Reviewerly/exHarmony huggingface dataset:
Usability implications in software architecture: The case study of a mobile app

Abstract Usability is a highly desired but often ignored software quality. Effective user interfaces tend to increase learnability, effectiveness and user satisfaction. But usability is often neglected in the early stages of software development and is thus frequently not address in a system's architectural design. Furthermore, because usability is often neglected or deferred, it is difficult to estimate the effort specifically spent on adding or improving usability mechanisms in the software architecture and in code. In this work, we present a case study where we analyze the impact of introducing a variety of usability mechanisms to a mobile application and we report on the architectural changes that must be made in the software architecture to accommodate them. We also report on the additional code required to implement these usability mechanisms and we investigate the users' satisfaction of combining and using several usability mechanisms in a mobile application. Finally, we evaluated user responses—both positive and negatives—to these usability mechanisms via a questionnaire involving 25 users from different roles and different organizations and including contextual interviews to analyze the qualitative responses of the subjects. Our results show that apart from the relatively modest modeling and coding effort, the combination of several usability mechanisms seems beneficial for users of mobile systems and increases the satisfaction during the interaction with the system.
Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.
Firm investment decisions for information security under a fuzzy environment: a game-theoretic approach

Purpose This paper aims to examine optimal decisions for information security investments for a firm in a fuzzy environment. Under both sequential and simultaneous attack scenarios, optimal investment of firm, optimal efforts of attackers and their economic utilities are determined. Design/methodology/approach Throughout the analysis, a single firm and two attackers for a “firm as a leader” in a sequential game setting and “firm versus attackers” in a simultaneous game setting are considered. While the firm makes investments to secure its information assets, the attackers spend their efforts to launch breaches. Findings It is observed that the firm needs to invest more when it announces its security investment decisions ahead of attacks. In contrast, the firm can invest relatively less when all agents are unaware of each other’s choices in advance. Further, the study reveals that attackers need to exert higher effort when no agent enjoys the privilege of being a leader. Research limitations/implications In a novel approach, inherent system vulnerability of the firm, financial benefit of attackers from the breach and monetary loss suffered by the firm are considered, as fuzzy variables in the well-recognized Gordon – Loeb breach function, with the help of fuzzy expectation operator. Practical implications This study reports that the optimal breach effort exerted by each attacker is proportional to its obtained economic benefit for both sequential and simultaneous attack scenarios. A set of numerical experiments and sensitivity analyzes complement the analytical modeling. Originality/value In a novel approach, inherent system vulnerability of the firm, financial benefit of attackers from the breach and monetary loss suffered by the firm are considered, as fuzzy variables in the well-recognized Gordon – Loeb breach function, with the help of fuzzy expectation operator.
Development of a real-time security management system for restricted access areas using computer vision and deep learning

The safety of railways, the nation's main transportation network, is currently drawing attention. This is mainly because of recent terrorist attacks aimed at private multipurpose facilities in a number of foreign countries. This article proposes a system for real-time monitoring of railway facilities and secure areas. Access control will be obtained using Raspberry Pi, an inexpensive micro-controller connected to the cloud via Amazon Web Service. Real-time surveillance is demonstrated by implementing computer vision and deep learning, and Twilio API. Intruders in restricted areas (such as tracks and electrical installations) can be detected with high precision and notifications can be sent to the safety and security managers in real time via short message service through cloud applications. The proposed system will assist the safety and security managers in responding swiftly and effectively to prevent or minimize risks that arise due to intruders.
Fuzzy logic based performance analysis of educational mobile game for engineering students

Abstract The aim of this study is to examine the effect of an educational android mobile game on the attitude of engineering students. For this purpose, an educational mobile game is developed and a fuzzy logic based attitude determination system is modeled. The study is carried out with 30 students of mechatronic engineering. A mobile game which is called Select Box—Solve Question has been developed in the android studio environment. Besides this; to analyse the performance of the game on the success of education, a fuzzy logic‐based attitude determination system, which consists of four number of inputs and outputs, is modeled. These input and output values constitute the factors of the attitude scale. The Triangular Membership Function is used as the Fuzzification step of the Fuzzy Logic Technique. Four different defuzzification methods are used and the results of these methods are compared and analysed.
Automatic Assignment of Reviewers to Papers Based on Vector Space Text Analysis Model

