Reviewerly/exHarmony huggingface dataset:
A two-step passenger flow anomaly detection scheme based on machine learning methods

Authors:
raw_author_name: Lujie Ruan
raw_affiliation_string: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming,  650500, Yunnan, China
raw_author_name: Xin Ouyang
raw_affiliation_string: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
raw_author_name: WenCong Wang
raw_affiliation_string: Hekou Yao Autonomous County Transportation Bureau, Hekou, 661300, Yunnan, China

Abstract: Abstract Subway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.
Statistical Fault Analysis of TinyJambu

Authors:
raw_author_name: Iftekhar Salam
raw_affiliation_string: School of Computing and Data Science, Xiamen University Malaysia, 43900, Sepang, Selangor, Malaysia
raw_author_name: Janaka Alawatugoda
raw_affiliation_string: Research & Innovation Centers Division, Rabdan Academy, P.O. Box 114646, Abu Dhabi, United Arab Emirates

Abstract: Abstract The resource-constrained IoT devices have limited resources such as processing power, memory, and battery capacity. Therefore it is challenging to adopt traditional cryptographic algorithms on them. In order to find a solution, the National Institute of Standards and Technology (NIST) initiated the Lightweight Cryptography (LWC) competition to standardize cryptographic algorithms for resource-constrained devices. The primary aim of our work is to implement and analyse selected finalist algorithms from the NIST competition using modern cryptanalysis techniques, with a focus on statistical fault attacks. Traditional analysis methods, such as linear and differential analysis, were not prioritized as most finalist algorithms have established defences against these methods. We implemented six of the selected finalist algorithms from the competition: Ascon, Elephant, GIFT-COFB, ISAP, TinyJambu, and Xoodyak. We chose TinyJumbu for statistical fault analysis because of its attractiveness, compact block size, and provision of a more lightweight keyed permutation.
Embrace sustainable AI: Dynamic data subset selection for image classification

Authors:
raw_author_name: Zimo Yin
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai 200433, China
raw_author_name: Jian Pu
raw_affiliation_string: Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
raw_author_name: Xiangyang Xue
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai 200433, China

Abstract: Data selection is commonly used to reduce costs and energy usage by training on a subset of available data. However, determining the appropriate subset size requires extensive dataset knowledge and experimentation, limiting transferability. Varying the validation set also produces unstable results and wastes computational resources. In this paper, we propose a data selection method for dynamically determining subset ratios based on model performance using only a training set. The data search space is narrowed through weighted sampling, leveraging statistical selection patterns. Parallel analysis of class distributions identifies the most representative samples with high selection potential. Extensive experiments validate our approach and demonstrate improved training efficiency. Our method speeds up various subset ratios by up to 2.2x on CIFAR-10, 1.9x on CIFAR-100, 2.0x on TinyImageNet, and 2.1x on ImageNet with negligible accuracy drops.
Discriminating quartz host rock based on its trace element chemistry using machine learning-a new tool for sedimentary provenance studies

Authors:
raw_author_name: Rupashree Saha
raw_affiliation_string: Department of Geology and Geophysics, Indian Institute of Technology, Kharagpur 721302, India
raw_author_name: Dewashish Upadhyay
raw_affiliation_string: Department of Geology and Geophysics, Indian Institute of Technology, Kharagpur 721302, India
raw_author_name: Biswajit Mishra
raw_affiliation_string: Department of Geology and Geophysics, Indian Institute of Technology, Kharagpur 721302, India

Abstract: Quartz is one of the most abundant mineral in the continental crust, occurring in a wide variety of igneous, metamorphic, and sedimentary rocks. It incorporates several trace elements such as Ti, Al, Li, Ge, B, and P in its structure during crystallization/precipitation from magma/fluid, which can potentially be used as petrogenetic indicators. Being physically and chemically resilient during sedimentary recycling, detrital quartz can retain the signatures of the physicochemical environment of their formation. Earlier studies have mostly attempted to discriminate quartz from magmatic and magmatic-hydrothermal systems using bivariate or ternary discrimination diagrams. In this study, we developed three robust machine learning (ML) methods (XGBoost, LightGBM, and CatBoost models) to discriminate quartz from a wide variety of magmatic/magmatic-hydrothermal host rocks based on their trace element chemistry. For this purpose, we compiled published 11,267 trace element analyses of quartz grains considering 50 trace elements from 14 different host rocks, namely A-, S-, I- types granitoids and pegmatites, A-, I- types volcanic rocks, A-, S- types granite greisen, hydrothermal veins associated with A-, S-, I- type intrusions, and metamorphic rocks. The three ML classifiers can successfully discriminate quartz from the aforementioned groups with an average accuracy of ~95%. Quartz hosted in each system can be correctly predicted with >75% classification accuracy, except for I-type pegmatite. The Shapley Additive exPlanations (SHAP) algorithm, which measures the contribution of each element to the output predicted by the ML models, indicates that Ti, Li, Ge, Al, B, and P exert the highest control on the ML classifiers. We propose our ML models as a new tool for constraining sedimentary provenance and demonstrate the usefulness of our approach using the trace element data of detrital quartz from sediments in the catchment area of the Bega River in the Lachlan Fold Belt, Australia.
Evaluation of 3D optical techniques for modal analysis during climatic test inside environmental chamber

