Reviewerly/exHarmony huggingface dataset:
Traditional Colombian Games in Virtual and in-Person Education

The main object of this research study was to enhance the Communicative Competence of fifth graders through the use of traditional Colombian games in virtual and in-person settings in an EFL classroom. The participants of this research study were thirty-three (33) students from a public school in Tunja, Boyacá, Colombia. This study is framed as qualitative Action Research. The methodological procedure implemented to collect data was based on the application of seven (7) workshops based on traditional Colombian games where the students were able to strengthen their Communicative Competence and use language in authentic school contexts, virtually and in-person classes. Three different instruments were taken into account in order to collect data: Video recordings, semi-structured interviews and a field journal. The findings of this research study revealed that the use of traditional Colombian games developed the Communicative Competence of the students, giving them tools to communicate in an authentic school context and not only during the development of the workshops but from now on as well. Regarding the motivation, it was evident that being able to interact with their classmates and teachers during the implementation of the games, made the students feel more comfortable and willing to participate in the activities proposed. Finally, the versatility of the workshops is a useful tool that allows us to cover much more topics, levels of language and settings (virtual and in-person) than usual
DAPID: A Differential-adaptive PID Optimization Strategy for Neural Network Training

Derived from automatic control theory, the PID optimizer for neural network training can effectively inhibit the overshoot phenomenon of conventional optimization algorithms such as SGD-Momentum. However, its differential term may unexpectedly have a relatively large scale during iteration, which may amplify the inherent noise of input samples and deteriorate the training process. In this paper, we adopt a self-adaptive iterating rule for the PID optimizer's differential term, which uses both first-order and second-order moment estimation to calculate the differential's unbiased statistical value approximately. Such strategy prevents the differential term from being divergent and accelerates the iteration without increasing much computational cost. Empirical results on several popular machine learning datasets demonstrate that the proposed optimization strategy achieves favorable acceleration of convergence as well as competitive accuracy compared with other stochastic optimization approaches.
Personalized Gestures Through Motion Transfer: Protecting Privacy in Pervasive Surveillance

With the growing ubiquitousness of pervasive sensing and toward ambient intelligence, pervasive surveillance becomes a very real privacy threat, where private gesture interaction is likely to be observed and automatically interpreted by other (even benign) pervasive intelligence tools. We propose motion transfer, the example-guided modification of motion to translate from default motion and gesture interaction alphabets to personal ones. Apart from privacy, incentive to use personalized gesture interaction alphabets include convenience as well as physical handicaps (i.e., inability to conduct certain movements). We demonstrate the concept using motion transfer in RGB-video data. We further show that the approach is feasible also for point-cloud-based gesture recognition methods. In particular, we implemented an end-to-end model for human motion transfer with 3D (x-y-time) or 4D (x-y-z-time) point-cloud datasets. Point-cloud-based motion transfer is a privacy protecting way of customizing gestures to control devices, hence lowering the risk of disclosing the nature of interaction to surrounding pervasive surveillance installations.
Memory Enhanced Spatial-Temporal Graph Convolutional Autoencoder for Human-Related Video Anomaly Detection

Human-related video anomaly detection is a challenging problem due to unclear definitions of anomalies and insufficient training data. Pose-based methods have attracted widespread attention by exploiting highly structured skeleton data that are robust to background noise and illumination changes. However, existing methods use recurrent neural network to extract temporal information while ignoring the spatial dependencies between skeleton joints, which are crucial to reason behaviors. Additionally, commonly-used methods are expected to produce larger reconstruction errors for anomalies than normal samples to achieve anomaly detection. But in practice, due to the strong generalization ability of these models, they fail to obtain significant reconstruction errors for abnormal samples, resulting in missing anomalies. In this paper, we propose a novel framework Memory Enhanced Spatial-Temporal Graph Convolutional Autoencoder(Mem-STGCAE) to address these problems. We use spatial-temporal graph convolution as an encoder to capture discriminative features in spatial and temporal domains. We enhance the autoencoder with a memory module that records normal patterns. The encoded representation is used as a query to retrieve the most relevant patterns. Thus, the decoder reconstructs anomalies using normal patterns, resulting in significant reconstruction errors. Different from traditional autoencoders, two branches of decoder are introduced to reconstruct past and predict future pose sequences respectively. Extensive experiments on two challenging video anomaly datasets, ShanghaiTech and IITB-Corridor, show that our proposed network outperforms other state-of-the-art methods.
How Personality and Visual Channels Affect Insight Generation

