Reviewerly/exHarmony huggingface dataset:
Are unit and integration test definitions still valid for modern Java projects? An empirical study on open-source projects

Unit and integration testing are popular testing techniques. However, while the software development context evolved over time, the definitions remained unchanged. There is no empirical evidence, if these commonly used definitions still fit to modern software development. We analyze, if the existing standard definitions of unit and integration tests are still valid in modern software development contexts. Hence, we analyze if unit and integration tests detect different types of defects, as expected from the standard literature. We classify 38,782 test cases into unit and integration tests according to the definition of the IEEE and use mutation testing to assess their defect detection capabilities. All integrated mutations are classified into five different defect types. Afterwards, we evaluate if there are any statistically significant differences in the results between unit and integration tests. We could not find any evidence that one test type is more capable of detecting certain defect types than the other one. Our results suggest that the currently used definitions do not fit modern software development contexts. This finding implies that we need to reconsider the definitions of unit and integration tests and suggest that the current property-based definitions may be exchanged with usage-based definitions.
Requirements specification for developers in agile projects: Evaluation by two industrial case studies

An inadequate requirements specification activity acts as a catalyst to other problems, such as low team productivity and difficulty in maintaining software. Although Agile Software Development (ASD) has grown in recent years, research pointed out several limitations concerning its requirements engineering activities, such as Software Requirements Specification (SRS) provided in high level and targeted to the customer, lack of information required to perform design activities and low availability of the customer. To overcome these issues, the RSD (Requirements Specification for Developers) approach was proposed to create an SRS that provides information closer to development needs. In addition, existing literature reviews identify a demand for more empirical studies on the requirements specification activity in ASD. Face to this, this work presents the evaluation of the RSD approach with respect to how it affects the teamwork and to identify its strengths and limitations. This evaluation was performed by means of two industrial case studies conducted using a multiple-case design, focusing on software engineers as the analysis unit. Data were collected during 15 months from documents, observations, and interviews. They were triangulated, analyzed, and synthesized using techniques of grounded theory. The findings pointed out that the readability of SRS was compromised when several requirements are specified in the same RSD artifact. Evaluation also indicated the need of prioritization and categorization of the acceptance criteria, a tool for creating, searching and tracing the artifacts, and obtaining acceptance tests from acceptance criteria. On the other hand, the findings showed that the practices used to specify requirements using the RSD approach have the potential to produce a more objective SRS, tailored for the development team. As a consequence, the structure of the RSD artifact was considered as a factor that improved the team performance in the two case studies.
ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection

Online recruitment fraud (ORF) is a new challenge in the cyber security area. In ORF, scammers give job seekers lucrative job offers and in-return steal their money and personal information. In India, scammers have stolen millions of moneys from innocent job seekers. Hence, it is important to find solution to this problem. In this paper, we propose, ORFDetector, an ensemble learning based model for ORF detection. We test the proposed model on publicly available dataset of 17,860 annotated jobs. The proposed model is found to be effective and give average f1-score and accuracy of 94&#x0025; and 95.4, respectively. Additionally, it increases the specificity by 8&#x0025; as compared to the baseline classifiers.
The mediating effect of teamwork on the relationship between strategic orientation and performance of Nigerian banks

Purpose The purpose of this study is to empirically explore the mediating effect of teamwork on the relationship between strategic orientation and organizational performance. Design/methodology/approach This study adopted a descriptive research design. A self-reported questionnaire was used to collect data from 253 bank managers representing 20 commercial banks in Nigeria. The author used SmartPLS-SEM to analyze the data collected. Findings The results of the structural models showed a significantly positive relationship between strategic orientation and organizational performance on the one hand and between strategic orientation and teamwork on the other. It was also found that teamwork had a significantly positive link with organizational performance. Further analysis revealed that teamwork fully mediated the relationship between strategic orientation and organizational performance. Research limitations/implications This study focuses on the Nigerian banking sector. Thus, it limits the generalizability of its findings to other sectors not covered. Future researchers could extend the study to other sectors to corroborate the findings presented. Practical implications The findings of this study provide some practical implications for business organizations and managers. Business organizations must be strategically positioned so that they can compete in today’s highly dynamic and competitive business environment and achieve superior performance. Likewise, business managers should make sure that all employees and sections in their organizations work cooperatively as a team by creating a collaborative climate where team spirit and teamwork thrive. Originality/value To the best of the author’s knowledge, this study is the first to provide empirical evidence of the mediating effect of teamwork on the relationship between strategic orientation and organizational performance. In that regard, it makes a valuable contribution to the field of strategic management and enhances the applicability and the generalizability of contingency and resource-based view theories across different environmental settings.
Protocol for Precise Field Sensing in the Optical Domain with Cold Atoms in a Cavity

