Reviewerly/exHarmony huggingface dataset:
UJN-Traffic: A Benchmark Dataset for Performance Evaluation of Traffic Element Classification

The detection of traffic element in remote sensing imagesplays an important role in the construction of traffic infrastructure, and can provide dynamic monitoring and quality evaluation for traffic construction projects. The convolutional neural network (CNN)-based method has achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, the existing dataset can only be used for object detection, and the sample size and diversity need to be improved. These problems have brought challenges to the improvement and application of existing traffic element detection methods. This paper proposes a large-scale highresolution traffic element dataset based on multi-method detection analysis, and validates it based on the CNN-based object detection method. We screened more than 100 satellite images from high-resolution satellite images with a spatial resolution of 0.8 m. Manually labeled 5 categories and 100,000 traffic element samples. In the experiment, three CNN-based detection methods were used as the experimental baseline method. The experimental results proved effectiveness of the UJN-traffic traffic element dataset and provided a useful reference for traffic construction engineering and management.
Perceptual Evaluation of Driving Scene Segmentation

Human visual perception forms different levels of abstractions expressing the essential semantic components in the scene at different scales. For real-world applications such as driving scene perception, abstractions of both coarse-level, such as the spatial presence of the lead vehicle, and the fine-level, such as the words on a traffic sign, serve as important signals for driver's decision making. However, the granularities of perception required for levels of abstractions are generally different. While current computer vision research makes significant progress in tasks of understanding the global scene (image classification), and dense scene (semantic segmentation), our work takes steps to explore the gap in between. In this paper, we propose Multi-class Probability Pyramid as a representation built on the top of pixel-level semantic scene labels. This representation forms region-level abstractions by controlling the granularity of local semantic information, and thus disentangles the variation of scene semantics at different resolutions. We further show how such representation can be effectively used for evaluation purposes, including interpretable evaluation of scene segmentation and unsupervised diagnosis of segmentation predictions.
Ontology-Based Software Graphs for Supporting Code Comprehension During Onboarding

Software engineers in modern development settings often face the challenge of contributing to large existing projects. The comprehension of foreign software code presents a time consuming obstacle, especially in contexts like onboarding. New employees have little knowledge of the software project they are supposed to contribute to. Therefore, tools supporting developers with their code comprehension are desirable to help them contribute to the best of their ability as soon as possible. Such tools must be flexible enough to work with any software project, while offering means for adjustments to very specific tasks.In this paper, we present an approach to visualize source code as node-link diagrams, using expert-designed ontologies to group various source code elements such as classes or methods. We evaluate our approach with nine advanced computer science students simulating an onboarding in a software project consisting of almost 100k lines of code. The results show that our approach supports code comprehension by utilizing expert knowledge of the visualized project, while also pointing to other use cases such as legacy code migration.
Big Five Personality Traits Inference from Five Facial Shapes Using CNN

The discipline of Computer Vision has generated interested in computer scientists, psychologists and neuroscientists. In this, Computer vision has been successfully used in face detection, face recognition, facial expression recognition and emotion identification. The next great step in computer vision is to identify personality traits from facial appearance. From ancient times, it has been proved that it is possible to assess personality traits and types from facial appearance or features of the face. While personality traits can be assessed using psychological or psychometric tests, interviews, and direct observation it is important, as the fields of AI and deep learning advance, that the psychological tests are automated to build and develop digital physiognomy software. The advantages of automatic personality traits assessment from facial appearances are far and wide ranging. This automation can be easily arrived at using deep learning algorithms especially by retraining the Inception v3 CNN model.
JobNet: 2D and 3D Visualization for Temporal and Structural Association in High-Performance Computing System

Performance monitoring in a High-Performance Computing (HPC) system is an essential and challenging task. With a large number of system components, coupled with health metrics that need to be reported, visualizing the system’s internal structure over time will uncover patterns and enable insights, empowering analysis from monitoring. This paper presents a visualization tool that visualizes the temporal and structural association of HPC system components using a force-directed graph layout algorithm. The visualization contains 2D and 3D representation, supporting a complete analysis of the compute usage, how users and job submission are interconnected throughout the observational interval. Design alternatives for time representation are discussed and depicted in 2D and 3D visualization encodings, with animation and exclusive presentation. The interaction capabilities of the tool assist visual exploration of health metrics and changes in system status over time. The tool’s usefulness and effectiveness in the monitoring task are demonstrated by a case study on a real-world HPC dataset.
Extensive Analysis of Global Presidents’ Speeches Using Natural Language

