Reviewerly/exHarmony huggingface dataset:
Roadblocks to Attracting Students to Software Testing Careers: Comparisons of Replicated Studies

Context. Recently, a family of studies highlighted the unpopularity of software testing careers among undergraduate students in software engineering and computer science courses. The original study and its replications explored the perception of students in universities in four countries (Canada, China, India, and Malaysia), and indicated that most students do not consider a career in software testing as an option after graduation. This scenario represents a problem for the software industry since the lack of skilled testing professionals might decrease the quality of software products and increase the number of unsuccessful projects. Goal. The present study aims to replicate, in Brazil, the studies conducted in the other four countries to establish comparisons and support the development of strategies to improve the visibility and importance of software testing among undergraduate students across the globe. Method. We followed the same protocol in the original study to collect data using a questionnaire and analyzed the answers using descriptive statistics and qualitative data analysis. Results. Our findings indicate similarities among the results obtained in Brazil in comparison to those obtained from other countries. We observed that students are not motivated to follow a testing career in the software industry based on a belief that testing activities lack challenges and opportunities for continuous learning. Conclusions. In summary, students seem to be interested in learning more about software testing. However, the lack of discussions about the theme in software development courses, as well as the limited offer of courses focused on software quality at the university level reduce the visibility of this area, which causes a decrease in the interest in this career.
Almost aggregations in the gravitational clustering to perform anomaly detection

Anomaly detection is the process of identifying observations that differ significantly from the norm in a dataset. Since there is not a proper formal definition of anomaly, different algorithms have arised to cope with the different variations of this idea, like novelty detection or outlier detection. Some of these algorithms have traditionally relied on prior knowledge on the data domain, or some degree of supervised learning in order to detect the irregular samples. In this work we propose a simulation based algorithm to detect those observations, based on gravitational forces, that requires not prior knowledge nor data labels to identify spurious observations. We do so by studying different generalizations of the aggregation of gravitational forces, and the resulting clusters obtained when the particles attract each other. We also compare our algorithm with other unsupervised anomaly detection algorithms, obtaining favourable results to our proposal.
Weak relevant justification logics

Abstract This paper will develop ideas from [44]. We will generalize their work in two directions. First, we provide axioms for justification logics over the base logic B and show that the logic permits a proof of the internalization theorem. Second, we provide alternative frames that more closely resemble the standard versions of the ternary relational frames, as well as a more general approach to the completeness proof. We prove that soundness and completeness hold for justification logics over a wide variety of base logics. Finally, we will strengthen Belnap’s variable sharing for the justification logic context, demonstrating that the justifications are properly relevant justifications.
Text analytics for customer satisfaction prediction: A case study in the banking domain

Customer centricity is key to the success of modern business companies. Recently, a lot of attention has been paid on machine learning methods for customer satisfaction analysis and monitoring of trends in customer opinions and sentiments. The current study aims at predicting customer satisfaction from contact center chat services – a domain largely underrepresented in the existing literature devoted on sentiment analysis. Main goal of the current paper is to analyze chat communication between operators in a call center and clients of a large bank and develop a methodology for sentiment analysis based only on chat data. This study is the first explicitly aimed at service rating prediction based only on textual features combined with grammatical information extracted from chats. The proposed approach is purely statistical and does not rely on sentiment lexicons. Drawn are valuable conclusions regarding the usefulness of different feature sets, text normalization techniques and levels of chat data representation. Main practical benefit of analyzing customer satisfaction is that it may lead to large improvement of customer services quality, as well as better understanding of customers’ experience, pain points and needs which underlies its importance in the financial services domain and not only. The study contributes to the literature devoted on practical applications of text analytics in Bulgarian and sentiment analysis for low-resource languages. To the best of the author's knowledge, the current study is the first for this combination of task, language, and data domain. The use case under study is in the banking domain but results and findings might be helpful in any other domain in which similar communication is being analyzed.
A fast stochastic approximation-based subgradient extragradient algorithm with variance reduction for solving stochastic variational inequality problems

In this paper, we propose a fast stochastic approximation-based subgradient extragradient algorithm with variance reduction for solving the stochastic variational inequality, where the Lipschitz constant is not necessarily known. Each iteration of our algorithm requires only one projection and one oracle call, and so reducing the computation time. By combining the iterative variance reduction procedure and the stochastic approximation approach, we discuss the asymptotic convergence, the optimal oracle complexity and the sublinear convergence rate in terms of the mean natural residual function. We also obtain the linear convergence rate with finite computational budget under the assumption of the strongly Minty variational inequality and the bounded projection error bound condition, respectively. Finally, several numerical experiments illustrate the efficiency and competitiveness of the proposed algorithm.
User-centric explainable AI: design and evaluation of an approach to generate coherent counterfactual explanations for structured data

