Reviewerly/exHarmony huggingface dataset:
Design of L-IDAKA Protocol for Lightweight Access Authentication and Encrypted Transmission of New Power System-Aware Terminals Based on Hash Pre-authentication

The introduction of massive heterogeneous IoT terminals poses new challenges to the access security of the edge-aware side of the power IoT. Marked cipher technology can realize direct authentication based on device identity without relying on digital certificates, which significantly simplifies the difficulty of key management and authentication process and has natural advantages in the large-scale deployment of power IoT terminal scenario. However, identification cryptography is based on bilinear pair operation, which has high operational overhead and is difficult to apply to resource-constrained terminals. In this paper, we design a lightweight authentication key negotiation protocol based on hash pre-certification based on the four-layer architecture of power IoT application-platform-network-awareness for the characteristics of huge scale and resource-constrained power IoT devices, which reduces the number of bilinear pairs in the traditional SM9 algorithm from two to one and improves the protocol efficiency by more than 50%. Meanwhile, a security analysis of the protocol based on the classical Dolev-Yao threat model shows that this scheme can achieve efficient and secure applications in resource-constrained terminals of power IoT.
A denoising method based on CNN through multi-layer separation

In the process of electromagnetic information leakage detection, the traditional denoising methods have the problem of insufficient ability to identify useful information under low SNR. This problem results in the useful information being easily removed as a noise signal in the denoising process. Aiming at this problem, this paper proposes a denoising method based on convolutional neural network (CNN), which is achieved by decomposing red and black signals layer by layer. This method can not only show a better denoising effect under a low SNR condition, but also locate, identify and enhance the useful information effectively.
Research on Personalized Tourism Recommendation Technology Based on Improved S-TrustSVD Model

With the rapid development of information technology, the problem of information overload is becoming more and more prominent. The recommendation system provides an effective way to solve the problem of information overload. The recommendation system is one of machine learning algorithms and has been widely used in areas Compared with other recommendation systems, tourism recommendation systems consider more factors, and there are data sparsity and cold start problems. In recent years, researchers have improved and proposed various tourism recommendation methods, and developed corresponding tourism recommendation systems. In this paper, after analyzing the different problems for the tourism recommendation system compared with other information recommendation systems, aiming at the problem that the existing scenic spot recommendation method does not consider users' personal scoring habits, the viewing effect of scenic spots, as well as the sparsity and cold start of users' scenic spot check-in data, Firstly we integrate the social information of users on the tourism platform into the TrustSVD model to improve it, Secondly, for the similarity calculation method in the TrustSVD model is the Pearson similarity calculation, and its calculation results have shortcomings, we introduce the category label of scenic spots and adopts the improved category similarity calculation method. An improved S-TrustSVD model of time evolution scenic spot recommendation with improved category similarity is proposed. The gradient descent method is used to solve the S-TrustSVD model, and the S-TrustSVD tourist attraction recommendation algorithm and experiment are completed. The experimental results show that proposed method not only improves the recommendation accuracy, but also alleviates the problems of data sparsity and cold start.
UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios

Stereo matching is a fundamental task in three-dimensional scene reconstruction. Recently, deep learning-based methods have proven effective on some benchmark datasets, such as KITTI and SceneFlow. Unmanned aerial vehicles (UAVs) are commonly used for surface observation, and the images captured are frequently used for detailed 3D reconstruction because of their high resolution and low-altitude acquisition. Currently, mainstream supervised learning networks require a significant amount of training data with ground-truth labels to learn model parameters. However, owing to the scarcity of UAV stereo-matching datasets, learning-based stereo matching methods in UAV scenarios is not fully investigated yet. To facilitate further research, this study proposes a pipeline for generating accurate and dense disparity maps using detailed meshes reconstructed based on UAV images and LiDAR point clouds. Through the proposed pipeline, we constructed a multi-resolution UAV scenario dataset called UAVStereo, with over 34,000 stereo image pairs covering three typical scenes. To the best of our knowledge, UAVStereo is the first stereo matching dataset for UAV low-altitude scenarios. The dataset includes synthetic and real stereo pairs to enable generalization from the synthetic domain to the real domain. Furthermore, our UAVStereo dataset provides multi-resolution and multi-scene image pairs to accommodate various sensors and environments. In this study, we evaluated traditional and state-of-the-art deep learning methods, highlighting their limitations in addressing challenges in UAV scenarios and offering suggestions for future research. Our dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/rebecca0011/UAVStereo.git.</uri>
Integrating Heterogeneous Graphs Using Graph Transformer Encoder for Solving Math Word Problems

