite. Moreover, we investigate whether there are characteristics of test and production code influencing the generation of smelly tests. Our study shows that all the considered tools tend to generate a high quantity of two specific test smell types, i.e.,Assertion Roulette and Eager Test, which are those that previous studies showed to negatively impact the reliability of production code. We also discover that test size is correlated with the generation of smelly tests. Based on our findings, we argue that more effective automated generation algorithms that explicitly take into account test code quality should be further investigated and devised.
A realist evaluation of student use of a virtual reality smartphone application in undergraduate legal education

Abstract This paper provides a realist evaluation of the Open Justice virtual reality (VR) smartphone app, designed to develop presentation skills for students on an undergraduate legal education module. This work addresses two research questions: what proportion of students engaged with the Open Justice app, and what worked for whom in what circumstances and why? Questionnaire and interviews were conducted and analysed using a realist evaluation approach. This approach is particularly pertinent to the evaluation of how emerging educational technologies are used, as it can point to the potential affordances of a technological intervention, in addition to assessing its current use. In keeping with the realist evaluation approach, data analysis was completed using the context–mechanism–outcome framework. The findings suggest that more than half of students did not engage with the app during their studies, but this was not surprising as it was a pilot project into using VR with our students and such innovations can be difficult to manage in distance education contexts. Those who did engage with it recognised the potential of immersive VR to contribute to legal skills development, but they found the application of the technology, in this context, to be of limited use. By utilising a realist evaluation framework, this study contributes to the emerging field of programme theory‐based evaluations of educational technology, which might form the basis of further research. Practitioner Notes What is already known about this topic Smartphone‐based virtual reality technology has developed exponentially and is now widely and cheaply available. Smartphone‐based virtual reality technology has the potential to develop professional skills, and thereby provide a useful addition to experiential learning pedagogies. This is of particular relevance to the development of professional legal skills in the context of clinical legal education. Realist evaluation methodology is increasingly common in health literature to evaluate the impact of education programmes. What this paper adds This is the first empirical study of the use of VR technology to develop professional skills in a distance learning setting. This is the first paper to use realist methodology to evaluate virtual reality technology in legal education. It provides a granular analysis of particular contexts and user responses that can facilitate or inhibit learner engagement with VR technology. Implications for practice and/or policy It provides an analysis of VR used to support teaching and learning, where the students are distance learners—this highlights issues and challenges to be overcome. It provides an evidence base to inform the development of, and further research into, smartphone‐based virtual reality for legal education settings. It provides a detailed example of how realist methodology can be utilised to evaluate educational technology in real‐world settings.
InsideInsights: Integrating Data‐Driven Reporting in Collaborative Visual Analytics

Abstract Analyzing complex data is a non‐linear process that alternates between identifying discrete facts and developing overall assessments and conclusions. In addition, data analysis rarely occurs in solitude; multiple collaborators can be engaged in the same analysis, or intermediate results can be reported to stakeholders. However, current data‐driven communication tools are detached from the analysis process and promote linear stories that forego the hierarchical and branching nature of data analysis, which leads to either too much or too little detail in the final report. We propose a conceptual design for integrated data‐driven reporting that allows for iterative structuring of insights into hierarchies linked to analytic provenance and chosen analysis views. The hierarchies become dynamic and interactive reports where collaborators can review and modify the analysis at a desired level of detail. Our web‐based I nside I nsights system provides interaction techniques to annotate states of analytic components, structure annotations, and link them to appropriate presentation views. We demonstrate the generality and usefulness of our system with two use cases and a qualitative expert review.
Protean Authentication Scheme – A Time-Bound Dynamic KeyGen Authentication Technique for IoT Edge Nodes in Outdoor Deployments