Gaining insight is considered one of the relevant purposes of visual data exploration, yet studies that categorize insights are rare. This paper reports on a study to understand if the categorization model used to describe insights and personality factors affect insight-based evaluations’ findings. Participants completed a set of tasks with three hierarchical visualizations and then reported what insights they could gather from them. Results show that the insight categorization taxonomies produce different descriptions of insights based on the same corpus of responses. In addition, our findings suggest that the openness to experience trait positively influences the number of reported insights. Both these factors may create obstacles to the design of insight-based evaluations and, consequently, should be controlled in the experimental design. We discuss the study implications, lessons learned, and future work opportunities.
Dismon-Gan: 24×7 All-Weather Optical Domain Surveillance Using Progressively Growing Adversarial Networks with Patch Discriminator

Cyclones and floods are significant disasters seen by south- Asian countries. With cloud occlusions on the affected regions, monitoring the disaster becomes tedious. Synthetic Aperture Radars (SAR) can penetrate through the clouds (due to their microwave frequencies) and image the area under- Being active sensors, they also can image around the clock. These properties enable the domain experts to use SAR images for disaster monitoring; however, even professionals find it challenging to interpret the data since the human eye is unfamiliar with the impact of distance-dependent imaging, signal intensities observed in the radar spectrum, and image features associated to speckle or postprocessing procedures. This manuscript exploits the valuable imaging properties of SAR images to propose a Generative Adversarial Network (GAN) to synthesize realistic and semantic optical images by conditioning them over the microwave satellite images. It en- the model to synthesize optical images of an affected area in all weather and around the clock conditions, making it a critical disaster monitoring assistance tool.
Training Aware Sigmoidal Optimization

Proper optimization of deep neural networks is an open research question since an optimal procedure to change the learning rate throughout training is still unknown. Manually defining a learning rate schedule involves troublesome, time-consuming try and error procedures to determine hyperparameters such as learning rate decay epochs and learning rate decay rates. Although adaptive learning rate optimizers automatize this process, recent studies suggest they may produce overfitting and reduce performance compared to fine-tuned learning rate schedules. Considering that deep neural networks loss functions present landscapes with much more saddle points than local minima, we proposed the Training Aware Sigmoidal Optimizer (TASO), consisting of a two-phase automated learning rate schedule. The first phase uses a high learning rate to fast traverse the numerous saddle point, while the second phase uses a low learning rate to approach the center of the local minimum previously found slowly. We compared the proposed approach with commonly used adaptive learning rates schedules such as Adam, RMSProp, and Adagrad. Our experiments showed that TASO outperformed all competing methods in both optimal (i.e., performing hyperparameter validation) and suboptimal (i.e., using default hyperparameters) scenarios.
Detection and Recognition of Software Design Patterns Based on Machine Learning Techniques: A Big Step Towards Software Design Re-usability

Design Patterns are techniques in software designing for addressing the frequently occurred issues in a relevant context. Understanding the design patterns used in the design helps to dive deeper into the design. Hence, mapping the design pattern is very necessary and valuable for Software designers to extract important information during the re-engineering process. Along with the detection of design patterns, it is also desired to recognize the design pattern from the source code. In this paper we presented an approach for design pattern detection and recognition using a machine learning techniques and metrics based training dataset.
T-Evos: A Large-Scale Longitudinal Study on CI Test Execution and Failure

Continuous integration is widely adopted in software projects to reduce the time it takes to deliver the changes to the market. To ensure software quality, developers also run regression test cases in a continuous fashion. The CI practice generates commit-by-commit software evolution data that provides great opportunities for future testing research. However, such data is often unavailable due to space limitation (e.g., developers only keep the data for a certain period) and the significant effort involved in re-running the test cases on a per-commit basis. In this paper, we present T-Evos, a dataset on test result and coverage evolution, covering 8,093 commits across 12 open-source Java projects. Our dataset includes the evolution of statement-level code coverage for every test case (either passed and failed), test result, all the builds information, code changes, and the corresponding bug reports. We conduct an initial analysis to demonstrate the overall dataset. In addition, we conduct an empirical study using T-Evos to study the characteristics of test failures in CI settings. We find that test failures are frequent, and while most failures are resolved within a day, some failures require several weeks to resolve. We highlight the relationship between code changes and test failure, and provide insights for future automated testing research. Our dataset may be used for future testing research and benchmarking in CI. Our findings provide an important first step in understanding code coverage evolution and test failures in a continuous environment.
Gait Differences in the Real World and Virtual Reality: The Effect of Prior Virtual Reality Experience

