Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of quantum private comparison with six-particle maximally entangled states

The measurement property of the six-particle maximally entangled state was used by Sun [Mod. Phys. Lett. A 37, 2250149 (2022)] to design a quantum private comparison (QPC) protocol. However, this study points out that the entanglement property of the six-particle maximally entangled state is inconsequential as a security measure; that is, participants waste resources to store the entanglement state and perform the Bell measurement without obtaining any additional security advantage. Although a third party (TP) cannot obtain information regarding the participants’ secret messages through an attack, an attack renders the six-particle entanglement redundant. Accordingly, this study proposed an improved QPC protocol to prevent third-party attacks.
Deep learning with connectomes

Deep learning is a class of machine learning methods that have recently produced state-of-the-art results in a number of classic computer vision and image analysis problems. These methods use highly nonlinear neural network models to learn useful representations of the data for the prediction task at hand. This chapter covers the application of deep learning models to connectome data analysis, reviewing popular types of neural network architectures, practical steps for training and evaluating a deep learning model, and limitations and potential future work in developing deep learning models for connectomes.
The specifics of the development of spiritual and moral values of a junior school student by means of musical folklore

Importance. The problems of spiritual and moral education of children at the stage of primary school education are described. The actualization of the issue of the development of spiritual and moral values of junior school students from the position of socio-cultural prerequisites for the development of society, state requirements for the upbringing of the younger generation and from the side of educational practice has been worked out. Research methods. The methodological basis of the conducted research was philosophical-anthropological, axiological and personality-oriented approaches that determine the content guidelines of the concept of spiritual and moral education of a junior school students. Within the framework of these approaches, methods of theoretical analysis, synthesis and generalization of scientific literature, as well as personalized observation of primary school students are used. Results and Discussion. The spiritual and moral values are substantiated in the context of the pedagogical category. The characteristics of the age characteristics of junior school students in the process of spiritual and moral education are given, and musical folklore as the leading means of forming spiritual and moral values in junior school students is also substantiated. Conclusion. The leveling effect of modern trends in the development of society and the presence of crises in various areas of society’s life prove the need for the development of spiritual and moral values from an early age of students. It is proved that the appeal to the origins of folk culture in a musical format contributes to a more in-depth and penetrating course of the process of spiritual and moral education of a junior school student.
Some Observations on Huffman Trees

Information theory and coding theory are very important in communication engineering. Data is transferred across the network from source to destination via some communication channel. Data Streams can be very lengthy. Moreover, if we consider all characters in English, the data stream may or may not contain all possible characters in this character set. Hence, we need not use all the bits used to represent these characters. Here, data compression comes into play. Huffman Coding is one such algorithmic technique used to create codes for each of the characters. Apart from compression, they are used in other tasks like cryptography, error detection and correction. In this paper, our concern is data compression. We recommend to club different techniques like run- length encoding, use of special characters, contexts in character stream together with Huffman Coding to perform effective compression.This preliminary work may be cited as: K. Viswanathan Iyer, Seshadri, K. and Ghosh, S. (2023). ‘’Some observations on Huffman trees,’’ in: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds.) Proc. of the Int’l. Conf. on Cognitive and Intelligent Computing. Cognitive Science and Technology, Springer, Singapore - https://doi.org/10.1007/978-981-19-2358-6_33.
Library of children’s reading conceptual modeling by teachers

Relevance. The place of children’s reading in the spatially developing environment of a preschool educational institution as functionally significant for the child’s intellectual development is reflected. The problem of book fund’s lack a in a kindergarten, created according to science’s rules, is posed. The purpose of the research is to elaborate a concept for the formation of a library of children’s books in a preschool educational institution. Materials and methods. The library formation theoretical foundations for children in a preschool educational institution are revealed. Historical-literary, book-study and culturological principles of selection and grouping of children’s books and works for younger, middle and older age groups of young listeners are considered. The books of the picture, game, activity type and the classic type of book publication for children are characterized. Method algorithms of viewing a children’s book, organizing listening, playing, and creative tasks are proposed. Result and Discussion. The psychological, pedagogical and literary rules of children’s reading range formation are formulated: the rule of age restrictions, age correspondences and age perspective, the rule of meaningful peace, thematic richness and genre diversity. The methods aspects of the child’s interaction with the book and magazine are reflected. Appeal to children’s literature motives dynamics of preschoolers from one to two years before the beginning of school education is revealed. The results of the author’s scientific and pedagogical statements about a high-quality library of children’s reading in a preschool educational institution are concluded. Conclusion. The concept of an integrated scientific approach to the libraries formation in kindergartens is declared and the experience of this activity, repeatedly tested by us, is reflected. As a visual generalization, a drawing of the conceptual model is given. This model can be used to develop the reading range of younger schoolchildren.
Modified SkipGram Negative Sampling Model for Faster Convergence of Graph Embedding

