Reviewerly/exHarmony huggingface dataset:
Two-Stream Mixed Convolutional Neural Network for American Sign Language Recognition

The Convolutional Neural Network (CNN) has demonstrated excellent performance in image recognition and has brought new opportunities for sign language recognition. However, the features undergo many nonlinear transformations while performing the convolutional operation and the traditional CNN models are insufficient in dealing with the correlation between images. In American Sign Language (ASL) recognition, J and Z with moving gestures bring recognition challenges. This paper proposes a novel Two-Stream Mixed (TSM) method with feature extraction and fusion operation to improve the correlation of feature expression between two time-consecutive images for the dynamic gestures. The proposed TSM-CNN system is composed of preprocessing, the TSM block, and CNN classifiers. Two consecutive images in the dynamic gesture are used as inputs of streams, and resizing, transformation, and augmentation are carried out in the preprocessing stage. The fusion feature map obtained by addition and concatenation in the TSM block is used as inputs of the classifiers. Finally, a classifier classifies images. The TSM-CNN model with the highest performance scores depending on three concatenation methods is selected as the definitive recognition model for ASL recognition. We design 4 CNN models with TSM: TSM-LeNet, TSM-AlexNet, TSM-ResNet18, and TSM-ResNet50. The experimental results show that the CNN models with the TSM are better than models without TSM. The TSM-ResNet50 has the best accuracy of 97.57% for MNIST and ASL datasets and is able to be applied to a RGB image sensing system for hearing-impaired people.
LLAMA: A Low Latency Math Library for Secure Inference

Secure machine learning (ML) inference can provide meaningful privacy guarantees to both the client (holding sensitive input) and the server (holding sensitive weights of the ML model) while realizing inferenceas-a-service. Although many specialized protocols exist for this task, including those in the preprocessing model (where a majority of the overheads are moved to an input independent offline phase), they all still suffer from large online complexity. Specifically, the protocol phase that executes once the parties know their inputs, has high communication, round complexity, and latency. Function Secret Sharing (FSS) based techniques offer an attractive solution to this in the trusted dealer model (where a dealer provides input independent correlated randomness to both parties), and 2PC protocols obtained based on these techniques have a very lightweight online phase. Unfortunately, current FSS-based 2PC works (AriaNN, PoPETS 2022; Boyle et al. Eurocrypt 2021; Boyle et al. TCC 2019) fall short of providing a complete solution to secure inference. First, they lack support for math functions (e.g., sigmoid, and reciprocal square root) and hence, are insufficient for a large class of inference algorithms (e.g. recurrent neural networks). Second, they restrict all values in the computation to be of the same bitwidth and this prevents them from benefitting from efficient float-to-fixed converters such as Tensorflow Lite that crucially use low bitwidth representations and mixed bitwidth arithmetic. In this work, we present LLAMA – an end-to-end, FSS based, secure inference library supporting precise low bitwidth computations (required by converters) as well as provably precise math functions; thus, overcoming all the drawbacks listed above. We perform an extensive evaluation of LLAMA and show that when compared with non-FSS based libraries supporting mixed bitwidth arithmetic and math functions (SIRNN, IEEE S&amp;P 2021), it has at least an order of magnitude lower communication, rounds, and runtimes. We integrate LLAMA with the EzPC framework (IEEE EuroS&amp;P 2019) and demonstrate its robustness by evaluating it on large benchmarks (such as ResNet-50 on the ImageNet dataset) as well as on benchmarks considered in AriaNN – here too LLAMA outperforms prior work.
Holistic Research on Blockchain’s Consensus Protocol Mechanisms with Security and Concurrency Analysis Aspects of CPS

