Reviewerly/exHarmony huggingface dataset:
Rebasing in Code Review Considered Harmful: A Large-Scale Empirical Investigation

Code review has been widely acknowledged as a key quality assurance process in both open-source and industrial software development. Due to the asynchronicity of the code review process, the system's codebase tends to incorporate external commits while a source code change is reviewed, which cause the need for rebasing operations. External commits have the potential to modify files currently under review, which causes re-work for developers and fatigue for reviewers. Since source code changes observed during code review may be due to external commits, rebasing operations may pose a severe threat to empirical studies that employ code review data. Yet, to the best of our knowledge, there is no empirical study that characterises and investigates rebasing in real-world software systems. Hence, this paper reports an empirical investigation aimed at understanding the frequency in which rebasing operations occur and their side-effects in the reviewing process. To achieve so, we perform an in-depth large-scale empirical investigation of the code review data of 11 software systems, 28,808 code reviews and 99,121 revisions. Our observations indicate that developers need to perform rebasing operations in an average of 75.35% of code reviews. In addition, our data suggests that an average of 34.21% of rebasing operations tend to tamper with the reviewing process. Finally, we propose a methodology to handle rebasing in empirical studies that employ code review data. We show how an empirical study that does not account for rebasing operations may report skewed, biased and inaccurate observations.
Face Recognition System Based on Raspberry Pi Platform

Human face recognition plays an important role in video surveillance, human-computer interface, personalizing different applications. In this paper, we present an approach to detect and identify a human face from the real-time video that tracks a face and compares it with stored data of known individuals. Our approach recognizes an individual within a fraction of a second which completely ignores any background effect. It also shows additional information about that individual. Besides, this method also works on different lighting conditions which make it suitable to execute its purpose in a wide variety of environment without encountering any significant error. We have implemented our work using a Raspberry Pi. And this approach utilizes the benefits offered by different python libraries like OpenCV and numPy. SQLite database management system is used as the backbone of its memory. Through its faster and authentic performance, this system is worthy enough to be integrated with any applications to boost up automation and user-friendliness.
Superword: A honeyword system for achieving higher security goals

Generating honeywords for each user's account is an effective way to detect whether password databases are compromised. However, there are several underlying security issues associated with honeyword techniques that need to be addressed, for example, (1) How to make it more difficult for an attacker to find an accurate match of "username-real password"? (2) How to prevent the intersection attack in multiple systems caused by password reuse without reducing usability? (3) How to reduce the success rate of targeted password guessing? In this study, we first propose a "matching attack" model and find that although Erguler's honeyword system can achieve perfect flatness, the success rate of the attacker is 100% under matching attack. Secondly, we propose a new honeyword approach named Superword that isolates the direct relationship between username and the corresponding hashed password in password files. Additional honeypots are mixed with real accounts to detect online guessing attacks. The analysis reveals that our approach makes a matching attacker difficult to find a real password from N password hashes. Since there is no connection between the username and password in password files, our honeyword system also alleviates the multiple systems intersection attack and targeted password guessing.
DQPFS: Distributed quadratic programming based feature selection for big data

With the advent of the Big data, the scalability of the machine learning algorithms has become more crucial than ever before. Furthermore, Feature selection as an essential preprocessing technique can improve the performance of the learning algorithms in confront with large-scale dataset by removing the irrelevant and redundant features. Owing to the lack of scalability, most of the classical feature selection algorithms are not so proper to deal with the voluminous data in the Big Data era. QPFS is a traditional feature weighting algorithm that has been used in lots of feature selection applications. By inspiring the classical QPFS, in this paper, a scalable algorithm called DQPFS is proposed based on the novel Apache Spark cluster computing model. The experimental study is performed on three big datasets that have a large number of instances and features at the same time. Then some assessment criteria such as accuracy, execution time, speed-up and scale-out are figured. Moreover, to study more deeply, the results of the proposed algorithm are compared with the classical version QPFS and the DiRelief, a distributed feature selection algorithm proposed recently. The empirical results illustrate that proposed method has (a) better scale-out than DiRelief, (b) significantly lower execution time than DiRelief, (c) lower execution time than QPFS, (d) better accuracy of the Naïve Bayes classifier in two of three datasets than DiRelief.
Approaching the quantum limit of precision in absorbance estimation using classical resources

This work investigates precision bounds in absorbance sensing via the Beer-Lambert law for quantum and classical sources of light. The authors find that by optimizing the length of the sample, classical sources of light are able to reach within 83% of the absolute quantum limit in precision. A bulk optical experiment is used to demonstrate optimization of thermal and Fock states of light
Parallel connected-Component-Labeling based on homotopy trees

