Reviewerly/exHarmony huggingface dataset:
Linus's Law

Linus's Law states that "given enough eyeballs, all bugs are shallow". In other words, given a large enough number of developers, almost every programming flaw is characterized and fixed quickly. Although there is much debate about this subject, we still lack empirical evidence to support this law. Given that this theme has, and still is, motivating business decisions in software development, we investigate the implications of Linus's Law in two empirical studies on open source projects mined from GitHub. In the first pilot study, we mined seven popular Java projects from GitHub and investigated the correlation between committers and programming flaws in source code files. Results of this pilot study suggest a positive correlation between the number of developers and programming flaws. We cross-validate these results in a second study with almost one hundred Python projects from GitHub. In this second study, we analyzed the correlation between the number of forks - i.e., a proxy for number of developers - and programming flaws identified in projects. In both studies, programming flaws were detected by using static code analysis tools. As a result of the second study, we could not observe a correlation between the number of developers and the number of programming flaws in Python projects. From both studies we conclude that we were unable to find evidence to support the Linus's Law.
A Mini-sized Agent Testbed for Applications in Mobile Robotics

In the last years, the growing interest in consensus and coordinated control of multi-agent mobile systems has originated the development of a variety of testbeds. These experimental platforms constitute an essential component in the research and education process in the robotics field. This paper describes the design and implementation of a testbed based on the position control and trajectory tracking of mini-sized ground robots. The testbed architecture consists of a monitoring system composed by a web cam and a control algorithm running in a main computer that communicates with the robots wirelessly. A unique feature of the testbed is the use of ArUco markers on top of the mobile platforms for motion tracking purposes using OpenCV and Visual Basic. Additionally, the testbed has a user graphic interface that allows to manage and visualize results of the robot behavior for the tracking of different pre-defined trajectories.
ImCAPTCHA: Imperceptible CAPTCHA Based on Cursor Trajectories

In the process of network interaction, CAPTCHA (completely automated public Turing test to tell computers and humans apart) is usually required. At present, the main method for differentiating between humans and machines is to use CAPTCHA, which includes images and text. Users need to interface with CAPTCHA for verification, which prolongs the interaction step. The interactive experience is poor, and the process of verification is not concealed. To simplify the verification process, optimize the interaction behavior and increase the security of verification, this paper proposes a CAPTCHA method based on a user's mouse operation behavior, and carries out a study on cursor trajectory recognition. This method can reduce the manual procedure and improve the user interactive experience. In this article, we propose an ensemble learning algorithm model based on sliding sampling to classify and recognize cursor trajectory data. Experiments show that the classification performance of the model is reliable for recognition based on human-machine cursor trajectories, which is a new concept for CAPTCHA.
Minimization Response Time Task scheduling Algorithm

Abstract Cloud computing is a type of computing that base on shared computer resources for delivering information technology services. Distributed Computational system has been considered as best systematic model for sharing resources. Minimization of response time is the requirement in the distributed computational system. Previous Algorithm belongs to the optimization in which every new Virtual Machine reaching as a request to the cloud computing manager. This creates disturbance in the processing during life cycle of others Virtual Machines. For overcome this problem, there is a proposed an algorithm for minimization response time in distributed computational system by using the multi agents cloud structure. The overall system is architecture as a distributed over multi layered model. This leads to a situation that demands high care in allocating a Virtual Machine (VM) to a server machine in order to improve allocation time and efficiency of resources. Focus of this work remains to decrease time of allocation to optimize response time using a Virtual Machine (VM) placement and allocation algorithm that accounts Virtual. Machines (VM), their types and available resources among the data center server machines. Presented algorithm is implemented by using cloud simulator tool, through this tool, the calculated results of presented algorithm have been quite better with respect to traditional algorithm, and the difference presented with graphs for the both algorithms. Results support proposed methodology.
Graph convolutional neural network for multi-scale feature learning