Graph embedding techniques have been introduced in recent years with the aim of mapping graph data into low-dimensional vector spaces, so that conventional machine learning methods can be exploited. In particular, in the DeepWalk model, truncated random walks are employed in random walk-based approaches to capture structural links-connections between nodes. The SkipGram model is then applied to the truncated random walks to compute the embedded nodes. In this work, the proposed DeepWalk model provides a faster convergence speed than the standard one by introducing a new trainable parameter in the model. Furthermore, experimental results on real-world datasets show that the performance in downstream community detection and link prediction task is improved by using the proposed DeepWalk model.
Face Presentation Attack Detection (PAD) Challenges

In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges.
Evaluating Deep Learning Models for the Automatic Inspection of Collective Protective Equipment

Deep Learning models are becoming widely used in many applications but they can often be improved through a fine-tuning process capable of enhancing their performances in specific scenarios. In this paper we tackle the problem of autonomously inspecting the conditions of Collective Protection Equipment (CPE) such as fire extinguishers, warning signs, ground and wall signalization and others. Work ministry imposes that such CPE are in good conditions to prevent accidents, carrying out periodic mandatory in loco auditions. Industry is increasingly applying the potential of Deep Learning (DL) models to automatize such Computer Vision (CV) tasks and a fiber-optic component provider proposed this demand. Specifically, we assessed the performances of four DL models, namely, MobileNet V2 SSDLite, FPN Resnet-50 SSD, Inception Resnet v2 Faster R-CNN with Atrous Convolution and EfficientNet B0 SSD in the evaluation of CPE conditions. We provide results that highlight each architecture’s advantages and drawbacks in the aforementioned scenario. Indeed, experiments have shown their potential in reducing time and costs of periodic inspections in factories.
HyDensity: A Hyper-Volume-Based Density Metric for Automatic Cluster Evaluation

As the systems produce ever more, becoming more and more complex and universal, the time spent on product validation narrows. The main alternative to the examination of every final product is a statistical study, with a confidence interval; another solution may be an automated validation, possibly integrated to the system’s core. Some works addressed this topic using unsupervised learning, and especially clustering, but one challenge remains, that of supporting the information brought by these blind, automatized techniques. In this chapter, we address this problem of cluster validation using a data-driven metric, based on the hyper-volume theory, to estimate the clusters’ density, so as to estimate how representative they are. We apply this metric to real industrial data, and show how resilient and representative this metric is, and we extend it to two other metrics from literature to provide a hybrid quantifier, which allows to automatically validate or reject a cluster. The accepted clusters can eventually be assimilated to true regions of the feature space, whose representativeness and meaningfulness are given by this hybrid quantifier.
Reliable Classification of Images by Calculating Their Credibility Using a Layer-Wise Activation Cluster Analysis of CNNs

An image classification model based on a Convolutional Neural Network architecture generally achieves a high classification performance over a wide range of image domains. However, the model is only able to obtain such a high performance on in-distribution samples. On out-of-distribution samples, in contrast, the performance of the model may be significantly decreased. To detect out-of-distribution samples, Papernot and McDaniel [38] introduced a method named DkNN, which is based on calculating a sample credibility score by a nearest neighbor classification in feature space of the hidden layers of the model. However, a nearest neighbor classification is memory-intensive and slow at inference. To address these issues, Lehmann and Ebner [26] suggested a method named LACA, which calculates the credibility score based on clustering instead of a nearest neighbor classification. Lehmann and Ebner [26] showed that for out-of-distribution samples with respect to models trained on MNIST, SVHN, or CIFAR-10, LACA is significantly faster at inference compared to DkNN, while obtaining a similar performance. In this work, we conducted additional experiments to test LACA on more complex datasets (Imagenette, Imagewoof). Our experiments show that LACA is significantly faster at inference compared to DkNN also for these more complex datasets. Furthermore, LACA computes meaningful credibility scores, while DkNN fails on these datasets.
Leader-Assisted Client Selection for Federated Learning in IoT via the Cooperation of Nearby Devices

