Reviewerly/exHarmony huggingface dataset:
THE MODEL FOR FORMING AND IMPROVING VOCATIONAL EDUCATION TEACHERS’ SKILLS BASED ON THE MONITORING OF THE EDUCATIONAL SPHERE AND THE LABOR MARKET

The article defines a priority task of the system of vocational and pedagogical education to ensure a high level of competence of teachers in professional educational organizations. The article indicates the need for a fundamental revision of the approaches and mechanisms of the formation and improvement of the skills of pedagogical workers taking into account the results of monitoring of the educational sphere and the labor market. In this connection, the purpose of the article is determined – to develop and scientifically and methodologically substantiate a model for the formation and improvement of the skills of teaching staff in educational institutions of the secondary vocational education system that includes the following elements: training in both basic and additional educational programs; organizational and pedagogical support of professional selfdetermination of students and graduates of specialized sectoral educational programs, employees of enterprises, people who lost their jobs (having a industry-specific education); scientific and methodological support for practicing teachers, including diagnostics of the level of competence, professional deficiencies and assistance in professional development and replenishment of deficiencies in order to retain the teaching employees of the vocational education system in the workplace; labor market monitoring; the results of an independent assessment of the qualifications of teachers, primary and periodic certification; determination of the satisfaction of teachers and their employers with the development of skills through the additional professional education system. The authors conclude that the developed model can form the basis for the formation and development of a modern training system of professional and pedagogical personnel, as well as of additional professional training of secondary vocational education teachers at the federal, regional and interregional levels.
Gesture based Real-Time Sign Language Recognition System

Real-Time Sign Language Recognition (RTSLG) can help people express clearer thoughts, speak in shorter sentences, and be more expressive to use declarative language. Hand gestures provide a wealth of information that persons with disabilities can use to communicate in a fundamental way and to complement communication for others. Since the hand gesture information is based on movement sequences, accurately detecting hand gestures in real-time is difficult. Hearing-impaired persons have difficulty interacting with others, resulting in a communication gap. The only way for them to communicate their ideas and feelings is to use hand signals, which are not understood by many people. As a result, in recent days, the hand gesture detection system has gained prominence. In this paper, the proposed design is of a deep learning model using Python, TensorFlow, OpenCV and Histogram Equalization that can be accessed from the web browser. The proposed RTSLG system uses image detection, computer vision, and neural network methodologies i.e. Convolution Neural Network to recognise the characteristics of the hand in video filmed by a web camera. To enhance the details of the images, an image processing technique called Histogram Equalization is performed. The accuracy obtained by the proposed system is 87.8%. Once the gesture is recognized and text output is displayed, the proposed RTSLG system makes use of gTTS (Google Text-to-Speech) library in order to convert the displayed text to audio for assisting the communication of speech and hearing-impaired person.
AR-Based Remote Command and Control Service: Self-Driving Vehicles Use Case

The recent technological advances in many fields have significantly contributed to the development of the Advanced Driver Assistance System (ADAS), which in turn will greatly contribute to the flourishing of self-driving vehicles that can operate autonomously in all road scenarios. Until then, keeping the human input in the loop remains vital to either make decisions in unseen situations or approve vehicles' proposed decisions. In this article, we leverage VR technology to provide remote assistance for self-driving in critical situations. Specifically, we study the delivery of a 360° live stream at high resolution (4K) to a remote operation center for supporting self-driving vehicles' decisions when, for example, merging onto the highway. The 360° video stream will be consumed by a human operator wearing a head-mounted display for increased flexibility, faster control, and an immersive experience. In addition, the 360° stream is augmented with relevant context data, such as the vehicle's speed and distance to other road objects, in order to increase the human operator's awareness of the vehicle and its surroundings. Depending on the human operator's proximity to the source, the video stream can either be viewed through the cloud or the edge, which further reduces the glass-to-glass latency. Experimental results demonstrate the effectiveness of employing VR technology to remotely command and control self-driving vehicles in critical situations. The results show that a 360° stream at 4K resolution can be delivered in sub-second glass-to-glass latency, which allows the operator to make timely decisions.
Tailoring Self-Supervision for Supervised Learning

Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tasks are specialized for unsupervised representation learning. To this end, we begin by presenting three desirable properties for such auxiliary tasks to assist the supervised objective. First, the tasks need to guide the model to learn rich features. Second, the transformations involved in the self-supervision should not significantly alter the training distribution. Third, the tasks are preferred to be light and generic for high applicability to prior arts. Subsequently, to show how existing pretext tasks can fulfill these and be tailored for supervised learning, we propose a simple auxiliary self-supervision task, predicting localizable rotation (LoRot). Our exhaustive experiments validate the merits of LoRot as a pretext task tailored for supervised learning in terms of robustness and generalization capability. Our code is available at https://github.com/wjun0830/Localizable-Rotation .
Speaker Adaptation Experiments with Limited Data for End-to-End Text-To-Speech Synthesis using Tacotron2

Speech synthesis has the aim of generating humanlike speech from text. Nowadays, with end-to-end systems, highly natural synthesized speech can be achieved if a large enough dataset is available from the target speaker. However, often it would be necessary to adapt to a target speaker for whom only a few training samples are available. Limited data speaker adaptation might be a difficult problem due to the overly few training samples. Issues might appear with a limited speaker dataset, such as the irregular allocation of linguistic tokens (i.e., some speech sounds are left out from the synthesized speech). To build lightweight systems, measuring the number of minimum data samples and training epochs is crucial to acquire a reasonable quality. We conducted detailed experiments with four target speakers for adaptive speaker text-to-speech (TTS) synthesis to show the performance of the end-to-end Tacotron2 model and the WaveGlow neural vocoder with an English dataset at several training data samples and training lengths. According to our investigation of objective and subjective evaluations, the Tacotron2 model exhibits good performance in terms of speech quality and similarity for unseen target speakers at 100 sentences of data (pair of text and audio) with a relatively low training time.
A Hoare Logic with Regular Behavioral Specifications

We present a Hoare logic that extends program specifications with regular expressions that capture behaviors in terms of sequences of events that arise during the execution, akin session types and process-like behavioral contracts. In comparison, the approach presented here strikes a particular balance between expressiveness and proof automation. The approach is modular and integrates well with auto-active verification tools. We describe and demonstrate our prototype implementation in SecC using two case studies: A matcher for E-Mail addresses and a specification of the game steps in the VerifyThis Casino challenge.
V2GNet: Robust Blockchain-Based Energy Trading Method and Implementation in Vehicle-to-Grid Network

Nowadays, energy trading policies are revolutionizing the efforts and policies geared toward addressing global carbon emissions and protecting the environment. Smart grids and electric vehicles (EVs) are energy-saving tools for efficient power management. Although EVs can act as both energy consumers and suppliers, the effort required to balance the energy supply and demand in typical centralized trading systems inevitably reduces trading reliability. Another challenge is distributing EVs’ energy rationally to achieve better demand response and energy utilization. To manage the market securely and efficiently, we propose V2GNet(AEBiS Project: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://web-ext.u-aizu.ac.jp/misc/benablab/aebis.html</uri> ), a blockchain-based energy trading system using the vehicle-to-grid (V2G) network. The system combines a blockchain of energy exchanges (BoE) and a blockchain of EVs (BoEV), with the distinct transmission of energy requests and offers. Furthermore, to consider energy management from an economic viewpoint, we address the attack issue by proposing a robust energy trading (RET) algorithm. The proposed system demonstrates high robustness to malicious attacks. Our experimental results show that the RET reduces 30% energy loss when 20% of consumers are attacked. Moreover, malicious exchanges are excluded progressively from the trading market during each trading round. Also, the RET algorithm achieves better energy fulfillment and higher profit compared to state-of-the-art approaches.
Understanding the public, private and consortium consensus algorithms in blockchain technology

Blockchain has become one of the most demanding technologies of the decade. Blockchain is a decentralised digital record of information stored in a form of blocks in a chain of blocks called blockchain. It is totally dependent on the addition of new block in its chain. At a single time, there are a number of blocks ready to be added in the blockchain; choosing one valid block from this number of blocks is a complex and most important part of blockchain architecture. As we know that blockchain is a non-centralised system, it has peer-to-peer network in which blocks are added one by one to the chain of blocks only when a consensus is reached amongst the participants of the network. This paper presents a theory summarising of some consensus algorithm used in public, private and consortium blockchain technology along with pros and cons of each algorithms.
A Deep Unfolding Method for Satellite Super Resolution