Taking advantage of the topological and isotopic properties of binary digital images, we present here a new algorithm for connected component labeling (CLL). A local-to-global treatment of the topological information within the image, allows us to develop an inherent parallel approach. The time complexity order for an image of m × n pixels, under the assumption that a processing element exists for each pixel, is near O(log(m+n)). Additionally, our method computes both the foreground and background CCL, and allows a straightforward computation of topological features like Adjacency Trees. Experiments show that our method obtains better performance metrics than other approaches. Our work aims at generating a new class of labeling algorithms: those centered in fully parallel approaches based on computational topology, thus allowing a perfect concurrent execution in multiple threads and preventing the use of critical sections and atomic instructions.
Role clarity deficiencies can wreck agile teams

One of the twelve agile principles is to build projects around motivated individuals and trust them to get the job done. Such agile teams must self-organize, but this involves conflict, making self-organization difficult. One area of difficulty is agreeing on everybody's role.What dynamics arise in a self-organizing team from the negotiation of everybody's role?We conceptualize observations from five agile teams (work observations, interviews) by Charmazian Grounded Theory Methodology.We define role as something transient and implicit, not fixed and named. The roles are characterized by the responsibilities and expectations of each team member. Every team member must understand and accept their own roles (Local role clarity) and everbody else's roles (Team-wide role clarity). Role clarity allows a team to work smoothly and effectively and to develop its members' skills fast. Lack of role clarity creates friction that not only hampers the day-to-day work, but also appears to lead to high employee turnover. Agile coaches are critical to create and maintain role clarity.Agile teams should pay close attention to the levels of Local role clarity of each member and Team-wide role clarity overall, because role clarity deficits are highly detrimental.
Semantic-enhanced discrete matrix factorization hashing for heterogeneous modal matching

The simultaneous hash representation of heterogeneous modalities has shown its excellent performance in the multi-modal learning community. However, the binary discrete constraint of hash brings a great challenge to the common hash representation of heterogeneous modal data. To resolve the issue, this paper proposes a novel discrete hash method, referred to as Semantic enhanced Discrete Matrix Factorization Hashing (SDMFH). SDMFH directly extracts the common discrete hash representation of all modalities from the reconstructed semantic intermodal similarity graph, which makes the hash codes more discriminative. Meanwhile, the semantic labels are regressed to the extracted discrete hash book, so as to further strengthen the discriminating ability of the learned discrete hash book. Moreover, a linear embedding from the kernel space of the original data to the Hamming space is explored to generate the common hash codes of each modality to ensure the consistency of the hash codes of heterogeneous modalities. More importantly, we develop an efficient discrete iterative algorithm based on Stiefel manifold that can directly learn the discrete hash book in a closed form, thus both avoiding quantization loss caused by discrete relaxation and reducing computational complexity. Experimental results on three benchmark data sets show that SDMFH performs better than several state-of-the-art methods for heterogeneous modal matching tasks.
SenseChain: Blockchain based Reputation System for Distributed Spectrum Enforcement

Distributed enforcement of spectrum policies require fusion of sensing results from a set of spatially scattered sensors to detect anomalous behavior with the highest possible accuracy. Central to this problem is the lack of trust or reputation of the participating sensors, which often leads to incorrect and biased inferences. In SenseChain, we leverage the distributed consensus mechanism employed in Blockchain networks to capture the reputation of the sensors, leading to a highly reliable and accurate enforcement system. Specifically, we define and analyze a detection mechanism to identify falsifying sensors using a distributed anomaly detection system and use the Blockchain to record the individual's behavior. The reputation is then based on the combination of the difficulty level of the consensus method and the degree of falsehood in the reported sensor values. We evaluate SenseChain using an integrated Blockchain and anomaly detection simulator to show that DLTs can be used to track reputation of distributed sensors for distributed enforcement of spectrum policies.
Robust multi-view clustering via inter-and-intra-view low rank fusion