In the present era, the consensus for blockchain is of three types: consortium/permissioned, decentralized/permissionless, and somewhat decentralized. Presently, security and privacy of blockchain scenarios are in four directions: auditability and transparency, accountability and nonrepudiation, contract privacy, and transactional privacies. Blockchain works on multilayered architectures with its consensus mechanisms. In this paper, important mechanisms of various consensus protocols for application specific usage are analyzed. In general, these consensus mechanisms have four groups of properties; all are examined and discussed. Moreover, the security analysis is shown. Furthermore, the paper examines the elliptic curve digital signature algorithm (ECDSA), which is in use by the cryptocurrencies along with many blockchain-based systems. Moreover, a variant of ECDSA (vECDSA) is also considered. In particular, ECDSA and vECDSA are compared in this research. In addition, modeling and analysis aspects related to the security and concurrency aspects of CPS are discussed. In particular, Petri-net-based models of CPS are considered, especially in terms of liveness and boundedness properties of the system.
A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System

In recent years cryptocurrencies are emerging as a prime digital currency as an important asset and financial system is also emerging as an important aspect. To reduce the risk of investment and to predict price, trend, portfolio construction, and fraud detection some Artificial Intelligence techniques are required. The Paper discusses recent research in the field of AI techniques for cryptocurrency and Bitcoin which is the most popular cryptocurrency. AI and ML techniques such as SVM, ANN, LSTM, GRU, and much other related research work with cryptocurrency and Bitcoin have been reviewed and most relevant studies are discussed in the paper. Also highlighted some possible research opportunities and areas for better efficiency of the results. Recently in the past few years, artificial intelligence (AI) and cybersecurity have advanced expeditiously. Its implementation has been extensively useful in finance as well as has a crucial impact on markets, institutions, and legislation. It is making the world a better place. AI is responsible for the simulation of machines that are replicas of human beings and are intelligent enough. AI in finance is changing the way we communicate with money. It helps the financial industry streamline and optimize processes from credit judgments to quantitative analysis marketing and economic risk management. The main goal of this research has been investigating certain impacts of artificial intelligence in this contemporary world. It's centered on the appeal of artificial intelligence, confrontation, chances, and its influence on professions and careers. The research paper uses AI to enable banks to generate financial resources and to provide valuable customer services. The application of the growing Indian banking sector is part of everyday life made up of several banks like RBI, SBI, HDFC, etc. and these banks have digitally implemented using chat-bots that have brought benefits to the customers.
Formation of informational and digital competence of secondary school students in laboratory work in physics

The article deals with the formation of informational and digital competence of high school students. First and foremost, the existing digitalization strategies for society already approved in the world and in Ukraine, including the implementation of STEM education and the Digital Agenda, are considered. On the other hand, attention is paid to the inconsistency of the level of ownership and frequency of use of digital technologies with the requirements of these initiatives. The concept of informational and digital competence is analyzed in detail. Existing publications identify key components, skills and competencies required to achieve this competence. A survey is conducted to better understand the current situation. One of the tasks is to determine the level of use of digital information in the classroom by teachers and in students’ preparation at home. The second task was to show how developing students’ informational and digital competence can be done by active introduction of existing software and hardware in the educational process in physics, in particular, a laboratory workshop. The example of laboratory work carried out in educational institutions shows how modern software can be used to analyze the movement of bodies and determine the physical characteristics of this movement. The concrete ways of performing laboratory work, analyzing its results and drawing conclusions are given. It is in the combination of existing teaching practices with modern gadgets, specialized and general programs that the basic way of forming informational and digital competence is seen. Further ways of modernization and improvement of described methods for increasing the level of information and digital competence are proposed.
Fast multiple graphs learning for multi-view clustering

Graph-based multi-view clustering has become an active topic due to the efficiency in characterizing both the complex structure and relationship between multimedia data. However, existing methods have the following shortcomings: (1) They are inefficient or even fail for graph learning in large scale due to the graph construction and eigen-decomposition. (2) They cannot well exploit both the complementary information and spatial structure embedded in graphs of different views. To well exploit complementary information and tackle the scalability issue plaguing graph-based multi-view clustering, we propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer. Finally, we develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model. Extensive experimental results on several datasets indicate that our proposed method outperforms some state-of-the-art multi-view clustering algorithms.
Modeling the training system of masters of public service using Web 2.0

