Reviewerly/exHarmony huggingface dataset:
Research on Dual-Arm Control of Lunar Assisted Robot Based on Hierarchical Reinforcement Learning under Unstructured Environment

When a lunar assisted robot helps an astronaut turn over or transports the astronaut from the ground, the trajectory of the robot’s dual arms should be automatically planned according to the unstructured environment on the lunar surface. In this paper, a dual-arm control strategy model of a lunar assisted robot based on hierarchical reinforcement learning is proposed, and the trajectory planning problem is modeled as a two-layer Markov decision process. In the training process, a reward function design method based on the idea of the artificial potential field method is proposed, and the reward information is fed back in a dense reward method, which significantly reduces the invalid exploration space and improves the learning efficiency. Large-scale tests are carried out in both simulated and physical environments, and the results demonstrate the effectiveness of the method proposed in this paper. This research is of great significance in respect of human–robot interaction, environmental interaction, and intelligent control of robots.
RT-libSGM: An Implementation of a Real-time Stereo Matching System on FPGA

Stereo depth estimation has become an attractive topic in the computer vision field. Although various algorithms strive to optimize the speed and the precision of estimation, the energy cost of a system is also an essential metric for an embedded system. Among these various algorithms, Semi-Global Matching (SGM) has been a popular choice for some real-world applications because of its accuracy-and-speed balance. However, its power consumption makes it difficult to be applied to an embedded system. Thus, we propose a robust stereo matching system, RT-libSGM, working on the Xilinx Field-programmable gate array (FPGA) platforms. The dedicated design of each module optimizes the speed of the entire system while ensuring the flexibility of the system structure. Through an evaluation running on a Zynq FPGA board called M-KUBOS, RT-libSGM achieves state-of-the-art performance with lower power consumption. Compared with the original design (libSGM), when working on the Tegra X2 GPU, RT-libSGM runs 2 × faster at a lower energy cost.
Proposal of a Model for the Development of University Teacher Training Through Virtual Courses

In times of pandemic, the education sector, worldwide, was affected. To minimize the problem, they carried out strategies that allow the start of virtual classes optimally. Likewise, teachers had to be trained, to be at the forefront of the context. Training should be asynchronous. In this sense, the objective of the research was to implement a model of teacher training through virtual courses. These courses are made up of the teaching-learning strategy, pedagogical resources and materials, the learning evaluation system, curricular planning, as well as teacher induction. Prototypes of the virtual courses were made to be implemented in the future. Also, the methodology carried out in the research, was a first moment, an analysis of the current situation and the proposal to be considered; then to carry out the validation of the model, with experts in educational pedagogy and others in technology oriented education. Likewise, a 10-question instrument was developed for expert validation of the proposed model; resulting in 85.5% acceptance.
TermitUp: Generation and enrichment of linked terminologies

Domain-specific terminologies play a central role in many language technology solutions. Substantial manual effort is still involved in the creation of such resources, and many of them are published in proprietary formats that cannot be easily reused in other applications. Automatic term extraction tools help alleviate this cumbersome task. However, their results are usually in the form of plain lists of terms or as unstructured data with limited linguistic information. Initiatives such as the Linguistic Linked Open Data cloud (LLOD) foster the publication of language resources in open structured formats, specifically RDF, and their linking to other resources on the Web of Data. In order to leverage the wealth of linguistic data in the LLOD and speed up the creation of linked terminological resources, we propose TermitUp, a service that generates enriched domain specific terminologies directly from corpora, and publishes them in open and structured formats. TermitUp is composed of five modules performing terminology extraction, terminology post-processing, terminology enrichment, term relation validation and RDF publication. As part of the pipeline implemented by this service, existing resources in the LLOD are linked with the resulting terminologies, contributing in this way to the population of the LLOD cloud. TermitUp has been used in the framework of European projects tackling different fields, such as the legal domain, with promising results. Different alternatives on how to model enriched terminologies are considered and good practices illustrated with examples are proposed.
Understanding the Experience of Code Review: Misalignments, Attention, and Units of Analysis

