Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal Information Fusion and Filtration for Human Action Recognition

Human action recognition (HAR) as the most representative human-centred computer vision task is critical in human resource management (HRM), especially in human resource recruitment, performance appraisal, and employee training. Currently, prevailing approaches to human action recognition primarily emphasize either temporal or spatial features while overlooking the intricate interplay between these two dimensions. This oversight leads to less precise and robust action classification within complex human resource recruitment environments. In this paper, we propose a novel human action recognition methodology for human resource recruitment environments, which aims at symmetrically harnessing temporal and spatial information to enhance the performance of human action recognition. Specifically, we compute Depth Motion Maps (DMM) and Depth Temporal Maps (DTM) from depth video sequences as space and time descriptors, respectively. Subsequently, a novel feature fusion technique named Center Boundary Collaborative Canonical Correlation Analysis (CBCCCA) is designed to enhance the fusion of space and time features by collaboratively learning the center and boundary information of feature class space. We then introduce a spatio-temporal information filtration module to remove redundant information introduced by spatio-temporal fusion and retain discriminative details. Finally, a Support Vector Machine (SVM) is employed for human action recognition. Extensive experiments demonstrate that the proposed method has the ability to significantly improve human action recognition performance.
Quantum Query Lower Bounds for Key Recovery Attacks on the Even-Mansour Cipher

The Even-Mansour (EM) cipher is one of the famous constructions for a block cipher. Kuwakado and Morii demonstrated that a quantum adversary can recover its n-bit secret keys only with $$\mathcal {O}(n)$$ nonadaptive quantum queries. While the security of the EM cipher and its variants is well-understood for classical adversaries, very little is currently known of their quantum security. Towards a better understanding of the quantum security, or the limits of quantum adversaries for the EM cipher, we study the quantum query complexity for the key recovery of the EM cipher and prove every quantum algorithm requires $$\Omega (n)$$ quantum queries for the key recovery even if it is allowed to make adaptive queries. Therefore, the quantum attack of Kuwakado and Morii has the optimal query complexity up to a constant factor, and we cannot asymptotically improve it even with adaptive quantum queries.
Multi-Load Agent Path Finding for Online Pickup and Delivery Problem

The Multi-Agent Pickup and Delivery (MAPD) problem, a variant of the lifelong Multi-Agent Path Finding (MAPF) problem, allows agents to move from their initial locations via the pickup locations of tasks to the delivery locations. In general MAPD problem, the agent is single-load and completes only one task at a time. However, many commercial platforms, e.g., Amazon and JD, have recently deployed multi-load agents to improve efficiency in their automated warehouses. As the multi-load agents can complete multiple tasks at once instead of just one, existing solutions for the general MAPD are unsuitable for the multi-load agent scenario. Meanwhile, a few works focus on the schedule of multi-load agents because it is hard to assign tasks to suitable multi-load agents and find conflict-free paths in real-time for multi-load agents. Therefore, in this paper, we formally define the Multi-Load Agent Pickup and Delivery (MLAPD) problem, in which the multi-load agents complete the real-time pickup-and-delivery tasks and avoid conflicts with each other to minimize the sum of the travel costs and the delay costs. For solving the MLAPD problem, we propose an efficient task assignment algorithm and a novel dynamic multi-agent path finding algorithm. Extensive experiments show that compared with the state-of-the-art, our solution can complete an additional 4.31% $$\sim $$ 138.33% of tasks and save 0.38% $$\sim $$ 12.41% of total costs while meeting real-time requirements.
Greedy Gray Codes for Dyck Words and Ballot Sequences