Authors:
raw_author_name: Manuel Pastor-Cintas
raw_affiliation_string: Departamento de Ingeniería Mecánica y Minera, Campus Científico Tecnológico de Linares, Universidad de Jaén, 23700 Linares, Spain
raw_author_name: Luis Felipe-Sesé
raw_affiliation_string: Departamento de Ingeniería Mecánica y Minera, Campus Científico Tecnológico de Linares, Universidad de Jaén, 23700 Linares, Spain
raw_author_name: Ángel J. Molina-Viedma
raw_affiliation_string: Departamento de Ingeniería Mecánica y Minera, Campus Científico Tecnológico de Linares, Universidad de Jaén, 23700 Linares, Spain
raw_author_name: Elías López-Alba
raw_affiliation_string: Departamento de Ingeniería Mecánica y Minera, Campus Las Lagunillas, Universidad de Jaén, 23071 Jaén, Spain
raw_author_name: Francisco A. Díaz
raw_affiliation_string: Departamento de Ingeniería Mecánica y Minera, Campus Las Lagunillas, Universidad de Jaén, 23071 Jaén, Spain

Abstract: The increasing demand for ensuring the reliability of industrial components under diverse temperature and dynamic solicitations has led to develop standards and testing methods that incorporate both thermal and vibration loads to analyse the dependency of mechanical properties with temperature. This research presents an assessment of a novel experimental approach that combines 3D full-field optical techniques and an environmental chamber to perform modal identification under different temperatures. The study performs a thorough comparative of scanning Laser Doppler Vibrometry (SLDV) and Digital Image Correlation (DIC) techniques. By integrating the environmental chamber with these optical methodologies, valuable insights into the behaviour of components were obtained. Both SLDV and DIC successfully identify variations in modal parameters due to temperature changes. Nevertheless, the results obtained employing SLDV were proved less effective for a reasonable test duration.
A new McEliece-type cryptosystem using Gabidulin-Kronecker product codes

Authors:
raw_author_name: Zhe Sun
raw_affiliation_string: Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, 300071, China
raw_author_name: Jincheng Zhuang
raw_affiliation_string: Quan Cheng Laboratory, Jinan, 250103, China
raw_author_name: Fang-Wei Fu
raw_affiliation_string: Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, 300071, China

Abstract: This paper presents a new McEliece-type cryptosystem using Gabidulin-Kronecker product codes in the rank metric. The contributions of this paper are as follows. Firstly, we propose a new Gabidulin-Kronecker product code which is a kind of block circulant code, and give an efficient decoding algorithm. Secondly, we design a one-way secure public key encryption scheme based on the Gabidulin-Kronecker product codes. Thirdly, we obtain an IND-CCA2 secure public key encryption scheme by converting our one-way secure public key encryption scheme under the hardness assumption of the RSD Dual Problem. In terms of efficiency, our scheme has a smaller public key size by taking advantage of the block circulant structure. For 128-bit security, the public key size of our proposal is 13% of Lau-Tan's cryptosystem (in the rank metric), and 19% of BIKE (in the Hamming metric). In terms of security, our scheme can resist Overbeck attack, Coggia-Couvreur attack and Sendrier attack.
Sparsifying Count Sketch

Authors:
raw_author_name: Bhisham Dev Verma
raw_affiliation_string: Indian Institute of Technology Mandi, Himachal Pradesh, India
raw_author_name: Rameshwar Pratap
raw_affiliation_string: Indian Institute of Technology Hyderabad, Telangana, India
raw_author_name: Punit Pankaj Dubey
raw_affiliation_string: Indian Institute of Technology Mandi, Himachal Pradesh, India