Code review is a common practice in software development and numerous studies have described different aspects of the process; its characteristics, the expectations on that process, issues around reviewer allocation, and more. However, one aspect that has not been studied to a large extent is the experience of the developers in the code review process. This is unfortunate given the significant amount of time that developers spend on this activity, where problems that degrade developers’ experience on a daily basis can create work environment issues.
NSSIA: A New Self-Sovereign Identity Scheme with Accountability

Self-sovereign identity (SSI) is a new distributed method for identity management, commonly used to address the problem that users are lack of control over their identities. However, the excessive pursuit of self-sovereignty in the most existing SSI schemes hinders sanctions against attackers. To deal with the malicious behavior, a few SSI schemes introduce accountability mechanisms, but they sacrifice users’ privacy. In addition, the digital identities (static strings or updatable chains) in the existing SSI schemes are as inputs to a third-party executable program (mobile app, smart contract, etc.) to achieve identity reading, storing and proving, and users’ self-sovereignty are weakened. To solve the above problems, we present a new self-sovereign identity scheme to strike a balance between privacy and accountability and get rid of the dependence on the third-party program. In our scheme, one and only individual-specific executable code is generated as a digital avatar-i for each human to interact with others in cyberspace without a third-party program, in which the embedding of biometrics enhances uniqueness and user control over their identity. In addition, a joint accountability mechanism, which is based on the shamir (t, n) threshold algorithm and a consortium blockchain, is designed to restrict the power of each regulatory authority and protect users’ privacy. Finally, we analyze the security, SSI properties and conduct detailed experiments in terms of the cost of computation, storage, and blockchain gas. The analysis results indicate that our scheme resists the known attacks and fulfills all the six SSI properties. Compared with the state-of-the-art schemes, the extensive experiment results show that the cost is larger in server storage, blockchain storage, and blockchain gas, but is still low enough for practical situations.
Integrating mobile technologies in a smart classroom to improve the quality of the educational process: Synergy of technological and pedagogical tools

The article describes the possibilities of mobile learning to implement the innovative educational concept of Smart Classroom. To increase the effectiveness of international partnerships and to develop opportunities for creating innovative projects in relevant socio-economic areas, an online forum "Engineering of educational courses in the medical and pharmaceutical industry" was held. Proficonf videoconferencing system made it possible to organize five online meetings on digital technologies in the training of modern pharmaceutical personnel within the concept of "Smart Classroom". The study results show that under digital transformation of the educational paradigm, it is advisable to consider a curriculum development strategy in the university ("Smart Campus"). Based on the study results, one can conclude that implementing the concept of "Smart Classroom" allows one to improve teaching methods and interaction between students at all stages of international educational relations.&#x0D; &#x0D; Keywords: digital technology, mobile learning, Smart Classroom, Smart Environment, Smart Technology, student interaction.
Background to the development of technology of formation of teachers readiness for distance learning

The current stage of distance education development demands enhancement of standards for training of teachers working in that area. Only professional teachers are able to provide quality education for people with different abilities, interests and needs, considering their personal characteristics and specifics of distance education. It is critically important to analyse the experience and professional qualities of teachers involved in distance learning given the uniqueness of the current situation and a high probability of transition to distance education. The study was focused on establishing the actual preparedness level of teachers for distance learning and identifying prerequisites for formation of teachers’ readiness to provide students distance education. Key words: teacher, readiness, distance education, distance learning technologies, information and communication technologies.
FACIAL EMOTION DETECTION AND RECOGNITION

Facial emotional expression is a part of face recognition, it has always been an easy task for humans, but achieving the same with a computer algorithm is challenging. With the recent and continuous advancements in computer vision and machine learning, it is possible to detect emotions in images, videos, etc. A face expression recognition method based on the Deep Neural Networks especially the convolutional neural network (CNN) and an image edge detection is proposed. The edge of each layer of the image is retrieved in the convolution process after the facial expression image is normalized. To maintain the texture picture's edge structure information, the retrieved edge information is placed on each feature image. In this research, several datasets are investigated and explored for training expression recognition models. The purpose of this paper is to make a study on face emotion detection and recognition via Machine learning algorithms and deep learning. This research work will present deeper insights into Face emotion detection and Recognition. It will also highlight the variables that have an impact on its efficacy.
Solids on Soli

