Reviewerly/exHarmony huggingface dataset:
Updatable searchable symmetric encryption: Definitions and constructions

Authors:
raw_author_name: Xiwen Wang
raw_affiliation_string: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
raw_author_name: Kai Zhang
raw_affiliation_string: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
raw_author_name: Junqing Gong
raw_affiliation_string: Software Engineering Institure, East China Normal University, Shanghai, China
raw_author_name: Jianting Ning
raw_affiliation_string: College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China; Fujian Provincial Key Laboratory of Network Security and Cryptology, China

Abstract: Searchable symmetric encryption (SSE) allows a client to search over encrypted data. To address the real threat of key compromise in practice, this work initiates the study of key rotation for SSE and introduces the notion of updatable SSE (USSE). In USSE, a client can issue a single update token that permits the server to convert existing encrypted data from the old key to the new key. In particular, we formalize the syntax of USSE and define the security model that captures the inference of key, search token, update token and encrypted data with bi-/uni-/no-directional key updates and bi-/uni-directional encrypted data updates. we present a USSE scheme that supports conjunctive queries with sub-linear complexity, and prove its security with no-directional key update and bi-directional encrypted data update. We also give extensions for concerning different key/encrypted data updates. we implement our USSE schemes and evaluate the performance with real-world dataset, which illustrates that our schemes achieve practically acceptable computational overhead and communication cost.
Energy-based structural least squares twin support vector clustering

Authors:
raw_author_name: Jiao Zhu
raw_affiliation_string: School of Mathematics and Physics, Anqing Normal University, Anqing, Anhui, 246133, China
raw_author_name: Sugen Chen
raw_affiliation_string: International Joint Research Center of Simulation and Control for Population Ecology of Yangtze River in Anhui Province, Anqing, Anhui, 246133, China; School of Mathematics and Physics, Anqing Normal University, Anqing, Anhui, 246133, China
raw_author_name: Yufei Liu
raw_affiliation_string: School of Mathematics and Physics, Anqing Normal University, Anqing, Anhui, 246133, China
raw_author_name: Cong Hu
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, 214122, China

Abstract: Clustering is an unsupervised learning algorithm and it is widely used in machine learning. Twin support vector clustering (TWSVC) is a new plane-based clustering algorithm, which exploits information from both within and between clusters to generate plane for each cluster. However, TWSVC suffered from relatively poor performance because it ignores the intrinsic structural information of data and solves a series of quadratic programming problems (QPPs). In order to address these problems, in this paper, we propose a novel energy-based structural least squares twin support vector clustering, termed as ESLSTWSVC. Firstly, based on least squares twin support vector clustering (LSTWSVC), we introduce within-class covariance matrix into the objective function of LSTWSVC to obtain more intrinsic structural information. ESLSTWSVC solves a series of system of linear equations rather than to solve QPPs in TWSVC, it leads to simple algorithm and less computation time. In addition, ESLSTWSVC converts the constraints of LSTWSVC into energy-based model by introducing an energy parameter for each cluster that makes ESLSTWSVC more robust. Experiments are performed on artificial datasets as well as UCI datasets, and the experimental results illustrate the effectiveness of the proposed algorithm.
Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Authors:
raw_author_name: Zakia Labd
raw_affiliation_string: Ibn Tofail University
raw_author_name: Said Bahassine
raw_affiliation_string: Hassan II University
raw_author_name: Khalid Housni
raw_affiliation_string: Ibn Tofail University
raw_author_name: Fatima Zahrae Ait Hamou Aadi
raw_affiliation_string: Ibn Tofail University
raw_author_name: Khalid Benabbes
raw_affiliation_string: Ibn Tofail University

Abstract: Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.
A descriptive behavior intention inference framework using spatio-temporal semantic features for human–robot real-time interaction

Authors:
raw_author_name: Liangliang Wang
raw_affiliation_string: School of Computer Science and Artificial Intelligence, Wuhan University of Technology, 430070, Wuhan, China
raw_author_name: Guanglei Huo
raw_affiliation_string: Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology, 362008, Quanzhou, China
raw_author_name: Ruifeng Li
raw_affiliation_string: State Key Laboratory of Robotics and System, Harbin Institute of Technology, 150001, Harbin, China
raw_author_name: Peidong Liang
raw_affiliation_string: Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology, 362008, Quanzhou, China