Walking through immersive virtual environments is one of the important parts of Virtual Reality (VR) applications. Prior research has established that users’ gait in virtual and real environments differs; however, little research has evaluated how users’ gait differs as users gain more experience with VR. We conducted experiments measuring novice and experienced subjects’ gait parameters in VR and real environments. Results showed that subjects’ performance in VR and Real World was more similar in the last trials than in the first trials; their walking dissimilarity in the start trials diminished by walking more trials. We found trial as a significant variable affecting the walking speed, step length, and trunk angle for both groups of users. While the main effect of expertise was not observed, an interaction effect between expertise and the trial number was shown. Trunk angle increased over time for novices but decreased for experts.
Application of Fractional Kalman Filter in Modal Analysis of Buck-Boost Converter

In practical application, both inductance and capacitor are fractional order in nature. Based on the fractional order model of Buck - boost converter operating in discontinuous mode of inductance current, the simulation application of fractional order Kalman filter is studied. The existing research results have proved that the fractional order mathematical simulation model can more accurately describe the working characteristics of Buck - boost converter in the discontinuous mode of inductor current. Therefore, the filtering accuracy of the fractional order model is higher than that of the integer order model. The simulation results verify the effectiveness and feasibility of the proposed algorithm.
Blockchain based Federated Learning for Object Detection

Object detection based on deep learning needs to collect and centralize a large amount of training data from multiple parties, which leads to data privacy problems. Federated learning has become an effective way to conduct deep learning when training data are distributed. However, traditional federated learning still faces the challenge of security and data heterogeneity. Specifically, the central server will introduce a single point of failure. Malicious clients and statistical heterogeneity of data will affect the performance of the model. To solve the above problems, this paper proposes FedDetectionBC, a federated learning framework for object detection model training, which takes advantage of blockchain to ensure the robustness and auditability of federated learning. To deal with data heterogeneity, a new algorithm (Exchange-FedAvg) is also proposed. Experimental results that FedDetectionBC is more robust than traditional federated learning when there exist malicious clients in the system. The proposed Exchanged-FedAvg can achieve higher accuracy with fewer communication rounds in non-IID settings.
Anomaly Detection of E-commerce Econnoisseur Based on User Behavior

Abstract Econnoisseur refers to users who obtain high returns from the Internet at low cost. It is of great significance for platform to identify econnoisseur to reduce unnecessary losses. At present, econnoisseur is mainly intercepted by rules. This method will fail when the new get the best deal method appears, and there is a certain lag. This paper identifies the econnoisseur from Knownsec Security Intelligence Brain’s e-commerce website visitors. First of all, it is found that the precision and recall of the Isolation Forest are better than the Local Outlier Factor and DBSCAN in econnoisseur detection. Secondly, we merged the similar URLs visited by users with Bi-directional Long Short-Term Memory (BiLSTM), then use the merged data in Isolation Forest Model. It is found that the improved Isolation Forest model based on BiLSTM can further improve the detection ability. Practical case studies showed that this method has certain validity and reference for the detection of econnoisseur.
Semantic Modeling Method of Highly Elastic Power Grid Based on OWL

As an important supporting technology for the development of power grid, highly elastic power grid is the intelligent development of power grid, which can effectively improve the information level of power system. However, due to the massive terminal data, various communication modes and independent application platforms, it is very difficult to extract a unified and effective data expression format from complex equipment, which hinders the power system to achieve intelligent services. In this paper, the ontology description language OWL is used for semantic extension, and the understanding gap between heterogeneous identifiers is eliminated by adding device semantics. Through the establishment of equipment information, service overview and quality of service ontology model, the interconnection between heterogeneous identities of highly elastic power grid terminals can be realized, which can conveniently carry out intelligent control of transmission/transformer/distribution equipment.
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning

To collaborate well with robots, we must be able to understand their decision making. Humans naturally infer other agents' beliefs and desires by reasoning about their observable behavior in a way that resembles inverse reinforcement learning (IRL). Thus, robots can convey their beliefs and desires by providing demonstrations that are informative for a human learner's IRL. An informative demonstration is one that differs strongly from the learner's expectations of what the robot will do given their current understanding of the robot's decision making. However, standard IRL does not model the learner's existing expectations, and thus cannot do this counterfactual reasoning. We propose to incorporate the learner's current understanding of the robot's decision making into our model of human IRL, so that a robot can select demonstrations that maximize the human's understanding. We also propose a novel measure for estimating the difficulty for a human to predict instances of a robot's behavior in unseen environments. A user study finds that our test difficulty measure correlates well with human performance and confidence. Interestingly, considering human beliefs and counterfactuals when selecting demonstrations decreases human performance on easy tests, but increases performance on difficult tests, providing insight on how to best utilize such models.
Construction of a Technological Component to Support ISMS for the Detection of Obfuscation in Computer Worm Samples

In the world of malware, there is a category called computer worms, this type has a complex technological structure, but an ability to replicate itself automatically without the intervention of a human and can be distributed to other computers that are connected to the network, with a malicious code that can infect a computer that will later be used to infect others, spreading through the network. This malicious code used by computer worms could be found with ob- obfuscated code that allows it to hide part of its code when analyzed, not being able to classify it. In this framework, the research proposes the creation of a technological component (a piece of software) that allows a faster categorization of computer worms that have obfuscation in their code, proposing the development of a component that allows selecting a malicious file (computer worm) and that through the selection of a rule with the YARA (Yet Another Ridiculous Acronym) tool, verifies the obfuscation in it, having also a button that allows verifying the hash in the Virus Total platform. The purpose of this component is to help and support the Information Security Management Systems for the analysis of malware in investigations of threat intelligence units or cybersecurity teams that need to categorize this type of malware, automating the manual processes and validating the obfuscation in this type of malware.
Personalization services in art education environments: first survey results

Personalization is the main vehicle in many learning environments for the provision of enhanced services to the end-users. This paper deals with the problem of providing personalization services in an art education environment. Specifically, we are interested in assisting art teachers to initiate an art exhibition for their students, art students to create virtual exhibitions and art exhibition visitors to have immersive and interactive experiences. The results discussed here are one of the first outputs of an EU-funded research project, namely CREAMS which aims to design, develop end evaluate a framework and open-source tools based on virtual, augmented, and mixed reality for the creation of virtual exhibitions for art students. Specifically, in this paper, we present a literature review concerning eXtended Reality (XR) technologies and personalization for art education purposes. Based on the findings of the literature review, along with expert opinions, we present a system design incorporating specific personalization services. In order to emphasize the strength of the proposed personalization services in the framework, we present a set of motivating scenarios.
Analysis of Different Deep Learning Architectures to Learn Generalised Classifier Stacking on Riemannian and Grassmann Manifolds

This paper considers different deep learning architectures to learn patterns that are objects lying on the Riemannian and Grassmann manifolds. Among them, we considered cascades of classifier ensembles (CCEs), convolutional neural networks (CNNs), and deep neural forests (DNFs). All aforementioned architectures have linearized and nonlinearized versions. Patterns that are objects of Riemannian manifolds are classifier prediction pairwise matrices (CPPMs) while objects of the Grassmann manifolds are obtained using decision profiles (DPs). We also compared our architectures with CCEs that operate in the Euclidean geometry. As seen from the experimental results deep learning architectures based on CNNs provided the best results.
Self-reflection Doll: A Generative Tool for Participatory Design Research on Student Burnout

Design and HCI researchers are increasingly exploring sensitive topics. Despite attempts to involve participants to better understanding their needs, it remains challenging for researchers to elicit information from vulnerable participants. To involve marginalized voices in participatory design, we need to search beyond traditional research tools. We propose a Self-Reflection Doll, a generative tool that aims at helping participants overcome psychological barriers to share emotionally sensitive experiences during participatory design research. To examine the feasibility and benefits of the new method, a comparative case study on the topic of burnout syndrome was conducted using the Self-Reflection Doll in comparison with Focus Group and Cultural Probe. The paper discusses the strengths of the new generative tool in making participants feel more at ease to disclose personal stories and eliciting both implicit and explicit information for design research regarding Burnout Syndrome and other sensitive topics.