Automatic deformable 3D modeling is computationally expensive, especially when considering complex position, orientation and scale variations. We present a volume segmentation framework to utilize local and global regularizations in a data-driven approach. We introduce automated correspondence search to avoid manually labeling landmarks and improve scalability. We propose a novel marginal space learning technique, utilizing multi-resolution pooling to obtain local and contextual features without training numerous detectors or excessively dense patches. Unlike conventional convolutional neural network operators, graph-based operators allow spatially related features to be learned on the irregular domain of the multi-resolution space, and a graph-based convolutional neural network is proposed to learn representations for position and orientation classification. The graph-CNN classifiers are used within a marginal space learning framework to provide efficient and accurate shape pose parameter hypothesis prediction. During segmentation, a global constraint is initially non-iteratively applied, with local and geometric constraints applied iteratively for refinement. Comparison is provided against both classical deformable models and state-of-the-art techniques in the complex problem domain of segmenting aortic root structure from computerized tomography scans. The proposed method shows improvement in both pose parameter estimation and segmentation performance.
Hidden Markov Model-based Load Balancing in Data Center Networks

Abstract Modern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.
An Agile Method of Representing, Organizing, and (Re) Prioritizing Requirements in a Large Enterprise

Agile and lean are now the mainstream tenets of software development. This development methodology is attracting attention because it can develop software that operates quickly despite uncertain situations and limited information (requirements), and it can adaptively develop while checking feedback both frequently and regularly. The key is to welcome change without resistance, which is not limited to development but also requires changes in requirement management. However, there is a lack of practical guidance on how to manage changes in agile requirements engineering. Although we need to consider all areas of requirements engineering, the author has seen three key themes: (1) How to focus on customer value and represent it clearly for smooth communication between teams in a large enterprise? (2) What are the requirements for organizing elements that make it easy to accept changes and assess impacts? (3) What reasonable algorithm is easy to use and reliable to prioritize or reorder requirements based on values regularly? The author wants to suggest practical ways to address these themes and propose a way to deal with three themes without compromising on the essence of agile. The author is going to do case studies with some organizations that are already using or first adopting agile software development. The author could get the strengths and weaknesses in terms of collaboration promotion and change both the acceptance and qualitative feedback on application results.
Image Steganographic Scheme Based on Doublelayer Magic Matrix

Data hiding is a technique used for embedding secret messages in a carrier medium which cannot be perceived by human eye. In the data hiding scheme based on the magic matrix, previous methods exploited a magic matrix with various shapes to carry secret messages, but the embedding capacity was often limited. Inspired by the magic matrix scheme using magic signet proposed by Lee et al., the proposed method develops a double-layer magic matrix scheme to enhance the embedding capacity. Under the framework of a double-layer matrix, we tested many combinations of different signet sizes which can carry secret data in various notational number systems. The experimental results show that the proposed scheme had a higher embedding capacity and good image quality compared to the previous methods.
Related Attention Network for Person Re-Identification

Person Re-identification (ReID) is a critical technology in intelligent video surveillance. In practice, person ReID remains challenging due to pedestrian misalignment and background clutter. Pedestrian images are generated by manually cropping or pedestrian detection algorithms in most existing datasets, which mainly cause two drawbacks. On the one hand, detection errors may lead to pedestrian misalignment and cluttered background. On the other hand, hand-drawn bounding boxes are highly accurate but with inconsistent scales. In order to solve these problems, we make two contributions. Firstly, we design a simple and effective data pre-processing algorithm, which aligns pedestrian images into a standard template based on keypoints. Secondly, the Related Attention Network (RAN) is proposed to focus on human body regions by the pixel-level correlation, which improves ReID performance significantly. Experimental results on Market-1501 and DukeMTMC-reID datasets demonstrate the effectiveness of our method.
Making the Invisible Audible

We describe how Embosonic Sketching, a novel approach to qualitative data sonification, was employed in the design of a sound art installation. The method was conceived to minimise designer bias in the creation of sound artworks which seek to faithfully represent the lived experience of a sample group. The 'Her[sonifications]' project was driven by the notion that much exists beyond sight, yet remains undiscovered, and explored bringing the unseen to light through the transformative medium of sound. We ran a series of workshop and focus group sessions to engage with women from a range of backgrounds including artists, writers, designers and researchers to explore how we could harness the emotive qualities of sound to communicate the visceral experience of womanhood. We contribute insight into how Embosonic Sketching can be employed to enrich the process of qualitative data collection in group-based settings; to produce sound sketches to inform the sound design process; and most significantly, to create self-actualised sonifications of visceral and experiential phenomena. Our findings demonstrate that Embosonic Sketching is a useful and accessible tool for exchanging ideas about sound and promotes heightened participant engagement with participatory design processes.
An Analogical Interpolation Method for Enlarging a Training Dataset