Over the last many years, sentiment analysis has evolved. A substantial part of the work rotates around the examination of textual feelings utilizing the tools of text mining. In any case, audio sentiment analysis is still coming to the present stage to investigate network gatherings. The following article proposes to use Natural Language Processing (NLP) to identify the effect of presidential speeches across the world from 1970 to 2019 on the public's mood, with positive and negative statements presented. This paper includes an analysis of different speeches for sentiment analysis in real time given by Global Presidents (positive and negative opinion) by implementing different classification techniques to predict the accuracy. Naïve Bayes performed well without overfitting and underfitting with an accuracy of 86%.
USB Keypress Injection Attack Detection via Free-Text Keystroke Dynamics

Universal Serial Bus (USB) devices are a standard method for information exchange. The growth in the usage of USB devices has increased the risk of vulnerabilities through which an adversary can exploit connected systems. Mostly, attackers tend to hamper the firmware of USB devices and try to inject malicious scripts into the system through keypresses, causing a keypress injection attack. This paper aims to develop a framework based on free-text keystroke dynamics for detecting keypress injection attacks. The mechanism uses various machine learning models trained over different keystroke features such as duration time features (Hold Time) and latency features (Up-Down Time and Down-Down Time). We implement a realtime continuous checking over the latency features of user keystrokes. The trained models subsequently predict whether the user activity lies in the genuine or malicious region. We have used different baseline classifiers including k-Nearest Neighbour(KNN), Support Vector Machine(SVM), Random Forest, XGBoost, and investigated three custom-made keypress injection attacks in each model. The results indicate that Random Forest provides the best detection accuracy over all the three applied attacks. Furthermore, we compared the models trained with our dataset to those trained with the Buffalo dataset. Interestingly, our trained models demonstrate comparable accuracy with the former one, thereby vindicating the utility of the dataset.
Quick remedy commits and their impact on mining software repositories

Most changes during software maintenance and evolution are not atomic changes, but rather the result of several related changes affecting different parts of the code. It may happen that developers omit needed changes, thus leaving a task partially unfinished, introducing technical debt or injecting bugs. We present a study investigating "quick remedy commits" performed by developers to implement changes omitted in previous commits. With quick remedy commits we refer to commits that (i) quickly follow a commit performed by the same developer, and (ii) aim at remedying issues introduced as the result of code changes omitted in the previous commit (e.g., fix references to code components that have been broken as a consequence of a rename refactoring) or simply improve the previously committed change (e.g., improve the name of a newly introduced variable). Through a manual analysis of 500 quick remedy commits, we define a taxonomy categorizing the types of changes that developers tend to omit. The taxonomy can (i) guide the development of tools aimed at detecting omitted changes and (ii) help researchers in identifying corner cases that must be properly handled. For example, one of the categories in our taxonomy groups the reverted commits, meaning changes that are undone in a subsequent commit. We show that not accounting for such commits when mining software repositories can undermine one's findings. In particular, our results show that considering completely reverted commits when mining software repositories accounts, on average, for 0.07 and 0.27 noisy data points when dealing with two typical MSR data collection tasks (i.e., bug-fixing commits identification and refactoring operations mining, respectively).
How Is Twitter Talking About COVID-19?

The novel coronavirus (COVID-19 or SARS-CoV-2) virus spread rapidly, both as a pandemic and as a viral topic of conversation. Social networks, especially after the boom of smartphones, completely revolutionised the speed and channels where information spreads. A clear example of this is how fast information spreads across Twitter, a platform famous for creating trends and spreading the news. This work focuses on the analysis of the overall opinion of the COVID-19 pandemic on Twitter. We attempted to study the polarity and emotional impression of the people applying a series of natural language processing techniques to a total of 270,000 tweets identified as related to COVID-19.
Blending agile methodologies to support automotive SPICE compliance

