Reviewerly/exHarmony huggingface dataset:
What Edits are Done on the Highly Answered Questions in Stack Overflow? An Empirical Study

Stack Overflow is the most-widely-used online question-and-answer platform for software developers to solve problems and communicate experience. Stack Overflow believes in the power of community editing, which means that one is able to edit questions without the changes going through peer review. Stack Overflow users may make edits to questions for a variety of reasons, among others, to improve the question and try to obtain more answers. However, to date the relationship between edit actions on questions and the number of answers that they collect is unknown. In this paper, we perform an empirical study on Stack Overflow to understand the relationship between edit actions and number of answers obtained in different dimensions from different attributes of the edited questions. We find that questions are more commonly edited by question owners, on bodies with relatively big changes before obtaining an accepted answer. However, edited questions that obtained more answers in a shorter time, were edited by other users rather than question owners, and their edits tended to be small, focused on titles and in adding addendums.
Multi-Scale Gradient Image Super-Resolution for Preserving SIFT Key Points in Low-Resolution Images

Low-resolution images present challenges to a variety of object recognition problems in a variety of surveillance and navigation applications. In recent years, deep learning has advanced the state of the art in image super-resolution (SR) in terms of pixel domain peak signal to noise ratio (PSNR)/ mean square error (MSE). Inspired by the recent advances of deep convolutional neural networks in general image SR tasks, we develop a computer vision task-driven image SR solution by learning super-resolved gradient images using multiple convolutional neural networks for different scales. Recovering super-resolved gradient images at multiple scales, enables the system to recover more information useful for high level vision tasks than simply SR in the pixel domain. In particular, we propose a residual learning framework to perform image SR in the Difference of Gaussian (DOG) domain. The trained residual network models are then adapted to drive a widely adopted key point algorithm for image recognition, i.e. the SIFT detection and matching. Experimental results show that the proposed approach can significantly improve the SIFT keypoints repeatability compared to the state of the art in pixel domain image SR solutions.
MCP: A Security Testing Tool Driven by Requirements

We present MCP, a tool for automatically generating executable security test cases from misuse case specifications in natural language (i.e., use case specifications capturing the behavior of malicious users). MCP relies on Natural Language Processing (NLP), a restricted form of misuse case specifications, and a test driver API implementing basic utility functions for security testing. NLP is used to identify the activities performed by the malicious user and the control flow of misuse case specifications. MCP matches the malicious user's activities to the methods of the provided test driver API in order to generate executable security test cases that perform the activities described in the misuse case specifications. MCP has been successfully evaluated on an industrial case study.
Evaluation of Textual Similarity Techniques in Code Level Traceability

Automatic recovery of test-to-code traceability links is an important task in many areas of software engineering, like quality assurance and code maintenance. The research community has shown great interest in such a topic and has developed several techniques that already made significant advances in the field. These techniques include text-based learning algorithms, of which corpus is built from the source code of the software components. Several techniques based on information retrieval have been benchmarked, but the capabilities of many learning algorithms have not yet been tested. In this work we examine the textual similarity measures produced by three different machine learning techniques for the recovery of traceability information while also considering various textual representations of the source code. The obtained results are evaluated on 4 open source systems based on naming conventions. We have been able to improve the current textual similarity based state-of-the-art results in the case of each evaluated system.
Attitudes of Professionals Toward the Need for Assistive and Social Robots in the Healthcare Sector

We conducted three studies among healthcare professionals and explored the need for service and social robots in the healthcare sector. The methods consisted of cross-sectional surveys and literature reviews. The survey data were analyzed with cross-tabulations, a logistic regression model, a Pearson correlation test, and a factor analysis. The literature reviews showed that there were only a few papers which discussed the use of service and social robots as tools by healthcare workers. Both professional care workers and healthcare educators perceived that robots were able to increase productivity. The results also showed that robots are able to reduce the mental workload of workers and to increase the diversity of work. Robots were also considered as good devices for activating the patients' motoric and cognitive skills and for making them happy. Even if the attitudes were positive and people were not afraid that robots may take over workplaces, the ecosystem of social robotics is still fragmented and the number of intervention studies among professional care workers is small. Policymakers should create a strategy for promoting service and social robots in the healthcare sector. The strategy should take into account robotics in education and implementation of robots in healthcare facilities.
Comparing the EvoStreets Visualization Technique in Two-and Three-Dimensional Environments A Controlled Experiment