Abstract: Visual behavior intention inference is crucial for enabling escort robots to interact naturally with humans, which is very challenging due to the big inner-class similarity and the small intra-class distinguishability of successive actions in the assistive scenario. To attain a reliable behavior intention inference, not only the current state of behaviors is concerned, but also the semantic information in both spatial and temporal domains plays an important role. This paper presents a segmentation–detection–recognition hierarchical system to represent the spatio-temporal semantic features for formulating descriptions of body parts, trajectories and deep relationships of sub-behaviors. Specifically, a dense trajectory matching scheme based on temporal sampling and Binarized Normed Gradients (BING) algorithm is formulated to segment the 3-Dimensional (3D) behavior cubes, based on which, local trajectories are obtained by clustering dense trajectories according to the distance similarity, and the body parts are then detected by multi-kernel learning of the encoded local features. Moreover, a global three-stream context Convolutional Neural Networks (CNN) is proposed for behavior classification by designing a texture module using expansion, connection and 1D convolution implementations. Based on transfer learning, scene information is also recognized efficiently. Finally, the semantic descriptors are modeled by two cascaded And-Or Graphs (AoGs) constraining the spatial scenarios and temporal sequences. Our unified approach is demonstrated on two public benchmarks containing long-term activities and on an escort robot for real-world applications.
Self-supervised learning with randomized cross-sensor masked reconstruction for human activity recognition

Authors:
raw_author_name: Aleksej Logacjov
raw_affiliation_string: Department of Computer Science, Norwegian University of Science and Technology, Trondheim, 7034, Trøndelag, Norway
raw_author_name: Kerstin Bach
raw_affiliation_string: Department of Computer Science, Norwegian University of Science and Technology, Trondheim, 7034, Trøndelag, Norway

Abstract: Self-supervised learning (SSL) has gained prominence in the field of accelerometer-based human activity recognition (HAR) due to its ability to learn from both labeled and unlabeled data. While labeled data acquisition is costly, it is relatively easy to accumulate unlabeled sensor data. However, few works utilize large-scale, unlabeled datasets for pre-training despite its positive impact on downstream HAR performance, shown in recent work. Cross-sensor upstream training has also received limited attention. We introduce a new auxiliary task, randomized cross-sensor masked reconstruction (RCSMR), for SSL. We pre-train a transformer encoder on the large-scale HUNT4 dataset with RCSMR. The resulting model exhibits better performance on two downstream datasets with the same sensor setup as HUNT4 (HARTH and HAR70+), achieving an average F1-score of 74.03%, surpassing two other auxiliary tasks (70.51% to 72.78%) and five supervised baselines (47.51% to 58.84%). Moreover, when applied to three datasets with sensor configurations distinct from HUNT4 (USC-HAD, PAMAP2, MobiAct), RCSMR outperforms nine state-of-the-art SSL methods, with an F1-score of 72.99% compared to F1-scores ranging from 51.46% to 69.88%. We further show that certain activities exhibit improved separability when utilizing latent representations learned through RCSMR, indicating reduced sensor position and orientation bias. Our method is applied in large-scale epidemiological studies, offering valuable insights into the impact of physical activity behavior on public health.
Multi-level attention network: Mixed time–frequency channel attention and multi-scale self-attentive standard deviation pooling for speaker recognition

Authors:
raw_author_name: Lihong Deng
raw_affiliation_string: School of Computer and Artificial Intelligence of Southwest Jiaotong University, China
raw_author_name: Fei Deng
raw_affiliation_string: College of Computer Science and Cyber Security, Chengdu University of Technology, China
raw_author_name: Peifan Jiang
raw_affiliation_string: College of Computer Science and Cyber Security, Chengdu University of Technology, China
raw_author_name: Gexiang Zhang
raw_affiliation_string: School of Automation, Chengdu University of Information Technology, China
raw_author_name: Qiang Yang
raw_affiliation_string: School of Automation, Chengdu University of Information Technology, China