Abstract Currently, agile methodologies are widely used as a light‐weight development process model based on short development cycles, which can satisfy frequent change requests from customers and stakeholders, and reach an efficient product delivery. On the other hand, most suppliers in the automotive industry are currently facing a challenge to comply to automotive SPICE (ASPICE) standard, which is a domain‐specific model of ISO/IEC 15504. Moreover, original equipment manufacturers (OEMs) have increasingly requested ASPICE compliance from their suppliers to accurately assess their process quality capability. This paper analyzes to what extent the ASPICE process areas were covered by agile and aims to propose a model based on integrating agile methodologies, as well as customize some agile practices so that it can fit in the Automotive SPICE process model and support automotive suppliers to achieve the required ASPICE rating. MAN.03, which is the process area for project management, was selected from the process reference model (PRM). Then, each base practice (BP) was mapped to the suitable agile methodology. Moreover, MAN.03 consists of 10 BPs, and each BP rating was measured to be N, P, L, or F. For Level 1, we applied Kanban on BP.4 and Scrum on the remaining BPs. For Level 2, we applied Scrum on all generic practices. We presented our results after deploying Kanban, and a restructured version of Scrum, which makes it in compliance with the automotive industry. The approach taken in this study could be applied in other process areas in the PRM afterwards.
Characterisation of User-Contexts for Context-Aware Recommendation Systems

There is a growing interest in context-aware recommendation systems (CARS) due to their ability to enable novelty, dynamism and serendipity in resulting recommendations. The incorporation of different user-contextual parameters is important in CARS. However, the problem of identifying the most optimal subset of user-contextual parameters that add real value to resulting recommendations from CARS remain unresolved. This paper investigates an approach to characterise user-contextual parameters and identify the optimal subset of parameters that enables best results from CARS algorithms. In this paper, two data sets were collected from different people in Africa, each data set had 21 classified user-contexts and corresponding likes. An association rule mining (apriori) algorithm was used to generate rules from the user profiles (user contextual-parameters) from both data sets, to identify user-contextual parameters which are strongly associated with certain user preferences. Five(5)[Hobbies, Mood, Family setup, Religion and Gender] user-contextual parameters out of the 21 were identified as the optimal subset of parameters for the domain and the data sets in this study. This optimal subset was found promising in helping CARS as the parameters which can estimate best the preferences of the users and hence led to best recommendations. This paper contributes a new method of identifying the optimal subset of user-contextual parameters in any domain, which enables CARS lgorithms to produce the most appropriate recommendations for each user.
Ensemble Based Predictive Model for Streaming Data

The Internet of Things (IoT) is employed in diverse applications to provide intelligent solutions. While IoT improves the user experience, the primary difficulty in realizing such applications is the large volume of data generated from the devices. Suitable analytics techniques are demanded to derive valuable insights from this data. the analysis becomes further challenging when real-time processing is expected. This paper evaluates the performance of regression models in an IoT dataset utilizing ensembling and adaptive windowing concepts. One of the critical factors that impact prediction accuracy in real-time streams is the training window size. We examine the impact of stream window size on prediction error by applying fixed-sized and adaptive windows. Also, a rolling variance-based model training is utilized to detect the change in the data stream. The results show that the ensembling technique with an adaptive windowing scheme and rolling-variance-based change detection provides better predictions than independent models.
Methodological Proposal for Privilege Escalation in Windows Systems

One of the operating systems most used today is Windows, created by the Microsoft company since its launch on November 20, 1985. As a result of the popularity that the use of the Windows operating system generated in companies and individuals, also called the attention of Cybercriminals who began to exploit different security breaches in these systems, and in a few years an exponential exploitation of equipment, theft of confidential information, impersonation, among many other attacks began. With the evolution of offensive security, there are different tasks that must be executed to evaluate the level of security of the systems. For this, researchers use methodologies that contribute to the development of their activities. The purpose of this work is to propose a procedural alternative to execute actions to raise privileges in Windows 7, 8, 8.1 and 10 systems to support post-exploitation work by professionals in the offensive security area.From the work methodology exposed in this research, which allows to be able to evaluate in an orderly way different activity associated with the enumeration, which becomes a background for the selection of techniques for raising or escalating privileges, within of a post-exploitation process.
Analysing Distance Measures in Topsis: A Python-Based Tool

The technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is an MCDM method that chooses the best option from a finite number of alternatives. It is based on the technique to choose an alternative that has the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution. This is achieved by evaluating the distance of the alternatives to the ideal solutions. For the purpose of finding the distance, the classical method uses Euclidean distance. In an attempt to recreate this, this paper uses ten different distance measures of the Lp Minkowski family, L1 family and the $$\chi^{2}$$ family which are then compared with that of the standard method.
Global presence of open-source research data management platform for libraries: the Dataverse project