Despite that existing deep-learning-based super resolution methods for satellite images have achieved great performance, these methods are generally designed to stack unaccountable and dense modules (i.e., residual blocks and dense blocks) to reach an optimal mapping function between low-resolution and high-resolution patches/images at the expense of computing resources. To address this challenge, we propose a deep unfolding method (LDUM) that includes two major components: 1) the pretreatment network and 2) the unfolding blocks. The main responsibility of the pretreatment network is to generate initial high-resolution images. Further, we model high-resolution images with the prior information, which can be seen as a combination of low-resolution and high-frequency residual images, and solve the optimization problem via the iterative proximal strategy. Specifically, we unfold the iterative process into a deep neural network to refine the reconstructed results, as each layer serves as an iterative step of the proposed optimization model. Thus, the proposed method is able to iteratively generate residual maps and high-resolution images by combining the powerful feature extraction capability of data-driven deep-learning-based methods and the interpretability of traditional model-driven algorithms. Experiments show that the proposed method, featured by its interpretable and lightweight merits, outperforms other state-of-the-art methods from quantitative and qualitative perspectives. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jiaming-wang/LDUM</uri> .
Robust Perception Architecture Design for Automotive Cyber-Physical Systems

In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems related to sensor selection/placement, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We present PASTA, a novel framework for global co-optimization of deep learning and sensing for robust vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA can find robust, vehicle-specific perception architecture solutions.
A Transformer-Based Decoder for Semantic Segmentation with Multi-level Context Mining

Transformers have recently shown superior performance than CNN on semantic segmentation. However, previous works mostly focus on the deliberate design of the encoder, while seldom considering the decoder part. In this paper, we find that a light weighted decoder counts for segmentation, and propose a pure transformer-based segmentation decoder, named SegDeformer, to seamlessly incorporate into current varied transformer-based encoders. The highlight is that SegDeformer is able to conveniently utilize the tokenized input and the attention mechanism of the transformer for effective context mining. This is achieved by two key component designs, i.e., the internal and external context mining modules. The former is equipped with internal attention within an image to better capture global-local context, while the latter introduces external tokens from other images to enhance current representation. To enable SegDeformer in a scalable way, we further provide performance/efficiency optimization modules for flexible deployment. Experiments on widely used benchmarks ADE20K, COCO-Stuff and Cityscapes and different transformer encoders (e.g., ViT, MiT and Swin) demonstrate that SegDeformer can bring consistent performance gains. Code is available at https://github.com/lygsbw/segdeformer .
Spotting Temporally Precise, Fine-Grained Events in Video

We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur). Precise spotting requires models to reason globally about the full-time scale of actions and locally to identify subtle frame-to-frame appearance and motion differences that identify events during these actions. Surprisingly, we find that top performing solutions to prior video understanding tasks such as action detection and segmentation do not simultaneously meet both requirements. In response, we propose E2E-Spot, a compact, end-to-end model that performs well on the precise spotting task and can be trained quickly on a single GPU. We demonstrate that E2E-Spot significantly outperforms recent baselines adapted from the video action detection, segmentation, and spotting literature to the precise spotting task. Finally, we contribute new annotations and splits to several fine-grained sports action datasets to make these datasets suitable for future work on precise spotting.
A Meta Model Based Particle Swarm Optimization for Enhanced Global Search

The performance of a metaheuristic algorithm depends on the appropriate selection of its behavioral parameters. A good selection of parameters increases the search ability of an algorithm and avoids premature convergence. Particle swarm optimization (PSO) is swarm-based metaheuristic algorithm, which needs few parameter adjustments and less computational time. Meta-optimization has been used to tune the parameters and to get better results. Previously, authors applied meta optimization techniques to specific problems to tune the parameters and to get better results for specific case studies in different fields, but the application of meta optimization in benchmark functions are limited. The present study proposes meta optimization-based PSO to minimize the computational effort required for manual trial and error-based parameter selection. The proposed algorithm is tested for 14 benchmark functions (8 unimodal and 6 multimodal), and its efficiency and robustness are assessed via statistical analysis. The algorithm outperforms other renowned established algorithms (GA, PSO), and its performance remains consistent with increasing modality and dimensionality.
Sequential Voting with Relational Box Fields for Active Object Detection

A key component of understanding hand-object interactions is the ability to identify the active object-the object that is being manipulated by the human hand. In order to accurately localize the active object, any method must reason using information encoded by each image pixel, such as whether it belongs to the hand, the object, or the background. To leverage each pixel as evidence to determine the bounding box of the active object, we propose a pixel-wise voting function. Our pixel-wise voting function takes an initial bounding box as input and produces an improved bounding box of the active object as output. The voting function is designed so that each pixel inside of the input bounding box votes for an improved bounding box, and the box with the majority vote is selected as the output. We call the collection of bounding boxes generated inside of the voting function, the Relational Box Field, as it characterizes a field of bounding boxes defined in relationship to the current bounding box. While our voting function is able to improve the bounding box of the active object, one round of voting is typically not enough to accurately localize the active object. Therefore, we repeatedly apply the voting function to sequentially improve the location of the bounding box. However, since it is known that repeatedly applying a one-step predictor (i.e., auto-regressive processing with our voting function) can cause a data distribution shift, we mitigate this issue using reinforcement learning (RL). We adopt standard RL to learn the voting function parameters and show that it provides a meaningful improvement over a standard supervised learning approach. We perform experiments on two large-scale datasets: 100DOH and MECCANO, improving AP50 performance by 8% and 30%, respectively, over the state of the art. The project page with code and visualizations can be found at https://fuqichen1998.github.io/SequentialVotingDet/.
Divergent thinking of students: categorical and conceptual analysis

Pedagogical aspects of the formation of the interdisciplinary category “divergent thinking” are considered. The purpose of the study is to construct the definition of “divergent thinking of university students” on the basis of the essential and specific characteristics of the named phenomenon, taking into account the criteria of necessity and sufficiency. To achieve a goal, the following methods were used: content analysis of scientific literature, methods of categorical-system methodology: “Two-level triadic decoding of categories”, “Formal-logical method of determining the concept”. The essential characteristics and nature of divergent thinking are identified and described, a two-level triadic model of this category is developed. On the basis of the named model, the definition of the concept of “divergent thinking” is constructed, which, in comparison with other definitions found in the literature, most fully and holistically reflects its key essential characteristics. As a result of the application of the formal-logical method, the definition of “divergent thinking of university students” has been developed, which allows interpreting the named concept and its manifestations from the point of view of personal and creative development of students in the conditions of the educational process of the university. The obtained theoretical results of the analysis of the formation of the category “divergent thinking of students” can be used in the development of technology for the formation and development of this type of thinking of students in the educational process of the university.
Average-case algorithms for testing isomorphism of polynomials, algebras, and multilinear forms

We study the problems of testing isomorphism of polynomials, algebras, and multilinear forms. Our first main results are average-case algorithms for these problems. For example, we develop an algorithm that takes two cubic forms $f, g\in \mathbb{F}_q[x_1,\dots, x_n]$, and decides whether $f$ and $g$ are isomorphic in time $q^{O(n)}$ for most $f$. This average-case setting has direct practical implications, having been studied in multivariate cryptography since the 1990s. Our second result concerns the complexity of testing equivalence of alternating trilinear forms. This problem is of interest in both mathematics and cryptography. We show that this problem is polynomial-time equivalent to testing equivalence of symmetric trilinear forms, by showing that they are both Tensor Isomorphism-complete (Grochow-Qiao, ITCS, 2021), therefore is equivalent to testing isomorphism of cubic forms over most fields.
Netiquette and Ethics Regarding Digital Education Across Institutions: A Narrative Review

Information Technology (IT) has made rapid advancement since the turn of this century. Ergo the scope and extent of IT has also grown, resulting in increase in the number of internet users, who have differing reasons for using the IT resources. The users of this IT revolution include students worldwide; who interact with technology based on their needs, with relative ease and have found the whole experience enriching. Progress in IT has not only changed the way humans interact with technology but has also influenced the way of interaction between the people. The interaction online is mostly informal and comes without set guidelines and compounded with the relative anonymity provided by internet, also it has complicated an individual’s ability to interact in a respectful and responsible way. The problems relating to internet safety arise from such interactions. Flame wars and cyberbullying are some of the other risky behavioural interactions displayed online and can lead to severe consequences. The unethical use of IT resources in the form of plagiarism, piracy, identity theft etc also questions the moral of an individual. Whilst there may be some idea, multitude of issues regarding Netiquette and ethical use of IT resources are often poorly understood and the knowledge imparted is also fragmented. Moreover, contemporary educators are much in need of such information. Thus, there is a need to include and summarise current priority areas that relate to etiquette and ethics in digital education.