The paper outlines a series of experiments aiming to test whether the vector space text analysis model could be successfully and reliably used to automatically determine the level of competence of a reviewer to evaluate a specific paper. The model corresponds to an implicit method of describing papers and reviewers' competences that does not require users to perform any additional actions. The similarity factors between papers and reviewers are calculated based on content analysis of publications' abstracts. The document collection consists of all submitted papers and reviewers' previous publications. The latter are retrieved from external Internet resources -- Semantic Scholar and DBLP.
Application of machine learning algorithms for code smell prediction using object-oriented software metrics

Code smells are generally not considered as bugs; instead, they point out certain shortcomings in the software design or code. Identification of code smell is a necessary step for improving the software quality and reducing the maintenance effort. In this study, we introduce a bad smell prediction technique based on object-oriented software metrics that use Decision Tree (DT) and Random Forest (RF) machine learning algorithm. An open-source project, namely JHOTDRAW, was used as our dataset, for which values of object-oriented software metrics were calculated. Two feature selection methods-Random Forest Importance (RFI) and Information Gain (IG) were applied to extract the most relevant attributes for the prediction of code smells, namely, Feature envy, Dispersed coupling, refused parent bequest, and God class. The random-search algorithm was used to tune the parameters of Random Forest and Decision Tree. Results show that the best classification accuracy for Decision Tree was obtained at 99.13% for refused parent bequest code smells. Results also show that after using the Random Forest classifier, refused parent bequest smell was predicted with the best accuracy of 99.14%. Finally, in this research study, a set of code smell prediction rules were extracted using Decision Tree.
Bivariate Polynomial Coding for Straggler Exploitation with Heterogeneous Workers

Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed matrix multiplication. Previous works employ univariate polynomials to encode matrix partitions. Such schemes greatly improve the speed of distributed computing systems by making the task completion time to depend only on the fastest workers. However, they completely ignore the work done by the slowest workers resulting in inefficient use of computing resources. In order to exploit the partial computations of the slower workers, we further decompose the overall matrix multiplication task into even smaller subtasks, and we propose bivariate polynomial codes. We show that these codes are a more natural choice to accommodate the additional decomposition of subtasks, and to exploit the heterogeneous storage and computation resources at workers. However, in contrast to univariate polynomial decoding, guarantying decodability with multivariate interpolation is much harder. We propose two bivariate polynomial coding schemes and study their decodability conditions. Our numerical results show that bivariate polynomial coding considerably reduces the computation time of distributed matrix multiplication.
Low Complexity Distributed Computing via Binary Matrices with Extension to Stragglers

We consider the distributed computing framework of MapReduce, which consists of three phases, the Map phase, the Shuffle phase and the Reduce phase. For this framework, we propose the use of binary matrices (with 0,1 entries) called computing matrices to describe the map phase and the shuffle phase. Similar binary matrices were recently proposed for the coded caching framework. The structure of ones and zeroes in the binary computing matrix captures the map phase of the MapReduce framework. We present a new simple coded data shuffling scheme for this binary matrix model, based on a identity submatrix cover of the computing matrix. This new coded shuffling scheme has in general a larger communication load than existing schemes, but has the advantage of less complexity overhead than the well-known earlier schemes in literature in terms of the file-splitting and associated indexing and coordination required. We also show that there exists a binary matrix based distributed computing scheme with our new data-shuffling scheme which has strictly less than twice than the communication load of the known optimal scheme in literature. The structure of this new scheme enables it to be applied to the framework of MapReduce with stragglers also, in a straightforward manner, borrowing its advantages and disadvantages from the no-straggler situation. Finally, using binary matrices derived from combinatorial designs, we show specific classes of computing schemes with very low file complexity (number of subfiles in the file), with marginally higher communication load compared to the optimal scheme for equivalent parameters.
4P based forensics investigation framework for smart connected toys