We present a simple greedy algorithm for generating Gray codes for Dyck words and fixed-weight Dyck prefixes. Successive strings in our listings differ from each other by a transposition, that is, two bit changes. Our Gray codes are both homogeneous and suffix partitioned. Furthermore, we use our greedy algorithm to produce the first known homogeneous 2-Gray code for ballot sequences, which are Dyck prefixes of all weights. Our work extends a previous result on combinations by Williams [Conference proceedings: Workshop on Algorithms and Data Structures (WADS), LNTCS 8037:525-536, 2013].
Revisiting Probabilistic Relation Analysis: Using Probabilistic Relation Graphs for Relational Similarity Analysis of Words in Short Texts

Relation graphs provide useful tools for structural and relational analyses of highly complex multi-component systems. Probabilistic relation graph models can represent relations between system components by their probabilistic links. These graph types have been widely used for the graphical representation of Markov models and bigram probabilities. This study presents an implication of relational similarities within probabilistic graph models of textual entries. The article discusses several utilization examples of two fundamental similarity measures in the probabilistic analysis of short texts. To this end, the construction of probabilistic graph models by using bigram probability matrices of textual entries is illustrated, and vector spaces of input word-vectors and output word-vectors are formed. In this vector space, the utilization of cosine similarity and mean squared error measures are demonstrated to evaluate the probabilistic relational similarity between lexeme pairs in short texts. By using probabilistic relation graphs of the short texts, relational interchangeability analyses of lexeme pairs are conducted, and confidence index parameters are defined to express the reliability of these analyses. Potential applications of these graphs in language processing and linguistics are discussed on the basis of the analysis results of example texts. The performance of the applied similarity measures is evaluated in comparison to the similarity index of the word2vec language model. Results of the comparative study in one of the illustrative examples reveal that synonyms with 0.18157 word2vec similarity value scored 1.0 cosine similarity value according to the proposed method.
A Sub-quadratic Time Algorithm for Computing the Beacon Kernel of Simple Polygons

In 2011, Biro et al. [4] initiated the concept of beacon attraction trajectory motivated by routing messages in sensor network systems. Let P be a polygonal region such that there is a point particle at each point in P. When we activate a beacon at a point $$b\in P$$ , each particle in P greedily moves toward b. For a point $$p\in P$$ , if the particle at p reaches b, we say b attracts p. We call a point $$b\in P$$ a beacon kernel point of P if a beacon at b attracts all points in P. The beacon kernel of P is defined as the set of all beacon kernel points of P. In 2013 [3] Biro presented a naive quadratic time algorithm to compute the beacon kernel of polygonal domains and showed that this bound is tight. But, obtaining a sub-quadratic time algorithm for computing the beacon kernel of simple polygons remained open. In this paper, we answer to this open problem by presenting an $$O(n^{1.5}\log ^2 n)$$ time algorithm for computing the beacon kernel of simple polygons.
Variants of Euclidean k-Center Clusterings

Fix two constant integers k and r. Given a set P of n points on a plane, the Euclidean k-center r-gather clustering problem is the problem to compute a set $$\{ c_1,c_2,\cdots ,c_k \}$$ of k points on the plane and a partition $$P_1\cup P_2\cup \cdots \cup P_k$$ of P such that each $$P_i$$ contains r or more points in P, and the maximum radius of $$P_1, P_2, \cdots , P_k$$ is minimized, where the radius of $$P_i$$ (with center at $$c_i$$ ) is the maximum distance from $$p\in P_i$$ to $$c_i$$ . If r is 0 then the problem is the ordinary Euclidean k-center clustering problem. This is a geometric version of the k-anonymity which is an important model for privacy preserving. In this paper we design a polynomial-time algorithm to solve the Euclidean k-center r-gather clustering problem when k and r are constant integers. We also design polynomial-time algorithms to solve some related problems.
Aircraft Parameter Estimation Using Gaussian Sum Filter With Lyapunov Stability Analysis

