Reviewerly/exHarmony huggingface dataset:
Feature Clustering and Ensemble Learning Based Approach for Software Defect Prediction

Objective:: Defects in delivered software products not only have financial implications but also blemish the reputation of the organisation and lead to wastage of time and human resource. This paper aims to detect defects in software modules. Methods:: Our approach sequentially combines SMOTE algorithm to deal with class imbalance problem, K - means clustering algorithm to obtain a set of key features based on inter-class and intra-class coefficient of correlation and ensemble modelling to predict defects in software modules. After cautious examination, an ensemble framework of XGBoost, Decision Tree and Random Forest is used for prediction of software defects owing to numerous merits of ensembling approach. Results:: We have used five open-source datasets from NASA Promise Repository for Software Engineering. The result obtained from our approach has been compared with that of individual algorithms used in ensemble. A confidence interval for the accuracy of our approach with respect to performance evaluation metrics namely Accuracy, Precision, Recall, F1 score and AUC score has also been constructed at a significance level of 0.01. Conclusion:: Results have been depicted pictographically.
Multimedia resources as a support for requirements engineering and software maintenance

Abstract Textual documentations are frequently used in the software development process to outline features and behaviors of an application. For some people, textual descriptions may not be enough to understand what is being developed. In this scenario, multimedia resources appear as an option for software documentation, providing other ways to observe and interpret information. Objective To identify and characterize the approaches and techniques which promote the use of multimedia in requirements engineering (RE) to support software development and maintenance. Method A systematic mapping was conducted to find the primary studies in the literature and collect evidence for directing future research. Results Only 27.66% of the approaches found validated their solutions through controlled experiments, showing the need to increase the use of scientific method in this area, with replications of studies that will allow to evaluating if other researchers independently will come up with the same results. In this context, the approaches / techniques identified were TRECE, MURMER, Wiki System Multimedia, Storytelling, Virtual World Environment, VisionCatcher, PRESTO4U, ReqVidA, CrowdRE, AVW, The Software Cinema Technique, Dolli Project, UTOPIA, and approaches without explicit names, which, as a rule, use multimedia resources as an additional support. Conclusions There was a favorable consensus regarding the use of multimedia in RE. The selected studies demonstrated to be favorable to the adoption of media to persist and store the requirements of a system. Moreover, multimedia resources can improve the process of understanding the code and decrease evolution and maintenance costs. General terms are design, documentation, experimentation, human factors, multimedia, reliability, software engineering, verification, and security.
Non-Rigid Structure-from-Motion and Shading

We show how photometric and motion-based approaches can be combined to reconstruct the 3D shape of deformable objects from monocular images. We start by motivating the problem using real-world applications. We give a comprehensive overview of the state-of-the-art approaches and discuss their limitations for practical use in these applications. We then introduce the problem of Non-Rigid Structure-from-Motion and Shading (NRSfMS), where photometric and geometric information is used for reconstruction, without prior knowledge about the shape of the deformable object. We present in detail the first technical solution to NRSfMS and close the chapter with the main remaining open problems.
Fast Multiplication in Binary Fields on ARMv8 Processors

Efficient algorithms performing binary field operations are often required in cryptography such as for digital signatures on binary elliptic curves. The most important operation that affects the performance of such algorithms is the multiplication since most microprocessors do not support instruction sets to carry out polynomial multiplications. This paper describes a new multi-layer algorithm for the multiplication of two polynomials in binary field. This algorithm combines the Karatsuba algorithm, the 128-bit and the 192-bit multipliers on 32-bit (AArch32) and 64-bit (AArch64) ARMv8 processors. The paper illustrates the performance improvement of using multi-layer algorithm in ECDSA, ECDH, ECMQV cryptographic primitives on the curves NIST_B283, NIT_B409 and NIST_B571. Our evaluation results showed that the proposed algorithm is 4, 5 and 5 times faster than the corresponding previous techniques in GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">283</sup> ), GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">409</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">571</sup> ) while being executed on the same hardware as Samsung Galaxy Tab S2 (AArch32) or IMX8M Kit (AArch64).
Adaptive Feature Enhancement Network for Semantic Segmentation