In the context of quantum metrology, optical cavity-QED platforms have primarily been focused on the generation of entangled atomic spin states useful for next-generation frequency and time standards. Here, we report a complementary application: The use of optical cavities to generate non-classical states of light for electric field sensing below the standard quantum limit. We show that cooperative atom-light interactions in the strong collective coupling regime can be used to engineer generalized atom-light cat states which enable quantum enhanced sensing of small displacements of the cavity field even in the presence of photon loss. We demonstrate that metrological gains of 10-20 dB below the standard quantum limit are within reach for current cavity-QED systems operating with long-lived alkaline-earth atoms.
A Novel Method for High Capacity Reversible Data Hiding Scheme Using Difference Expansion

Data hiding techniques are very significant in the research area of information security. In this article, the authors propose a new reversible data hiding (RDH) scheme using difference expansion. At first, the original image is partitioned into 3 × 3 pixel blocks, then marked Type-one and Type-two pixels based on their coordinate values. After that, the authors find correlated pixels by computing correlation coefficients and the median of Type-one pixels. Next, secret data bits are embedded within Type-two pixels based on correlated pixels and Type-one pixels based on the stego Type-two pixels. The data extraction process successfully extracts secret data as well as recovers the cover image. The authors observed the effects of the proposed method by performing experiments on some standard cover images and found significantly better result in terms of data hiding capacity compared with existing data hiding schemes.
Repeat-Free Codes

In this paper we consider the problem of encoding data into repeat-free sequences in which sequences are imposed to contain any k-tuple at most once (for predefined k). First, the capacity and redundancy of the repeat-free constraint are calculated. Then, an efficient algorithm, which uses a single bit of redundancy, is presented to encode length-n sequences for k = 2 + 2 log n. This algorithm is then improved to support any value of k of the form k = a log n, for 1 <; a ≤ 2, while its redundancy is o(n). Lastly, we also calculate the capacity of this constraint when combined with local constraints which are given by a constrained system.
Efficient Verifiable Multi-Key Searchable Encryption in Cloud Computing

The notion of Multi-Key Searchable Encryption (MKSE) enables data owners to outsource their data into a cloud server, while supporting fine-grained data sharing with the authorized users. Note that the traditional MKSE is vulnerable to data leakage. That is, the malicious data owner may collude with the server and recover the search queries of authorized users. Recently, Hamlin et al. (PKC'18) presented a new MKSE construction that can ensure data privacy between data owner and authorized users, where the share key is generated depending on data owner, authorized user and the specific document. However, their scheme cannot support verifiable search in the case of the malicious cloud server. In this paper, we propose a new verifiable MKSE (VMKSE) scheme by leveraging Garbled Bloom Filter, which can simultaneously support verifiability of search result and secure data sharing in multi-user setting. Compared to the state-of-the-art solution, the proposed scheme is superior in efficiency and verifiability. The experiment results demonstrate the efficiency of our scheme.
A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.
An Empirical Evaluation of K-Means Clustering Algorithm Using Different Distance/Similarity Metrics

k-means is an effective and efficient clustering algorithm. It uses distance/similarity metric to find out the distance/similarity among the data objects. The objects which are closer/similar to each other are assigned to the same cluster where as distant/dissimilar objects are assigned to different clusters. Most of the implementations of k-means are based on Euclidean/Squared Euclidean distance metrics. In order to find out the possibility of different distance/similarity metrics to be used with k-means algorithm, an empirical evaluation has been performed. In this paper, accuracy, performance and reliability of 13 different distance/similarity measures over 6 different variations of data using k-means algorithm have been compared based on empirical evaluation on well-known benchmark IRIS data set. Accuracy is measured in terms of similarity of cluster assignment between ground truth and machine clustering. Performance is measured in terms of the number of iterations used for convergence of the final cluster assignment. Reliability is measured on the basis of correctness of the cluster assignment.
The design of a rotary-wing unmanned aerial vehicles–payload drop mechanism for fire-fighting services using fire-extinguishing balls