This paper introduces a novel method that integrates structural information with training deep neural models to solve math word problems. Prior works adopt the graph structure to represent rich information residing in the input sentences. However, they lack the consideration of different relation types between other parts of the sentences. To provide various types of structural information in a uniform way, we propose a graph transformer encoder to integrate heterogeneous graphs of various input representations. We developed two types of graph structures. First, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dependency Graph</i> maintains long-distance lexical dependency between words and quantities. Second, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Question Overlap Graph</i> captures the gist within the problem body. The two graphs are encoded as a single graph for graph transformation. Experimental results show that our method produces competitive results compared to the baselines. Our model outperforms state-of-the-art models in Equation and Answer accuracy near three percent in SVAMP benchmark. Moreover, we discuss that integrating different types of textual characteristics may improve the quality of mathematical logic inference from natural language sentences.
Squeezing Area of the Versatile <i>GF</i> (2<sup> <i>m</i> </sup>) GNB Arithmetic Operators

Cryptography primitives have a prominent role in securing applications that may require low-area realizations, for example portable devices and other resource constrained devices. A given system may require support for different cryptography based protocols/ primitives. Many standardized and/or published primitives rely on arithmetic operations over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF\left ({2^{m}}\right)$ </tex-math></inline-formula> that occupy major area footprint. Therefore, versatile operators have been of interest to reduce the area penalty, in particular bit-serial multipliers. This paper introduces a novel scheme for versatile multiplication by the normal element in the Gaussian Normal Basis (GNB) leading to new low-area versatile GNB multiplier and inverter architectures that are presented for the first time, as far as we know. Specifically, the proposed inverters are the first versatile GNB inversion in open literature, to the best of our knowledge. Field Programmable Gate Arrays (FPGA) implementation results demonstrate that the proposed versatile multiplication and inversion techniques save almost 30% and 46%, and for Application Specific Integrated Circuits (ASIC) implementation the savings are up-to 29% and 35% respectively, in terms of area when compared to other counterparts.
Technologies for Organizing Creative Competitions to Support Regional Social Creativity

The paper focuses on the features of technology for organizing creative competitions in the regional creative fashion industry. The authors consider the socio-cultural context of the origin and development of creative competitions in the 20th–twenty-first centuries. Moreover, the authors analyze the history of the Altai fashion industry and its current development level. Studying innovative fashion projects as an important condition for developing social creativity in the region determines the research purpose. The authors used such research methods as cultural-historical, comparative, activity-oriented, and systemic, including a socio-cultural approach. The role of competition is determined by the need to introduce new innovative projects and approbate (1) innovative and creative ideas, (2) social initiatives, and (3) creative activities to provide effective and sustainable development of the creative regional economy. Fashion and style contests are studied not only from the standpoint of its organization, but as an innovative form of developing the fashion industry. In the Altai Krai, the fashion industry has its own history, traditions, and art school of modeling. The research novelty lies in consideration of the competition as an open, communicative, and interactive platform for developing professional creativity, providing creative opportunities, ensuring the intellectual development of innovation, which meets the urgent tasks of socio-economic transformation and modernization of the region.
Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Autonomous mobile service robots are used to complete many tasks, such as cleaning, transporting goods and monitoring. Such tasks usually require uninterrupted and continuous service. However, the battery of the robot is limited and must be charged frequently. For a large number of robots, it is essential to select a suitable charging pile. For this issue, we propose a path planning model for robots to intelligently access a limited number of charging piles distributed on the map. The traditional path planning model mainly considers the shortest path criterion to generate the path. Different from this, the path planning model in this paper not only considers the shortest path, but also the service position of the robot after charging, the remaining power of the robot, the state of the charging pile and the position of the robot in the map. Our path planning assigns the most suitable charging pile to the robot that needs charging. To solve the problem of high memory consumption and slow search speed when traditional A* algorithm is used for path planning, we propose local memorial path planning (LMPP) algorithm to quickly generate effective paths. The simulation results show that the proposed robot charging path planner can improve the robot service satisfaction and plan the effective path to the available charging piles.
Ontological Modeling for Contextual Data Describing Signals Obtained From Electrodermal Activity for Emotion Recognition and Analysis