Semantic segmentation is a fundamental and challenging problem in computer vision. Recent studies attempt to integrate feature information of different depths to improve the performance of segmentation tasks, and a few of them enhance the features before fusion. However, which areas of the feature should be strengthened and how to strengthen are still inconclusive. Therefore, in this work we propose an Adaptive Feature Enhancement Module (AFEM) that utilizes high-level features to adaptively enhance the key areas of low-level features. Meanwhile, an Adaptive Feature Enhancement Network (AFENet) is designed with AFEM to combine all the enhanced features. The proposed method is validated on representative semantic segmentation datasets, Cityscapes and PASCAL VOC 2012. In particular, 79.5% mIoU on the Cityscapes testing set is achieved without using fine-val data, which is 1.1% higher than the baseline network and the model size is smaller. The code of AFENet is available at https://github.com/KTMomo/AFENet .
Thunderbird Add-On “SmartMail” for More Convenient E-Mail Writing

In this paper we introduce “SmartMail”, an add- on for Mozilla's e-mail client Thunderbird. It helps to increase productivity of users, especially the ones with international and multi-lingual e-mail recipients. We conducted a usability study which shows a significant acceleration of time spent writing e-mails as well as a reduced error rate for the chosen language and gender. After filling a questionnaire based on the System Usability Scale, the majority of test users rate the user experience satisfactory. Half of them even prefer using Thunderbird with this add-on over their own e-mail client.
NMIEDA: Estimation of distribution algorithm based on normalized mutual information

Summary A new estimation of distribution algorithm based on normalized mutual information (NMIEDA) is proposed for overcoming the premature convergence of bivariate estimation of distribution algorithms. NMIEDA first uses normalized mutual information to measure the interaction between two variables and then generate a dependency forest model. Second, based on the concept of sporadic model building and a reward and punishment scheme in Selfish Gene, NMIEDA provides a new updating mechanism that accelerates the convergence speed. Finally, a new sampling mechanism is adopted in NMIEDA to improve the efficiency of sampling, which combines stochastic sampling, the opposition‐based learning scheme and the mutation operator. The simulation results on benchmark problems and real‐world problems demonstrate that NMIEDA often outperforms several other bivariate algorithms.
FAmINE4Android: Empowering Mobile Devices in Distributed Service-Oriented Environments

The domain of distributed services is constantly growing, enabling interoperability between applications that run on different operating systems. The increasing availability and use of wireless mobile devices gave rise to opportunities for new types of mobility-distributed applications. This poster presents the FAmINE4Android middleware, which facilitates the development of service-oriented mobile applications by providing all the necessary mechanism and tools, through a seamless and intuitive Application Programming Interface (API). In addition, it caters for the creation of distributed services by enabling the exposure of software and hardware resources to the service-oriented environment. In order to demonstrate the features of the middleware and its suitability for the inclusion of mobile devices in distributed computing platforms, a mobile museum guide application has been developed as a case study, communicating with a Windows-based person tracking service. The preliminary evaluation of the mobile guide application verified the effectiveness and efficiency of the proposed middleware.
The Advent of Data Science Platforms

As a reader of IEEE Potentials, you are very likely an undergraduate or graduate student who likes technology. There is a good chance that you are reading this article on your smartphone, that little electrical device that you can hold in a pocket. The device is quite common, considering that about 40% of the worldwide population has one (O'Dea, 2020).
The Establishment of the Logical Model of the Internet of Things Application System

Abstract In order to build the Internet of things system, it is necessary to establish a feasible and reasonable mathematical model. Based on the application of embedded technology, Internet technology, communication technology and other scientific and technological methods of Internet of things system, a complete logic model of Internet of things application system is established. The logical model is the theoretical basis for the formation of the Internet of things system. It is the supplement and improvement of Internet of things application system. It is the collection and induction of discrete Internet of things. Practice has proved that the establishment of the logical model of the Internet of things system makes up for the deficiency of the application technology of the Internet of things and makes the application system of the Internet of things more perfect. It realizes the real-time sharing of environment and state information between the objects of the Internet of things, and realizes the collection, transmission, processing and application of the Internet of things system.
Information security management of enterprise mobile device

Abstract The development of the modern labour market involves increasing the level of mobility of personnel, so the current trend is the spread of mobile devices in the implementation of professional activities. This practice improves the efficiency of enterprise, but also has a negative impact on the security of information systems of enterprises. The growing requirements of employers have led to the growth of the functionality of mobile devices. However, this also causes an increase in the number of vulnerabilities in them, leading to an increase in the complexity of the security processes. The combination of the above problems determines the need to organize the process of information security management of enterprise mobile devices. The model of information security management of enterprise mobile devices is described in the article. It allows identifying vulnerabilities of operating system objects of enterprise mobile devices by modelling combinations of dangerous permissions more accurately.
Programming of the Average Windward Area of Finite Element Fragments Based on MCSPS