The article concerns grounding the technology of training masters of public service with the use of Web 2.0. This technology is based on the concept of sign-contextual learning, the positions of the laboratory-brigade method, the concept of Web 2.0, case technology, project method, problem learning. The main features of this technology are changes in the correlation between theoretical and practical training, in-class and individual studying; changing teachers’ functions; extensive use of information technology capabilities in learning.
Knowledge-aware attentional neural network for review-based movie recommendation with explanations

Abstract In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation tasks by extracting knowledge entities from movie reviews and capturing understandable interactions between users and movies at the knowledge level. In most recommendation systems, review information is already widely utilized to uncover the explicit preferences of users for items, especially for domains including movie recommendations, music recommendations, and book recommendations, as reviews are full of knowledge entities relevant to the domain. When processing review information, current methods usually use word embeddings to represent reviews for modeling users and items. As a result, they may split the meaning of a phrase, and thereby induce erroneous predictions. Moreover, most methods capture high-order interactions between users and items after obtaining latent low-dimensional representations, which means they cannot discover understandable interactions or provide knowledge-level explanations. By incorporating knowledge graph representation into movie recommendation tasks, the proposed KANN can not only capture the inner attention among user (movie) reviews but also compute the outer attention values between users and movies before generating corresponding latent vector representations. These characteristics enable the explicit preferences of users for movies to be learned and understood. We test our model on two datasets (IMDb and Amazon) for the movie rating prediction task and the click-through rate prediction task and show that it outperforms some of the existing state-of-the-art models and gains outstanding prediction performances in cases with a very small amount of reviews. Furthermore, we demonstrate the high explainability of the proposed KANN by visualizing the interaction between users and movies through a case study. Our results and analyses highlight the relatively high effectiveness and reliability of KANN for movie recommendation tasks.
Informatics teacher’s training for design of innovative learning aids

In accordance with its goal, the paper covers practical aspects and experience of Informatics teachers’ preparation for the design of innovative learning aids as one of the important components of the renewed model of teachers’ training. Theoretical background of the research includes holistic educational approach and functional basics of electronic didactic aids development. The specific example of such an experience (students’ project activity on the design of English multimedia tutorial for schoolchildren) is depicted in details. The prospects of further research are outlined.
Tourists and AI: A political ideology perspective

The tourism industry has adopted AI agents as substitutes for human contact. We examine how tourists respond to AI in hotel service settings during a pandemic. Four studies show that services featuring human interaction is preferred to AI enabled interaction. Moreover, subjective happiness is identified as the underlying causal mechanism that drives this effect. We provide further nuanced insights by showing that politically conservative tourists discriminate between service agent types more. Theoretically we contribute to a more fine-grained understanding of tourists' responses towards AI by examining an affective and an individual difference factor (political ideology). Practically, marketers are encouraged to incorporate tourists' ideologies and psychographics into segmentation, targeting, and positioning considerations, thereby enhancing their marketing effectiveness.
Rain Rendering and Construction of Rain Vehicle Color-24 Dataset

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new RainVehicleColor-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the VehicleColor-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset RainVehicleColor-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at humingdi2005@github.com.
Invertible Quadratic Non-Linear Layers for MPC-/FHE-/ZK-Friendly Schemes over Fnp