Analyzing and maintaining large software systems is a challenging task due to the sheer amount of information contained therein. To overcome this problem, Steinbrückner developed a visualization technique named EvoStreets. Utilizing the city metaphor, EvoStreets are well suited to visualize the hierarchical structure of a software as well as hotspots regarding certain aspects. Early implementations of this approach use three-dimensional rendering on regular two-dimensional displays. Recently, though, researchers have begun to visualize EvoStreets in virtual reality using head-mounted displays, claiming that this environment enhances user experience. Yet, there is little research on comparing the differences of EvoStreets visualized in virtual reality with EvoStreets visualized in conventional environments. This paper presents a controlled experiment, involving 34 participants, in which we compared the EvoStreet visualization technique in different environments, namely, orthographic projection with keyboard and mouse, 2.5D projection with keyboard and mouse, and virtual reality with head-mounted displays and hand-held controllers. Using these environments, the participants had to analyze existing Java systems regarding software clones. According to our results, it cannot be assumed that: 1) the orthographic environment takes less time to find an answer, 2) the 2.5D and virtual reality environments provide better results regarding the correctness of edge-related tasks compared to the orthographic environment, and 3) the performance regarding time and correctness differs between the 2.5D and virtual reality environments.
Comparing Bug Replication in Regular and Micro Code Clones

Copying and pasting source code during software development is known as code cloning. Clone fragments with a minimum size of 5 LOC were usually considered in previous studies. In recent studies, clone fragments which are less than 5 LOC are referred as micro-clones. It has been established by the literature that code clones are closely related with software bugs as well as bug replication. None of the previous studies have been conducted on bug-replication of micro-clones. In this paper we investigate and compare bug-replication in between regular and micro-clones. For the purpose of our investigation, we analyze the evolutionary history of our subject systems and identify occurrences of similarity preserving co-changes (SPCOs) in both regular and micro-clones where they experienced bug-fixes. From our experiment on thousands of revisions of six diverse subject systems written in three different programming languages, C, C# and Java we find that the percentage of clone fragments that take part in bug-replication is often higher in micro-clones than in regular code clones. The percentage of bugs that get replicated in micro-clones is almost the same as the percentage in regular clones. Finally, both regular and micro-clones have similar tendencies of replicating severe bugs according to our experiment. Thus, micro-clones in a code-base should not be ignored. We should rather consider these equally important as of the regular clones when making clone management decisions.
Basics of Supervised Deep Learning

The use of supervised and unsupervised deep learning models has grown at a fast rate due to their success with learning of complex problems. High-performance computing resources, availability of huge amounts of data (labeled and unlabeled) and state-of-the-art open-source libraries are making deep learning more and more feasible for various applications. Since the main focus of this chapter is on supervised deep learning, Convolutional Neural Network (CNN or ConvNets) that is one of the most commonly used supervised deep learning models is discussed in this chapter.
On the Alternatives for Composing Batch Refactoring

Code refactoring is often performed for improving code structures through code transformations. Many transformations, e.g., extracting or moving a method, are applied for at least partially removing code smells. Each code smell is a symptom of a poor code structure that makes hard to read and change the program. Developers often compose two or more interrelated transformations in conjunction (batch refactoring) rather than applying a single transformation. For instance, developers often compose method extractions with method motions to better organize the features realized by classes. We have recently observed cases of batch refactoring performed along with code review in open source projects. We then noticed that composing batches capable of fully removing code smells is quite challenging. Especially, it requires carefully discussing on how two or more transformations complement one another and what to expect from the batch effect on code smell. This position aims to reason about multiple alternatives to support developers on composing their batches. These alternatives should make it easier to compose batches that remove code smells. For this purpose, we exemplify the role of semi-automated tools in gradually recommending transformations, thereby guiding the batch composition in each alternative.
Contrast Enhancement via Dual Graph Total Variation-Based Image Decomposition