The IoT edge/sensor nodes are exposed to large attack surface and could easily succumb to several well-known attacks in the wireless sensor network (WSN) domain.Authenticating edge nodes before they join a network, especially after a sleep state, is a critical step to maintain the overall trust of any given IoT local area network (IoT LAN).The low resources and computational constraints of such IoT nodes make this a challenging and non-trivial problem.As many IoT deployments are in uncontrolled environments, the IoT devices are often placed in the open so that physical access to them is inevitable.Due to easy physical access of the devices, common attacks, including cloning of devices or stealing secret keys stored in an edge node, are some of the most common attacks on the IoT deployments.This paper focuses on developing an extremely lightweight authentication scheme for constrained end-devices that are part of a given IoT LAN.Authentication occurs between the end-device and the gateway that acts as an edge computing device.The proposed authentication scheme is put through both formal and informal security verification.Voltage drop, current, and power are measured to gauge the overall impact of the security scheme.All the three parameters were measured while configuring the edge node as an end-device or as a router.Our testing results show that the impact on the resources was minimal.
A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, &amp; Safer Presentation for Investigating Child Sexual Abuse Material (CSAM)

For those investigating cases of Child Sexual Abuse Material (CSAM), there is the potential harm of experiencing trauma after illicit content exposure over a period of time. Research has shown that those working on such cases can experience psychological distress. As a result, there has been a greater effort to create and implement technologies that reduce exposure to CSAM. However, not much work has explored gathering insight regarding the functionality, effectiveness, accuracy, and importance of digital forensic tools and data science technologies from practitioners who use them. This study focused specifically on examining the value practitioners give to the tools and technologies they utilize to investigate CSAM cases. General findings indicated that implementing filtering technologies is more important than safe-viewing technologies; false positives are a greater concern than false negatives; resources such as time, personnel, and money continue to be a concern; and an improved workflow is highly desirable. Results also showed that practitioners are not well-versed in data science and Artificial Intelligence (AI), which is alarming given that tools already implement these techniques and that practitioners face large amounts of data during investigations. Finally, the data exemplified that practitioners are generally not taking advantage of tools that implement data science techniques, and that the biggest need for them is in automated child nudity detection, age estimation and skin tone detection.
Imputing missing values using cumulative linear regression

The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly. Of late, Python and R provide diverse packages for handling missing data. In this study, an imputation algorithm, cumulative linear regression, is proposed. The proposed algorithm depends on the linear regression technique. It differs from the existing methods, in that it cumulates the imputed variables; those variables will be incorporated in the linear regression equation to filling in the missing values in the next incomplete variable. The author performed a comparative study of the proposed method and those packages. The performance was measured in terms of imputation time, root-mean-square error, mean absolute error, and coefficient of determination . On analysing on five datasets with different missing values generated from different mechanisms, it was observed that the performances vary depending on the size, missing percentage, and the missingness mechanism. The results showed that the performance of the proposed method is slightly better.
Channel Attention Networks

Multi-band images beyond RGB are becoming popular in both commercial applications and research datasets, yet existing deep learning models were designed for academic RGB datasets. In this talk, we propose Channel Attention Networks (CAN), a deep learning model that uses soft attention on individual channels. We jointly train this model end-to-end on Spacenet, a challenging multi-spectral semantic segmentation dataset. In a comparative study, CAN outperforms previous models. We also demonstrate that CAN is significantly more robust to noise in individual bands than the other models, because the attention network allocates attention away from the noisy channels. Our proposed method marks the first step in designing deep learning algorithms specifically for multi-spectral imagery. Semantic Segmentation; Convolutional Neural Networks; Attention.
Efficient Signcryption With Proxy Re-Encryption and its Application in Smart Grid

Smart grid utilizes intelligent metering and other monitoring devices that frequently collect and send a customer's usage report of energy consumption to an energy service provider (ESP). By mere behavioral inference from usage reports, its viable to adjudge the daily routine, location, and the kind of electrical devices of a customer. The collection and transmission of such data raises security concerns. Hence, its vital to secure ESP's access to a customer's data. In perspective of this, we introduce a data access control scheme which is suitable for smart grids with secure customer data access. In our scheme, the customer is a data owner while an ESP or other third party stakeholder (TPS) is the data user. A data user can have secure access to a customer's data via a gateway device such as energy service interface (ESI). The ESI fits in as proxy that re-encrypts data for authorized data users based on the delegation command from the data owner. Here, ESI is unable to acquire plaintext information on the data. Moreover, only authorized data users are eligible for decrypting and verifying the integrity and authentication of data. We achieve the data access control via a certificateless signcryption with proxy re-encryption (CLSPRE) scheme. Detailed security analysis shows that our CLSPRE scheme is secure in the random oracle model (ROM). Moreover, extensive performance evaluation indicates its efficiency with respect to communication overhead and computation complexity.
Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