Most of the research in the field of emotion recognition is based on datasets that contain data obtained during affective computing experiments. However, each dataset is described by different metadata, stored in various structures and formats. This research can be counted among those whose aim is to provide a structural and semantic pattern for affective computing datasets, which is an important step to solve the problem of data reuse and integration in this domain. In our previous work, the ROAD ontology was introduced. This ontology was designed as a skeleton for expressing contextual data describing time series obtained in various ways from various signals and was focused on common contextual data, independent of specific signals. The aim of the presented research is to provide a carefully curated vocabulary for describing signals obtained from electrodermal activity, a very important subdomain of emotion analysis. We decided to present it as an extension to the ROAD ontology in order to offer means of sharing metadata for datasets in a unified and precise way. To meet this aim, the research methodology was defined, mostly focusing on requirements specification and integration with other existing ontologies. Application of this methodology resulted firstly in sharing the requirements to allow a broader discussion and secondly development of the EDA extension of the ROAD ontology, validated against the MAHNOB-HCI dataset. Both these results are very important with respect to the vast context of the work, i.e. providing an extendable framework for describing affective computing experiments. Introducing the methodology also opens the way for providing new extensions systematically just by executing the steps defined in the methodology.
High Precision and Large Dynamic Range Measurement of Laser Triangulation Displacement Sensor Using Diffraction Grating

The conventional laser triangulation displacement sensor (LTDS) is difficult to achieve large dynamic range and high precision measurement simultaneously, since LTDS is affected by factors such as limited detector size, nonlinear input and output variables, laser power fluctuation, speckle noise, electronic noise, etc. Given the above background, we proposed a modified LTDS using a diffraction grating to improve the dynamic range and precision simultaneously. Different from the conventional LTDS, the modified LTDS generates more than one spots on the image sensor, thereby the displacements with different ranges can be obtained from the multiple order diffraction spots. Ultimately, the dynamic range and measurement accuracy will be improved simultaneously by integrating these displacements. In this paper, the principle of the modified LTDS for large dynamic range and high precision measurement is described in detail, and we verified the validity and effectiveness of this idea through the experiment. Compared with the traditional LTDS, the experimental results show that the linearity of the modified LTDS is improved by a factor of 1.5685 and the dynamic range is improved 1.2986 times as well as maintaining the same linearity.
A Tiny Accelerator for Mixed-Bit Sparse CNN Based on Efficient Fetch Method of SIMO SPad

Convolution neural networks (CNNs) have been implemented with custom hardware on edge devices since its algorithms were successful in many artificial intelligence applications. Although lots of unstructured pruning and mix-bit quantization algorithms have been proposed to successfully compress CNNs, there are few hardware accelerators which can support both sparse and mix-bit CNNs. Besides, sparse matrix computation consumes lots of hardware resources such as registers or BRAM to fetch the needed input activations into processing element (PE). This brief presents a tiny accelerator for mixed-bit sparse CNNs featuring a novel scheme of single vector-based compressed sparse filter (CSF) method and single input multiple output scratch pad (SIMO SPad) to effectively compress weight and fetch the needed input activation. SIMO SPad is shared by multiple PEs, which saves 13.34% CLB LUTs, 46.24% CLB Registers. Furthermore, the accelerator supports mixed-bit sparse computation to obtain better accuracy and performance. When tested on VGG16, compared with 8-bit non-sparsity baselines, the performance of mixed-bit sparsity on Cifar10 and ImageNet improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.85\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.33\times $ </tex-math></inline-formula> , respectively, with small accuracy decrease degradation. Compared to state-of-the-art accelerators, the accelerator achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.40\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.98\times $ </tex-math></inline-formula> greater DSP efficiency, and offers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.91\times $ </tex-math></inline-formula> greater energy efficiency.
Teaching Efficiency in the Conditions of Active Development of IT Technologies