Multi-view Clustering has become a vital task with the rapidly growing amount of data in multiple representations. Under the context of unsupervised learning, the existing clustering methods show limited capability in leveraging the discriminative information from multiple views. View-independent noises and distribution differences among views are two main causes that may degrade the clustering performances. By introducing the inter-and-intra-view low rank decomposition, we propose Multi-view Clustering via Intra and Inter-view Low Rank Fusion (MCIIF). The proposed method facilitates clustering with the discriminative intra-view low rank structures that complement the shared inter-view transition. By recovering complementary low rank transitions from the differences between the shared inter-view transition and individual-view transitions, we pursue a more accurate transition probability matrix by combining the complementary intra-view transitions with the shared inter-view transition in an additive manner. To solve the corresponding optimization problem, we propose a procedure based on the Alternating Direction Method of Multipliers (ADMM) scheme with convergence guarantees. Experimental results on various real-world datasets verify the effectiveness of the proposed method over the state-of-art multi-view clustering methods.
Universal and Culture-dependent Employee Compliance of Information Systems Security Procedures

Employee information systems security behavior (ISSB) is a key concern for organizations. Previous studies have proposed models aimed at explaining employees' ISSB and related behavioral change. While these studies have contributed to our understanding of the reasons for ISSB (change), there is a lack of research related to cultural differences and distinguishing cultural-specific reasons for ISSB. This paper takes the first step in addressing this research gap by theorizing about employee ISSB based on empirical material collected in Finland, Switzerland, the UAE, and China. This paper suggests that ISSB constitute a learned information systems security (ISS) conventions that may be somewhat generic across different cultures; however, different paradigms of learning seem to be effective in different cultures for supporting behavioral change. From a theoretical perspective, the results help us to understand why employees comply or do not comply with ISS procedures. This study also highlights the need for future research on employee compliance to understand cultural differences regarding key ISS interventions. Finally, from a managerial perspective, the theory suggests that different cultures require different ISS interventions.
Adversarial Joint-Distribution Learning for Novel Class Sketch-Based Image Retrieval

In the information retrieval task, sketch-based image retrieval (SBIR) has drawn significant attention owing to the ease with which sketches can be drawn. The existing deep learning methods for the SBIR are very unrealistic in the real scenario, and its performance reduces drastically for unseen class test examples. Recently, Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) has drawn a lot of attention due to its ability to retrieve the novel/unseen class images at test time. These methods try to project sketch features into the image domain by learning a distribution conditioned on the sketch. We propose a new framework for ZS-SBIR that models joint distribution between the sketch and image domain using a generative adversarial network. The joint distribution modeling ability of our generative model helps to reduce the domain gap between the sketches and images. Our framework helps to synthesize the novel class image features using sketch features. The generative ability of our model for the unseen/novel classes, conditioned on sketch feature, allows it to perform well on the seen as well as unseen class sketches. We conduct extensive experiments on two widely used SBIR benchmark datasets-Sketchy and Tu-Berlin and obtain significant improvement over the existing state-of-the-art. We will release the code publicly for reproducibility of results.
Automatic Classification of Apps Reviews for Requirement Engineering: Exploring the Customers Need from Healthcare Applications

In one year, more than 6.5 million mobile applications have been listed for download on the application stores. That is, they are used by millions (or billions) of users across the world. Users express their daily experience of applications as reviews on those stores. This experience may include reporting bugs, demanding new features, posting feedback with regards to performance, reporting security issues, demanding user interface enhancements, and other needs. Interestingly, reviews could contain valuable information for the interest of application vendors and developers. However, the volume of such data is as huge, that is, traditional searching algorithms may not be efficient in extracting such useful information. Machine learning and data mining techniques are one of the popularly used algorithms to efficiently extracting significant information for Software Requirement Engineering; a key phase in the Software Engineering Life Cycle. In this paper, we experience machine learning algorithms and natural language processing techniques to classify a set of reviews about healthcare-domain applications into multiple types of categories such as bug reports, new feature requests, application performance, and user interface. For this purpose, we could extract more than 7500 reviews of ten different health-related mobile applications. More importantly, those reviews were annotated manually by software experts. In our experiments, we use the Weka tool employing different machine learning algorithms. We will also show what algorithms and features will perform better; in terms of accuracy using different evaluation metrics, when classifying reviews about mobile apps into various classes; bugs, new features, sentimental, general bug, usability, security, and performance. Moreover, the conducted experiments show that the overall performance improves when we use the data subset with highly confident labeling; when two experts agree on the same class. For the imbalanced-data problem, this research will show the effect of applying resampling techniques on improving classification accuracy as well.
A modified moving least-squares suitable for scattered data fitting with outliers