The use of Gaussian Sum Filter (GSF) as a state/parameter estimation tool for aerodynamic modelling is relatively unexplored in the literature. In this paper GSF has been employed for aircraft parameter estimation. The stability analysis of GSF has also been discussed using the normalized Lyapunov energy functional and the conditions for the convergence of the GSF are derived by using the proposed nonlinear observer which is a novel approach. An approach to prune the weights of GSF based on Pearson coefficient is considered. The performances of the three recursive parameter estimation algorithms: Extended Kalman Filter (EKF), the simplified version of the GSF and the improved version of the GSF are evaluated for aircraft parameter estimation using simulated flight data in the MATLAB implementations. The numerical results show that pruning the weights reduces the computational load by nearly 75%, and still provides good performance compared to the conventional GSF.
MTHI‐former: Multilevel attention for two‐handed reconstruction from RGB image

Abstract Hand reconstruction is the foundation of virtual reality and human–computer interaction, but currently it still faces challenges such as blurred interaction edge and inter‐hand occlusion. To solve these challenges, in this letter, the authors propose a framework called Multilevel Two Hand Interactive Former (MTHI‐Former) which considers the vertices of a hand model as graph structures and learn the connectivity relationships information between vertices. In this framework, the authors introduce two novel modules. The first is the Multi‐branch Image Feature Extraction Module , which is utilized to obtain accurate hand features. The second is the Multilevel Two Hand Interaction Module , which is utilized to fuse interactive hand information to enhance the attention features of interactive edges, and fuse vertex relationships to determine the occlusion relationship between interactive hands. The authors compare their method with recent methods on the InterHand 2.6M dataset, and the experimental results show that their method outperforms other representative methods, achieving a result of 13.15 mm and an improvement in performance of about 18%.
Blockchain and crypto forensics: Investigating crypto frauds

Abstract In the past few years, cryptocurrency has gained widespread acceptance because of its decentralized nature, quick and secure transactions, and potential for investment and speculation. But the increased popularity has also led to increased cryptocurrency fraud, including scams, phishing attacks, Ponzi schemes, and other criminal activities. Although there is little documentation of cryptocurrency fraud, an in‐depth study is essential to recognize various scams in different cryptocurrencies. To fill this gap, a study investigated cryptocurrency‐related fraud in various cryptocurrencies and provided a taxonomy of crypto‐forensics and forensic blockchain. In addition, we have introduced an architecture that integrates artificial intelligence (AI) and blockchain technologies to investigate and protect against instances of cryptocurrency fraud. The suggested design's effectiveness was evaluated using several machine learning (ML) classification algorithms. The conclusion of the evaluation confirmed that the random forest (RF) classifier performed the best, delivering the highest level of accuracy, that is, 97.5%. Once the ML classifiers detect cryptocurrency fraud, the information is securely stored in the InterPlanetary File System (IPFS); the document's hash is also stored in the blockchain using smart contracts. Law enforcement can leverage blockchain technology to secure access to fraudulent cryptographic transactions. The proposed architecture was tested for bandwidth utilization. Despite the potential benefits of blockchain and crypto‐forensics, several issues and challenges remain, including privacy concerns, standardization, and difficulty identifying fraud between crypto‐currencies. Finally, the paper discusses various problems and challenges in blockchain and crypto forensics to investigate cryptocurrency fraud.
The architecture of a reasoning system for Defeasible Deontic Logic

We present the architecture of Houdini-2.0, a reasoning system that computes the extension of a defeasible deontic theory given as input, the process of computing the consequences of the rules expressed in the theory itself. The decision process is a sceptical, non-monotonic, and it allows us to determine which prescriptive behaviours are in force (obligations, permissions, prohibitions) along with propositional ones. The system is based on pre-existing algorithmic solutions, and it is implemented as an online platform to deploy the results of a computation in several use cases, including those that pertain legal domain.
MAB-optimized binary PSO-based feature selection for enhanced classification performance

