Reviewerly/exHarmony huggingface dataset:
Using or Misusing?

Today's cyberphysical systems are increasingly prone to misuse. To secure existing and future software systems, introducing concepts of IT-Security and Secure Software Engineering (SecSE) in Software Engineering (SE) courses is essential for academic education of future software engineers. This is not only important for computer science students, but also for engineering students studying topics of computing and SE. However, only little research exists on integrating these topics into traditional SE courses, especially for engineering students in non-computer science majors. To narrow this gap, this paper contributes with the design and evaluation of an exercise on modeling misuse cases alongside use cases, based on the inductive teaching method problem-based learning (PBL). The exercise is part of an educational design research investigating which learning content and teaching methods are suitable for integrating IT-Security and SecSE topics into traditional SE education of engineering students to convey factual knowledge as well as raise awareness and interest for both topics during software development. We present the integration of the exercise design into a traditional SE course for engineering students and its evaluation to examine its suitability. We evaluated the exercise design regarding the suitability of the design components, the learning content of misuse cases and the intended learning goals as well as its impact on students' motivation, and their interest in IT-security. The paper then presents indications on the feasibility and success of the exercise design for teaching misuse cases to engineering students and sparking their interest in IT-Security.
A Query Framework for Massive RDF Graph Data in Pay-As-You-Go Fashion

In the context of big data, faster and more accurate methods are required for RDF data retrieval. The current research on querying RDF graph data has made some progress, but it has a certain delay and high up-front cost. Given the above shortcomings, we propose a more efficient framework for querying RDF graph data based on the pay-as-you-go (PAYG) approach. Firstly, we annotate the evolution process of data content and association and then construct the evolution update operation set and dynamic incremental graph to describe the dynamic data. Secondly, we design a query algorithm supporting the best-effort query, which returns the data information with the highest similarity to the user, thus improving the search efficiency. Finally, we apply the investment income theory and information retrieval evaluation methods to construct an evaluation mechanism for PAYG RDF data management.
Soccer Player Recognition using Artificial Intelligence and Computer Vision

Groundbreaking features and functionalities are available in sports broadcasting programs, specifically in soccer games, such as post-game analysis, tracking of players, tracking of the ball, and associated teams’ and players’ statistical information. However, viewers don’t have control to choose which features to view on-demand and aren’t able to interact with any of these functionalities. Our system aims for the fundamental system of an on-demand viewer-driven application that would be able to track players on the field, identify formation changes, follow team strategy changes, and gather all statistical information of the player and the team on a single screen. In order to realize such an application, we focus on developing a method to track players on the field using multiple Artificial Intelligence (AI) and computer vision techniques where our application employs facial recognition and jersey number recognition algorithms. As a live soccer game broadcast would have various camera views of the players, our custom-made database contains captured images from different scenarios of camera views such as different angles and zooms of the player and is used to train the model using Convolutional Neural Network (CNN). Our system is scalable to different types of sports, such as basketball, baseball, volleyball, and more where players have their numbers on their jerseys and would be feasible to apply our system since soccer involves more players on the field than other sports games.
Gradient harmonized loss: Improving the performance of intelligent diagnosis models in large imbalance scenarios

The natural distribution of monitoring data is imbalanced, which has a negative impact on the training of intelligent diagnosis models. Although researchers have proposed data-level and algorithm-level methods to solve this problem, these methods are only applicable to small imbalance scenarios. In order to correct the anomalies of model training under large imbalance scenarios, this paper proposes a gradient harmonized loss that coordinates the gradients of each class to prevent the majority class in the imbalanced data from dominating the training. The coordination of gradients is based on the similarity of the sample gradients, and the compression of similar gradients is achieved by defining different penalty rules for each class. Taking into account the computational efficiency and the training difficulty, the proposed method is further optimized in terms of gradient dimensionality reduction and parameter simplification respectively. The proposed method was verified using two sample sets with different imbalance ratios and compared with traditional methods. The results showed that the proposed method greatly improved the performance of the DCNN model in large imbalance scenarios.
iOS Based Pose Estimation and Gesture Recognition for Robot Manipulation

