Reviewerly/exHarmony huggingface dataset:
An exploratory study of MVC-based architectural patterns in Android apps

Mobile app development now represents a significant part of the software industry, with Android being the largest ecosystem. Android development comes with its own design practices and templates (layouts, activities, etc.). Developers also use different established architectural patterns for designing interactive software such as MVC, MVP and MVVM. They implement these patterns based on their understanding and experience. Thus, the choice and the implementation of such patterns varies from a developer to another. To the best of our knowledge, there is no work that provides a comprehensive view of the use of these patterns in mobile apps. Moreover, there is no clear understanding of which pattern to use and what is the trend for designing mobile apps using such patterns. In this paper, we propose an automatic approach to identify which MVC-based architectural pattern (MVC, MVP and MVVM) is used predominantly in a given app. For this purpose, we defined each of these patterns through a number of heuristics according to the pattern's potential implementations within the Android framework. We conducted an empirical study on a large set of mobile apps downloaded from the Google Play Store. We found, not surprisingly, a dominance of the popular MVC pattern, a rare use of MVP while MVVM is almost unused and a significant number of apps do not follow any pattern. The empirical study also enabled us to analyse the use of these patterns by domain, size and last-update date of the apps. We observed that MVC has been the most used pattern over the past years and it continues to gain popularity, and that small-size apps are mostly the ones that do not use any pattern.
Securing the testing process for industrial automation software

The testing of automation applications has become a crucial pillar of every production systems engineering (PSE) project with the proliferation of cyber-physical systems (CPSs). In light of new attack vectors against CPSs, caused, inter alia, by increased connectivity, security aspects must be considered throughout the PSE process. In this context, software testing represents a critical activity, as a lack of adequate security mechanisms puts a variety of valuable assets (e.g., system configurations and production details) at risk of information theft and sabotage. Thus, organizations must analyze the security of their software testing process on a regular basis in order to counter these threats. Yet, due to the required security knowledge or budget constraints for security-related expenses, these undertakings may be destined to fail. In this work, we present a framework that supports the semi-automated security analysis of an organization’s software testing process for industrial automation software. This framework is based on the VDI/VDE 2182 guideline and integrates an ontological approach to model the necessary background knowledge, including, e.g., data flows, assets, entities, threats, and countermeasures. The framework comprises a default model of the testing process, which users can adapt so that the target of inspection accurately reflects their software testing environment. In particular, the testing process considered for creating the default model is based on best practices observed at a major system integrator, aligned with the ISO/IEC/IEEE 29119 series of software testing standards. Moreover, we developed a tool that enables the automatic generation of attack–defense trees from such formal models of the organization’s software testing process. We demonstrate how the proposed framework can be applied to a generic software testing process to answer essential questions in conducting a security risk analysis. The results of the exemplary security analysis provide guidance, should raise awareness in the industrial domain, and support effective, yet cost- and time-efficient security analyses. Finally, we evaluate the presented framework by performing a comprehensive comparison of suitable security analysis tools.
Detection of trust links on social networks using dynamic features

Social networks have turned into a popular medium for diffusion of information, providing connections between people through access to several networks and shared personal opinions, thoughts, information, and experiences. A web-based social network consists of users who are increasingly concerned with protection of their privacy, which is considered as an important concern about user privacy due to the nature of social networks and privacy protection. Therefore, every network user prefers to identify people trusted by him and to communicate with them in order not to be abused by untrusted people. In this paper, we seek to predict trust links by utilizing the most important features of each user on a social network, so that the users can pursue their everyday purposes without much concern. In order to provide trust in social networks, we made use of several features of the users at the same time, the most important of which include the extent to which they trust each other, the amount of similarity between their trust, and each one’s reputation. Using these measures, a trust path was created on the network for each connected user, compared with four common methods for verification of its truth. The results demonstrate that the proposed method has been capable of outperforming the Katz, h Trust, TP, and RS methods in terms of effectiveness, efficiency, and strength, and can more accurately present a more reliable path within an acceptable runtime.
Lung cancer recognition and prediction according to random forest ensemble and RUSBoost algorithm using LIDC data