Images captured in low lighting environment suffer from both low luminance contrast and noise corruption. However, most existing contrast enhancement algorithms only consider contrast boosting, which tends to reveal or amplify noise that is originally not visible in the dark areas. In this paper, we propose a joint contrast enhancement and denoising algorithm, which is based on structure/texture layer decomposition via minimization of dual forms of graph total variation (GTV). Specifically, the structure layer is expected to be generally smoothing but with sharp edges at the foreground background boundaries, for which we propose a quadratic form of GTV (QGTV) as the prior that promotes signal smoothness along graph structure. For the texture layer, a re-weighted GTV (RGTV) is tailored to noise removal while preserving true image details. We provide theoretical analysis about the filtering behavior of these two priors. Furthermore, a boost factor is derived per patch via optimal contrast-tone mapping to improve the overall brightness level of the patch. Finally, an optimization objective function is formulated, which casts image decomposition, brightness boosting, and noise reduction into a unified optimization framework. We further propose a fast approach to efficiently solve the optimization and provide analysis about the convergency. The experimental results show that the proposed method outperforms the state-of-the-art works in subjective, objective, and statistical quality evaluation.
A fuzzy framework for prioritization and partial selection of security requirements in software projects

Resource limitations in software projects rarely allow for the security requirements to be fully realized. As such, Prioritization and Selection (PAS) techniques are used to find an optimal subset of the requirements. Consequently, some of the security requirements will be ignored. But ignoring se curity requirements may (a) leave some of the security threats unattended and (b) negatively impact the effectiveness of the selected requirements. To mitigate this, we have proposed a fuzzy framework, referred to as Prioritization And Partial Selection (PAPS), that reduces the number of ignored security requirements by allowing for partial satisfaction of those requirements. We achieve this by relaxing the satisfaction conditions of security requirements, when tolerated, based on their priorities specified by a fuzzy inference system. Taking into account the partiality of security in PAPS mitigates the adverse impact of ignoring security requirements and enhances the accuracy of prioritization and selection. Our proposed framework is scalable to a large number of requirements.
Basic Concepts of Data Stream Mining

Data stream mining, as its name suggests, is connected with two basic fields of computer science, i.e. data mining and data streams. Data mining [1–4] is an interdisciplinary subfield of computer science whose main aim is to develop tools and methods for exploring knowledge from large datasets. Data mining is strictly related to statistics, pattern recognition [5] and machine learning [6], using methods like neural networks, decision trees, Bayesian networks or support vector machines. Neural networks are often considered a method belonging to soft computing or computational intelligence [7]. Another soft computing concept used in data mining is fuzzy logic. It should be noted that all mentioned above subfields of computer science are not strictly defined and overlap in many issues. Moreover, data mining is something more than learning or extracting knowledge, and includes, among others, database systems or data visualization as well. However, this book focuses mainly on the learning aspect of data mining.
Application of public blockchain to control the immutability of data in online scientific periodicals

Purpose The purpose of this paper is to address the problem of content immutability and integrity of online scientific periodicals on the sites of small publishers that can be violated not only by the external hack of the publisher’s site but also by publisher’s and author’s misconduct or by submitting different versions of a periodical to different sites. Design/methodology/approach The authors defined a list of requirements that verify online scientific publications immutability and integrity. Then, the authors analyzed existing projects and recently emerged information on security technologies and identified challenges met during the development and testing. The use of the public blockchain network Ethereum as a secure storage location for data was explained. Findings The authors developed the method of checking online scientific periodicals for immutability and presented ecosystem architecture to control immutability and integrity of data. On the example of the online periodical “Istoriya”, it was demonstrated how the immutability of online scientific publication has been verified with the use of the public blockchain over a six-month period. First, operating results were evaluated; challenges hampering the implementation of the suggested ecosystem on Ethereum now were identified; and potential advantages of the suggested approach as compared to similar projects were discovered. Research limitations/implications The considered prototype is not a ready-to-use system, but in future providing higher transparency and the development of general distributed ecosystem small publishers will have new opportunities for development given that the issues of scalability, reliability and operating speed on a public blockchain will be addressed. Introduction of the described ecosystem may even provoke some changes on such conservative market as that of publishing of academic papers. Originality/value This research is one of the first attempts to expand digital object identifier technology with the use of additional verifications based on the data storage and search in the public blockchain. The suggested idea is the example of “blockchainified science” that was brought to implementation in a real online journal. This method has some advantages compared to Crossmark service.
Identifying Entity Properties from Text with Zero-shot Learning