Interfacing between robots and humans has come a long way in the past few years, and new methods for smart, robust interaction are needed. Typically, a technician has to program a routine for a robot in order for the robot to be useful. This puts up a significant barrier to entry into the field of automating tasks using robots—not only is a technician and a computer required, but the robot is not adaptive to the immediate needs of the user. The robot is only capable of executing a pre-determined task and for any change to be made the entire system needs to be paused. This project seeks to bridge the gap between user and robot interface, creating an easy-to-use system that allows for adaptive robot control. Using a combination of computer vision and a monocular camera system and integrated LiDAR sensor on an iPhone, gesture recognition and pose estimation was conducted within an independent system to control the Baxter humanoid robot. The gathered data was sent wirelessly to the robot to be interpreted and then replay actions performed by the user.
Effective attributed network embedding with information behavior extraction

Network embedding has shown its effectiveness in many tasks, such as link prediction, node classification, and community detection. Most attributed network embedding methods consider topological features and attribute features to obtain a node embedding but ignore its implicit information behavior features, including information inquiry, interaction, and sharing. These can potentially lead to ineffective performance for downstream applications. In this article, we propose a novel network embedding framework, named information behavior extraction (IBE), that incorporates nodes' topological features, attribute features, and information behavior features within a joint embedding framework. To design IBE, we use an existing embedding method (e.g., SDNE, CANE, or CENE) to extract a node's topological features and attribute features into a basic vector. Then, we propose a topic-sensitive network embedding (TNE) model to extract a node's information behavior features and eventually generate information behavior feature vectors. In our TNE model, we design an importance score rating algorithm (ISR), which considers both effects of the topic-based community of a node and its interaction with adjacent nodes to capture the node's information behavior features. Eventually, we concatenate a node's information behavior feature vector with its basic vector to get its ultimate joint embedding vector. Extensive experiments demonstrate that our method achieves significant and consistent improvements compared to several state-of-the-art embedding methods on link prediction.
Measuring the Technical Debt

Technical debt measurement is still an open issue considering the absence of consistency in the existing tools at the methodology level as well as at the applicability of the rulesets level. In this paper we present the evolvability metrics which measure the technical debt of a software system at multiple levels of its program structure based on the functional hierarchy of its components. The metrics suite also estimates the efforts required for each evolution when applied to a system thus validating the impact of technical debt on the evolvability of the system.
A Novel DCT-based Just Noticeable Difference Model for Videos Based on Structure Complexity

The Just Noticeable Difference (JND) model involves the minimum level of visibility where visual content can be distinguished, which plays a significant role in terms of the perceptual image/video. For the estimation of the JND threshold, the contrast masking effect evaluation is a critical task and has room for manoeuvre. Considering the important role of structural information for contrast masking evaluation, a structure complexity descriptor based on orientation selectivity characteristics of the human visual system (HVS) was introduced and a new contrast masking model based on structure complexity in the discrete cosine transformation (DCT) domain was estimated. Then, combining with the spatio-temporal CSF and luminance adaptation, a novel JND model for videos was proposed in the DCT domain. The experimental results of the subjective quality evaluation tests demonstrate that the proposed JND threshold can hide more noises under the same perceived quality, which is highly consistent with human subjective visual perception.
Taking a Cognitive Approach to Organizing Students’ Independent Work in IT-enhanced Foreign Language Teaching

Driven by advances in IT development and conditioned by the COVID‑19 pandemic, the current transformation of the learning process activates the teaching profession and encourages a search for optimum pedagogical solutions. The research aims to analyze the organization of IT-enhanced foreign language teaching from a cognitive perspective. As an object of multidisciplinary research, students’ independent work is considered in respect of the psychophysiological, sociological and pedagogical aspects, which enables the authors to identify the preconditions for successful foreign language teaching. The authors study a wide range of cognitive and metacognitive features of new knowledge acquisition. The authors emphasize the necessity of increasing the percentage of students’ individual and group independent work in the instructional time in online learning environments.
Simple Concurrent Connected Components Algorithms