Lung cancer is increasing day by day. According to the report published by WHO in 2017, number of death people due to lung cancer in Bangladesh reached 12,075 or 1.53% of total deaths. It is a matter of great sorrow that this lung cancer detection does not occur at an early stage for many people. I t is very important to recognize lung cancer and detect the location as well as an accurate prediction. Many researchers applied techniques like Fast Fourier Transform (FFT) for image enhancement, thresholding for the purpose of segmentation and binarization system for extraction, etc. Basically recognition process consists of three stages like enhancement, segmentation and feature Extraction, lung cancer images can be used as inputs and maintaining these stages give more quality and accuracy in the detection of lung cancer. Approaches developed by the earlier researchers fail to produce accuracy in real-time applications. Hence, in order to mitigate the drawbacks of these approaches a hybrid method to detect lung cancer. Gabor filter has been used for image enhancement of input image. Marker-Controlled Watershed algorithm has been applied for segmentation purpose which will help to provide the exact location of the infected region in the input pictures as well as Out-of-bag (OOB) error rate for different iteration (trees) has been observed graphically according to Random Forest Ensemble and OOB rate for the different class also been determined. When the number of tree is increasing, OOB rate in percentage is decreasing. The highest OOB value is 23.40% which provides an initial tree. Distribution curve of Mean decrease in accuracy, Mean decrease in Gini index and standard error of importance measure according to Random Forest Ensemble has also been observed graphically. RUSBoost algorithm has been introduced finally in order to evaluate accuracy. Performance of RUSBoost algorithm has been visualized in this paper for the purpose of Subtlety, Spiculation, Sphericity, Texture, Margin, Malignancy, Lobulation, where Texture provides the highest accuracy and Spiculation provides second highest accuracy.
Deliver Security Awareness Training, then Repeat: {Deliver; Measure Efficacy}

Organisational information security policy contents are disseminated by awareness and training drives. Its success is usually judged based on immediate post-training self-reports which are usually subject to social desirability bias. Such self-reports are generally positive, but they cannot act as a proxy for actual subsequent behaviours. This study aims to formulate and test a more comprehensive way of measuring the efficacy of these awareness and training drives, called ASTUTE. We commenced by delivering security training. We then assessed security awareness (post-training), and followed up by measuring actual behaviours. When we measured actual behaviours after a single delivery of security awareness training, the conversion from intention to behaviour was half of the desired 100%. We then proceeded to deliver the training again, another two times. The repeated training significantly reduced the gap between self-reported intention and actual secure behaviours.
Densely connected AutoEncoders for image compression

Image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it prohibitive to storage and communications of image data without the use of compression. However, the traditional compression methods, such as JPEG, may introduce the compression artefact problems. Recently, deep learning has achieved great success in many computer vision tasks and is gradually being used in image compression. To solve the compression atrefact problem, in this paper, we present a lossy image compression architecture, which utilizes the advantages of the existing deep learning methods to achieve a high coding efficiency. We design a densely connected autoencoder structure for lossy image compression. Firstly, we design a densely autoencoder structure to get richer feature information from image which can be helpful for compression. Secondly, we design a U-net like network to decrease the distortion caused by compression. Finally, an improved binarizer is adopted to quantize the output of encoder. In low bit rate image compression, experiments show that our method significantly outperforms JPEG and JPEG2000 and can produce a better visual result with sharp edges, rich textures, and fewer artifacts.
Single and Sequential Viewports Prediction for 360-Degree Video Streaming

Sending only the viewport of interest provides a solution for 360-degree video streaming under the current bandwidth-constrained infrastructure. To this end, the user viewport requires to be prefetched in advance by conducting viewport prediction. To more accurately capture the nonlinear and long-term dependent relation between the future and past viewports, we develop a single viewport prediction model using convolutional neural network (CNN), in which the pooling layers are dropped and more convolutional layers are added for stronger nonlinear fitting ability. Further, we design a viewport trajectory prediction model based on recurrent neural network (RNN) which learns long-term dependency in sequential viewports. Specially, it is capable to estimate future viewport trajectory and support variable-size prediction window with low complexity. Finally, a correlation filter-based viewport tracker (CFVT) is proposed to perform content-aware viewport prediction. The combination of the RNN and the CFVT through a fusion model enables them to complement each other which is validated by significant improvement in prediction accuracy.
Sparse phase information for secure optical double-image encryption and authentication

A novel optical encryption and authentication method by sparse phase information was proposed. Phase-only component of double-image encryption function was generated and considered for sparse strategy. Partial information of these phase values was subjected to the authentication process. The sparse strategy of the phase data permits simultaneous data reduction to achieve the general requirements of storage and transmission. Cryptanalysis of the proposed method was carried out in terms of chosen-plaintext and chosen-ciphertext attacks. The sparse strategy for phase data makes it difficult to recognize the confidential information shown by double-image encryption and authentication system, thus, the security level of the overall process was increased. Computational simulations displayed the feasibility and more security of the proposed method.
Uncovering Security Vulnerabilities in the Belkin WeMo Home Automation Ecosystem