Purpose This paper aims to provide statistical information on the worldwide spread of the open-source research data management application, the Dataverse Project, to librarians, data managers and information managers who are considering using the application at their own institution. Design/methodology/approach To produce a list of dataverse repositories, the official Dataverse website was evaluated, and JSON data were downloaded and parsed. Data standardisation was performed to assess the state of installations in various nations and continents across the world. Findings Globally, the Dataverse repositories have seen a rise in overall installations. The year 2020 alone saw a 23.21% rise. In a country-by-country comparison, the USA (13) has the most dataverse installations, while Europe (25) has the highest number of installations worldwide. Originality/value This research will be useful to librarians, data managers and information managers, among others, who want to learn more about Dataverse repositories throughout the world before deploying at their local level.
Quantum Private Comparison Based on GHZ-type States

In this paper, an efficient quantum private comparison (QPC) protocol based on GHZ-type states is proposed. Two participants can compare the equality of two classical bits in each comparison, which could greatly reduce comparison times and increase efficiency. A semi-honest third-party (TP) is involved in assisting the participants to compare their secrets. TP may misbehave on her own, but she is not allowed to conspire with any participants. Besides, the proposed protocol needs Hadamard operation as well as single-particle measurements and Bell measurements, which are easy to implement with current technologies. Finally, the analysis shows the proposed protocol is correct and it can resist various attacks including outside attacks and dishonest participant attacks.
Implementation of Hyperledger-Based Secure Firmware Update Delivery for IoT Devices

An increasing reliance on smart and connected devices in our homes, workplaces, and in everyday life has led to the rapid growth of the Internet-of-Things (IoT) technology. While some IoT devices communicate without the involvement of their users, their functionalities must be protected against various attacks. The firmware update process is a fundamental security challenge in the world of embedded devices. Therefore, this research work proposes and implements a secure firmware update delivery mechanism for IoT devices. To assure a secure firmware update, the process should be conducted through a trusted network. The proposed solution is therefore deployed in a blockchain network. This mechanism ensures that the firmware version of the IoT device is verified while also validating the integrity of the file itself and then downloading the latest version of the firmware update. All these tasks are performed securely through a Hyperledger blockchain network. The main objective of this solution is to mitigate attacks on the firmware update process by ensuring that IoT firmware is up to date and that it has not been modified either during the transfer process or as it is installed on the IoT device.
Software refactoring side effects

Abstract Software refactoring solutions aim at mitigating the negative effects of code and design smells on the overall software quality. Many efforts have been exerted to improve the software refactoring process. However, most of these efforts, despite their contributions, overlooked the side effects of the identified refactoring opportunities that may lead to new smells that will go unnoticed. This paper addresses the side effects of software refactoring and proposes sound solutions for handling them. Unlike current practices in software maintenance, we recommend three different approaches to handle the refactoring side effects. In the first approach, called the baseline , we opt to ignore the smells, caused by refactoring, while executing the identified refactoring decisions. In the second one, refactoring decisions are continually updated to fix all smells caused by side effects. In the last approach, only a subset of these smells is appended to the original smell sequence during the execution of the refactoring decisions. Thanks to the proposed approaches, optimal refactoring decisions are identified using a multi‐objective (MO) optimization algorithm commonly known as the MO covariance matrix adaptation evolution strategy (MO‐CMA‐ES). Experiment results corroborate our assumptions and show the superiority of the second approach over the other ones.
Privacy in Ubiquitous Mobile Computing

Mobile sensing applications exploit big data to measure and assess human-behavioural modelling. However, big data profiling and automated decision practices, albeit powerful and pioneering, they are also highly unregulated and thereby unfair and intrusive. Their risk to privacy has been indeed identified as one of the biggest challenges faced by mobile computing applications. In this Chapter, we delve into the privacy risks arising from the ubiquitous mobile computing and sensing applications and, in particular, from the big data algorithmic processing which infers sensitive personal details such as people’s social behaviour or emotions. In this respect, we thoroughly discuss the risks of profiling which are further elaborated in the tax and financial context. We also explore strategies towards mitigating these privacy risks, and we investigate the extent to which the GDPR protects against these threats, especially against aggressive profiling and automated decision-making. To mitigate these risks, we explore implementation challenges and we introduce countermeasures in the context of financial privacy that can adhere to the privacy requirements of the GDPR.
Scalability in Blockchain

Blockchain Technology is considered a nondestructive way to record changes in ledgers or database. The best part of BCT is the recording of data over a decentralized system that establishes trust in the system among all nodes where ledger copies are stored thus allowing no data tampering to occur at all. Lately the technology has suffered challenges as the number of participants using the technology is increasing. This work is a novel study of scalability issues in blockchain technology focusing healthcare sector as a use case. The paper proposes solutions using smart contracts in hierarchical models.