Reviewerly/exHarmony huggingface dataset:
ISC-MTI: An IPFS and smart contract-based framework for machine learning model training and invocation

Due to centralized storage, centralization problems are common in machine learning model training and invocation, which makes train data and trained models extremely vulnerable to tampering and stealing. A safe framework for training and invoking models called ISC-MTI (IPFS (InterPlanetary File System) and Smart Contract-Based Method for Storage and Invocation of Machine Learning Mobel) is proposed in this paper. The framework uses IPFS as the storage solution, EOS (Enterprise Operation System) blockchain as the smart contract platform, RSA and AES as the implementation of encrypted communication. The Action responsible for invoking the training data and trained models in the smart contract and the model training, uploading, and invoking methods are designed. The experimental results demonstrate that ISC-MTI can improve the safety of model training and invocation with losing a little efficiency. Simultaneously, ISC-MTI can provide anti-theft model capabilities, traceability, tamper resistance, reliability, and privacy for the process.
Development of the evaluative activities of teachers in the conditions of updated education

The aim of this research is to reveal the opinions of teachers about the development of assessment activities in updated educational conditions. The study was conducted with the phenomenology pattern, one of the qualitative research designs. The study data were collected with semi-structured interview forms applied to primary, secondary and high school teachers. The participants of the study consisted of 20 primary school teachers, 20 secondary school teachers and 20 high school teachers who were working in various schools in Kazakhstan in the 2021–2022 academic year and agreed to participate voluntarily in the research. As a result of the research, among the answers given to the question about the advantages of online education – financial advantages, time advantages and transportation advantages – the majority of the participants answered the question as financial advantages. Among the answers given to the question about the disadvantages of online education – adaptation problems, problems in social relationships and connection problems – the majority of the participants answered the question as connection problems. Among the answers given to the question about the advantages of evaluation activities in online education – speed–time advantages, storage advantages and alternative methods’ advantages – the majority of the participants answered the question speed–time advantages. Among the answers given to the question about the disadvantages of assessment activities in online education – connection problems, cheating and distractions – most of the participants answered cheating. Among the answers given to the question regarding the development of assessment activities in online education – improving technology, establishing a counselling unit and preventing copying – majority of the participants answered the question as improving technology.&#x0D; Keywords: online education training, assessment, development of assessment
Combining Type Checking and Set Constraint Solving to Improve Automated Software Verification

In this paper we show how prescritive type checking and constraint solving can be combined to increase automation during software verification. We do so by defining a type system and implementing a typechecker for {log} (read `setlog'), a Constraint Logic Programming (CLP) language and satisfiability solver based on set theory. Hence, we proceed as follows: a) a type system for {log} is defined; b) the constraint solver is proved to be safe w.r.t. the type system; c) the implementation of a concrete typechecker is presented; d) the integration of type checking and set constraint solving to increase automation during software verification is discussed; and f) two industrial-strength case studies are presented where this combination is used with very good results.
Optimal bounding function for GNR-enumeration

The proposed pruning technique by Gama-Nguyen-Regev for enumeration function makes this pruned enumeration (GNR-enumeration) as a claimant practical solver for SVP.The total cost of GNR-enumeration over a specific input lattice block with pre-defined enumeration radius and success probability would be minimized, just if this enumeration uses an optimal bounding function for pruning.Unfortunately, the running time of the original proposed algorithm of searching optimal bounding function by the work of Chen-Nguyen (in 2011) is not analyzed at all, so our work in this paper tries to introduce some efficient searching algorithms with exact analysis of their time/space complexity.In fact, this paper proposes a global search algorithm to generate the optimal bounding function by a greedy idea.Then, by using our greedy strategy and defining the searching steps based on success probability, a practical search algorithm is introduced, while it's time-complexity can be determined accurately.Main superiorities of our algorithm include: complexity analysis, using high-performance version of each sub-function in designing search algorithm, jumping from local optimums, simple heuristics to guide the search, trade-off between quality of output and running time by tuning parameters.Also by using the building blocks in our practical search algorithm, a high-quality and fast algorithm is designed to approximate the optimal bounding function.
Non-Parallel Voice Conversion System Using An Auto-Regressive Model

Much existing voice conversion (VC) systems are attractive owing to their high performance in terms of voice quality and speaker similarity. Nevertheless, without parallel training data, some generated waveform trajectories are not yet smooth, leading to degraded sound quality and mispronunciation issues in the converted speech. To address these shortcomings, this paper proposes a non-parallel VC system based on an auto-regressive model, Phonetic PosteriorGrams (PPGs), and an LPCnet vocoder to generate high-quality converted speech. The proposed auto-regressive structure makes our system able to produce the next step outputs from the previous step acoustic features. Further, the use of PPGs aims to convert any unknown source speaker into a specific target speaker due to their speaker-independent properties. We evaluate the effectiveness of our system by performing any-to-one conversion pairs between native English speakers. Objective and subjective measures show that our method outperforms the best non-parallel VC method of Voice Conversion Challenge 2018 in terms of naturalness and speaker similarity.
Towards automated library migrations with error prone and refaster

Software projects use a significant number of external libraries to speed up development and deliver high-quality software in a cost-effective way. Requirements placed on external libraries can change over time and libraries may cease to be maintained or evolve in incompatible ways. Consequently, a library may no longer be fit for purpose and require replacement. Such a library migration is generally performed manually and may be complex, error-prone, and time-consuming, especially for large codebases. It follows that an automated approach to library migrations is preferred.
The Design of Project Work Based on the Multiple Intelligences in Junior High School

Based on the theory of multiple intelligences, combined with the homework design scheme of a module in junior high school English textbook of foreign research edition, this paper expounds the application of project-based homework in junior high school English teaching. It can cultivate students' speech language intelligence, logical mathematical intelligence, visual spatial intelligence, interpersonal intelligence and self-awareness intelligence.
Image Multithreshold Segmentation Method Based on Improved Harris Hawk Optimization

In order to improve the accuracy and performance of traditional image threshold segmentation algorithm, this paper proposes a multithreshold segmentation method named improved Harris hawk optimization (IMHHO). Firstly, IMHHO adopts Tent map and elite opposition-based learning to initialize population and enhance the diversity. Secondly, IMHHO uses quadratic interpolation to generate new individuals and enhance the local search ability. Finally, IMHHO adopts improved Gaussian disturbance method to disturb optimal solution, which coordinates the local and global search ability. Then, the performance of IMHHO is tested based on 14 benchmark functions. In image segmentation, different algorithms are tested to compare the comprehensive performance based on Otsu and Renyi entropy. Experiments show that IMHHO performs better in the three kinds of benchmark functions; the segmentation effect is directly proportional to the number of thresholds; compared with other algorithms, IMHHO has better comprehensive performance.
Context matters!

Context is crucial for identifying semantic relations between input sequences embedded in a document. This paper studies how different levels of context, coming from discourse markers or from a paragraph-wide context window, impact the capability of language models to identify inference relations. Empirical results for the task of argumentative relation identification show that the proposed paragraph-aware model provides considerable improvements over a context-agnostic baseline, surpassing the usage of discourse markers alone.
Performance Evaluation of Convolutional Networks on Heterogeneous Architectures for Applications in Autonomous Robotics

Humanoid robots find application in human-robot interaction tasks. However, despite their capabilities, their sequential computing system limits the execution of computationally expensive algorithms such as convolutional neural networks, which have demonstrated good performance in recognition tasks. As an alternative to sequential computing units, Field-Programmable Gate Arrays and Graphics Processing Units have a high degree of parallelism and low power consumption. This study aims to improve the visual perception of a humanoid robot called NAO using these embedded systems running a convolutional neural network. The methodology adopted here is based on image acquisition and transmission using simulation software: Webots and Choreographe. In each embedded system, an object recognition stage is performed using commercial convolutional neural network acceleration frameworks. Xilinx® Ultra96™, Intel® Cyclone® V-SoC and NVIDIA® Jetson™ TX2 cards were used, and Tinier-YOLO, AlexNet, Inception-V1 and Inception V3 transfer-learning networks were executed. Real-time metrics were obtained when Inception V1, Inception V3 transfer-learning and AlexNet were run on the Ultra96 and Jetson TX2 cards, with frame rates between 28 and 30 frames per second. The results demonstrated that the use of these embedded systems and convolutional neural networks can provide humanoid robots such as NAO with greater visual recognition in tasks that require high accuracy and autonomy.
Application of agent-based approach for heat conduction processes simulation

Heat transfer processes are the basis of the most technological processes of the energy sector. Thus, the development of modern approaches for computer simulation and visualization of the phenomena of thermal energy transfer in various objects are of great importance and is relevant. Classical models in the form of differential equations of various types describe processes in continuous space and time. So, it is difficult to apply classical models to study nonlinear phenomena, and processes in inhomogeneous media in the presence of discontinuous solutions at the boundaries. In these cases, simplifying assumptions are used, thus, the adequacy of the models is reduced. It is of great interest to apply fundamentally different approaches to describe transfer processes, which include discrete dynamic models. The purpose of this project is to study the possibilities to apply discrete approaches to simulate nonlinear heat transfer processes under conditions of material inhomogeneity and the presence of volume sources of variable power. The paper studies the possibilities to apply the agent-based approach to simulate models of complex systems. This approach allows us to consider a continuum as a set of interacting elements (agents). The behavior of the elements is completely described by local dependencies. At the same time, the laws of functioning of individual elements are accepted as deterministic and they correspond to the fundamental principles of the theory of heat transfer. The possibility to apply a discrete approach for simulating the process of heat transfer by the molecular mechanism has been studied. The general methodology to develop an agent-based deterministic model is described. Its applicability to describe quasi-linear and nonlinear heat conduction processes is considered. The examples of simulation of combustion processes complicated by exothermic and endothermic effects are considered. The advantages and disadvantages of the proposed method are indicated. The results of the study have shown that discrete agent models are a good alternative to classical continuum approaches to study heat transfer processes in inhomogeneous media. The results obtained do not contradict modern approaches to the description of thermal processes. It has also been found that the simulation algorithms used in the agent-based approach are quite universal and easily adapt to changes under the conditions of problem setting. The analysis of the results makes it possible to recommend a discrete agent-based approach to develop simulation models of complex technological processes and systems.
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles

Adversarial attacks can make deep neural network (DNN) models predict incorrect output labels, such as misclassified traffic signs, for autonomous vehicle (AV) perception modules. Resilience against adversarial attacks can help AVs navigate safely on the road by avoiding misclassication of signs or objects. This DNN-based study develops a resilient traffic sign classifier for AVs that uses a hybrid defense method. We use transfer learning to retrain the Inception-V3 and Resnet-152 models as traffic sign classifiers. This method also utilizes a combination of three different strategies: random filtering, ensembling, and local feature mapping. We use the random cropping and resizing technique for random filtering, plurality voting as ensembling strategy and an optical character recognition model as a local feature mapper. This DNN-based hybrid defense method has been tested for the no attack scenario and against well-known untargeted adversarial attacks (e.g., Projected Gradient Descent or PGD, Fast Gradient Sign Method or FGSM, Momentum Iterative Method or MIM attack, and Carlini and Wagner or C&W). We find that our hybrid defense method achieves 99% average traffic sign classification accuracy for the no attack scenario and 88% average traffic sign classification accuracy for all attack scenarios. Moreover, the hybrid defense method, presented in this study, improves the accuracy for traffic sign classification compared to the traditional defense methods (i.e., JPEG filtering, feature squeezing, binary filtering, and random filtering) up to 6%, 50%, and 55% for FGSM, MIM, and PGD attacks, respectively.
Quantum-Inspired Complex-Valued Language Models for Aspect-Based Sentiment Classification

Aiming at classifying the polarities over aspects, aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. The vector representations of current models are generally constrained to real values. Based on mathematical formulations of quantum theory, quantum language models have drawn increasing attention. Words in such models can be projected as physical particles in quantum systems, and naturally represented by representation-rich complex-valued vectors in a Hilbert Space, rather than real-valued ones. In this paper, the Hilbert Space representation for ABSA models is investigated and the complexification of three strong real-valued baselines are constructed. Experimental results demonstrate the effectiveness of complexification and the outperformance of our complex-valued models, illustrating that the complex-valued embedding can carry additional information beyond the real embedding. Especially, a complex-valued RoBERTa model outperforms or approaches the previous state-of-the-art on three standard benchmarking datasets.
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions. During a conversation involving various people, a person's emotions are influenced by the other speaker's utterances and their own emotional state over the utterances. In this paper, we propose COntextualized Graph Neural Network based Multimodal Emotion recognitioN (COGMEN) system that leverages local information (i.e., inter/intra dependency between speakers) and global information (context). The proposed model uses Graph Neural Network (GNN) based architecture to model the complex dependencies (local and global information) in a conversation. Our model gives state-of-the-art (SOTA) results on IEMOCAP and MOSEI datasets, and detailed ablation experiments show the importance of modeling information at both levels.
Exam Proctoring System

Abstract: Due to the rise of Coronavirus Pandemic, the majority of people were forced to follow a lot of protocols from masks and social distancing to online presence at work places. One of those places are educational institutions. The lectures could be easily conducted using online video conferences, but exams are very difficult to be conducted online. And the part that makes it hard is invigilation of those exams. Invigilation is a very important part of any examination. Even though it is evident that examinations are for the candidates to evaluate their performance, they still try to cheat to get good results which may get them benefits they do not deserve. We propose a system that proctors or invigilates students while giving online exams. The system collects candidates' behavior projected on the camera at browsers and extracts information from those images, like ‘looking left’, ‘missing test’ etc. and logs them to a SQL database. The system also has a threshold for fouls candidate commits and would submit the exam when threshold is crossed. Keywords: Proctoring System, Head Pose Estimation, behaviour logging, Online Proctoring System, Anti-cheat System
Infrastructure for rapid open knowledge network development

Abstract The past decade has witnessed a growth in the use of knowledge graph technologies for advanced data search, data integration, and query‐answering applications. The leading example of a public, general‐purpose open knowledge network ( aka knowledge graph) is Wikidata, which has demonstrated remarkable advances in quality and coverage over this time. Proprietary knowledge graphs drive some of the leading applications of the day including, for example, Google Search, Alexa, Siri, and Cortana. Open Knowledge Networks are exciting: they promise the power of structured database‐like queries with the potential for the wide coverage that is today only provided by the Web. With the current state of the art, building, using, and scaling large knowledge networks can still be frustratingly slow. This article describes a National Science Foundation Convergence Accelerator project to build a set of Knowledge Network Programming Infrastructure systems to address this issue.
The success of Conversational AI and the AI evaluation challenge it reveals

Abstract Research interest in Conversational artificial intelligence (ConvAI) has experienced a massive growth over the last few years and several recent advancements have enabled systems to produce rich and varied turns in conversations similar to humans. However, this apparent creativity is also creating a real challenge in the objective evaluation of such systems as authors are becoming reliant on crowd worker opinions as the primary measurement of success and, so far, few papers are reporting all that is necessary for others to compare against in their own crowd experiments. This challenge is not unique to ConvAI, but demonstrates as AI systems mature in more “human” tasks that involve creativity and variation, evaluation strategies need to mature with them.
MobileNetv3-CenterNet :A target recognition method for avoiding missed detection effectively based on a lightweight network

Abstract To solve the problems in online target detection on the embedded platform, such as high missed detection rate, low accuracy, and slow speed, a lightweight target recognition method of MobileNetv3-CenterNet is proposed. This method combined the anchor-free Centernet network with the MobileNetv3 small network and trained on the UA-DETRAC and PASCAL VOC07 + 12 standard datasets. While reducing the scale of the network model, the MobileNetv3-Centernet model shows a good balance in the accuracy and speed of target recognition and effectively solves the problem of missing detection of dense and small targets in online detection. To verify the recognition performance of the model, it is tested on 2683 images of the UA- DETRAC and PASCAL VOC 2007 datasets, and compared with the analysis results of CenterNet-DLA34, CenterNet-ResNet18, CenterNet-MobileNetv3-large, YOLOv3, MobileNetv2-YOLOv3, SSD, MobileNetv2-SSD and Faster R-CNN models. The results show that the MobileNetv3-CenterNet model accurately recognized the dense targets and small targets missed by other methods, and obtains a recognition accuracy of 99.4% with a running speed of FPS53. The comprehensive detection effect of MobileNetv3-CenterNet is better than other methods. The MobileNetv3-CenterNet lightweight target recognition method will provide effective technical support for the target recognition of deep learning networks in embedded platforms and online detection.
Formation of the image of the world during the digital transformation of education

The formation of the world's image in a person at each age stage of their development is a relevant problem due to this phenomenon's multidisciplinary and unique nature. This article focuses on the concept of the professional image of the world and reveals its integrity and consistency in the generalized views of future teachers about the learning process. The authors considered the research problem from the global digital transformation of education. To assess the manifestations, levels, and quality of the formed image of the world of the individual during the digital transformation of education, the authors experimented in Yelets State Bunin University. The research is useful for educators designing the content, technologies, and management tools for the professional training of a future teacher.