In classification problems, it happens that the training set remains scarce. Given a data set, described in terms of discrete, ordered attribute values, we propose an interpolation-based approach in order to predict new examples useful for enlarging the original data set. The proposed approach relies on the use of continuous analogical proportions that are statements of the form "a is to x as x is to c". The prediction is made on the basis of pairs of examples (a, c) present in the data set, for which one can find a value for x for each attribute value as well as for the corresponding class label of the example thus created. The first option that we consider is to select x as the midpoint between a and c, attribute by attribute. To extend the search space, we may also choose x as any randomly selected value between the values of a and c. We first propose a basic algorithm implementing these two interpolation definitions, then we extend it to two improved algorithms. In the former, we only consider the nearest neighbor pairs (a, c) to x for prediction, while, in the latter, we further restrict the search to those pairs (a, c) having the same class label. The experimental results, for classical ML classifiers applied to the enlarged data sets built by the proposed algorithms, show the effectiveness of analogical interpolation methods for enlarging data sets.
Digital camera identification based on analysis of optical defects

Abstract In this paper we deal with the problem of digital camera identification by photographs. Identifying camera is possible by analyzing camera’s sensor artifacts that occur during the process of photo processing. The problem of digital camera identification has been popular for a long time. Recently many effective and robust algorithms for solving this problem have been proposed. However, almost all solutions are based on state-of-the-art algorithm, proposed by Lukás et al. in 2006. Core of this algorithm is to calculate the so-called sensor pattern noise based on denoising images with wavelet-based denoising filter. Such technique is very efficient, but very time consuming. In this paper we consider tracing cameras by analyzing defects of their optical systems, like vignetting and lens distortion. We show that analysis of vignetting defect allows for recognizing brand of the camera. Lens distortion can be used to distinguish images from different cameras. Experimental evaluation was carried out on 60 devices (compact cameras and smartphones) for a total number of 12 051 images, with support of the Dresden Image Database. Proposed methods do not require denoising images with wavelet-based denoising filter what has a significant influence for speed of image processing, compared with state-of-the-art algorithm.
Requirements Reference Models Revisited: Accommodating Hierarchy in System Design

Reference models such as Parnas' four-variable model, Jackson's and Zaves' world machine model, and Gunther et al.'s WRSPM model abstractly define and relate key artifacts in requirements engineering. Such reference models are intended to serve as a frame of reference for engineers to understand and reason about the artifacts involved in requirements engineering. However, when discussing the requirements of modern systems that are developed in a hierarchical and middle-out manner, these reference models do not provide a framework in which the relationship between requirements and architecture is explicitly discussed. Conceptual clarity about this relationship is crucial since the architecture and requirements for such systems become intrinsically intertwined as the architectural choices made during development influence the requirements and vice-versa. Hence, to precisely determine the scope of specifying requirements, distinguish requirements from architecture details, reason about the requirements, and determine how the requirements are realized in the system, we argue that a requirements reference model intended as a reference for such systems must explicitly discuss the architecture - requirements relationship. To that end, we define a hierarchical reference model that formally, yet abstractly, captures the intertwined relationship between the architecture and requirements in a way that will serve the same purpose as other models, but be more suitable for modern systems where architecture and requirements co-evolve. To illustrate the concepts in this model, we use a generic patient-controlled analgesic infusion pump system as a case example.
Applying Ontology in Computational Creativity Approach for Generating a Story

Computational creativity is a young multidisciplinary field which has a promising future for effectively giving options in tackling and handling many automated systems. It is very useful for example, in creating a narrative story for various purposes, which is actually a human art. In this research, we are investigating methods that can be applied as into computational approach for generating structured narratives automatically, to suit education purposes. In this paper, we present a literature review of the work done in this field so far, and we propose a framework that is designed to generate educational stories using computational creativity approach. The major contribution of this paper is a proposed computational creativity approach consisting of hybrid Artificial Intelligence methods to generate educational stories.
Proof-of-Work (PoW) Blockchain Network and Its Viability as a Payment System