This paper proposes a novel algorithm built on a reinforcement learning approach as an alternative to standard hyperparameter tuning methods for improving the feature selection process to obtain better classification performance. Various techniques have been proposed to handle the problem, and among them are metaheuristics, such as Particle Swarm Optimization (PSO). However, a new problem arises as learning algorithms must often be tuned in terms of their hyperparameters. We need to solve this problem separately, and vary of methods can be used for this matter. The hyperparameter optimization for the learning process of PSO can be seen as a sequence decision-making process. Hence, we can solve it with the multi-armed bandit problem algorithms.
Preliminary evaluation of virtual tourism content by using a neuroscience approach for the elderly

Japan's tourism market is facing challenges due to the impact of COVID-19 and an aging population. To address this issue, we intend to use virtual (VR) technology to provide virtual travel experiences for older individuals who may otherwise have difficulty traveling due to health concerns. In this preparatory experiment with young participants, we compared several virtual travel conditions using questionnaires and near-infrared spectroscopy (NIRS) technology. Our findings suggest that VR glasses are more effective and popular among young people than smartphones. Both forms of virtual travel have a relaxing effect on young people. This may be because virtual travel is not particularly exciting for them. However, it may have a different effect on older individuals.
LWO vs. LWE Lattice Problems in Human-Computable Passwords Protocol

In this paper, we will compare the results of introducing the well-known Learning with Errors (LWE) and the newly discovered Learning with Options (LWO) lattice problems to a human-computable one-time passwords (OTP) protocol, designed for this purpose. This innovative post-quantum secure challenge-response protocol allows the user to calculate safely many thousands of such passwords, e.g. 6-digit OTP, in about 10 seconds. As a method of human-computer identification, safe also in untrusted systems and environments (Zero Trust), it allows the elimination of any supplementary gadgets/devices or theft-sensitive biometric data used by the Multi-Factor Authentication (MFA), and using only one secret as a universal private key for all obtainable online accounts. Moreover, all these accounts can use OTP calculated from this one secret known only by the user, not the verifier. The secret has the form of an outline similar to a handwritten autograph, designed in invisible ink on the mapping grid. The password generation process requires following such an invisible contour on the challenge matrix created randomly by the verifier and reading values from secret fields to calculate the OTP.
An U-Net Semantic Segmentation Vision System on a Low-Power Embedded Microcontroller Platform

In recent years, real-time semantic segmentation on embedded devices has become increasingly popular, largely driven by the growing interest in smart vehicles and robots. The rising of autonomous driving has brought about new challenges for these systems, such as the need for low latency and computation-intensive operations, which can lead to excessive energy consumption and computing power. To address these challenges, this paper focuses on the critical task of semantic segmentation, which is essential for accurate environment perception, and proposes an implementation that achieves high accuracy and low complexity using a U-Net as base architecture. The goal is to enable real-time semantic segmentation on low-power cores while preserving performance, which is crucial for the success of autonomous vehicles and robots. The lightweight U-Net architectures have been implemented in a STM32 microcontroller, namely STM32L4R5, as a severe benchmark to meet the low-power, low-cost requirements.
Improving Student Performance Prediction Using a PCA-based Cuckoo Search Neural Network Algorithm

The ANN is a commonly used network for pattern recognition, and has been trained for various tasks such as prediction, classification, and engineering. However, this model faces challenges such as local minima and slow convergence, which have been addressed through different strategies such as combining the artificial neural network (ANN) with optimised models like the cuckoo search (CS) algorithm. However, for large datasets, the hybrid ANN-based CS algorithm can lead to overfitting. To overcome this issue, the authors propose a new algorithm called Principal Component Analysis with Cuckoo Search Neural Network (PCACSNN). The performance of this algorithm is compared to other commonly used algorithms such as ANN, backpropagation neural network (BPNN), and cuckoo search backpropagation (CSBP), using the Mean Square Error (MSE) and accuracy on classification problems. The simulations were performed on the Student Performance dataset taken from the UCIMLR. The results show that the proposed model performs better than the other models, achieving high accuracy and low MSE for both mathematics and Portuguese student datasets. For the mathematics students, the suggested model attained an accuracy of (99.32%) with MSE of 2.77E-07 for 70% training data and an accuracy of 98.52% with MSE of 2.50E-04 for 30% training data. Similarly, for the Portuguese student dataset, the proposed model obtained (99.38%) accuracy with MSE of 1.09E-08 for 70% training data and 98.72% accuracy with MSE of 1.01E-04 for 30% training data.
Implementation of a hybrid certificate approach: enhancing efficiency and credibility in ICDL certification process