This study aims to encourage the idea of utilizing unmanned aerial vehicles in the fire-fighting application. In this paper, we present a remote-controlled rotary-wing unmanned aerial vehicle made from metal alloys. This unmanned aerial vehicle, using a payload drop mechanism, can carry fire-extinguishing balls and release them to combat fires, especially in places not easily accessible by humans. The payload drop mechanism is a novel design that has an advantage of dropping two (maximum four) fire-extinguishing balls one at a time using one servo motor. Hence, the servo motor is controlled by one radio channel of the remote controller. Besides, comparing it to our (mechanism A) first designed mechanism, this payload drop mechanism shows smaller horizontal area for fitting most of the drones as well as less effect of vibrations during flight. The whole system is designed using Solidworks platform and manufactured with computer numerical control machining and three-dimensional printing. Thus, the potential use of unmanned aerial vehicles to carry out fire-fighting operations using the proposed payload drop mechanism is successfully examined.
An Information Theoretic Interpretation to Deep Neural Networks

It is commonly believed that the hidden layers of deep neural networks (DNNs) attempt to extract informative features for learning tasks. In this paper, we formalize this intuition by showing that the features extracted by DNN coincide with the result of an optimization problem, which we call the "universal feature selection" problem, in a local analysis regime. We interpret the weights training in DNN as the projection of feature functions between feature spaces, specified by the network structure. Our formulation has direct operational meaning in terms of the performance for inference tasks, and gives interpretations to the internal computation results of DNNs. Results of numerical experiments are provided to support the analysis.
Level Design Patterns in 2D Games

Videogame designers use tips and tricks and tools of the trade to design levels. Some of these tips are based on their gut feeling and others have been known in the game industry for the last 30 years. In this work, we discuss six of common level design patterns present in 2D videogames. The patterns under discussion are the product of an exploratory analysis of over thirty 2D games. We choose to focus on patterns that are both common and impactful for the overall player experience. We discuss in detail the rationale for and advantages of each pattern, showing examples of games that make use of such. We conclude with a discussion of the usage and understanding of these patterns from the perspective of level design and how other technical approaches can benefit from them.
Human behaviour modelling for welfare technology using hidden Markov models

Human behaviour modelling for welfare technology is the task of recognizing a person's behaviour patterns in order to construct a safe environment for that person. It is useful in building environments for older adults or to help any person in his or her daily life. The aim of this study is to model the behaviour of a person living in a smart house environment in order to detect abnormal behaviour and assist the person if help is needed. Hidden Markov models, location of the person in the house, posture of the person, and time frame rules are implemented using a real-world, open-source dataset for training and testing. The proposed model presented in this study models the normal behaviour of a person and detects anomalies in the usual pattern. The model shows good results in the identification of abnormal behaviour when tested.
Supervised discrete cross-modal hashing based on kernel discriminant analysis

Cross-modal hashing methods have drawn considerable attention due to the rapid growth of multi-modal data. To obtain efficient binary codes in a low-dimensional Hamming space, most existing approaches relaxed the discrete constraint, which could cause quantization loss and even result in performance degradation. In order to avoid this bottleneck, some scholars employed iterative discrete cyclic coordinate descent (DCC) to learn hash codes bit by bit, but this was very time-consuming. To counter this problem, a simple yet novel supervised discrete cross-modal hashing framework is represented to directly learn the unified discrete binary codes with a close-form, rather than bit by bit. Furthermore, to preserve label separability, the kernel discriminant analysis is fused into the proposed framework to enrich the discrimination ability of the learned binary codes. The goal of the proposed method is to obtain the common discrete binary codes of different modalities in a shared latent Hamming space so that the different modalities of a sample can be effectively connected. Experimental study shows the encouraging results of the proposed algorithm in comparisons to the state-of-the-art baseline approaches on four real-world datasets. Especially on the LabelMe dataset, the superiority of the proposed method is obvious, with an average improvement of 9% over the best available results.
Error Exponents of Typical Random Codes for the Colored Gaussian Channel