Many Artificial Intelligence (AI) systems are black boxes, which hinders their deployment. Explainable AI (XAI) approaches which automatically generate counterfactual explanations aim to assist users in scrutinising AI decisions. One property of explanations crucial for their acceptance by users is their coherence. Users perceive counterfactual explanations as coherent if they present a realistic/typical counterfactual scenario that is suitable to explain the factual situation. We design an optimisation-based approach to generate coherent counterfactual explanations applicable to structured data. We demonstrate its applicability and rigorously evaluate its efficacy through functionally grounded and human-grounded evaluation. Results suggest that our approach indeed produces counterfactual explanations that are perceived as coherent by users. More specifically, they are perceived as more realistic, typical, and feasible than state-of-the-art explanations.
A novel optimized tiny YOLOv3 algorithm for the identification of objects in the lawn environment

Based on the problem of insufficient accuracy of the original tiny YOLOv3 algorithm for object detection in a lawn environment, an Optimized tiny YOLOv3 algorithm with less computation and higher accuracy is proposed. Three reasons affect the accuracy of the original tiny YOLOv3 algorithm for detecting objects in a lawn environment. First, the backbone of the original algorithm is composed of a stack of a single convolutional layer and a max-pooling layer, which results in insufficient ability to extract feature information of objects. An enhancement module is proposed to enhance the feature extraction capability of the shallow layers of the network. Second, the information of the shallow convolutional layers of the backbone is not fully used, which results in insufficient detection capability for small objects. Third, the deep part of the backbone uses a convolutional layer with an excessive number of channels, which results in a large amount of computation. A multi-resolution fusion module is proposed to enhance the information interaction capability of the deep and shallow layers of the network, and reduce the computation. To verify the accuracy of this Optimized tiny YOLOv3 algorithm, the algorithm was tested on the dataset containing trunk, spherical tree and person, and compared with the current research. The results show that the algorithm proposed in this paper improves the detection accuracy while reducing the calculation.
An Interpretable Image Tampering Detection Approach Based on Cooperative Game

In order to reply the potential security issues caused by the tampering of digital images, many image forensics approaches based on deep learning have been proposed in recent years. However, the interpretability of deep learning-based approaches has not been fully considered. In this paper, an interpretable image tampering detection approach is proposed. It consists of suspicious tampered region detection (STRD) module and cooperative game module. The STRD module, inspired by YOLO, combines the shallow-level and deep-level features to discriminate different tampering types of suspicious tampered regions in complex scenes, and also performs well in detecting small tampered regions. The prediction of STRD module could be extended to suspicious box and the payoff of cooperative game module. The cooperative game module utilizes the Shapley interaction index as the strategy to measure the information gain of image pixels. The Shapley interaction index disentangles the multi-order interaction between the pixels of the image, and we discover that image tampering mainly affects low-order interaction of the image. The final detection result is obtained by combining the pixels that contribute greatly to the payoff with the suspicious box. The proposed approach provides a new thought for the interpretability of image forensics and could be broadly applied to other digital image forensic approaches. Extensive experimental results have demonstrated the proposed approach outperforms SOTA approaches, which also has good interpretability and robustness.
Block-Fading Non-Stationary Channel Estimation for MIMO-OFDM Systems via Meta-Learning

Deep learning (DL)-based channel estimations for multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have shown remarkable performance at the cost of huge sample size and complexity. While such complexity can be offloaded onto offline training phase for stationary channels, this becomes problematic when non-stationary channels are arisen. In this letter, we resolve this issue by proposing meta-learning-aided online training that only requires small sample size with reduced complexity. Numerical results under 3GPP channel models verify that proposed meta-learning approach outperforms not only conventional DL-based estimators but also conventional model-based estimators, e.g., least squares and linear minimum mean square error estimators, especially in the small sample size/low complexity regime.
Modeling Speech Sound Radiation With Different Degrees of Realism for Articulatory Synthesis