Smart Connected Toys (SCTs) have the potential to collect terabytes of sensitive personal, contextual, and usage information which may be a subject of cybercrime or used as a conduit for cybercrime resulting in a digital forensic investigation which requires the examination of the digital artifact stored, processed or transmitted by the SCT. SCT forensics is challenging in most cases due to non-availability of specialized forensics tools and standardized evidence acquisition interface port. We explore the various privacy and security challenges plaguing the SCT industry and the possible safety risk SCT poses to children as a result of a lack of serious consideration technical controls surrounding the collection, processing, and storage of children's information and possible exposure to crime which will require digital forensic investigation. As a result of this gap in research and industry, we investigate current digital forensic solutions for SCTs and present an abstract forensics investigation framework with the focus on using non-conventional means which allow Investigators to successfully "Plan," "Preserve" "Process" and "Present" (4P) as a systematic means to conduct digital forensic analysis on an SCT in a situation where SCT is complicit in a criminal investigation or a subject of crime.
Sparse Road Network Model for Autonomous Navigation Using Clothoids

To autonomously navigate in traffic roads, an Autonomous Vehicle must take into account perception information, as well as the topological and geometric structure of the environment it is inserted in. Specifically in urban scenarios, the vehicle has to plan its path across intersections, roundabouts and perform lane changes to obey traffic rules. In addition, the planning algorithm must also consider the kinematics constraints of the vehicle and comfort parameters to the passengers. This paper proposes a road network model based on clothoids, which embraces the geometric and topological representation of the environment in a compact data structure. Piecewise linear continuous-curvature paths composed of clothoids, circular arcs, and straight lines are used for this purpose. The proposed approaches are evaluated in an urban scenario composed of curved and straight roads with single and double lanes, roundabouts, and intersections. As a result, a navigation architecture for Autonomous Vehicles was developed using the model, including global planning with continuous-curvature paths.
Learn to Pay Attention Via Switchable Attention for Image Recognition

Attention mechanisms are rapidly being noticed in the computer vision community. However, existing works focus on the design of a single attention module, and then employ the same attention module in all attention layers of an entire network, rendering sub-optimal performance. In this paper, we address a learning-to-attention problem by proposing Switchable Attention (SA), which learns to select different kinds of attention modules for variant blocks of a Deep Neural Network (DNN). SA employs three distinct scopes to compute the attention map, including local spatial attention (LSA), global spatial attention (GSA) and channel attention (CA). Especially, the introduced trainable parameters can be optimized with the network in an end-to-end manner. SA is a light-weight module which can be embedded in existing networks with little overhead of computation. We conduct several quantitative experiments, and SA boosts the performance of the baseline on various challenging benchmarks, such as CIFAR-100, ImageNet-1K, and MS COCO and different computer vision tasks, such as Image Classification and Object detection. Notably, SA outperforms the most attention methods with ResNet-50 on ImageNet-1K.
Landmarks as Navigation-Aids for Multiple Robots

The paper presents selected landmarks as navigation-aids or waypoints for multiple car-like robots in a contained workspace cluttered with randomly fixed obstacles and landmarks. A new metrics is designed to select specific landmarks (which are treated as waypoints) falling in the robots’ field of view and with a minimum distance away from each other and their targets. A new metric is also defined to obtain the robot’s field of view at every iteration. Using the Lyapunov-based control scheme (LbCS) nonlinear acceleration-based stabilizing control laws are derived for navigation amongst obstacles and landmarks en route the final destination via selected landmarks or waypoints. The proposed technique and the new control laws are verified via interesting computer simulations.
Radar Signal Recognition Based on Squeeze-and-Excitation Networks

Classification and recognition of radar signals is an important part of modern warfare. Traditional radar signal recognition method based on the pulse description word may be not suitable for the complex electromagnetic environment. In this paper, a novel radar signal recognition method based on Squeeze-and-Excitation networks is proposed. Firstly, time-domain, frequency-domain and time-frequency-domain features of five kinds of radar signals are transformed into images. Then the images are classified by convolutional neural networks and output the result of each domain. Finally, the results of the three domains are combined to obtain the correct identification results. And the recognition effects of three different modules are compared. The experiments prove the efficiency of our method, especially on different SNR condition and center frequency. And confusion matrixes demonstrate that under the low SNR condition, our method still has a good recognition effect and are better than traditional radar signal recognition method based on PDW.
Ghostbusting