We propose a method for identifying a set of entity properties from text. Identifying entity properties is similar to a relation extraction task that can be cast as a classification of sentences. Normally, this task can be achieved by distant supervised learning by automatically preparing training sentences for each property; however, it is impractical to prepare training sentences for every property. Therefore, we describe a zero-shot learning problem for this task and propose a neural network-based model that does not rely on a complete training set comprising training sentences for every property. To achieve this, we utilize embeddings of properties obtained from a knowledge graph embedding using different components of a knowledge graph structure. The embeddings of properties are combined with the model to enable identification of properties with no available training sentences. By using our newly constructed dataset as well as an existing dataset, experiments revealed that our model achieved a better performance for properties with no training sentences, relative to baseline results, even comparable to that achieved for properties with training sentences.
Practical quantum key distribution with geometrically uniform states

In this paper we propose a practical quantum key distribution protocol based on geometrically uniform states and a standard decoy state technique. The protocol extends the ideas used in SARG04 to the limit where the core quantum communication is secure against unambiguous state discrimination and provides some level of inherent resistance to photon number splitting attacks. An additional decoy state overlay ensures its conformity to the conventional security requirements. The protocol security is analyzed by an explicit construction of the most optimal unitary attack, which is known to reach the tight security bound in the case of BB84.
Online Incremental Classification Resonance Network and Its Application to Human–Robot Interaction

In human-robot interaction (HRI), classification is one of the most important problems, and it is essential particularly when the robot recognizes the surroundings and chooses a reaction based on a certain situation. Each interaction is different since new people appear or the environment changes, and the robot should be able to adapt to different situations during a brief interaction. Thus, it is imperative that the classification is performed incrementally in real time. In this sense, we propose an online incremental classification resonance network (OICRN) that enables incremental class learning in multi-class classification with high performance online. In OICRN, a scale-preserving projection process is introduced to use the raw input vectors online without a normalization process in advance. The integrated network of the convolutional neural network (CNN) for feature extraction and the OICRN for classification is applied to a robotic system that learns human identities through HRIs. To demonstrate the effectiveness of our network, experiments are carried out on benchmark data sets and on a humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory, KAIST.
Joint Learning of Multiple Latent Domains and Deep Representations for Domain Adaptation

In domain adaptation, the automatic discovery of multiple latent source domains has succeeded by capturing the intrinsic structure underlying the source data. Different from previous works that mainly rely on shallow models for domain discovery, we propose a novel unified framework based on deep neural networks to jointly address latent domain prediction from source data and deep representation learning from both source and target data. Within this framework, an iterative algorithm is proposed to alternate between 1) utilizing a new probabilistic hierarchical clustering method to separate the source domain into latent clusters and 2) training deep neural networks by using the domain membership as the supervision to learn deep representations. The key idea behind this joint learning framework is that good representations can help to improve the prediction accuracy of latent domains and, in turn, domain prediction results can provide useful supervisory information for feature learning. During the training of the deep model, a domain prediction loss, a domain confusion loss, and a task-specific classification loss are effectively integrated to enable the learned feature to distinguish between different latent source domains, transfer between source and target domains, and become semantically meaningful among different classes. Trained in an end-to-end fashion, our framework outperforms the state-of-the-art methods for latent domain discovery, as validated by extensive experiments on both object classification and human action-recognition tasks.
Super-resolution using multi-channel merged convolutional network

Single-image super-resolution (SISR) has been an important topic due to the demand for high-quality virtual images in the field of visual artificial intelligence. Methods based on deep learning have achieved great success based on the excellent capability of grasping complicated features of deep convolutional networks. The performance can be improved slightly but not obviously by simply widening or deepening the network. In this paper, we propose a merged convolutional network for super-resolution, which extracts more adequate details to restore high-resolution images. We used dense blocks for feature extraction to concatenate deep features with shallow features in depth. We also designed two sub-nets with distinct convolution kernels as different branches of the network, which can widen the network and improve the performance of the system. Finally, we employed sub-pixel layers to avoid feature distortion for up-sampling at the very end. Our method was evaluated using several standard benchmark datasets. The results demonstrate superior performance and good robustness compared with state-of-the-art methods.