Federated learning is a form of distributed learning in which each participating node is handled as a client and is responsible for the training of a model using only its local data. The participation of inappropriate clients, on the other hand, may have a substantial impact on reliability, which would entail a significant consumption of resources. Due to this difficulty, the aspect of client selection is challenging. Most of the proposed approaches are based on selecting clients from their resource’s information by gathering them in a centralized manner, which may have many drawbacks. In this paper, we propose a decentralized client selection. The gathering of resource information is accomplished through the collaboration of neighboring nodes, coordinated by the leader. The leader is elected using a leader election algorithm. Based on the gathered information, the leader then trains a lightweight deep learning model to select clients throughout the IoT context. The proposed approach has been developed and validated by experimenting with different complex scenarios.
Phisherman: Phishing Link Scanner

Phishing intention is to track and steal sensitive information and people often use the internet to purchase online. As a result of the increased demand for digital security and the growing number of phishing scams, the researchers decided to create a phishing link scanner. Current phishing prevention technology was studied, and with gaps identified, machine learning algorithms were used to improve and make it available to the public. The researchers created a phishing link-scanner application that runs in the background and alerts users to potentially malicious links. Implementing machine learning allows the application to adapt to uniquely structured emails and messages. Phisherman was validated using the ISO 9126 software quality model to test the application's quality, as part of the agile development methodology. The application was rated Highly Acceptable in terms of functionality, reliability, usability, efficiency, maintainability, and portability by 377 respondents, with a weighted mean of 4.5. The project met its goal of effectively verifying phishing links and informing users.
Fostering Professionalism in Software Engineering: An Early-Exposure Approach

In this article, we advocate for an early-exposure approach to professionalism in software engineering. We believe that it is important to start preparing students for the “culture” of software professionalism from early on, which gives them a competitive edge in their careers.
A Novel Solution to the Time-Varying Lyapunov Equation: The Integral Dynamic Learning Network

In this article, a novel approach of utilizing an integral dynamic learning network (IDLN) is presented for addressing a general time-varying Lyapunov matrix equation (TVLME). First, a cost function is defined by designing a variable unbounded vector/matrix-type error function. The goal is to make the cost function approximate to zero. Second, an integral neural dynamic equation with an odd activation function that is monotonically increasing is designed and applied to guarantee that the error function can converge to zero. Third, a novel IDLN with a recurrent topological structure is exploited to find the time-varying theoretical solution. The proposed IDLN with strong robustness to bounded noise with unknown amplitude regardless of the value of hyperparameters, can be implemented through electronic circuits as a method of parallel computing and achieve global convergence from any initial state. In addition, for better convergence rates, the novel linear-arcsine-type and softsign-arcsine-type activation functions are designed and utilized to the proposed IDLN. The effectiveness, stability, and practicability of the proposed IDLN are verified by comparative computer simulations and the application to the analysis of voltage stability in a single-machine infinite bus system.
Cross-Lingual Universal Dependency Parsing Only From One Monolingual Treebank

Syntactic parsing is a highly linguistic processing task whose parser requires training on treebanks from the expensive human annotation. As it is unlikely to obtain a treebank for every human language, in this work, we propose an effective cross-lingual UD parsing framework for transferring parser from only one source monolingual treebank to any other target languages without treebank available. To reach satisfactory parsing accuracy among quite different languages, we introduce two language modeling tasks into the training process of dependency parsing as multi-tasking. Assuming only unlabeled data from target languages plus the source treebank can be exploited together, we adopt a self-training strategy for further performance improvement in terms of our multi-task framework. Our proposed cross-lingual parsers are implemented for English, Chinese, and 29 UD treebanks. The empirical study shows that our cross-lingual parsers yield promising results for all target languages, approaching the parser performance which is trained in its own target treebank.
Quantum-Safe Secure and Authorized Communication Protocol for Internet of Drones