Abstract: The seminal work of Charikar et al. [1] called Count-Sketch suggests a sketching algorithm for real-valued vectors that has been used in frequency estimation for data streams and pairwise inner product estimation for real-valued vectors etc. One of the major advantages of Count-Sketch over other similar sketching algorithms, such as random projection, is that its running time, as well as the sparsity of sketch, depends on the sparsity of the input. Therefore, sparse datasets enjoy space-efficient (sparse sketches) and faster running time. However, on dense datasets, these advantages of Count-Sketch might be negligible over other baselines. In this work, we address this challenge by suggesting a simple and effective approach that outputs (asymptotically) a sparser sketch than that obtained via Count-Sketch, and as a by-product, we also achieve a faster running time. Simultaneously, the quality of our estimate is closely approximate to that of Count-Sketch. For frequency estimation and pairwise inner product estimation problems, our proposal Sparse-Count-Sketch provides unbiased estimates. These estimations, however, have slightly higher variances than their respective estimates obtained via Count-Sketch. To address this issue, we present improved estimators for these problems based on maximum likelihood estimation (MLE) that offer smaller variances even w.r.t. Count-Sketch. We suggest a rigorous theoretical analysis of our proposal for frequency estimation for data streams and pairwise inner product estimation for real-valued vectors.
Distributed representations of entities in open-world knowledge graphs

Authors:
raw_author_name: Lingbing Guo
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, China; Donghai Laboratory, China; Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph, China
raw_author_name: Zhuo Chen
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, China; Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph, China
raw_author_name: Jiaoyan Chen
raw_affiliation_string: Department of Computer Science, The University of Manchester, UK
raw_author_name: Yichi Zhang
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, China; Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph, China
raw_author_name: Zequn Sun
raw_affiliation_string: Department of Computer Science and Technology, Nanjing University, China
raw_author_name: Zhongpu Bo
raw_affiliation_string: Ant Group, China
raw_author_name: Yin Fang
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, China; Donghai Laboratory, China; Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph, China
raw_author_name: Xiaoze Liu
raw_affiliation_string: Elmore Family School of Electrical and Computer Engineering, Purdue University, USA
raw_author_name: Huajun Chen
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, China; Donghai Laboratory, China; Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph, China
raw_author_name: Wen Zhang
raw_affiliation_string: School of Software Technology, Zhejiang University, China; Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph, China

Abstract: Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge graphs where new entities emerge frequently. To address this limitation, we introduce Decentralized Attention Network (DAN). DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings. To effectively train a DAN, we introduce self-distillation, a technique that guides the network in generating desired representations. Theoretical analysis validates the effectiveness of our approach. We implement an end-to-end framework and conduct extensive experiments to evaluate our method, showcasing competitive performance on conventional entity alignment and entity prediction tasks. Furthermore, our method significantly outperforms existing methods in open-world settings.
Dataset for Bibliometric Data-Driven Research Team Formation: Case of Politehnica University of Timisoara scholars for the interval 2010-2022

Authors:
raw_author_name: Christian-Daniel Curiac
raw_affiliation_string: Computer and Information Technology Department, Politehnica University of Timisoara, V. Parvan 2, 300223 Timisoara, Romania
raw_author_name: Mihai Micea
raw_affiliation_string: Computer and Information Technology Department, Politehnica University of Timisoara, V. Parvan 2, 300223 Timisoara, Romania
raw_author_name: Daniel-Ioan Curiac
raw_affiliation_string: Automation and Applied Informatics Department, Politehnica University of Timisoara, V. Parvan 2, 300223 Timisoara, Romania
raw_author_name: Alex Doboli
raw_affiliation_string: Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794-2350, USA

Abstract: Data-driven expert team formation is a complicated and multifaceted process that requires access to accurate information to identify researchers’ areas and level of expertise and their collaborative prospects. In this respect, bibliometric data represents a valuable and reliable source of information that can be effectively employed in revealing key insights regarding candidates. Due to its complex and complete structure of publication metadata records, IEEE Xplore database may offer the possibility to compute an extensive set of indicators about researchers’ publication production and how they have interacted during time. Considering the case of Politehnica University of Timisoara scholars for the interval 2010-2022, current dataset encapsulates relevant and rich information for assembling multidisciplinary research teams, being also a testing ground for experimenting and calibrating the expert team formation methods and mechanisms.
Improved likelihood ratios for face recognition in surveillance video by multimodal feature pairing

Authors:
raw_author_name: Andrea Macarulla Rodriguez
raw_affiliation_string: Fundacion Vicomtech, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 57, Donostia-San Sebastian, 20009, Spain; Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands; University of Amsterdam, Science Park 904, Amsterdam, 1098XH, the Netherlands
raw_author_name: Zeno Geradts
raw_affiliation_string: Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands; University of Amsterdam, Science Park 904, Amsterdam, 1098XH, the Netherlands
raw_author_name: Marcel Worring
raw_affiliation_string: University of Amsterdam, Science Park 904, Amsterdam, 1098XH, the Netherlands
raw_author_name: Luis Unzueta
raw_affiliation_string: Fundacion Vicomtech, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 57, Donostia-San Sebastian, 20009, Spain