We study a class of simple algorithms for concurrently computing the connected components of an n -vertex, m -edge graph. Our algorithms are easy to implement in either the COMBINING CRCW PRAM or the MPC computing model. For two related algorithms in this class, we obtain Θ (lg n ) step and Θ (m lg n ) work bounds. 1 For two others, we obtain O (lg 2 n ) step and O (m lg 2 n ) work bounds, which are tight for one of them. All our algorithms are simpler than related algorithms in the literature. We also point out some gaps and errors in the analysis of previous algorithms. Our results show that even a basic problem like connected components still has secrets to reveal.
Analyzing Semantically Distinctive English Word Usages in Computer Science for English-as-a-Second-Language Learners

Computer science is full of terms that are used in unique ways - for example, the word "string'' for a sequence of characters in programming or the word "thread'' for a unit of parallel processing within a process. Simultaneously, computer science is an academic field studied by a large number of non-native English speakers (i.e., English-as-a-Second-Language (ESL) learners). Are these rare and distinctive computer science terminologies preventing ESL learners from studying computer science, or are there expressions that should be changed to make it easier for ESL learners to understand computer science? Few studies have addressed these critical issues. In this study, we evaluate how challenging it is for ESL learners to understand the distinctive terms used in computer science. We used state-of-the-art natural language processing techniques based on deep transfer learning, which determines if ESL learners can read the text considering semantics. We also used a standard dataset in which professional English teachers manually evaluated the complexity of English expressions for ESL learners. The experimental results showed that, while some expressions are distinctive to computer science, the number of expressions that are particularly confusing to ESL learners is limited. Our experimental results suggest that providing ESL learners with a list of such particularly confusing terms ahead of time may help them learn computer science.
Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.
The Constitution of the meaning of education and the cultural-semiotic environment ‒ mainstream revolution in modern pedagogy

The article deals with two main topics: an attempt is made to characterize the meaning of modern education and introduce the concept of a cultural-semiotic environment, the concept of which is proposed by the author and a group of methodologists and practitioners of pedagogy. The idea of the meaning of education is briefly explained, the process of meaning formation is set in more detail. Characterizing this process, the author analyzes one case and shows that meaning formation allows solving problematic situations by constructing narratives (schemes, metaphors, symbols, etc.) and a new objectivity; as a result, a new reality is created, as well as conditions for new activities. To define the meaning of modern education, the concept of an educational environment (as formed in education, related to pedagogical communication, semiotic loaded) is introduced. In addition, the current situation in the field of education is described. The leading position of the teacher is not the management and formation, but the support of the personality, stimulating and puzzling it, hints and help, organizing conditions and space for the student's activity and creativity, joint search for solutions, demonstration of their own position and work, willingness to discuss problems together with students and a number of other points that lie within the same reality. Four educational needs are analyzed, which form the appropriate pedagogical audiences: "universal" education, "specialized", "interest-based" and "missionary". The conclusion is made that the meaning of education is a complex field of meanings, assuming from the side of pedagogy and science the arrangement of the cultural and semiotic environment, and from the individual thinking, choice and actions. The complexity of building such a dual-use environment (providing the formation of an average type of person and the support of different types of personality) is that it should work for the four specified types of education, acquaint a developing young person with the main types of problem situations, and form the competencies necessary to resolve them.
Named Entity Recognition of Hazardous Chemical Risk Information Based on Multihead Self-Attention Mechanism and BERT

An approach based on self-attention mechanism and pretrained model BERT is proposed to solve the problems of entity recognition and relationship recognition of hazardous chemical risk information. The text of hazardous chemical risk information is coded at the character level by adding the pretrained language model BERT, which, when paired with a multihead self-attention mechanism, improves the ability to mine global and local aspects of texts. The experimental results show that the model’s F1 value is 94.57 percent, which is significantly higher than that of other standard models.
A Multivariate Time Series Anomaly Detection Method Based on Generative Model

Modern equipment is complex in structure, large in scale and highly integrated, in order to solve the problems of high dimension and a large amount of data collected by equipment, a multivariate time series anomaly detection method based on the deep generative model (DG-MTAD) has been proposed in this paper. Long short-term memory (LSTM) network is used to optimize the model structures of autoencoder (AE) and generative adversarial networks (GAN). While extracting time information, the bidirectional mapping of data between multi-dimensional feature space and low-dimensional latent space is completed. The reconstruction of normal time series in latent space is realized by GAN, and then the combination of generating loss and discriminant loss is calculated as the anomaly score, so as to realize the anomaly detection of multivariate time series. Experiments on the high-dimensional engine degradation monitoring data set published by NASA show that the accuracy of the method is over 90&#x0025;.
Design Flow and Implementation of a Vision-Based Gesture-Controlled Drone

The increasing efficiency of complex Neural Network architecture and the continuous improvement of embedded edge computing has reached a point that allows the deployment of advanced computer vision tasks on some of the most critical embedded applications, such as aerial drones. While similar applications were already possible by moving heavy processing on a ground station, an autonomous and centralized system significantly improves usability and security. The machine is thus self-sufficient and less prone to network attacks. Three main challenges stand-out during the deployment of our complex gesture recognition pipeline: (1) allowing user-defined controls, (2) ensuring robustness, and (3) on-board deployment. These challenges are tackled through handcrafted features to avoid the curse of dimensionality, neural network optimization on GPUbased companion computers, and data augmentation to cover reallife edges cases such as partial inputs.
Human gesture recognition of dynamic skeleton using graph convolutional networks

In this era, intelligent vision computing has always been a fascinating field. With the rapid development in computer vision, dynamic gesture-based recognition systems have attracted significant attention. However, automatically recognizing skeleton-based human gestures in the form of sign language is complex and challenging. Most existing methods consider skeleton-based human gesture recognition as a standard video recognition problem, without considering the rich structure information among both joints and gesture frames. Graph convolutional networks (GCNs) are a promising way to leverage structure information to learn structure representations. However, adopting GCNs to tackle such gesture sequences both in spatial and temporal spaces is challenging as graph could be highly nonlinear and complex. To overcome this issue, we propose the spatiotemporal GCNs model to leverage the powerful spatiotemporal correlations to adaptively construct spatiotemporal graphs, called Aegles. Our method could dynamically attend to relatively significant spatiotemporal joints and construct different graphs, including spatial, temporal, and spatiotemporal graph, and well capturing the structure information in gesture sequences. Besides, we introduce the second-order information of the gesture skeleton data, i.e., the length and orientation of bones, to improve the representation of human hands and fingers. In addition, with the public sign language datasets, we use OpenPose technology to extract human gesture skeleton and obtain human skeleton video, building four skeleton-based sign language recognition datasets. Experimental results show that this Aegles outperforms the state-of-the-art ones and that the spatiotemporal correlations effectively boost the performance of human gesture recognition.
Real-Time Gesture Recognition with Virtual Glove Markers

Due to the universal non-verbal natural communication approach that allows for effective communication between humans, gesture recognition technology has been steadily developing over the previous few decades. Many different strategies have been presented in research articles based on gesture recognition to try to create an effective system to send non-verbal natural communication information to computers, using both physical sensors and computer vision. Hyper accurate real-time systems, on the other hand, have only recently began to occupy the study field, with each adopting a range of methodologies due to past limits such as usability, cost, speed, and accuracy. A real-time computer vision-based human-computer interaction tool for gesture recognition applications that acts as a natural user interface is proposed. Virtual glove markers on users hands will be created and used as input to a deep learning model for the real-time recognition of gestures. The results obtained show that the proposed system would be effective in real-time applications including social interaction through telepresence and rehabilitation.