In today's rapidly evolving job market, qualification certificates play a crucial role in validating an individual's skills, knowledge, and abilities. This article explores the importance of qualification certificates in the modern workforce, their impact on employment prospects and professional development, and the emerging trends shaping their future. It highlights how qualification certificates bridge the skills gap and enhance employability by providing standardized evidence of an individual's proficiency in a specific field. Moreover, it emphasizes the benefits of certifications for professionals in staying relevant and competitive in rapidly changing industries. The article mostly discusses the significance of the printed form of qualification certificates, their role in authentication, credibility, and portability. It explores the emerging trend of digital alternatives to printed certificates and the challenges associated with verifying their authenticity. The proposed solution is a hybrid approach that combines electronic certificates with printed versions, allowing for easy verification of authenticity in both formats. The article presents a detailed description of this approach, focusing on secure QR codes with extended unique identifiers for efficient verification. It further examines the implementation of this approach within the context of the International Computer Driving License (ICDL) Certification Program in Poland. The study demonstrates the effectiveness of the hybrid certificate approach in simplifying the verification process while maintaining credibility and facilitating lifelong learning.
A New Branch and Bound Algorithm for Mining Frequent Conceptual Links in Social Networks

The frequent conceptual links is a descriptive data mining task which aims at describing a social network in term of the most connected type of nodes. This is done by grouping nodes into clusters or groups according to their attributes and checking the number of links between the nodes of each couple of groups, if this number is greater than a predefined threshold, the set of links is referred to as a frequent conceptual link (FCL). Although relatively recent, this task has received a number of research, chiefly in order to optimize the exploration of the search process. Indeed, the problem is defined as NP-hard, where the search process depends on the size of the network, the number of attributes and the set of their possible values whose combination can explode quickly. In this paper, we propose a new algorithm for mining the frequent conceptual links in a social network based on the technique of the branch and bound. In addition to defining an upper bound for the potential patterns in the search space, the algorithm implements other techniques which improve significantly the performance of the search process and allows to fix the shortcomings constated in the previous implementations.
Evaluation Methods and Differences between Three Dimensional And Two Dimensional Movies by Physiological Measurements Using A Commercial 6-Axis Sensor

The term "metaverse" has become a common word in recent years. This term, a virtual space on the Internet, is gaining importance in various fields in today's era of remarkable progress toward the fusion of virtual space and real space using digital science. The authors have been engaged in a project to develop educational applications using experiences in 3D virtual space with headset-type goggles. In this study, we investigated the physiological effects on the viewer by observing and analyzing the blink and discussed its applicability.
Data loss in quantum image representation methods

Quantum Image Representation is a well-studied area in quantum informatics. The first proposed algorithm was the qubit lattice method introduced in 2003. This method was not harnessing the full potential of the quantum system - using as many qubits as there were pixels in the image. The most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using a reduced number of qubits. In this paper, we want to compare these two methods, alongside Local Phase Image Quantum Encoding (LPIQE) method proposed in 2020, to see which method produces images with minimal data loss. Another comparison will be done with image encoded using the LPIQE method with Phase Distortion Unraveling (PDU) error mitigation method, which is done entirely on a classical computer. LPIQE implementation was intended for using the photonic quantum system since the phase-shift is easy to produce, and it will be implemented on real, photonic quantum computers after further development.