Reviewerly/exHarmony huggingface dataset:
Gradient-based conditional generative adversarial network for non-uniform blind deblurring via DenseResNet

Blind image deblurring aims to recover the sharp image from a blurry image. The problem is seriously ill-conditioned and many existing algorithms based on kernel estimation require heuristic parameter adjustments and high computational cost, and cannot perform well on non-uniform motion blurs. To address this issue, image deblurring is viewed as an image translation problem in this paper. The authors solve it based on a conditional generative adversarial network (GAN), where the sharp image is restored by an end-to-end trainable neural network. Different from the generative network in basic conditional GAN, the proposed generator is based on dense blocks and residual network (DenseResNet), aiming to mitigate the problems of overfitting and vanishing gradient, and strengthen the blur feature propagation. To generate clear structure, the basic conditional GAN formulation is further revised by introducing joint VGG features and L1-based gradient loss. Extensive experimental results demonstrate the superior performance of the proposed method.
The Effect of Personalization Techniques in Users' Perceptions of Conversational Recommender Systems

Conversational recommender systems provide users with individually tailored recommendations in a flowing dialogue. These require users to disclose information proactively or reactively for receiving personalized recommendations, which can trigger users' resistance to the platform and to the recommendations. Accordingly, this study examined the extent to which user-initiated and system-initiated recommendations provided by a conversational recommender system influenced users' perceptions of it. The results of an online experiment entail that when recommendations are system-initiated, as compared to user-initiated, users perceive to be in less control and perceive the system as riskier. Furthermore, the results stress that systems that provide user-initiated or system-initiated recommendations do not differ in users' perceptions of anthropomorphism.
Multisensory Augmented Reality in Cultural Heritage: Impact of Different Stimuli on Presence, Enjoyment, Knowledge and Value of the Experience

Little is known about the impact of the addition of each stimulus in multisensory augmented reality experiences in cultural heritage contexts.This paper investigates the impact of different sensory conditions on a user's sense of presence, enjoyment, knowledge about the cultural site, and value of the experience.Five different multisensory conditions, namely, Visual, Visual + Audio, Visual + Smell, and Visual + Audio + Smell conditions, and regular visit referred to as None condition, were evaluated by a total of 60 random visitors distributed across the specified conditions.According to the results, the addition of particular types of stimuli created a different impact on the sense of presence subscale scores, namely, on spatial presence, involvement, and experienced realism, but did not influence the overall presence score.Overall, the results revealed that the addition of stimuli improved enjoyment and knowledge scores and did not affect the value of the experience scores.We concluded that each stimulus has a differential impact on the studied variables, demonstrating that its usage should depend on the goal of the experience: smell should be used to privilege realism and spatial presence, while audio should be adopted when the goal is to elicit involvement.
Composite deep learning framework for absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation

Fourier transform profilometry (FTP) is a classic three-dimensional (3D) shape measurement technique that can retrieve the wrapped phase from a single fringe pattern. However, suffering from the spectral leakage and overlapping problems, it generally yields a coarse phase map with low spatial resolution and precision. Recently, deep learning has been introduced to the field of Fringe projection profilometry (FPP), revealing promising results in fringe analysis, phase unwrapping, depth constraint and system calibration. However, for absolute shape measurement of general objects, the inherent depth ambiguity problem of a single fringe is still insurmountable. In this work, we propose a composite deep learning framework for absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation. Our method combines the advantages of FPP techniques for high-resolution phase retrieval and speckle correlation approaches for robust unambiguous depth measurement. The proposed deep learning framework comprises two paths: one is a U-net-structured network, which is used to extract the wrapped phase maps from a single fringe pattern with high accuracy (but with depth ambiguities). The other stereo matching network produces the initial absolute (but with low resolution) disparity map from an additional speckle pattern. The initial disparity map is refined by exploiting the wrapped phase maps as an additional constraint and finally, a high-accuracy high-resolution disparity map for absolute 3D measurement can be obtained. Experimental results demonstrated that the proposed deep-learning-based method could realize high-precision absolute 3D measurement with an accuracy of 50 µm for measuring objects with complex surfaces.
Refactoring Test Smells