Abstract: In this paper, we propose a more efficient lightweight speaker recognition network, the multi-level attention network (MANet). MANet aims to generate more robust and discriminative speaker features by emphasizing features at different levels in the speaker recognition network through multi-level attention. The multi-level attention contains mixed time–frequency channel (MTFC) attention and multi-scale self-attentive standard deviation pooling (MSSDP). MTFC attention combines channel, time, and frequency information to capture global features and model long-term contexts. MSSDP can capture changes in frame-level features and aggregate frame-level features with different scales, generating a long-term, robust, and discriminative utterance-level feature. Therefore, MANet emphasizes the features of different levels. We performed extensive experiments on two popular datasets, Voxceleb and CN-Celeb. The proposed method is compared with the current state-of-the-art speaker recognition methods. It achieved EER/minDCF of 1.82%/0.1965, 1.94%/0.2059, 3.69%/0.3626, and 11.98%/0.4814 on the test sets Voxceleb1-O, Voxceleb1-E, Voxceleb1-H, and CN-Celeb, respectively. It is a more effective lightweight speaker recognition network, superior to most large speaker recognition networks and all lightweight speaker recognition networks tested, with an improved performance of 64% compared to the baseline system ThinResNet-34. Compared to the lightest EfficientTDNN-Small, it has only 0.6 million more parameters but 63% better performance. The performance of MANet is only 4% different compared to the state-of-the-art large model LE-Conformer. In the ablation experiments, our proposed attention method and aggregation model achieved the best experimental performance in Voxceleb1-O with EER/minDCF of 2.46%/0.2708, 2.39%/0.2417, respectively, which indicates that our proposed methods are a significant improvement over previous methods.
GCCF: A lightweight and scalable network for underwater image enhancement

Authors:
raw_author_name: Chufan Liu
raw_affiliation_string: School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China
raw_author_name: Xin Shu
raw_affiliation_string: School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China
raw_author_name: Dan Xu
raw_affiliation_string: School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China
raw_author_name: Jinlong Shi
raw_affiliation_string: School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China

Abstract: Underwater images suffer from color deviation and low contrast arising from light attenuation and scatter, making them act as a drag on exploring and exploiting ocean resources. Existed underwater image enhancement algorithms are either inflexible due to scene-dependent design or redundant for ignoring prior knowledge. To address the above problems, we propose a lightweight and scalable deep learning-based network that consists of a Grouped Color Compensation encoder and channel Fusion decoder, abbreviated GCCF. Concretely, the least absorbed green channel contains abundant information compared to the red and blue channels. Taking this into account, the encoder extracts information independently from three grouped features according to the color. Simultaneously, the encoder flexibly compensates for the red and blue channels from the green channel with a proposed learnable module. Then, the decoder gradually fuses the grouped features into a whole part and produces competitive outputs from the integrated features. Extensive experiments on real-world underwater images demonstrate that the proposed GCCF achieves outstanding results in subjective visual comparisons and objective metrics.
Multi-UAV airborne UV area coverage and task assignment method

Authors:
raw_author_name: Taifei Zhao
raw_affiliation_string: Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; Xi'an Key Laboratory of Wireless Optical Communication and Network Research, Xi'an, Shaanxi 710048, China
raw_author_name: Chenyu Ai
raw_affiliation_string: Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
raw_author_name: Jiatong Yao
raw_affiliation_string: Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
raw_author_name: Jiahao Guo
raw_affiliation_string: Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China

Abstract: To address the area coverage problem in large-scale reconnaissance and detection tasks, Unmanned Aerial Vehicles(UAVs) utilize Ultraviolet(UV) light for flight guidance. Additionally, a UV light power control method is implemented to assist UAVs in adapting to various terrains. Based on the flight characteristics of quadrotor UAVs, the target area is divided into a grid-like format. This transformation helps convert the area coverage task into a multi-UAV path planning problem on a raster map. To address this, constraints are established for assigning tasks to multiple UAVs. An algorithm is proposed to assign the target area uniformly based on the initial position of the UAV, ensuring full coverage. Additionally, the algorithm considers the differences in endurance among multiple UAVs. This improved algorithm is highly suitable for real-life application scenarios. The simulation results demonstrate the power control changes in wireless UV-guided UAV flight. Moreover, the assignment results of the improved algorithm align closely with the theoretical assignment results, considering different parameters like the number of UAVs, initial position, and endurance capacity.
Research on the construction scheme of smart library based on blockchain technology

Authors:
raw_author_name: Jin Shang
raw_affiliation_string: Control Technology Institute, Wuxi Institute of Technology, Wuxi, 214121, China