Motivated by new applications such as secure Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Zero-Knowledge proofs (ZK), many MPC-, FHE- and ZK-friendly symmetric-key primitives that minimize the number of multiplications over Fp for a large prime p have been recently proposed in the literature. This goal is often achieved by instantiating the non-linear layer via power maps x↦xd. In this paper, we start an analysis of new non-linear permutation functions over Fnp that can be used as building blocks in such symmetrickey primitives. Given a local map F : Fmp→ Fp, we limit ourselves to focus on S-Boxes over Fnp for n ≥ m defined as SF (x0, x1, . . . , xn−1) = y0|y1| . . . |yn−1 where yi := F(xi, xi+1, . . . , xi+m−1). As main results, we prove that• given any quadratic function F : F2p→ Fp, the corresponding S-Box SF over Fnp for n ≥ 3 is never invertible;• similarly, given any quadratic function F : F3p → Fp, the corresponding S-Box SF over Fnp for n ≥ 5 is never invertible.Moreover, for each p ≥ 3, we present (1st) generalizations of the Lai-Massey construction over Fnp defined as before via functions F : Fmp → Fp for each n = m ≥ 2 and (2nd) (non-trivial) quadratic functions F : F3p → Fp such that SF over Fnp for n ∈ {3, 4} is invertible. As an open problem for future work, we conjecture that for each m ≥ 1 there exists a finite integer nmax(m) such that SF over Fnp defined as before via a quadratic function F : Fmp →Fp is not invertible for each n ≥ nmax(m). Finally, as a concrete application, we propose Neptune, a variant of the sponge hash function Poseidon, whose non-linear layer is designed by taking into account the results presented in this paper. We show that this variant leads to a concrete multiplication reduction with respect to Poseidon.
SuperBall: A New Approach for MILP Modelings of Boolean Functions

Mixed Integer Linear Programming (MILP) solver has become one of the most powerful tools of searching for cryptographic characteristics. It has great significance to study the influencing factors of the efficiency of MILP models. For this goal, different types of MILP models should be constructed and carefully studied. As Boolean functions are the fundamental cryptographic components, in this paper, we study the descriptive models of Boolean functions. Here, a descriptive model of a Boolean function refers to a set of integer linear inequalities, where the set of the binary solutions to these inequalities is exactly the support of this Boolean function. Previously, it is hard to construct various types of descriptive models for study, one important reason is that only a few kinds of inequalities can be generated. On seeing this, a new approach, called SuperBall, is proposed to generate inequalities. The SuperBall approach is based on the method of undetermined coefficients, and it could generate almost all kinds of inequalities by appending appropriate constraints. Besides, the Sasaki-Todo Algorithm is also improved to construct the descriptive models from a set of candidate inequalities by considering both their sizes and strengths, while the strengths of descriptive models have not been considered in the previous works. As applications, we constructed several types of descriptive models for the Sboxes of Liliput, SKINNY-128, and AES. The experimental results first prove that the diversity of the inequalities generated by the SuperBall approach is good. More importantly, the results show that the strengths of descriptive model do affect the efficiencies, and although there is not a type of descriptive model having the best efficiency in all experiments, we did find a specific type of descriptive model which has the minimal size and relatively large strength, and the descriptive models of this type have better efficiencies in most of our experiments.
Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.
Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning

Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today's clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network's labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation.We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors.Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future.
A Novel Color Image Encryption Algorithm Based on 5-D Hyperchaotic System and DNA Sequence

Nowadays, it is increasingly necessary to improve the encryption and secure transmission performance of images. Therefore, in this paper, a bit-level permutation algorithm based on hyper chaos is proposed, with a newly constructed 5-D hyperchaotic system combined with DNA sequence encryption to achieve bit-wide permutation of plaintexts. The proposed 5-D hyperchaotic system has good chaotic dynamics, combining hyperchaotic sequence with bit-level permutation to enhance the pseudo-randomness of the plaintext image. We adopt a scheme of decomposing the plaintext color image into three matrices of R, G, and B, and performing block operations on them. The block matrix was DNA encoded, operated, and decoded. The DNA operation was also determined by the hyperchaotic sequence, and finally generated a ciphertext image. The result of the various security analyses prove that the ciphertext images generated by the algorithm have good distribution characteristics, which can not only resist differential attacks, but also have the advantages of large cryptographic space.