In the fitting of scattered data, there may be a few sample values that contain high noise, which are called outliers. In dealing with such scattered data, the approximation effect of the classical moving least squares (abbr. MLS) is greatly reduced due to the existence of outliers. In this paper, a modified moving least squares (abbr. MMLS) is proposed, which can recognize outliers automatically from scattered data and weaken the influence of the outliers in fitting by an added weight function in MLS. It is theoretically proven that if the only noise existing in scattered data is outliers, the solution of MMLS is close to that of MLS in the absence of outliers. Because the computational process of the proposed MMLS is consistent with the classical MLS, the computational efficiency of MMLS is higher than that of Levin's moving least-hardy method (abbr. MLH) which is proposed to also deal with the fitting of scattered data with outliers by iterative solution. The numerical experiments not only confirm the property of MMLS but also show that the approximation effect of MMLS is almost identical with that of MLH.
Recording device identification by ENF harmonics power analysis

Two important aspects of digital audio forensics are integrity validation of the recorded contents and identification of the acquisition device that produced the recording. One of the effective methods for integrity validation is based on induced electrical network frequency (ENF) signal analysis. The goal of this paper is to extend the usage of the ENF signal for the identification of the acquisition device. The proposed identification method is based on the harmonic amplitude coefficients of ENF signals that are effectively applied as feature vectors. The evaluation results demonstrate the efficacy of the proposed approach.
Deeplive: QoE Optimization for Live Video Streaming through Deep Reinforcement Learning

A new broad of video services that support live streaming has become tremendously popular in recent years. Compared with traditional video-on-demand (VOD) services, live video streaming has much higher requirements on Quality-of-Experience (QoE), including low rebuffering, high definition, low latency and low bitrate oscillations. While previous adaptive bitrate algorithms (ABR) solely optimize bitrate for ensuring QoE of VOD, live video streaming has a larger decision space, making the optimization problem more difficult to solve. We propose Deeplive, which maximizes QoE through deep reinforcement learning (DRL), so it does not rely on fixed rules. To accelerate the training process of Deeplive, we further propose optimization including window completion with historical data and quick-start with rate-based algorithm. We compare Deeplive with other advanced ABR algorithms in a frame-level dynamic adaptive video streaming simulator using different network traces, QoE definitions, and video categories. In all experiments, we find that Deeplive not only has significant improvement in training time, but also shows an average of 15-55% improvement on QoE than the state-of-the-art ABR algorithms.
The Image Analysis Using Fast Radial Symmetry Transform in Control Systems Base on the Computer Vision

Currently computer vision-based control systems are widely used. In such systems, procedures for detecting specified objects in images, calculating their characteristics and generating information for decision making are implemented. Mostly, in practice, the detected objects have the property of radial symmetry. For example, round markers are used as such objects, providing precise positioning in robotic systems. For their detection various methods of processing and analyzing images can be used. In this paper, we consider an approach based on the fast radial symmetry transform. As its application example, an algorithm for detecting centers of radially symmetric objects is considered. It is shown that this algorithm allows detecting the centers of radially symmetric objects in the conditions of noise that interfere with scene analysis in real conditions of the control systems operation. Besides, it shows how the algorithm is used in combination with the circles-detecting Hough method. The Hough method has a number of parameters, the setting of which is a time-consuming process as they are very sensitive to the size and location of the circles. The application of the algorithm for the circles' centers detecting makes it possible to refine the found circle by the degree of proximity of the center, obtained using the Hough method, to one of the centers obtained by fast radial symmetry transform.
tweeDe – A Universal Dependencies treebank for German tweets

We introduce the first German treebank for Twitter microtext, annotated within the framework of Universal Dependencies.The new treebank includes over 12,000 tokens from over 500 tweets, independently annotated by two human coders.In the paper, we describe the data selection and annotation process and present baseline parsing results for the new testsuite.
An efficient two-pass classifier system for patient opinion mining to analyze drugs satisfaction

Opinion mining is a well-known problem in natural language processing that increasing attention in recent years. With the rapid growth in e-commerce, reviews for popular products on the web have grown rapidly. In opinion mining, the greater part of the scientists has dealt with general domains, for example, electronic items, movies, and restaurants audit not much on health and medical domains. Therefore, in this paper, we focus on predicting the drug satisfaction level among the other patient who already experienced the effect of a drug using a novel Two-pass classifier. The Two-pass classifier is a combination of Support Vector Machine and Artificial Neural Network (SVMNN). Here, at first, we collect customer reviews from healthcare domain. After that, we extract the important features from each review and based on the features we generate the feature vector. Then, we apply two-pass classifier in order to predict the given customer review is positive or negative. The performance of the proposed approach is analyzed using precision, recall, and F-measures. The experimentation results show that the proposed system attains the better result associated with the available methods.