The paper reveals the need for an automated assessment of teaching effectiveness in training courses, which arises in connection with the development of information and communication technologies and their use as innovative forms of education. The study reveals that pedagogical and psychological factors are considered separately when determining the teaching effectiveness. They influence the process of assimilation of educational materials when using electronic information and educational technologies. The paper analyzes new methods for assessing teaching effectiveness using digital technologies. We offer an innovative assessment system that considers both pedagogical (qualitative) and quantitative criteria. Accordingly, we determine the influence of the quantitative characteristics (study duration and the number of students in the group) on teaching and learning success for an individual and an entire study group. The results obtained using the methodology considered in this work can serve as a basis for adjusting training courses: (1) changing the training course duration, (2) the number of students in the group, or (3) the content of the material presented. It will improve the efficiency of the educational process. We tested the technique in the educational process of the university when studying the course of graphic disciplines. It can be useful to study other disciplines at the university and educational institutions of all levels.
Digital Technologies in Career Guidance for Preschool and School-Age Children

The paper presents one of the ways to solve a significant social problem, such as early professional self-determination of children of different age groups using diagnostic tools to obtain a digital footprint formed in the process and as a result of career guidance work in a mobile application. Developing information and communication technologies based on gamification principles makes it possible to use gaming techniques in non-gaming issues, such as professional self-determination based on personal preferences and choices with obtaining feedback. The proposed diagnostic program will allow users to form an idea of ​​a predisposition to one of the types of professions (human–human, human-nature, human-technology, human-symbolic system, or human-artistic image). More than that, the program can also act as a tool for the work of a teacher-psychologist, a vocational guidance counselor, or a parent. Besides, children can use this program themselves when independently choosing a learning path. The development of a mobile application in a digital platform system based on gamification principles makes it possible to work at different stages of age development. Simultaneously, children are subjects of their development. Using gaming technologies will allow actualizing the mental activity of the personality of children and contributing to the development of professional heuristic and creative thinking. Thus, relying on meaningful experience, students will be able to choose a training profile and labor self-determination responsibly.
Seer: Learning-Based $360^{\circ }$ Video Streaming for MEC-Equipped Cellular Networks

The increasing popularity of virtual reality and 360-degree video streaming has created a demand for streaming high-quality video content over cellular networks. However, streaming 360-degree videos over cellular networks poses a challenge due to the need for ultra-low latency and high bandwidth. Although viewport adaptive streaming presents a promising solution to reduce the bandwidth requirement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video, it remains challenging to predict the Field of View (FoV) due to the adverse impact of prediction errors on the user's Quality of Experience (QoE). Moreover, bitrate adaptation gets complicated due to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> videos and the dynamic nature of the cellular network. Therefore, in this paper, we propose an intelligent framework, termed Seer, to fully realize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video streaming over cellular networks by leveraging Multi-access Edge Computing (MEC). It incorporates an FoV prediction scheme and a bitrate adaptation policy into the MEC platform. In particular, the FoV prediction scheme uses perceived video features coupled with time-series based viewport prediction to achieve higher prediction accuracy. For an effective bitrate selection in cellular network conditions, a Reinforcement Learning (RL) based bitrate adaptation policy is further employed. The results demonstrate that Seer's FoV prediction scheme delivers higher prediction accuracy, while the streaming framework significantly reduces the bandwidth requirements and improves the user's QoE.