Articulatory synthesis is based on modeling various physical phenomena of speech production, including sound radiation from the mouth. With regard to sound radiation, the most common approach is to approximate it in terms of a simple spherical source of strength equal to the mouth volume velocity. However, because this approximation is only valid at very low frequencies and does not account for the diffraction by the head and the torso, we simulated two alternative radiation characteristics that are potentially more realistic: the radiation from a vibrating piston in a spherical baffle, and the radiation from the mouth of a detailed model of the human head and torso. Using the articulatory speech synthesizer VocalTractLab, a corpus of 10 sentences was synthesized with the different radiation characteristics combined with three different phonation types. The synthesized sentences were acoustically compared with natural recordings of the same sentences in terms of their long-term average spectra (LTAS), and evaluated in terms of their naturalness and intelligibility. The intelligibility was not affected by the type of radiation characteristic. However, it was found that the more similar their LTAS was to real speech, the more natural the synthetic sentences were perceived to be. Hence, the naturalness was not directly determined by the realism of the radiation characteristic, but by the combined spectral effect of the radiation characteristic and the voice source. While the more realistic radiation models do not per se improve synthesis quality, they provide new insights in the study of speech production and articulatory synthesis.
Axial Assembled Correspondence Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims at training a model that can segment novel classes in a query image with only a few densely annotated support exemplars. It remains a challenge because of large intra-class variations between the support and query images. Existing approaches utilize 4D convolutions to mine semantic correspondence between the support and query images. However, they still suffer from heavy computation, sparse correspondence, and large memory. We propose axial assembled correspondence network (AACNet) to alleviate these issues. The key point of AACNet is the proposed axial assembled 4D kernel, which constructs the basic block for semantic correspondence encoder (SCE). Furthermore, we propose the deblurring equations to provide more robust correspondence for the aforementioned SCE and design a novel fusion module to mix correspondences in a learnable manner. Experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> reveal that our AACNet achieves a mean intersection-over-union score of 65.9% for 1-shot segmentation and 70.6% for 5-shot segmentation, surpassing the state-of-the-art method by 5.8% and 5.0% respectively.
Quantum Mating Operator: A New Approach to Evolve Chromosomes in Genetic Algorithms

Genetic Algorithms (GAs) are optimization methods that search near-optimal solutions by applying well-known operations such as selection, crossover and mutation. In particular, crossover and mutation are aimed at creating new solutions from selected parents with the goal of discovering better and better solutions in the search space. In literature, several approaches have been defined to create new solutions from the mating pool to try to improve the performance of genetic optimization. In this paper, the literature is enriched by introducing a new mating operator that harnesses the stochastic nature of quantum computation to evolve individuals in a classical genetic workflow. This new approach, named Quantum Mating Operator, acts as a multi-parent operator that identifies alleles' frequency patterns from a collection of individuals selected by means of conventional selection operators, and encodes them through a quantum state. This state is successively mutated and measured to generate a new classical chromosome. As shown by experimental results, GAs equipped with the proposed operator outperform those equipped with traditional crossover and mutation operators when used to solve well-known benchmark functions.
Multi-objective Grammar-guided Genetic Programming with Code Similarity Measurement for Program Synthesis

Grammar-Guided Genetic Programming (G3P) is widely recognised as one of the most successful approaches for program synthesis, i.e., the task of automatically discovering an executable piece of code given user intent. G3P has been shown capable of successfully evolving programs in arbitrary languages that solve several program synthesis problems based only on a set of input/output examples. Despite its success, the restriction on the evolutionary system to only leverage input/output error rate during its assessment of the programs it derives limits its scalabil-ity to larger and more complex program synthesis problems. With the growing number and size of open software repositories and generative artificial intelligence approaches, there is a sizeable and growing number of approaches for retrieving/generating source code (potentially several partial snippets) based on textual problem descriptions. Therefore, it is now, more than ever, time to introduce G3P to other means of user intent (particularly textual problem descriptions). In this paper, we would like to assess the potential for G3P to evolve programs based on their similarity to particular target codes of interest (obtained using some code retrieval/generative approach). Through our experimental evaluation on a well-known program synthesis benchmark, we have shown that G3P successfully manages to evolve some of the desired programs with all four considered similarity measures. However, in its default configuration, G3P is not as successful with similarity measures as it is with the classical input/output error rate when solving program synthesis problems. Therefore, we propose a novel multi-objective G3P approach that combines the similarity to the target program and the traditional input/output error rate. Our experiments show that compared to the error-based G3P, the multi-objective G3P approach could improve the success rate of specific problems and has great potential to improve on the traditional G3P system.
Adversarial Differential Evolution for Multimodal Optimization Problems