This paper analyzes PoW blockchain network’s long-term cost ef- ficiency and short-term stability. Our equilibrium model builds upon the optimization problems of blockchain users and miners, and allows us to simultaneously determine token price, network hashrate, and user transaction fee. We prove that transaction fee bidding mechanism creates two types of welfare loss in the long- term: over-competition among users leads to excessive transaction fee payment; failure to realize fee’s positive externality on network security in user fee decision making also leads to a non-optimal fee level. By studying short-term feedback loop between network hashrate and miner income, we also prove that the network over- corrects itself and diverges when subject to a hashrate shock. We conclude that it has unstable equilibrium.
On the Ethnic Classification of Pakistani Face using Deep Learning

Demographic-based identification plays an active role in the field of face identification.Over the past decade, machine learning algorithms have been used to investigate challenges surrouding ethnic classification for specific populations, such as African, Asian and Caucasian people.Ethnic classification for individuals of South Asian, Pakistani heritage, however, remains to be addressed.The present paper addresses a two-category (Pakistani Vs Non-Pakistani) classification task from a novel, purpose-built dataset.To the best of our knowledge, this work is the first to report a machine learning ethnic classification task with South Asian (Pakistani) faces.We conduted a series of experiments using deep learning algorithms for feature extraction and a linear support vector machine (SVM) for classification.The experimental results demonstrate ResNet-101 achieves the highest performance accuracy of 99.2% for full-face ethnicity classification, followed closely by 91.7% and 95.7% for the nose and mouth respectively.
Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity

In recent years, reversible data hiding (RDH) has become a research hotspot in the field of multimedia security that has aroused more and more researchers’ attention. Most of the existing RDH algorithms are aiming at continuous-tone images. For RDH in encrypted halftone images (RDH-EH), the original cover image cannot be recovered losslessly after the watermark is extracted. For some application scenarios such as medical or military images sharing, reversibility is critical. In this paper, a reversible data hiding scheme in encrypted color halftone images (RDH-ECH) is proposed. In the watermark embedding procedure, the cover image is copied into two identical images to increase redundancy. We use wet paper code to embed the watermark into the image blocks. Thus, the receiver only needs to process the image blocks by the check matrices in order to extract the watermarks. To increase embedding capacity, we embed three layers in the embedding procedure and combine the resulting images into one image for convenience of transmission. From the experimental results, it can be concluded that the original image can be restored entirely after the watermarks are extracted. Besides, for marked color halftone images, our algorithm can implement high embedding capacity and moderate visual quality.
Big Five Personality Traits and Ensemble Machine Learning to Detect Cyber-Violence in Social Media

Cyber-violence is a largely addressed problem in e-health researches, its focus is the detection of harmful behavior from online user-generated content in order to prevent and protect victims. In this work, we show how big five personality traits are correlated to the violent behavior of the cyber-violence perpetrator. We use a set of ensemble learning algorithms with engineered features related to the vocabulary used in each Big Five personality trait namely, Agreeableness, Conscientiousness, Extraversion, Neuroticism and Openness. The findings show a significant association between the individuals’ personality state and the harmful intention. This result can be a good indicator of online users’ susceptibility to cyber-violence and therefore can help in dealing with it.
Estimation of the Left Ventricle Volume Using Semantic Segmentation

The left ventricle (LV) volume is a major indicator of heart disease. Current clinical procedure for volume estimation from magnetic resonance (MR) images include manual inspection by the experienced cardiologists, which is often a time-consuming process. This paper presents an automated method for LV volume estimation at two points in the time. The method consists of training a convolutional neural network (CNN) with the MR images from the Sunnybrook dataset. After that, the trained model is used to predict left ventricular segments in MR images from the Kaggle dataset. From predicted segments, the ventricular volumes at end-systole (ESV) and end-diastole (EDV) are calculated using numerical integration. The obtained results estimate the ESV and EDV with a mean absolute error of 14.688 and 19.222 mL, respectively.