Gesture recognition with miniaturised radar sensors has received increasing attention as a novel interaction medium. The practical use of radar technology, however, often requires sensing through materials. Yet, it is still not well understood how the internal structure of materials impacts recognition performance. To tackle this challenge, we collected a large dataset of 14,090 radar recordings for 6 paradigmatic gesture classes sensed through a variety of everyday materials, performed by humans (6 materials) and a robot system (75 materials). Next, we developed a hybrid CNN+LSTM deep learning model and derived a robust indirect method to measure signal distortions, which we used to compile a comprehensive catalogue of materials for radar-based interaction. Among other findings, our experiments show that it is possible to estimate how different materials would affect gesture recognition performance of arbitrary classifiers by selecting just 3 reference materials. Our catalogue, software, models, data collection platform, and labeled datasets are publicly available.
Stranger Detection and Occupant Identification Using Structural Vibrations

Human-induced structural vibration provides valuable information about humans who interact with the structure, including people’s identity, characteristics, activities and health status. Among them, person identification is crucial because it is the premise for any personalized services. Our prior work using footstep-induced structural vibration has been promising in identifying a fixed group of people, but it is restricted to registered occupants, leading to errors whenever a stranger appears. Therefore, we introduce a stranger detection and occupant identification system using footstep-induced structural vibrations. There are two main challenges in developing this system: 1) the strangers’ walking patterns are unknown before being observed, and 2) the probability of the presence of a stranger varies at different times and locations. To overcome the first challenge, we model the occupants’ and strangers’ footstep-induced floor vibration data as a mixture of Gaussian distributions with varying number of components, representing the changing number of registered people. To address the second challenge, we reformulated the prior estimation in the mixture model by introducing an interpretable parameter that represents the expected probability of observing a stranger. Through an experiment conducted on a wood-framed structural platform, our method achieves an average accuracy of 89.2% in person identification among 10 people.
Detecting Interpersonal Conflict in Issues and Code Review: Cross Pollinating Open- and Closed-Source Approaches

Interpersonal conflict in code review, such as toxic language or an unnecessary pushback, is associated with negative outcomes such as stress and turnover. Automatic detection is one approach to prevent and mitigate interpersonal conflict. Two recent automatic detection approaches were developed in different settings: a toxicity detector using text analytics for open source issue discussions and a pushback detector using logs-based metrics for corporate code reviews. This paper tests how the toxicity detector and the pushback detector can be generalized beyond their respective contexts and discussion types, and how the combination of the two can help improve interpersonal conflict detection. The results reveal connections between the two concepts. LAY ABSTRACT Software engineers often communicate with one another on platforms that support tasks like discussing bugs and inspecting each others‘ code. Such discussions sometimes contain interpersonal conflict, which can lead to stress and abandonment. In this paper, we investigate how to automatically detect interpersonal conflict, both by analyzing the text of the what the engineers are saying and by analyzing the properties of that text.
Fashion Image Search via Anchor-Free Detector

Clothes image search is the key technique to effectively search the clothes items that are most relevant to the query clothes given by the customer. In this work, we propose an Anchor-free framework for clothes image search by adopting an additional Re-ID branch for similarity learning and global mask branch for instance segmentation. The Re-ID branch is to extract richer feature of target clothes, where we develop a mask pooling layer to aggregate the feature by utilizing the mask of target clothes as the guidance. In this way, the extracted feature will involve more information covered by the mask area of targets instead of only the center point; the global mask branch is to be trained with detection and Re-ID branches simultaneously, where the estimated mask of target clothes can be utilized in reference procedure to guide the feature extraction. Finally, to further enhance the performance of retrieval, we have introduced a match loss to further fine-tune the Re-ID embedding branch in the framework, so that the clothes target can be closer to the same one, while be farther away from different clothes targets. Extensive simulations have been conducted and the results verify the effectiveness of the proposed work.
Physical and morphological characterization of the 19 May 2021 ash cloud deposit at Stromboli (Italy)