Multimodal optimization problems (MMOPs) are sorts of optimization problems that have many global optima. To discover as many peaks as possible and increase the accuracy of the solutions, MMOP requires algorithms with great exploration and exploitation abilities. However, exploration and exploitation are in an adversarial relationship, since exploration aims to locate more optima via searching the global space rather than small regions, whereas exploitation targets to enhance the accuracy of solutions via searching in small areas. The key to efficiently solving MMOPs lies in striking a balance between exploration and exploitation. To achieve the goal, this paper proposes an adversarial differential evolution (ADE), containing an adversarial reproduction strategy and an adversarial selection strategy. Firstly, adversarial reproduction strategy generates offspring for exploration and offspring for exploitation and lets these two types of offspring compete for survival. Secondly, adversarial selection strategy employs a diversity-optimization-based selection and a crowding-based selection to select the offspring with both good diversity and good fitness. Diversity-optimization-based selection transforms the problem of selecting diverse individuals into an optimizing problem and solves it via an extra genetic algorithm to get the offspring with optimal diversity. Extensive experiments are conducted on CEC2013 MMOP benchmark to verify the effectiveness and efficiency of the proposed ADE. Experimental results show that ADE has advantages over the state-of-the-art MMOP algorithms.
Combining Soft-Actor Critic with Cross-Entropy Method for Policy Search in Continuous Control

In this paper, we propose CEM-SAC - a hybridization between the cross-entropy method (CEM), i.e., an estimation-of-distribution algorithm, and the soft-actor critic (SAC), i.e., a state-of-the-art policy gradient algorithm. Our work extends the evolutionary reinforcement learning (ERL) line of research on integrating the robustness of population-based stochastic black-box optimization, that typically assumes little to no problem-specific knowledge, into the training process of policy gradient algorithms, that exploits the sequential decision making nature for efficient gradient estimation. Our hybrid approach, CEM-SAC, exhibits both the stability of CEM and the efficiency of SAC in training policy neural networks of reinforcement learning agents for solving control problems. Experimental result comparisons with the three baselines CEM, SAC, and CEM-TD3, a recently-introduced ERL method that combines CEM and the twin-delayed deep deterministic policy gradient (TD3) algorithm, on a wide range of control tasks in the MuJoCo benchmarks confirm the enhanced performance of our proposed CEM-SAC. The source code is available at https://github.com/ELO-Lab/CEM-SAC.
Duplicate Individuals in Differential Evolution

Some modern computing architectures such as those which use GPUs offer significantly more computational capability in single-precision than double-precision floating point. However, when using single-precision math, evolutionary optimization algorithms such as differential evolution are much more prone to collision, where new individuals created by reproduction operators are duplicates of previously generated individuals. We show experimentally that on the CEC2014 single objective benchmarks and the BBOB benchmarks, search using the SHADE adaptive DE can waste more than 20% of the fitness evaluations due to collisions. We (1) discuss the causes of this collision, (2) detect collisions using hash to avoid unnecessary evaluations, and (3) propose a restart method based on collisions.
Boomerang Spectrum of A Class of Power Functions

In EUROCRYPT 2018, Cid et al. introduced a new concept on the cryptographic property of S-boxes to evaluate the subtleties of boomerang-style attacks. This concept was named as boomerang connectivity table (BCT for short). For a power function, the value distribution of BCT can be directly deter-mined by its boomerang spectrum. In this paper, we investigate the boomerang spectrum of a class of power functions over even characteristic finite fields via its differential spectrum, where the exponent is a Niho exponent.
A Clustering-based Differential Evolution Boosted by a Regularisation-based Objective Function and a Local Refinement for Neural Network Training

The performance of feed-forward neural networks (FFNN) is directly dependant on the training algorithm. Conventional training algorithms such as gradient-based approaches are so popular for FFNN training, but they are susceptible to get stuck in local optimum. To overcome this, population-based metaheuristic algorithms such as differential evolution (DE) are a reliable alternative. In this paper, we propose a novel training algorithm, Reg-IDE, based on an improved DE algorithm. Weight regularisation in conventional algorithms is an approach to reduce the likelihood of over-fitting and enhance generalisation. However, to the best of our knowledge, the current DE-based trainers do not employ regularisation. This paper, first, proposes a regularisation-based objective function to improve the generalisation of the algorithm by adding a new term to the objective function. Then, a region-based strategy determines some regions in search space using a clustering algorithm and updates the population based on the information available in each region. In addition, quasi opposition-based learning enhances the exploration of the algorithm. The best candidate solution found by improved DE is then used as the initial network weights for the Levenberg-Marquardt (LM) algorithm, as a local refinement. Experimental results on different benchmarks and in comparison with 26 conventional and population-based approaches apparently demonstrate the excellent performance of Reg-IDE.