The error exponent of the typical random code is defined as the asymptotic normalized expectation of the logarithm of the probability of error, as opposed to the traditional definition of the random coding exponent as the normalized logarithm of the expectation of the probability of error with respect to a given ensemble of codes. For a certain ensemble of independent codewords, with a given power spectrum, and a generalized stochastic mismatched decoder, we characterize the error exponent of the typical random codes (TRC) for the colored Gaussian channel, with emphasis on the range of low rates, where the TRC error exponent differs in value from the ordinary random coding error exponent. The error exponent formula, which is exponentially tight at some range of low rates, is presented as the maximum of a certain function with respect to one parameter only (in the spirit of Gallager’s formulas) in the case of matched decoding, and two parameters in the case of mismatched decoding. Several aspects of the main results are discussed. These include: general properties, a parametric representation, critical rates, phase transitions, optimal input spectrum (water pouring), and comparison to the random coding exponent.
Choi–Williams distribution in linear canonical domains and its application in noisy LFM signals detection

Due to the requirement of signal representation’s flexibility for strong noise interference in non-stationary signal processing, the traditional Choi–Williams distribution (CWD) is extended to the linear canonical domain by using a well-established closed-form instantaneous cross-correlation function (CICF) type of linear canonical transform (LCT) free parameters embedded approach. The derived CICF type of CWD (CICFCWD) unifies all of the linear canonical Choi–Williams distributions, and can be considered as the CWD’s closed-form representation in linear canonical domains. The CICFCWD of a discrete-time signal defined by the sampling equation is discussed for the requirement of digital signal analysis and processing. To break through the limitation that the existing research techniques are limited to quantitative analysis based on numerical simulations, a qualitative analysis to output signal-to-noise ratio (SNR) inequality between the traditional CWD and the CICFCWD for a general noisy signal is introduced, including the inequality modeling and solving. Within this output SNR improvement analysis framework, the strategies on determination LCT free parameters for linear frequency-modulated (LFM) signals added with white noise are developed. Numerical experiments are also carried out to validate the correctness of LCT free parameters selection results and the feasibility of output SNR improvement analysis approach.
Matrix Factorization for Personalized Recommendation With Implicit Feedback and Temporal Information in Social Ecommerce Networks

Collaborative filtering with implicit feedback is regarded as one of the most challenging issues in social ecommerce networks.However, the scarcity of negative feedback and the impact of time makes collaborative filtering difficult to use.Most models assign a uniform weight to missing data, but this method is invalid in the real world and leads to a biased representation of user profiles.In social ecommerce networks, the popularity of an item is implicit social information that is dynamic and can affect the preferences of a user.In this paper, we propose a smart model named TimeMF to address the above issues by incorporating implicit feedback and temporal information into social ecommerce recommendation.The weighting scheme is based on the dynamic popularity of an item.Then, we present an objective function and adopt an optimization strategy to enhance the efficiency.The experimental results for a real-world dataset reveal that our model outperforms the baselines on several metrics.
Low-Rank Regularized Multi-Representation Learning for Fashion Compatibility Prediction

The currently flourishing fashion-oriented community websites and the continuous pursuit of fashion have attracted the increased research interest of the fashion analysis community. Many studies show that predicting the compatibility of fashion outfits is a nontrivial task due to the difficulty in capturing the implicit patterns affecting fashion compatibility prediction and the complex relationships presented by raw data. To address these problems, in this paper, we propose a transductive low-rank hypergraph regularizer multiple-representation learning framework (LHMRL), whereby we formulate the processes of feature representation and fashion compatibility prediction in a joint framework. Specifically, we first introduce a low-rank regularized multiple-representation learning framework, in which the lowest-rank multiple representations of samples can be learned to characterize samples from different perspectives. In this framework, we maximize the total difference among multiple representations based on Grassmann manifold theory and incorporate a common hypergraph regularizer to naturally encode the complex relationships between fashion items and an outfit. To enhance the representation ability of our model, we then develop a supervised learning term by exploiting two types of supervision information from labeled data. Experiments on a publicly available large-scale dataset demonstrate the effectiveness of our proposed model over the state-of-the-art methods.