The popularity of smart home devices is growing as consumers begin to recognize their potential to improve the quality of domestic life.At the same time, serious vulnerabilities have been revealed over recent years, which threaten user privacy and can cause financial losses.The lack of appropriate security protections in these devices is thus of increasing concern for the Internet of Things (IoT) industry, yet manufacturers' ongoing efforts remain superficial.Hence, users continue to be exposed to serious weaknesses.In this paper, we demonstrate that this is also the case of home automation applications, as we uncover a set of previously undocumented security issues in the Belkin WeMo ecosystems.In particular, we first reverse engineer both the mobile app that enables users to control smart appliances and the communication logic implemented by WeMo devices.This enables us to compromise the passphrase guarding the communication over the local wireless network, opening the possibility of eavesdropping on user traffic.We further reveal how an attacker can present a fake device to a WeMo user, through which cross-site scripting can be exploited in order to mislead the user into disclosing private information.Lastly, we provide a set of security guidelines that can be followed to remedy the vulnerabilities identified.
A Gaze-Based Exploratory Study on the Information Seeking Behavior of Developers on Stack Overflow

Software developers use Stack Overflow on a daily basis to search for solutions to problems they encounter during bug fixing and feature enhancement. In prior work, studies have been done on mining Stack Overflow data such as for predicting unanswered questions or how and why people post. However, no work exists on how developers actually use, or more importantly, read the information presented to them on Stack Overflow. To better understand this behavior, we conduct an eye tracking study on how developers seek for information on Stack Overflow while tasked with creating human-readable summaries of methods and classes in large Java projects. Eye gaze data is collected on both the source code elements and Stack Overflow document elements at a fine token-level granularity using iTrace, our eye tracking infrastructure. We found that developers look at the text more often than the title in posts. Code snippets were the second most looked at element. Tags and votes are rarely looked at. When switching between Stack Overflow and the Eclipse Integrated Development Environment (IDE), developers often looked at method signatures and then switched to code and text elements on Stack Overflow. Such heuristics provide insight to automated code summarization tools as they decide what to give more weight to while generating summaries.
SCsec: A Secure Near Field Communication System via Screen Camera Communication

With the increasing popularity of short range communication services, such as contact less payment and electronic toll collection, screen camera communication and near field communication (NFC) have received considerable attention from both industry and academia. Existing short range communication technologies face challenges from deployment, compatibility, and security issues. In this work, we target the security challenges, specifically the eavesdropping attack, of screen camera communication systems. We propose SCsec, a secure screen-camera communication system. On one hand, SCsec achieves computationally secure communication between the transmitter and receiver, preventing the eavesdroppers beyond a certain range from learning anything about the content of the communication. On the other hand, SCsec achieves throughput that is comparable to existing screen camera communication schemes. We extensively evaluate the performance of our system with real world experiments on multiple devices. Numerous experiment results show that our system achieves both high security and comparable communication efficiency with existing screen camera communication systems.
Unwrapped phase correction for robust 3D scanning

A temporal phase unwrapping method is proposed to generate an unwrapped phase map for a robust three-dimensional (3D) scan. The proposed algorithm seeks to improve the accuracy of the 3D data points obtained through the phase unwrapping process. By applying the k-nearest-neighbor search method, the error bound of the wrapped phase is controlled with improved flexibility. To achieve the desired scanning quality, a series of fringe patterns is generated with multiple phases at three different frequencies. For this method, the pattern is shifted by utilizing a six-step temporal phase unwrapping process. In this unwrapping process, the error bound is controlled by employing the k-nearest-neighbor search method and spatial comparison method to obtain an accurate fringe order. Through our correction method, the wrapped phases can be unwrapped more accurately and thus enhance the robustness of the scanning system compared to previous phase unwrapping methods.
On the Security-Privacy Tradeoff in Collaborative Security: A Quantitative Information Flow Game Perspective

To contest the rapidly developing cyber-attacks, numerous collaborative security schemes, in which multiple security entities can exchange their observations and other relevant data to achieve more effective security decisions, are proposed and developed in the literature. However, the security-related information shared among the security entities may contain some sensitive information and such information exchange can raise privacy concerns, especially when these entities belong to different organizations. With such consideration, the interplay between the attacker and the collaborative entities is formulated as Quantitative Information Flow (QIF) games, in which the QIF theory is adapted to measure the collaboration gain and the privacy loss of the entities in the information sharing process. In particular, three games are considered, each corresponding to one possible scenario of interest in practice. Based on the game-theoretic analysis, the expected behaviors of both the attacker and the security entities are obtained. In addition, the simulation results are presented to validate the analysis.
Investigating the Role of User's English Language Proficiency in Using a Voice User Interface

Amazon's Echo, and Apple's Siri have drawn attention from different user groups; however, these existing commercial VUIs support limited language options for users including native English speakers and non-native English speakers. Also, the existing literature about usability differences between these two distinct groups is limited. Thus, in this study, we conducted a usability study of the Google Home Smart Speaker with 20 participants including native English and non-native English speakers to understand their differences in using the Google Home Smart Speaker. The findings show that compared with their counterparts, the native English speakers had better and more positive user experiences in interacting with the device. It also shows that users' English language proficiency plays an important role in interacting with VUIs. The findings from this study can create insights for VUI designers and developers for implementing multiple language options and better voice recognition algorithms in VUIs for different user groups across the world.
Correlated Microstructure Descriptor for Image Retrieval

In this paper, a novel feature descriptor named “correlated microstructure descriptor (CMSD)” is proposed for image retrieval. CMSD represents high level semantics by identifying microstructures via establishing correlations between texture orientation, color, and intensity features. The proposed CMSD follows a two-staged approach for feature extraction and their integration. Color information is extracted by exclusively quantizing each component of HSV color space. Richer edge orientation information is extracted by using the multi-directional Sobel operator. Local contrast information is obtained by quantizing V component of HSV. Correlated microstructures are then identified by correlations established on the basis of proximity and continuation relations. The identified microstructures are then mapped to color, texture orientation, and intensity features exclusively to obtain micro-color, micro-orientation, and micro-intensity maps, respectively. Proposed three concatenated micro-maps are represented through a 2-D histogram, incorporate information regarding semantic and spatial contents of an image. The experiments are performed on standard datasets, i.e., Corel 1k, Corel 5k, and Corel 10k. The results evaluation depicts the outstanding performance of proposed CMSD as compared to state-of-the-art methods including TCM, GLCM, MTH, MSD, MTSD, and SED.
A Preliminary Investigation of the Role of Anthropomorphism in Designing Telehealth Bots for Older Adults

Autonomous virtual agents (VAs) are increasingly used commercially in critical information spaces such as healthcare. Existing VA research has focused on microscale interaction patterns such as usability and artificial intelligence. However, the macroscale patterns of users' information practices and their relationship with the design and adoption of VAs have been largely understudied, especially when it comes to older adults (OAs), who stand to benefit greatly from VAs. We conducted a preliminary investigation to understand the role design elements, such as anthropomorphic aspects of VAs, play in OAs' perception of VAs and in OAs' preferences for VAs' participation within their health information practices. Some unexpected findings indicate that the fidelity of anthropomorphic features influences perception in ways that are dependent on the context of the information tasks. This suggests that research on improving the design and increasing the adoption of VAs should factor the interplay between fidelity of VA representation and information context.
Proof of Previous Transactions (PoPT): An Efficient Approach to Consensus for JCLedger

JCLedger is a BlockChain-based distributed ledger for JointCloud that can improve the reliability and convenience of cloud resource exchanges by empowering cooperation among multiple clouds. The biggest challenge for the implementation of JCLedger is the approach to consensus. The existing consensus algorithms for the public BlockChain, such as proof of work (PoW) or proof of stake (PoS) does not apply to the JointCloud, because they require a massive computing power with a low throughput or monopoly risk. In this paper, we propose a practical Byzantine-fault-tolerance (PBFT)-based consensus algorithm called proof of previous transactions (PoPT), in which the accountants are selected by a specific hash function from a certain number of candidates. The candidates are chosen according to the users' participation in JointCloud, and only candidates that join the PBFT-based consensus process instead of all users. We also propose a new BlockChain structure for parallel accounting to improve the scalability of JCLedger, and a consistent hashing algorithm is used to assign the transactions to different accountants. Simulation experiments show that the PoPT can shield the unequal computing power of the users to provide them equal accounting opportunities, and the parallel accounting can handle the massive and high-frequency transactions in JointCloud more efficiently.