Test smells are symptoms in the test code that indicate possible design or implementation problems. Their presence, along with their harmfulness, has already been demonstrated by previous researches. However, we do not know to what extent developers acknowledge the presence of test smells and how to refactor existing code to eliminate them in practice. This study aims to assess open-source developers' awareness about the existence of test smells and their refactoring strategies. We conducted a mixed-method study with two parts: (i) a survey with 73 experienced open-source developers to assess their preference and motivation to choose between 10 different smelly test code samples, found in 272 open-source projects, and their refactored versions; and (ii) the submission of 50 pull requests to assess developers' acceptance of the proposed refactorings. As a result, most surveyed developers preferred the refactored proposal for 78% of the investigated test smells, and the pull requests had an average acceptance of 75% among respondents. Additionally, we were able to provide empiric validation for literature-proposed refactoring strategies. This study demonstrates that although not always using the academic terminology, developers acknowledge both the negative impact of test smells presence and most of the literature's proposals for their removal.
A Hybrid Modified Method of the Sine Cosine Algorithm Using Latin Hypercube Sampling with the Cuckoo Search Algorithm for Optimization Problems

The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.
Green Cloud Software Engineering for Big Data Processing

Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.
Designing accurate emulators for scientific processes using calibration-driven deep models

Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.
Digital technologies in teaching and learning foreign languages: pedagogical strategies and teachers’ professional competence

Research problem: Today’s education system must adapt to the digital revolution and use it to best advantage. One of the most critical paths to pursue is to develop digital skills among student teachers. Many Russian researchers have focused on practices of digital technology integration in foreign language teaching, but, few among them have formulated systematic teaching strategies. The aim of the study: To elaborate the pedagogical strategies for developing digital competence among students on the basis of the complex approach and scientific achievements in this field. Research methods: The research is based on the statistics obtained during an online survey among university and school teachers, students. While conducting the interviews additional information appeared and teachers’ opinions were specified. The participants included 100 university foreign language teachers as well as 120 students from Central Russia. Results: The analysis revealed how the professors organized their digital learning spaces. Among others, the findings highlighted on which aspects they spent more Internet time, and the factors that limited digital use in class. The analysis of the students’ responses revealed some problems in using digital tools while learning a foreign language. The results can be used in developing students’ competences and in elaborating syllabus and teaching materials. Conclusions and recommendations. On the basis of recent achievements and collected data, the complex of pedagogical strategies to form digital competence among students during the foreign language learning have been elaborated. With the appropriate pedagogical accompaniment, digital technologies allow the development of student teachers’ digital competence to meet the demands of the modern society. Thus, it can increase the efficiency of foreign language teaching and will contribute to students’ personal development.
Lifelong Language Knowledge Distillation

It is challenging to perform lifelong language learning (LLL) on a stream of different tasks without any performance degradation comparing to the multi-task counterparts.To address this issue, we present Lifelong Language Knowledge Distillation (L2KD), a simple but efficient method that can be easily applied to existing LLL architectures in order to mitigate the degradation.Specifically, when the LLL model is trained on a new task, we assign a teacher model to first learn the new task, and pass the knowledge to the LLL model via knowledge distillation.Therefore, the LLL model can better adapt to the new task while keeping the previously learned knowledge.Experiments show that the proposed L2KD consistently improves previous state-ofthe-art models, and the degradation comparing to multi-task models in LLL tasks is well mitigated for both sequence generation and text classification tasks. 1
An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection

Road sweepers are ubiquitous machines that help preserve our cities cleanliness and health by collecting road garbage and sweeping out dirt from our streets and sidewalks. They are often very mechanical instruments, needing to operate in harsh conditions dealing with all sorts of abandoned trash and natural garbage. They are usually composed of rotating brushes, collector belts and bins, and sometimes water or air streams. All of these mechanical tools are usually high in power demand and strongly subject to wear and tear. Moreover, due to the simple working logic often implied by these cleaning machines, these tools work in an “always on”/“max power” state, and any further regulation is left to the pilot. Therefore, adding artificial intelligence able to correctly operate these tools in a semi-automatic way would be greatly beneficial. In this paper, we propose an automatic road garbage detection system, able to locate with great precision most types of road waste, and to correctly instruct a road sweeper in order to handle them. With this simple addition to an existing sweeper, we will be able to save more than 80% electrical power currently absorbed by the cleaning systems and reduce by the same amount brush weariness (prolonging their lifetime). This is done by choosing when to use the brushes and when not to, with how much strength, and where. The only hardware components needed by the system will be a camera and a PC board able to read the camera output (and communicate via CanBus). The software of the system will be mainly composed of a deep neural network for semantic segmentation of images, and a real-time software program to control the sweeper actuators with the appropriate timings. To prove the claimed results, we run extensive tests onboard of such a truck, as well as benchmark tests for accuracy, sensitivity, specificity and inference speed of the system.
Dense-RefineDet for Traffic Sign Detection and Classification

Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (RefineDet) to maintain a suitable accuracy-speed trade-off. We constructed a dense connection-related transfer-connection block to combine high-level feature layers with low-level feature layers to optimize the use of the higher layers to obtain additional contextual information. Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs (recall: 84.3%, 95.2%, and 92.6%; precision: 83.9%, 95.6%, and 94.0%). Moreover, experiments using the Caltech pedestrian dataset indicated that the miss rate of Dense-RefineDet was 54.03% (pedestrian height > 20 pixels), which outperformed other state-of-the-art methods.
Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) &gt; Cu (31.34 mg kg−1) &gt; Ni (20.79 mg kg−1) &gt; As (10.65 mg kg−1) &gt; Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) &gt; Cd (R2 = 0.68) &gt; Ni (R2 = 0.67) &gt; Cu (R2 = 0.62) &gt; As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.
Electronic Money Laundering, The Dark Side of Fintech

Fintech has significantly expanded the offer of financial services available to users, facilitating the spread of new financing, payment and exchange services in increasingly large sections of the population. However, there is a downside, represented by the lack of organic regulation that defines the perimeter of legality. World legislators, Europeans in particular, have started building a structured legal framework and to prevent (or to catch) opportunistic or criminal behaviors. Among the unavoidable challenges for the anti-money laundering system are those related to the development of FinTech which produces significant volumes of transactions. In this paper it is provided a systematic review of a) the most common money laundering patterns; b) the European anti-money laundering legal framework, and c) the most recent and relevant fraud cases detected by the enforcements agencies. The main objective of this research is to verify whether the behavior of economic players within the FinTech sector can be contained within the limits of legality. Another objective, far from secondary, is to analyze the current patterns that lead to money laundering, also verifying whether there are tools, within the FinTech itself, capable of exploiting technologies and identifying dark or illegal practices. The recent case study of scandals within the FinTech industry further helps to support the hypotheses and conclusions of the research itself.
A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories

Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of pre-trained source models can transfer to other new target models, thus pose a security threat to black-box applications (when the attackers have no access to the target models). Despite adopting diverse architectures and parameters, source and target models often share similar decision boundaries. Therefore, if an adversary is capable of fooling several source models concurrently, it can potentially capture intrinsic transferable adversarial information that may allow it to fool a broad class of other black-box target models. Current ensemble attacks, however, only consider a limited number of source models to craft an adversary, and obtain poor transferability. In this paper, we propose a novel black-box attack, dubbed Serial-Mini-Batch-Ensemble-Attack (SMBEA). SMBEA divides a large number of pre-trained source models into several mini-batches. For each single batch, we design 3 new ensemble strategies to improve the intra-batch transferability. Besides, we propose a new algorithm that recursively accumulates the “long-term” gradient memories of the previous batch to the following batch. This way, the learned adversarial information can be preserved and the inter-batch transferability can be improved. Experiments indicate that our method outperforms state-of-the-art ensemble attacks over multiple pixel-to-pixel vision tasks including image translation and salient region prediction. Our method successfully fools two online black-box saliency prediction systems including DeepGaze-II (Kummerer 2017) and SALICON (Huang et al. 2017). Finally, we also contribute a new repository to promote the research on adversarial attack and defense over pixel-to-pixel tasks: https://github.com/CZHQuality/AAA-Pix2pix.