Abstract In order to obtain the average windward area of the natural fragments in finite element method, a simulation model of the average windward area is established based on the Monte Carlo Subdivision Projection Method. The projection boundary of the fragments is obtained. Based on the cross multiplication, the simulation program of the windward area of the FE natural fragment is compiled. According to the concave and convex polygon vertices of the different quadrants, the average windward area of the finite element fragments is solved. The results show that the average windward area simulation program based on cross multiplication is applicable to the area of concave and convex polygons; the results of different quadrants of the same polygon are exactly the same; the program is applicable to the average windward area of simulation fragments with arbitrary irregular shape.
Automotive eHMI Development in Virtual Reality: Lessons Learned from Current Studies

More and more studies in automotive research and development are conducting user-centered development in the emerging field of external human-machine interfaces (eHMI) in virtual reality (VR). As time, cost and risk are decreasing with progressively affordable sophisticated VR technologies, researchers have shifted to virtual testing. Within this context, they use a variety of methods and technologies to develop new designs but so far little examination has been done towards validity of virtualization and description of the technical setup. As level of immersion is one of the current pillars in VR and technology evolves rapidly, study setups differ a lot in recent years, resulting in poorer comparability. In this paper, our goal is to review the current generation of VR studies in automotive eHMI development and extract in the sense of a lessons-learned approach best practices with regard to their VR setup. For that, we assessed a total of six current studies published between 2017 and April 2020 in automotive eHMI development to extract lessons learned from study designs and virtualization setups. We took a look at hardware and software used as well as study procedure. The results allow us to find useful conclusions on automotive eHMI development practices in VR.
Multiple Screen Control Application with Facial and Gesture Recognitions in a Manufacturing Control Room

In the age of Industry 4.0, the operators in a manufacturing control room require to work with digital content such as factory KPI dashboards, live dashboards and controls on multiple displays. Manipulating and arranging the displays and their layout with a user’s face and hand gesture could be more natural, automatic and interactive for the control room operators. Therefore, a display control framework is developed in the Manufacturing Intelligence Control Room (MICR) at the Advanced Remanufacturing and Technology Centre (ARTC), using the open-source FaceNet facial recognition and OpenPose hand feature recognition algorithms and integrating with the display control system; Nerve Center from Mezzanine system of Oblong Industries.
Towards Mitigating Straggler with Deep Reinforcement Learning in Parameter Server

Parameter server paradigm has shown great performance superiority for handling deep learning (DL) applications. One crucial issue in this regard is the presence of stragglers, which significantly retards DL training progress. Previous approaches for solving straggler may not consider the resource utilization of a cluster. This motivates us to make an attempt at designing a new scheme that mitigates straggler problem in DL from the perspective of dynamic balance workloads among workers. To optimize the method of mitigating straggler problem in the traditional parameter server, we propose Help-Reassign Synchronization (HRS) mechanism which has high flexibility to adapt to the dynamic cluster without parameter settings. Furthermore, we propose a Deep Reinforcement Learning (DRL)-based algorithm Decentralized Actor-critic-based Experience Replay (DAER) that can automatically identify and determine helper workers (helper) and helpee workers (helpee). The whole idea has been implemented in a scheme called FlexHS which mitigates straggler problem by creating a dynamic balance between the number of helper and backup overhead. Evaluation under various algorithms evidences the superiority of our scheme.
Fault Tolerant Suffix Trees

Classical algorithms and data structures assume that the underlying memory is reliable, and the data remain safe during or after processing.However, the assumption is perilous as several studies have shown that large and inexpensive memories are vulnerable to bit flips.Thus, the correctness of output of a classical algorithm can be threatened by a few memory faults.Fault tolerant data structures and resilient algorithms are developed to tolerate a limited number of faults and provide a correct output based on the uncorrupted part of the data.Suffix tree is one of the important data structures that has widespread applications including substring search, super string problem and data compression.The fault tolerant version of the suffix tree presented in the literature uses complex techniques of encodable and decodable error-correcting codes, blocked data structures and fault-resistant tries.In this work, we use the natural approach of data replication to develop a fault tolerant suffix tree based on the faulty memory random access machine model.The proposed data structure stores copies of the indices to sustain memory faults injected by an adversary.We develop a resilient version of the Ukkonen's algorithm for constructing the fault tolerant suffix tree and derive an upper bound on the number of corrupt suffixes.
Secure Data Assimilation of Cloud Sensor Networks