We report on the ash cloud related to the gravitational collapse of a portion of the Stromboli volcano crater rim that occurred on 19 May 2021. The collapse produced a pyroclastic density current (PDC) that spread along the northwest flank of the volcano and propagated in the sea for about 1 km. The PDC was associated with a convective ash cloud that rapidly dispersed eastward and deposited a thin layer (< 1 mm) of very fine pinkish ash over the village of Stromboli. The deposit was sampled shortly after the emplacement (within a few hours) and prior to any significant reworking or re-sedimentation. We present a comprehensive description of the deposit including dispersal, sedimentological characteristics and textural and geochemical features. We also compare the 19 May 2021 deposit with fine-ash deposits connected to other PDCs and landslides previously occurring at Stromboli and with the distal ash of a paroxysmal explosive eruption of Mt. Etna volcano. Results indicate that the distributions of the mass on the ground and of the grain size are not correlated with the distance from the source. Also, the componentry reflects a preponderance of remobilized material ingested by the PDC. Therefore, the great amount of fine ash can be ascribed to clasts comminution processes, although the prevalence of dense crystalline components records an overall equiaxial shape, revealing a paucity of elongated clast with complex morphology. Furthermore, the outcomes of this work aim to create a collection of data of a co-PDC ash cloud that may prove useful for comparison with other deposits worldwide.
iTiger: an automatic issue title generation tool

In both commercial and open-source software, bug reports or issues are used to track bugs or feature requests. However, the quality of issues can differ a lot. Prior research has found that bug reports with good quality tend to gain more attention than the ones with poor quality. As an essential component of an issue, title quality is an important aspect of issue quality. Moreover, issues are usually presented in a list view, where only the issue title and some metadata are present. In this case, a concise and accurate title is crucial for readers to grasp the general concept of the issue and facilitate the issue triaging. Previous work formulated the issue title generation task as a one-sentence summarization task. A sequence-to-sequence model was employed to solve this task. However, it requires a large amount of domain-specific training data to attain good performance in issue title generation. Recently, pre-trained models, which learned knowledge from large-scale general corpora, have shown much success in software engineering tasks. In this work, we make the first attempt to fine-tune BART, which has been pre-trained using English corpora, to generate issue titles. We implemented the fine-tuned BART as a web tool named iTiger, which can suggest an issue title based on the issue description. iTiger is fine-tuned on 267,094 GitHub issues. We compared iTiger with the state-of-the-art method, i.e., iTAPE, on 33,438 issues. The automatic evaluation shows that iTiger outperforms iTAPE by 29.7%, 50.8%, and 34.1%, in terms of ROUGE-1, ROUGE-2, ROUGE-L F1-scores. The manual evaluation also demonstrates the titles generated by BART are preferred by evaluators over the titles generated by iTAPE in 72.7% of cases. Besides, the evaluators deem our tool as useful and easy-to-use. They are also interested to use our tool in the future.
Deep Unsupervised Weighted Hashing for Remote Sensing Image Retrieval

Deep unsupervised hashing methods are gaining attention in the field of remote sensing (RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most previous unsupervised hashing research used only natural image-based pre-trained models to generate label matrices; however, this method cannot capture the semantic information of RS images well and limits the accuracy of retrieval. To solve this problem, the authors propose a deep unsupervised weighted hashing (DUWH) model that uses a similarity matrix updating strategy based on a weighted similarity structure to achieve the mutual optimization of the similarity matrix and hash network. The authors devise a novel combinatorial loss function to improve the hash performance that can be used to obtain higher quality hash codes by assigning different weights to the sample pairs with different difficulties. Experiments were conducted on two RS datasets to verify the excellent performance of the proposed method.
Users' Feedback on COVID-19 Lockdown Documentary: An Emotion Analysis and Topic Modeling Analysis

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose "The lockdown: One month in Wuhan" YouTube documentary, and applied emotion analysis as well as a machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation (LDA) topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust (22.8%), joy (15.4%), and anticipation (17.6%) are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing.