Reviewerly/exHarmony huggingface dataset:
Variability management meets microservices

A microservice implements a small unit of functionality that it provides through a network using lightweight protocols. So, microservices can be combined to fulfill tasks and implement features of a larger software system---resembling a variability mechanism in the context of a software product line (SPL). Microservices and SPLs have similar goals, namely facilitating reuse and customizing, but they are usually employed in different contexts. Any developer who has access to the network can provide a microservice for any task, while SPLs are usually intended to implement features of a specific domain. Due to their different concepts, using microservices to implement an SPL or adopting SPL practices (e.g., variability management) for microservices is a challenging cross-area research problem. However, both techniques can complement each other, and thus tackling this problem promises benefits for organizations that employ either technique. In this paper, we reason on the importance of advancing in this direction, and sketch six concrete challenges to initiate research, namely (1) feature identification, (2) variability modeling, (3) variable microservice architectures, (4) interchangeability, (5) deep customization, and (6) re-engineering an SPL. We intend these challenges to serve as a starting point for future research in this cross-area research direction---avoiding that the concepts of one area are reinvented in the other.
Can Clean New Code Reduce Technical Debt Density?

While technical debt grows in absolute numbers as software systems evolve over time, the density of technical debt (technical debt divided by lines of code) is reduced in some cases. This can be explained by either the application of refactorings or the development of new artifacts with limited Technical Debt. In this paper we explore the second explanation, by investigating the relation between the amount of Technical Debt in new code and the evolution of Technical Debt in the system. To this end, we compare the Technical Debt Density of new code with existing code, and we investigate which of the three major types of code changes (additions, deletions and modifications) is primarily responsible for changes in the evolution of Technical Debt density. Furthermore, we study whether there is a relation between code quality practices and the ‘cleanness’ of new code. To obtain the required data, we have performed a large-scale case study on twenty-seven open-source software projects by the Apache Software Foundation, analyzing 66,661 classes and 56,890 commits. The results suggest that writing “clean” (or at least “cleaner”) new code can be an efficient strategy for reducing Technical Debt Density, and thus preventing software decay over time. The findings also suggest that projects adopting an explicit policy for quality improvement, e.g., through discussions on code quality in board meetings, are associated with a higher frequency of cleaner new code commits. Therefore, we champion the establishment of processes that monitor the density of Technical Debt of new code to control the accumulation of Technical Debt in a software system.
Random walk based distributed representation learning and prediction on Social Networking Services

Social Networking Services (SNSs) provide online platforms for users with two kinds of behavior: user-user social behavior (e.g., following a user, making friends with others) and user-item consumption behavior (e.g., rating, showing likeness, clicking, giving thumbs up to items). With the increasing popularity of SNSs and demand for SNS features, predicting potential social links and recommending preferable items to users have become two hot research lines. However, previous works either modeled just one of these two kinds of behaviors in isolation or only considered the observed user behavior data. In fact, social scientists have long recognized that the user-user and user-item behaviors have a mutual reinforcement effect. On the one hand, the two behaviors have correlations, and they can influence each other. On the other hand, due to the sparsity of the observed user behavior data, the user behavior prediction performance is far from satisfactory, although, using two types of behavioral data at the same time can mitigate the sparsity problem. These two problems remains open: how to better model the correlation of user-user social and user-item consumption activities and how to mitigate the data sparsity issue. In this paper, we propose a random walk based distributed representation learning model to jointly predict these behaviors on SNSs. Specifically, we first construct a joint behavior graph that combines the two behaviors, with the edges denoting the sparse observed user behavior data. Then, we adopt a random walk to capture higher-order relationships between users and items. After that, we utilize a distributed learning approach to embed both users and items into a latent space. In this way, the behavior prediction tasks are transformed into similarity calculations in the latent space. Finally, extensive experimental results using two real-world datasets demonstrate the effectiveness of our proposed approach on the two behavior prediction tasks.
Product line adoption in industry

The software system controlling a train is typically deployed on various hardware architectures and must process various signals across those deployments. The increase of such customization scenarios and the needed adherence of the software to various safety standards in different application domains has led to the adoption of product line engineering within the railway domain. This paper explores the current state-of-practice of software product line development within a team developing industrial embedded software for a train propulsion control system. Evidence is collected using a focus group session with several engineers and through inspection of archival data. We report several benefits and challenges experienced during product line adoption and deployment. Furthermore, we identify and discuss improvement opportunities, focusing mainly on product line evolution and test automation.
Review of camouflage assessment techniques

In military operations signature reduction techniques such as camouflage nets, low-emissive paints, and camouflage patterns are typically deployed to optimize the survivability of high value assets by minimizing their detectability. Various methods have been developed to assess the effectiveness of these camouflage measures. There are three main approaches to the evaluation of camouflage measures: (1) a subjective approach through observer experiments, (2) an objective computational approach through image analysis, and (3) an objective approach through physical measurements. Although subjective evaluation methods have a direct relation with the operational practice, they are often difficult to implement because of time and budget restrictions, or simply because the associated conditions are not safe for the observers. Objective evaluation methods are typically based on the outcome of psychophysical laboratory experiments using simple artificial stimuli, presented under extremely restricted (impoverished) conditions, and in different experimental paradigms. Objective methods based on signal processing techniques have no obvious counterpart in human vision. So far, no attempts have been made to validate any of these objective metrics against the performance of human observers in realistic military scenarios. As a result, there are currently no standard and internationally accepted methods and procedures to evaluate camouflage equipment and techniques, and to indicate their military effectiveness. In this review paper we present an overview of the various subjective (psychophysical) and objective (computational, image or video based) evaluation methods that are currently available and that have been used to validate camouflage effectiveness. In addition, we will discuss the relative merits of field experiments versus laboratory experiments.
Not Afraid of the Unseen: a Siamese Network based Scheme for Unknown Traffic Discovery

As an essential task for network management and security, network traffic classification has attracted increasing attention in recent years. Traditional traffic classification methods achieve certain success in identifying specific application traffic but fail with un-predefined unknown classes. Existing unknown traffic discovery methods commonly pick out some unlabeled testing data as part of training data to train the classification models, which is not in line with the real-world open environments. In this paper, we propose a novel scheme named SEEN to achieve unknown traffic detection in network traffic classification. There are three crucial phases in the SEEN: unknown discovery, unknown clustering, and system update. In the first step, using a metric-based approach with siamese network, SEEN identifies unknown traffic as well as accurately classifies the traffic generated by pre-defined application classes. After discovery, unknown traffic is automatically clustered into more fine-grained categories in the unknown clustering step. In the system update step, inspired by low-shot learning, SEEN allows new classes to be added or unnecessary known classes to be deleted quickly without retraining from the sketch, which can complement the system’s knowledge. Experimental results exhibit that SEEN can achieve outstanding performances both on known and unknown traffic identification on two open real-world datasets, and the proposed scheme can address the problem of unknown traffic effectively.
Integrating Internet of Things, Provenance, and Blockchain to Enhance Trust in Last Mile Food Deliveries

In this article, we discuss our experience of realising a prototype IoT-based food safety monitoring solution which integrates inexpensive off-the-shelf open source IoT technology for monitoring food deliveries, semantic services for managing and reasoning about food safety provenance records, and private blockchain networks for persistent and secure storage of semantic provenance graphs. We describe how observation of real-world contexts was used to develop a prototype device, and the results of field trials deploying these prototypes as part of the food delivery process. Results indicate that continuous, context sensitive, trustworthy temperature measurement could provide benefits to multiple stakeholders across the delivery pathway. However, close attention has to be paid to the technology used - as cheap multi-functional IoT devices may produce low quality sensor observations which adversely affect the utility of the overall solution. Our experience also suggests that future food safety management systems may need to include machine-processable guidelines to support analysis of raw sensor data for food safety compliance.
DeepFisheye

Near-surface multi-finger tracking (NMFT) technology expands the input space of touchscreens by enabling novel interactions such as mid-air and finger-aware interactions. We present DeepFisheye, a practical NMFT solution for mobile devices, that utilizes a fisheye camera attached at the bottom of a touchscreen. DeepFisheye acquires the image of an interacting hand positioned above the touchscreen using the camera and employs deep learning to estimate the 3D position of each fingertip. We created two new hand pose datasets comprising fisheye images, on which our network was trained. We evaluated DeepFisheye's performance for three device sizes. DeepFisheye showed average errors with approximate value of 20 mm for fingertip tracking across the different device sizes. Additionally, we created simple rule-based classifiers that estimate the contact finger and hand posture from DeepFisheye's output. The contact finger and hand posture classifiers showed accuracy of approximately 83 and 90%, respectively, across the device sizes.
Understandable and trustworthy explainable robots: A sensemaking perspective

Abstract This article discusses the fundamental requirements for making explainable robots trustworthy and comprehensible for non-expert users. To this extent, we identify three main issues to solve: the approximate nature of explanations, their dependence on the interaction context and the intrinsic limitations of human understanding. The article proposes an organic solution for the design of explainable robots rooted in a sensemaking perspective. The establishment of contextual interaction boundaries, combined with the adoption of plausibility as the main criterion for the evaluation of explanations and of interactive and multi-modal explanations, forms the core of this proposal.
Lightweight Side-Channel Protection using Dynamic Clock Randomization

Power analysis attacks have evolved rapidly over the past two decades, recently strengthened by advanced deep learning algorithms. However, the application of effective countermeasures such as masking is often challenging in practice due to restricted power and area resources of cryptographic devices. On the other hand, lightweight hiding methods like random data delays often introduce only a small amount of entropy in the execution process, and thus provide only a moderate level of protection. In this work, we propose and evaluate a generic hiding countermeasure based on dynamic clock frequency randomization. We exploit runtime reconfiguration of modern reconfigurable devices to produce a highly unstable clock signal, which yields up to 20 million different execution times for an AES encryption operation. Our design not only creates heavy misalignments in the power traces, but is also highly customizable and can be easily composed with other side-channel countermeasures. We test our approach using recently proposed evaluation methods for desynchronized power traces including sliding-window correlation analysis and deep neural networks. The results show that none of the attacks is able to recover the secret key with one million power traces. Furthermore, we could not detect any first-order leakage in five million encryptions using state-of-the-art leakage assessment.
Locating feature revisions in software systems evolving in space and time

Software companies encounter variability in space as variants of software systems need to be produced for different customers. At the same time, companies need to handle evolution in time because the customized variants need to be revised and kept up-to-date. This leads to a predicament in practice with many system variants significantly diverging from each other. Maintaining these variants consistently is difficult, as they diverge across space, i.e., different feature combinations, and over time, i.e., revisions of features. This work presents an automated feature revision location technique that traces feature revisions to their implementation. To assess the correctness of our technique, we used variants and revisions from three open source highly configurable software systems. In particular, we compared the original artifacts of the variants with the composed artifacts that were located by our technique. The results show that our technique can properly trace feature revisions to their implementation, reaching traces with 100% precision and 98% recall on average for the three analyzed subject systems, taking on average around 50 seconds for locating feature revisions per variant used as input.
Dual Domain Multi-Task Model for Vehicle Re-Identification

Vehicle re-identification (re-id) is an essential task in the field of intelligent transportation systems (ITS). The main goal of re-id is to find the same vehicle in different scenarios, which can is still a challenging task in both ITS and computer vision (CV). The existing vehicle re-identification methods simply combine the coarse-grained and the fine-grained attributes together with multi-task training. However, such combination may still have limited performance in vehicles with trivial appearance differences, or with rare models and colors. To solve this problem, we propose a simple yet effective framework, called dual domain multi-task model (DDM), that divides the vehicle images into two domains based on the frequency. And then two parallel branches are proposed to recover the two domains. Furthermore, a multi-task method is proposed, which combines the classification loss in color and model together with triplet loss for fine-grained distance measurement. Besides, a progressive strategy is used in the training process. Two public datasets, PKU VehicleID and VeRi are used to validate the proposed DDM. The experimental results demonstrate that the proposed approach outperforms the existing methods on both datasets.
A Lightweight Signcryption Method for Perception Layer in Internet-of-Things

Internet-of-Things (IoTs) are one of the most promising technologies in the present world. It connects people, processes and things. The increasing number of devices in IoT invoking various security and privacy issues. Authentication is one of them which should be measured at the very first level when a device comes into the network. Many authentication schemes are in use which follow the traditional cryptographic techniques of first-sign-then-encryption. The low performance and high complexity of these techniques also have urged the development of signcryption and lightweight signcryption techniques which are more beneficial for low-powered devices in IoTs. The literature study also provides a scope of improvement in the existing signcryption techniques. With this objectives, in this paper, the work shows a lightweight signcryption scheme for perception layer devices in IoT. It uses PHOTON-256 as a lightweight hash function which makes the proposed scheme less complex and faster. The performance of the scheme has been measured in terms of complexity, cost analysis and energy consumption. AVISPA validation is performed to evaluate security. The analysis of attacks, comparative experimentation and energy consumption confirm the efficiency of the scheme.
A multi-scale and multi-level feature aggregation network for crowd counting