Remotely-Controlled Aerial Vehicles (RCAV), popularly known as drones, have gained wide popularity in several applications from military to civilian due to the usage of sensors, actuators, and processors with wireless connectivity for data collection and processing. However, data security and authentication are still challenging as sensitive data is collected and shared in RCAV using an open channel, i.e., the Internet. The existing security of Internet-of-Drones (IoD) communication mainly depends on the hardness of discrete logarithms and factorization problems. However, due to Shor's algorithm, both authorized and secure transmissions are challenging in the presence of highly scalable quantum computers. To mitigate the aforementioned issues and challenges, in this article, we propose an authorized and secure communication scheme for IoD based on Ring Learning With Error(RLWE) problem on lattices, which have the potential to sustain low computation and quantum attacks. The proposed scheme supports mutual authentication and has an efficient session establishment. The evaluation results show that the proposed scheme has superior performance in comparison to the existing state-of-the-art solutions on benchmark data sets using various evaluation metrics.
Multiscale Prototype Contrast Network for High-Resolution Aerial Imagery Semantic Segmentation

Semantic segmentation of high-resolution aerial images is a challenging task on account of complex scene-variation and large scale-difference. However, these two issues are inadequately addressed in general semantic segmentation methods. In this paper, we propose a Multi-scale Prototype Contrast Network (MPCNet) to improve the adaptive capability for different scenes and scales. Specifically, a novel multi-scale prototype transformer decoder (MPTD) is designed to extract dynamic scene-specific prototypes as pixel classifier by fusing information of feature maps and learnable class tokens. To exploit cross-scene context information and accommodate the large scale-difference in aerial image, we build a multi-scale prototype memory queue to store these multi-scale prototypes during training. Upon the multi-scale prototype memory queue, a novel multi-scale prototype contrastive loss is proposed to increase object feature discriminability across multiple scale, which brings better consistency of intermediate feature and boosts the convergence of network. Extensive experimental results on three publicly available datasets demonstrate the effectiveness and efficiency of our MPCNet over other state-of-the-art methods. The code is available at https://github.com/qixiong-wang/mmsegmentation-mpcnet.
Cross-Camera Trajectories Help Person Retrieval in a Camera Network

We are concerned with retrieving a query person from multiple videos captured by a non-overlapping camera network. Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. To address this issue, we propose a pedestrian retrieval framework based on cross-camera trajectory generation that integrates both temporal and spatial information. To obtain pedestrian trajectories, we propose a novel cross-camera spatio-temporal model that integrates pedestrians' walking habits and the path layout between cameras to form a joint probability distribution. Such a cross-camera spatio-temporal model can be specified using sparsely sampled pedestrian data. Based on the spatio-temporal model, cross-camera trajectories can be extracted by the conditional random field model and further optimised by restricted non-negative matrix factorization. Finally, a trajectory re-ranking technique is proposed to improve the pedestrian retrieval results. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset, the Person Trajectory Dataset, in real surveillance scenarios. Extensive experiments verify the effectiveness and robustness of the proposed method.
Efficient Graph-based Signal Motif Discovery with Performance Bounds

In this paper, we investigate the problem of signal motif discovery. In the formulation considered, the goal is to find an unknown pattern (or motif) repeated across multiple data sets. This problem formulation can be applied to various domains, such as DNA sequence alignment and matching, motif discovery in time-series, and object detection and localization in images. We take the approach of minimizing an objective that is the sum over a measure of the difference between a candidate instance in each signal collection (or set) and the unknown pattern. Additionally, a non-negative instance dependent penalty is introduced. The proposed general objective can be used to capture well-known problems (e.g., blind joint time-delay estimation). Due to the non-convex nature of the problem and often the integer programming flavor of the approach, brute-force solution is non-polynomial and computationally prohibitive for large scale problems. We propose an efficient polynomial time (quadratic in the number of instances) bipartite graph based approximation to solve the problem. We provide a theoretical analysis for the proposed solution including bounds on the gap from the optimal solution and conditions for optimality. In particular, we show that the objective value for the proposed solution is no more than twice the objective value of the optimal solution. To illustrate the merit of the proposed approach, we present qualitative and quantitative empirical analysis of the proposed approach on several applications and compare our method to appropriate alternatives.