Spectre attacks have drawn much attention since their announcement. Speculative execution creates so-called transient instructions, those whose results are ephemeral and not committed architecturally. However, various side-channels exist to extract these transient results from the microarchitecture, e.g., caches. Spectre Variant 1, the so-called Bounds Check Bypass, was the first such attack to be demonstrated. Leveraging transient read instructions and cache-timing effects, the adversary can read secret data.
Low-Rank Parity-Check Codes over the Ring of Integers Modulo a Prime Power

We define and analyze low-rank parity-check (LRPC) codes over extension rings of the finite chain ring Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pr</sub> , where p is a prime and r is a positive integer. LRPC codes have originally been proposed by Gaborit et al. (2013) over finite fields for cryptographic applications. The adaption to finite rings is inspired by a recent paper by Kamche et al. (2019), which constructed Gabidulin codes over finite principle ideal rings with applications to space-time codes and network coding. We give a decoding algorithm based on simple linear-algebraic operations. Further, we derive an upper bound on the failure probability of the decoder. The upper bound is valid for errors whose rank is equal to the free rank.
Integrity Checking of Railway Interlocking Firmware

While uses of trusted computing have concentrated on the boot process, system integrity and remote attestation of systems, little has been made on the higher use cases - particularly safety related domains - where integrity failures can have devastating consequences, eg: StuxNet and Triton. Understanding trusted systems and exploring their operation is complicated by the need for a core and hardware roots of trust, such as TPM module. This can be problematical, if not impossible to work with in some domains, such as Rail and Medicine, where such hardware is still unfamiliar. We construct a simulation environment to quickly prototype and explore trusted systems, as well as provide a safe means for exploring trust and integrity attacks in these vertical domains.
A Fast Single Image Fog Removal Method Using Geometric Mean Histogram Equalization

Fog is a natural phenomenon that affects scene visibility, it reduces the contrast of the image and causes color-fade. While various works in the literature have addressed this issue, a fast effective model is still lacking. In this paper, a single image fog removal based on Geometric Mean Histogram Equalization (GMHE) is proposed. In particular, the proposed method is composed of three steps. The primary step is to adaptively tune the performance of GMHE according to the properties of the color histogram of the foggy image. The obtained result then enters two levels of chromaticity enhancement using the Hue Saturation Value (HSV) and rotors color transformations, respectively. Extensive experiments demonstrate that the proposed method attains high performance compared to the state-of-the-art methods in terms of quality and execution time. The evaluation is performed qualitatively by visual assessment, and quantitatively using a set of full reference and no-reference-based measures. As well, we suggest an assessment criterion to combine the results of the standard measures in a single score to facilitate the comparisons between the different fog removal methods.
A Framework to Protect Against Phishing Attacks

Phishing is a social engineering attack which aims to manipulate people and encourage them to expose their confidential information. The most common methods and techniques used for phishing are emails, chats or websites. Furthermore, there are various types of phishing attacks, for instance, spear-phishing and smishing. Many solutions have been proposed to prevent and detect phishing attacks such as users' awareness methods and detection tools. However, phishing still on the top of attacks with a high number of victims. One of the main reasons is the lack of users' knowledge. This could have more severe effects on Arabic users as most of the proposed mitigation solutions have not considered Arabic users. Furthermore, there is an enormous lack of knowledge in computing and security. It is highly likely that they can be victimized using social engineering attacks. This paper presents a framework to protect against phishing attacks by enhancing the users' awareness to detect phishing attacks. The proposed framework has two major components, which are animation videos for training awareness and gamification part to assess the delivered knowledge.
Critical traffic analysis on the tor network

Tor is a widely-used anonymity network with more than two million daily users. A special feature of Tor is the hidden service architecture. Hidden services are a popular method for anonymous communication or sharing web contents anonymously. A specialty in Tor is that all data packets that are sent are structured completely identical for security reasons. They are encrypted using the TLS protocol and have a fixed size of exactly 512 bytes. In an earlier implementation, Tor was an example of networks without generated traffic noise to make traffic analysis more difficult. In this work we describe a method to deanonymize any hidden service on Tor based on traffic analysis, which is a threat to anonymity online. This method allows an attacker with modest resources to deanonymize any hidden services in less than 12.5 days.