Recently, crowd counting has drawn widespread attention in computer vision, but it is extremely challenging because of the varying scales and densities. Many existing methods focus on improving the multi-scale representation by utilizing multi-column or multi-branch architectures with different kernel sizes. However, such networks cannot extract the feature maps with large receptive fields due to limitation of depth. In addition, the importance of utilizing the multi-level feature information in a deep network is ignored. In this paper, we propose a multi-scale and multi-level features aggregation network (MFANet) for accurate and efficient crowd counting, and it can be trained by end-to-end. A vital component of the network is the scale and level aggregation module (SLAM), which can extract multi-scale features and make full use of multi-level feature information for more accurate estimation. When six SLAMs are stacked together and applied to our network, our method can achieve the best performance. Furthermore, we introduce a new loss function called normalized Euclidean loss (NEL) to balance the contribution of all samples to network training. To demonstrate the performance of the proposed method, extensive experiments are conducted on four benchmark crowd counting datasets, including ShanghaiTec Part A/B, UCF-CC-50, Mall, and UCF-QNRF. Experimental results show that our MFANet achieves state-of-the-art performance in crowd counting and crowd localization.
Role of global economic policy uncertainty on firms participation in innovation and new product introductions: an empirical study in African SMEs

This study investigates the impact of Global Economic Policy Uncertainty (GEPU) on firms’ key marketing strategies namely, innovation as characterised by R&D spending and new product introductions. Additional analyses investigate whether labour productivity is also impacted. Based on a sample of African SMEs in both the services and manufacturing sectors, the findings from this study indicate GEPU negatively impacts the firm’s propensity to undertake key marketing strategies (i.e. innovation and new product introductions). The implications from this research discuss how African SMEs could pursue less risky marketing strategies in order to survive during times of high economic uncertainty.
Applying social marketing to evaluate current security education training and awareness programs in organisations

The effectiveness of cybersecurity management programs is contingent on improving employee security behaviour. Security education, training, and awareness (SETA) programs aim to drive positive behaviour change in support of cybersecurity objectives. In this paper, we argue that existing SETA programs are suboptimal as they aim to improve employee knowledge acquisition rather than behaviour and belief. We apply social marketing principles to examine SETA practices across six organisations. We find that SETA programs fail to implement the key principles and concepts of social marketing that are essential for positive behaviour change. We therefore propose a novel development process for SETA based on a social marketing approach. We explain how the new approach can be used to develop SETA programs that are focused on behaviour change.
Human action identification by a quality-guided fusion of multi-model feature

Human motion recognition has become an active research area in the field of computer vision due to its wide range of implementations in domains of video monitoring, virtual reality, human–machine interaction. Dealing with the problem that the RGB images cannot provide enough depth information, a multi-modal depth neural network based on joint cost function is proposed for human motion recognition. In the architecture, the features of the RGB video frames are extracted by the 3D CNN architecture while the characteristics of human motion recognition in the SSDDI graphics utilizing depth map are extracted by the LSTM. Moreover, the model utilizes joint cost function including the cross-entropy loss and the distance constraint between the feature space of training samples and their center values within each category. The experimental results on the MSR Action 3D datasets suggest that the proposed model demonstrates a higher accuracy rate than do the other competing models.
Learning adversarial attack policies through multi-objective reinforcement learning

Deep Reinforcement Learning has shown promising results in learning policies for complex sequential decision-making tasks. However, different adversarial attack strategies have revealed the weakness of these policies to perturbations to their observations. Most of these attacks have been built on existing adversarial example crafting techniques used to fool classifiers, where an adversarial attack is considered a success if it makes the classifier outputs any wrong class. The major drawback of these approaches when applied to decision-making tasks is that they are blind for long-term goals. In contrast, this paper suggests that it is more appropriate to view the attack process as a sequential optimization problem, with the aim of learning a sequence of attacks, where the attacker must consider the long-term effects of each attack. In this paper, we propose that such an attack policy must be learned with two objectives in view. On the one hand, the attack must pursue the maximum performance loss of the attacked policy. On the other hand, it also should minimize the cost of the attacks. Therefore, in this paper we propose a novel modelization of the process of learning an attack policy as a Multi-objective Markov Decision Process with two objectives: maximizing the performance loss of the attacked policy and minimizing the cost of the attacks. We also reveal the conflicting nature of these two objectives and use a Multi-objective Reinforcement Learning algorithm to draw the Pareto fronts for four well-known tasks: the GridWorld, the Cartpole, the Mountain car and the Breakout.