Abstract: In forensic and security scenarios, accurate facial recognition in surveillance videos, often challenged by variations in pose, illumination, and expression, is essential. Traditional manual comparison methods lack standardization, revealing a critical gap in evidence reliability. We propose an enhanced images-to-video recognition approach, pairing facial images with attributes like pose and quality. Utilizing datasets such as ENFSI 2015, SCFace, XQLFW, ChokePoint, and ForenFace, we assess evidence strength using calibration methods for likelihood ratio estimation. Three models-ArcFace, FaceNet, and QMagFace-undergo validation, with the log-likelihood ratio cost (Cllr) as a key metric. Results indicate that prioritizing high-quality frames and aligning attributes with reference images optimizes recognition, yielding similar Cllr values to the top 25% best frames approach. A combined embedding weighted by frame quality emerges as the second-best method. Upon preprocessing facial images with the super resolution CodeFormer, it unexpectedly increased Cllr, undermining evidence reliability, advising against its use in such forensic applications.
A semi-automatic approach for joint orientation recognition using 3D trace network analysis

Authors:
raw_author_name: Jineon Kim
raw_affiliation_string: Department of Energy Resource Engineering, Seoul National University, Seoul, South Korea
raw_author_name: Jae-Joon Song
raw_affiliation_string: Department of Energy Resource Engineering, Seoul National University, Seoul, South Korea

Abstract: Identifying rock mass discontinuities and their plane orientation are crucial factors when determining rock mass characteristics. Rock mass discontinuity mapping is fundamentally dependent on joint trace surveying since traces of them are most often the only visible features in rock outcrops. Traditional methods for joint trace surveying using tape and a geological compass are challenging, time-consuming, and hazardous. Fortunately, non-contact measuring techniques offer the advantage of generating accurate objective records of rock masses and enabling the measurement of discontinuities from digital surface models and 3D point clouds of outcrops without requiring direct access to the rock mass and associated constraints. Herein, an innovative approach for identifying discontinuity planes in rock formations using 3D trace data is presented. We introduce the concept of curved and straight traces, with a curvature index indicating the trace's accuracy by representing its discontinuity plane. In addition, we identified co-planar traces by analyzing intersecting straight traces, thereby further contributing to discontinuity plane determination. The methodology's effectiveness was established through validation using a predefined 3D trace network of discontinuity planes with known orientations on a 3D digital rock outcrop model. The methodology was then applied to trace data collected from an actual rock outcrop. The algorithm successfully matched >66% of traces with their corresponding discontinuity planes. After clustering, 80% of the identified planes aligned with principal joint sets within the rock mass. These identified planes accurately aligned with their expected jointing pattern, thereby validating the robustness of the method. Despite challenges presented by discontinuous and complex trace segments obtained from semi-automatic trace detection techniques, the algorithm effectively processed such traces, thereby enabling a comprehensive understanding of the rock mass's jointing system. This enables swift identification of the main joint orientations. We leveraged stereonet analysis to identify principal joint sets using kernel-based and fuzzy C-means clustering techniques. Our approach represents an important advancement in the characterization of rock mass structural properties.
Security issues in visual cryptography scheme with known random number sequence

Authors:
raw_author_name: Zhengxin Fu
raw_affiliation_string: PLA SSF Information Engineering University, Zhengzhou 450000, China
raw_author_name: Xiaopeng Li
raw_affiliation_string: PLA SSF Information Engineering University, Zhengzhou 450000, China
raw_author_name: Liguo Fang
raw_affiliation_string: PLA SSF Information Engineering University, Zhengzhou 450000, China
raw_author_name: Yi Feng
raw_affiliation_string: PLA SSF Information Engineering University, Zhengzhou 450000, China
raw_author_name: Bin Yu
raw_affiliation_string: PLA SSF Information Engineering University, Zhengzhou 450000, China

Abstract: The visual cryptography scheme (VCS) is usually considered unconditionally secure, meaning that participants in the forbidden subset cannot access any secret information. However, this assumes that the random number sequence (RNS) used in the secret sharing process is completely inaccessible to attackers. In reality, the random number generator could have vulnerabilities or backdoors, allowing attackers to obtain RNS. Thus, we study an extreme scenario where attackers successfully acquire RNS. Our work explores the relationship between VCS security and RNS, revealing intriguing findings when there is complete or partial leakage of secret information. To enhance VCS security, we propose three innovative algorithms designed to prevent attackers from accessing all original secret image information, even when they possess RNS and shares within the forbidden subset. Theoretical and experimental results demonstrate the effectiveness of these proposed schemes in enhancing the security of existing VCSs.