In this paper we propose a novel approach for detecting and tracking objects in videos captured by moving cameras without any additional sensor. In such a video both the background and foreground change in each frame of the image sequence; making the separation of actual moving object from the background a challenging task. In this work, moving objects are detected as clusters of spatio-temporal blobs generated by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter and merged using Minimum Spanning Tree. Problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of Kalman filter. The major advantage of the proposed method is that, it does not require initialization or training on sample data to perform. Our algorithm demonstrated very satisfactory state-of-the-art result on benchmark videos. The performance of the algorithm is equivalent or superior to some benchmark algorithms.
AdaFlow: Domain-adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-domain Translation

We tackle unsupervised anomaly detection (UAD), a problem of detecting data that significantly differ from normal data. UAD is typically solved by using density estimation. Recently, deep neural network (DNN)-based density estimators, such as Normalizing Flows, have been attracting attention. However, one of their drawbacks is the difficulty in adapting them to the change in the normal data's distribution. To address this difficulty, we propose AdaFlow, a new DNN-based density estimator that can be easily adapted to the change of the distribution. AdaFlow is a unified model of a Normalizing Flow and Adaptive Batch-Normalizations, a module that enables DNNs to adapt to new distributions. AdaFlow can be adapted to a new distribution by just conducting forward propagation once per sample; hence, it can be used on devices that have limited computational resources. We have confirmed the effectiveness of the proposed model through an anomaly detection in a sound task. We also propose a method of applying AdaFlow to the unpaired cross-domain translation problem, in which one has to train a cross-domain translation model with only unpaired samples. We have confirmed that our model can be used for the cross-domain translation problem through experiments on image datasets.
A Double-Deep Spatio-Angular Learning Framework for Light Field-Based Face Recognition

Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field-based face recognition, which is able to model both the intra-view/spatial and inter-view/angular information using two deep networks in sequence. This is a novel recognition framework that has never been proposed in the literature for face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network, whose inputs are VGG-Face descriptions, computed using a VGG-16 convolutional neural network (CNN). The VGG-Face spatial descriptions are extracted from a selected set of 2D sub-aperture (SA) images rendered from the light field image, corresponding to different observation angles. A sequence of the VGG-Face spatial descriptions is then analyzed by the LSTM network. A comprehensive set of experiments has been conducted using the IST-EURECOM light field face database, addressing varied and challenging recognition tasks. The results show that the proposed framework achieves superior face recognition performance when compared to the state of the art.
LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks

Designing channel codes under low latency constraints is one of the most demanding requirements in 5G standards. However, sharp characterizations of the performances of traditional codes are only available in the large block lengths limit. Code designs are guided by those asymptotic analyses and require large block lengths and long latency to achieve the desired error rate. Furthermore, when the codes designed for one channel (e.g. Additive White Gaussian Noise (AWGN) channel) are used for another (e.g. non-AWGN channels), heuristics are necessary to achieve any non trivial performance - thereby severely lacking in robustness as well as adaptivity. Obtained by jointly designing recurrent neural network (RNN) based encoder and decoder, we propose an end-to-end learned neural code which outperforms canonical convolutional code under block settings. With this gained experience of designing a novel neural block code, we propose a new class of codes under low latency constraint - Low-latency Efficient Adaptive Robust Neural (LEARN) codes, which outperform the state-of-the-art low latency codes as well as exhibit robustness and adaptivity properties. LEARN codes show the potential of designing new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights.