Reviewerly/exHarmony huggingface dataset:
COMPLEX METHOD FOR AUTOMATIC RECOGNITION OF NATURAL LANGUAGE AND EMOTIONAL STATE

Current trends in NLP emphasize universal models and learning from pre-trained models. This article explores these trends and advanced models of pre-service learning. Inputs are converted into words or contextual embeddings that serve as inputs to encoders and decoders. The corpus of the author's publications over the past six years is used as the object of the research. The main methods of research are the analysis of scientific literature, prototyping, and experimental use of systems in the direction of research. Speech recognition players are divided into players with huge computing resources for whom training on large unlabeled data is a common procedure and players who are focused on training small local speech recognition models on pre-labeled audio data due to a lack of resources. Approaches and frameworks for working with unlabeled data and limited computing resources are almost not present, and methods based on iterative training are not developed and require scientific efforts for development. The research aims to develop methods of iterative training on unlabeled audio data to obtain productively ready speech recognition models with greater accuracy and limited resources. A separate block proposes methods of data preparation for use in training speech recognition systems and a pipeline for automatic training of speech recognition systems using pseudo marking of audio data. The prototype and solution of a real business problem of emotion detection demonstrate the capabilities and limitations of owl recognition systems and emotional states. With the use of the proposed methods of pseudo-labeling, it is possible to obtain recognition accuracy close to the market leaders without significant investment in computing resources, and for languages with a small amount of open data, it can even be surpassed.
Short Review of Probability and of Stochastic Processes

Given a probability space $$(\varOmega ,\mathscr {F},P)$$ , where Ω denotes a non-empty set, $$\mathscr {F}$$ a σ-algebra and P a probability measure on Ω
Generative UAP attacks against deep‐learning based modulation classification

Abstract Thanks for its powerful feature extraction capability and unprecedented success in computer vision and language processing tasks, deep learning (DL) has also been applied in recent years to wireless communications. However, DL models are proved to be inherently vulnerable to adversarial perturbations: carefully crafted perturbations that appear imperceptible but can fool the models to induce misclassification. Existing researches in wireless communications employ the gradient‐based or optimization based methods, for example, FGSM, PGD and iterative FGSM, to craft input‐dependent adversarial perturbations. However, these methods have a high computational complexity and the perturbations they craft cannot be readily applied to fool DL‐based algorithms in wireless communications. In this paper, the efficient generation of universal (input‐agnostic) adversarial perturbations (UAPs) used to attack DL‐based modulation classification algorithms is studied. Communication signals exhibit unique characteristics, for example, received modulated signals after transmitting through noisy channels are densely distributed around the modulation constellation points. In this work, the authors have proposed, by making use of this unique characteristics, a generative network to model the distribution of adversarial perturbations for high‐efficiency generation of UAPs, which can fool the DL‐based modulation classification algorithms for most inputs. Instead of attacking all regions of modulated signals, attention mechanisms are introduced in generative network design that enables the perturbations to be concentrated around the constellation points. In this way, a higher attack efficiency and a lower perceptibility are achieved by crafting UAPs with smooth variations and more focused attack. In addition, a diversity term is included in loss function to help capture a wide range distribution of adversary perturbations that will help the generation of more diverse adversary perturbations. Once trained, the generative network can construct with ease a large number of UAPs with some favorable features, which will benefit more efficient adversarial attack and faster adversarial training. Experimental results show the superiority of our proposed method in terms of attack efficiency, imperceptibility and diversity over existing methods.
Few-shot multi-modal registration with mono-modal knowledge transfer

Multi-modal registration is a key problem in many medical image analysis applications. Recent learning-based deformable image registration methods become attractive alternatives to traditional methods because of their great performance and fast run time. However, their success relies on large training datasets that are rarely available in multi-modal registration scenarios. To address this, we propose a novel knowledge transfer-based network (KT-Net) for few-shot multi-modal registration, which focuses on transferring knowledge of the mono-modal registration model to multi-modal registration. The contributions can be two-fold: (1) we propose model decoupling to disentangle the registration model into a feature learning network and an alignment learning network. The two networks are trained on large mono-modal datasets, making preparations for knowledge transfer. (2) a reverse teaching strategy is further designed to align the features of multi-modal images with a few samples, enabling the knowledge from mono-modal registration to transfer to multi-modal registration. Experimental results on multi-contrast brain MRI datasets demonstrate that our proposed method yields accurate and robust registration performance under the constraint of a few multi-modal samples. Compared with state-of-the-art registration methods, our proposed method achieves better registration performance with a high average Dice score of up to 83.5% and an average 95% percentile of Hausdorff distance as low as 1.26mm in various anatomical structures, showing great potential for mono-modal knowledge transfer to be applied in few-shot multi-modal registration.
VM-Studio: A Universal Crosschain Smart Contract Verification and Execution Scheme

Blockchain interoperability promotes value delivery, application expansion, and ecological compatibility across heterogeneous blockchain systems. However, the contract framework and virtual machine construction in these systems are significantly different, and crosschaining becomes a challenging issue for system universality and compatibility. Starting from this problem, in this study, we propose VM-Studio, a crosschain smart contract verification and execution scheme to migrate the virtual machines (VMs) from the origin blockchain to the target blockchain. In our scheme, the migrated VMs are loaded as independent components enclosed in containers. We also design a unified system schedule to enable VM-Studio to allocate transactions into different containers. Loaded with origin blockchain VMs, these containers can accordingly solve crosschain transaction execution and smart contract verification. We implement VM-Studio and evaluate the transaction execution performance in the origin environment with multiple blockchains and the container environment. Experiment results demonstrate that VM-Studio achieves broad universality without compromising the execution performance of original blockchain transactions.
Artificial Intelligence-Based Digital Media Design Effect Enhancement Mechanism

The advancement of AI technology has promoted the development speed of digital multimedia and brought a new experience to the digital media experience effect. In this paper, we aim to using artificial intelligence methods to enhance the digital media design experience. Specifically, we propose a method for low-light image enhancement using generative adversarial networks as a model framework. To better solve the problem, we design the following strategies in our proposed method. First, we preprocess the images into patches with a proper size. Second, we introduce the overall network structure of GAN. Third, we designed a multifeature extraction module with different sizes of convolution kernels to enhance the model’s ability for extracting features. Fourth, we propose a loss that combines the mean square error distance function with the adversarial error function to enable the model to learn the distributional features of the image. Finally, we verify the effectiveness of the proposed method on two datasets, namely, PASCAL VOC and MIT-Adobe FiveK. The results show that our proposed method performs well in the process of evaluation.
Efficient Noninvasive Fault Injection Method Utilizing Intentional Electromagnetic Interference

In the fault injection method, an electromagnetic (EM) wave is injected to temporarily cause a fault at a specific time of the encryption process, the faulty outputs are obtained from the cryptographic device, and the secret key is extracted by analyzing the faulty outputs. In the conventional method, the intentional electromagnetic interference (IEMI) wave is injected at a random time because it is difficult to obtain information on the start time of the encryption process. Thus, a cryptographic module must execute a large number of encryption trials before the occurrence of a fault that enables the secret key to be extracted. In this article, we propose a fault injection method that can generate the faults at a specific time with high probability, which is like the method of injecting an IEMI wave synchronized with the start time of the encryption process. The proposed method inserts glitches into the encryption process at fixed times by injecting a continuous sinusoidal wave of a specific frequency while controlling the amplitude and phase. This generates faults required for the secret key analysis method with a high probability even when the start time of the encryption process cannot be obtained. We experimentally demonstrate the impact of the aforementioned IEMI using the advanced encryption standard, which is an ISO/IEC 18033 block cipher, implemented as a module on a standard evaluation board. The conventional method requires more than 30 000 encryption processes to obtain the secret key. In contrast, the results indicate that we can obtain the secret key with approximately 22 encryption processes which is almost three orders of magnitude less than that with the conventional method. This confirms that secret keys can be extracted in a brief period of time. Moreover, devices previously excluded from IEMI-based fault injection because they can only be accessed for a brief period because their physical access was surveilled, may now be the target of the threat.
Learning Diverse Policies with Soft Self-Generated Guidance

Reinforcement learning (RL) with sparse and deceptive rewards is challenging because non-zero rewards are rarely obtained. Hence, the gradient calculated by the agent can be stochastic and without valid information. Recent studies that utilize memory buffers of previous experiences can lead to a more efficient learning process. However, existing methods often require these experiences to be successful and may overly exploit them, which can cause the agent to adopt suboptimal behaviors. This paper develops an approach that uses diverse past trajectories for faster and more efficient online RL, even if these trajectories are suboptimal or not highly rewarded. The proposed algorithm combines a policy improvement step with an additional exploration step using offline demonstration data. The main contribution of this paper is that by regarding diverse past trajectories as guidance, instead of imitating them, our method directs its policy to follow and expand past trajectories while still being able to learn without rewards and approach optimality. Furthermore, a novel diversity measurement is introduced to maintain the team's diversity and regulate exploration. The proposed algorithm is evaluated on discrete and continuous control tasks with sparse and deceptive rewards. Compared with the existing RL methods, the experimental results indicate that our proposed algorithm is significantly better than the baseline methods regarding diverse exploration and avoiding local optima.
Design of Circuits and Packaging Systems for Security Chips

Hardware oriented security and trust of semiconductor integrated circuit (IC) chips have been highly demanded. This paper outlines the requirements and recent developments in circuits and packaging systems of IC chips for security applications, with the particular emphasis on protections against physical implementation attacks. Power side channels are of undesired presence to crypto circuits once a crypto algorithm is implemented in Silicon, over power delivery networks (PDNs) on the frontside of a chip or even through the backside of a Si substrate, in the form of power voltage variation and electromagnetic wave emanation. Preventive measures have been exploited with circuit design and packaging technologies, and partly demonstrated with Si test vehicles.
The development of the application for support the intellectual analysis of texts of the humanities and socio-economic cycle

The article is devoted to the consideration of the problems of didactic design of teaching materials by a teacher of disciplines of the humanities and socio-economic cycle associated with the study of a significant amount of information from various sources, the need to select a number of literary sources taking into account their specifics for different educational tasks and types of educational activities, as well as ranking the most important didactic units for the formation of working programs of disciplines. The possibilities of using automation tools for the analysis of text materials are revealed, which will allow a teacher engaged in the didactic design of a particular discipline to conduct a syntactic analysis of methodological materials, significantly reduce the complexity of this process when developing, making changes and adapting courses of social and humanitarian orientation. As a result of the analysis of the subject area, the authors determined a list of functional requirements, based on which the results of the physical and logical design of the system were presented, including a database model, system architecture, as well as a deployment diagram using an object-oriented modeling language.
Bridge Pre-Training and Clustering: A Unified Contrastive Learning Framework for OOD Intent Discovery

Discovering Out-of-Domain (OOD) intents is essential for developing new skills in a task-oriented dialogue system. Previous methods suffer from poor knowledge transferability from in-domain (IND) intents to OOD intents, and inefficient iterative clustering. In this paper, we propose an efficient unified contrastive learning framework to discover OOD intents, bridging the gap between IND pre-training stage and OOD clustering stage. Specifically, we employ a supervised contrastive learning (SCL) objective to learn discriminative pre-trained intent features for clustering. And we introduce an efficient end-to-end contrastive clustering method to jointly learn representations and cluster assignments. Besides, we propose an adaptive contrastive learning (ACL) method to automatically adjust the weights of different negative sample pairs for a given anchor according to their semantic similarities. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods.
The pandemic period in Russia: overcoming the problems of the rapid transition to distance learning in the system of higher education

The article tests the hypothesis about the need to change the methodology concept for training economists in the higher education system when switching to distance learning. The authors use the empirical method of students and teachers’ survey in Russian universities, statistical methods for processing results, and an analysis of research results published by Russian scientists on the effectiveness of distance learning. The lack of proposals from specialists and scientists to overcome the problems of distance learning predetermine the need for this research. The authors divide problems into four groups: software-technical, socio-psychological, organizational and methodological. The analysis of systematized problems made it possible to propose a transition to an active interaction between a teacher and a student, which is of theoretical importance. Implementing this concept contribute to the restoration of social communication in the educational environment, creation of methodological support in the conditions of distance learning. It is of great practical importance for organizing the distance educational process. Keywords: higher education, distance learning, problems, socio-psychological, organizational, methodological.
Trust-Region Adaptive Frequency for Online Continual Learning

Abstract In the paradigm of online continual learning, one neural network is exposed to a sequence of tasks, where the data arrive in an online fashion and previously seen data are not accessible. Such online fashion causes insufficient learning and severe forgetting on past tasks issues, preventing a good stability-plasticity trade-off, where ideally the network is expected to have high plasticity to adapt to new tasks well and have the stability to prevent forgetting on old tasks simultaneously. To solve these issues, we propose a trust-region adaptive frequency approach, which alternates between standard-process and intra-process updates. Specifically, the standard-process replays data stored in a coreset and interleaves the data with current data, and the intra-process updates the network parameters based on the coreset. Furthermore, to improve the unsatisfactory performance stemming from online fashion, the frequency of the intra-process is adjusted based on a trust region, which is measured by the confidence score of current data. During the intra-process, we distill the dark knowledge to retain useful learned knowledge. Moreover, to store more representative data in the coreset, a confidence-based coreset selection is presented in an online manner. The experimental results on standard benchmarks show that the proposed method significantly outperforms state-of-art continual learning algorithms.
ANALISIS KETERAMPILAN KOMUNIKASI PADA PEMBELAJARAN PERKENALAN DIRI BAHASA INGGRIS KELAS X SMK

Communication skills are one of the characteristics of 21st century learning. Students are required to have good communication skills to compete with the outside world. These skills can be started with self-introduction material which is the first material taught to class X students. The purpose of this research is to analyze students' communication skills in self-introduction material. With a lesson study approach that has a plan, do, see stage, students' communication skills are expected to improve. The subjects of this study were students of class X Islamic Banking at SMK Muhammadiyah 2 Boja. This type of research is qualitative. While the data obtained through observation, questionnaires, and interviews. Students were initially asked to write a self-introduction text with a small group. In these groups, students help each other and correct the results of their work. Not only self-introduction texts but also students' pronunciation or articulation when introduced orally. Students practice many times in the group. Then students come forward to get acquainted with their classmates. The discussion stage carried out by students was proven to increase their confidence when they came to the front of the class. Students can also compose self-introduction texts with correct vocabulary and grammar. Therefore, the implementation of lesson study to improve students' communication skills proved successful
Intelligent identification of radar active jamming type based on multi-domain information fusion

Abstract Feature fusion is beneficial to improving the recognition rate of radar active jamming types, which usually costs a lot of computation and network complexity, however. Contrapose the disadvantage of low computational power and resource allocation of missile-borne radar, this paper introduces data enhancement theory into the feature fusion method and proposes an intelligent identification model of radar active jamming type based on multi-domain information fusion. The model first analyzes the time-frequency domain features of the signal and one-dimensional features such as its real part, imaginary part, frequency spectrum, and power spectrum, and then uses the Cutout&amp;Patchup algorithm to fuse the one-dimensional and two-dimensional features into a new multi-domain information fusion matrix as the input of the recognition network. The simulation results show that this method greatly improves the recognition accuracy of active jamming types. Under the WideResNet28_2 classifier, the classification accuracy of active interference type reaches 88.06%, which is 0.79% higher than that before fusion.
Intellectual volunteerism in the educational space: features of development, features and current formats

The article presents a comprehensive analysis of intellectual volunteering in the educational space. Approaches to the understanding of this direction of volunteer activity are presented. The authors consider intellectual volunteering as an effective resource of innovative development of the educational system. The article considers the specifics of the formats for implementing the helping behavior of professionals in relation to the field of education.
An efficient deep learning-based approach for human activity recognition using smartphone inertial sensors

Human activity recognition (HAR) has recently witnessed outstanding growth in health and entertainment applications. Owing to the availability of smartphones, many new methods and protocols for using the data from smartphones’ embedded sensors are emerging. Nonetheless, the methods carried out and published in the literature leave a wide area for improvement, in terms of accuracy, resource economy, and adaptation to real-world nuisances. On top of that, a novel classification method that is more economical and efficient is proposed in this paper using both 1D convolutional neural network (1D-CNN) parameters and handcrafted temporal and frequency features with the proficiency of a multilayer perceptron neural network (MLP) classifier. The method proposed requires only tri-axial accelerometer data, allowing it to be deployed even into lower equipment devices; it was tested within the two well-known benchmark datasets: UCI-HAR and Uni-MIB SHAR. Experimental results yield a classification accuracy exceeding 99%, outperforming many of the methods recently shown in the literature.
Multi-criteria system for evaluating students

The content of multi-criteria student assessment methodology is discussed, including several evaluation indicators — average learning score, test score and final grade received at the exam or pass-fail assessment. In addition, it is proposed to evaluate the quality of group training taking into account the pedagogical qualifications of the teachers who conducted the classes. The method allows you to improve the objectivity of current training quality assessment, as well as to reveal shortcomings of the educational process and on this basis to develop organizational and methodical measures aimed at improving the quality of the organization of students. The methodology is applicable to both higher and secondary educational institutions.