Cloud computing technologies (CCTs) enable a large-scale sensor network (LSN) to outsource the computations of data assimilation to improve its performance. However, the cyber-physical nature of cloud-enabled LSNs (CE-LSNs) introduces new challenges. Outsourcing the computations to an untrusted cloud may expose the privacy of the sensing data. To address the security issues, this chapter proposes a security mechanism to achieve data confidentiality in the outsourcing process. We develop our mechanism by combining a conventional homomorphic encryption and a customized encryption scheme. We present theorems to characterize the correctness of the encryption and investigate the estimation performance and the security of the proposed method. We also analyze the impacts of the quantization errors on the estimation performance. Finally, we present numerical experiments to consolidate our analytical results.
From Sentiment to Well-Being: Internet Users in an Uncertain and Risky Environment

This research is to investigate emerging perspectives on online user well-being and provide a new metric to capture this construct by using machine learning based sentiment analysis. In study 1, a corpus was compiled consisting of over 10,000 online reviews (in Chinese) collected from an online forum with Huawei’s Smart Watch users. In study 2, a sentiment analysis was conducted and the online reviews were rated by college students in southern China. The factor analysis and text analytics indicate that the new metric captures the concept of community member well-being, and the corpus can by used to predict well-being in the virtual community context.
The Influence of Social Embarrassment on Engagement with Publicly Displayed Digital Content

Public, large-screen touch displays offer advertisers, social activist groups, and other organizations a distinct opportunity to communicate their message interactively to users. While previous research has sought to identify strategies for encouraging users to interact with these displays, it is not clear how different levels or types of engagement may be encouraged by different solicitation strategies. Moreover, it is also not yet understood how the effectiveness of these strategies may depend on the nature of the content to interact with. In this research, we consider specifically how users may be encouraged to engage with mental health focused content presented on these types of displays, and propose an experiment for exploring the effectiveness of different context-sensitive solicitation strategies.
Smart Watchdog: A Lightweight Defending Mechanism Against Adversarial Transfer Learning

Most traffic sign recognition tasks rely on artificial neural network. As a kind of transfer learning method, knowledge distillation has improved the robustness of neural network models to a certain extent and saved time for model training. However, the weights of the original model (teacher model) and the new model (student model) are similar. The adversarial examples of the teacher model are easy to transfer and can successfully attack the student model. In order to solve this problem, this paper proposes a lightweight defense mechanism to reduce the similarity between the weight of the student model and the weight of the teacher model, and the dropout-randomization method is applied in the input layer of the student model to reduce the input probability of the adversarial examples. Moreover, we evaluate the precision and the recall of the improved model, the results show that the robustness of the model is significantly improved under the Carlini-Wagner (CW) attack and Project Gradient Descent (PGD) attack.
Logical Segmentation of Source Code

Many software analysis methods have come to rely on machine learning approaches.Code segmentation -the process of decomposing source code into meaningful blockscan augment these methods by featurizing code, reducing noise, and limiting the problem space.Traditionally, code segmentation has been done using syntactic cues; current approaches do not intentionally capture logical content.We develop a novel deep learning approach to generate logical code segments regardless of the language or syntactic correctness of the code.Due to the lack of logically segmented source code, we introduce a unique data set construction technique to approximate ground truth for logically segmented code.Logical code segmentation can improve tasks such as automatically commenting code, detecting software vulnerabilities, repairing bugs, labeling code functionality, and synthesizing new code.
A tale of two sequences : Interpretable and linguistically-informed deep learning for natural language processing

Deep Learning has swiftly taken over our field of NLP. It caused a shift from exploiting linguistic features and structures to relying solely on the input words. As performance records in NLP benchmarks keep being broken, we can ask ourselves: are linguistic structures now obsolete? In the first part of this thesis, we try to answer that question in the context of machine translation. We find that we can exploit a graph-convolutional network to condition an MT model on linguistic input structures, and that it brings performance improvements. We also investigate if we can induce structure in an MT setting, and find that it is possible to learn useful structure on top of embeddings and CNN representations, while obtaining trivial structure on top of LSTMs. In the second part of the thesis, we look at two common criticisms of neural networks: their lack of interpretability, and their hunger for labeled data to generalize well. We first study text classifiers, and make them interpretable by having them provide a rationale for their predictions. This is done by showing which part of the input text is used for classification. We show that our method is more aligned with human rationales than previous work. Finally, we investigate generalization of neural networks. We look at the SCAN benchmark and find obtaining a high score does not have to imply strong generalization behavior, due to the simple nature of the data set. We propose a remedy in the form of the NACS data set.