Abstract: Facial recognition technology and recommendation systems are the main technologies in the construction of intelligent libraries, but both technologies face privacy breaches and credibility issues. Blockchain, as an emerging technology, is having significant impact in many fields. This article delves into the role and core application value of blockchain technology in the construction of smart libraries in universities, and proposes smart library service architecture based on blockchain technology. This architecture provides a value-added path for smart library services, including secure storage, resource sharing, and optimizing book borrowing and returning systems. It can improve the quality of resource services and meet the increasing service needs of readers. The effectiveness and practicality of this method have been verified through experiments.
Some relations between the irreducible polynomials over a finite field and its quadratic extension

Authors:
raw_author_name: Ryul Kim
raw_affiliation_string: Faculty of Mathematics, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

Abstract: In this paper, we establish some relations between irreducible polynomials over a finite field Fq and its quadratic extension Fq2. First we consider a relation between the numbers of irreducible polynomials of a fixed degree over Fq and Fq2, and some relations between self-reciprocal irreducible polynomials over Fq and self-conjugate-reciprocal irreducible polynomials over Fq2. We also obtain formulas for the number and the product of all self-conjugate-reciprocal irreducible monic (SCRIM) polynomials over Fq2.
A differential evolution algorithm for solving mixed-integer nonlinear programming problems

Authors:
raw_author_name: Efrén Mezura-Montes
raw_affiliation_string: Artificial Intelligence Research Institute, University of Veracruz, Xalapa, Veracruz, Mexico
raw_author_name: Edgar Alfredo Portilla-Flores
raw_affiliation_string: UPIIT, Instituto Politécnico Nacional, Tlaxcala, Mexico
raw_author_name: Eduardo Vega-Alvarado
raw_affiliation_string: CIDETEC, Instituto Politécnico Nacional, Ciudad de México, Mexico

Abstract: Many engineering optimization problems fall into the category of Mixed-Integer Nonlinear Programming (MINLP) problems, which combine nonlinear relations, constraint conditions, and different types of variables, including continuous, integer, and/or discrete variables. Solving MINLP problems can be a challenging exploration process since their landscape can be composed of many discontinuous feasible parts with different sizes. In this scheme, Evolutionary Algorithms (EAs) often fail to generate enough diversity to explore the discontinuous feasible parts. Consequently, EAs are vulnerable to being attracted to larger discontinuous feasible parts, even if they are not promising regions. In this paper, a new proposal based on two fundamental strategies is presented to improve the performance of the differential evolution algorithm when solving MINLP problems. The first strategy considers a set of "good fitness-infeasible solutions" that contribute to exploring promising regions from infeasible contours. It reduces the vulnerability of the solutions to be attracted to larger discontinuous feasible parts with unpromising objective function values. The second is a composite trial vector generation to improve the combinatorial exploration while ensuring a robust convergence capacity toward the final solution. Sixteen well-known MINLP problems are used in several experiments to evaluate the performance of the proposed algorithm, comparing it to state-of-the-art EAs. The results provided by the proposal show a better performance in terms of quality, robustness, and computational cost.
CoCo-trie: Data-aware compression and indexing of strings

Authors:
raw_author_name: Antonio Boffa
raw_affiliation_string: Department of Computer Science, University of Pisa, L.go B. Pontecorvo 3, Pisa 56127, PI, Italy
raw_author_name: Paolo Ferragina
raw_affiliation_string: Department of Computer Science, University of Pisa, L.go B. Pontecorvo 3, Pisa 56127, PI, Italy
raw_author_name: Francesco Tosoni
raw_affiliation_string: Department of Computer Science, University of Pisa, L.go B. Pontecorvo 3, Pisa 56127, PI, Italy
raw_author_name: Giorgio Vinciguerra
raw_affiliation_string: Department of Computer Science, University of Pisa, L.go B. Pontecorvo 3, Pisa 56127, PI, Italy

Abstract: We address the problem of compressing and indexing a sorted dictionary of strings to support efficient lookups and more sophisticated operations, such as prefix, predecessor, and range searches. This problem occurs as a key task in a plethora of applications, and thus it has been deeply investigated in the literature since the introduction of tries in the ’60s. We introduce a new data structure, called the COmpressed COllapsed Trie (CoCo-trie), that hinges on a pool of techniques to compress subtries (of arbitrary depth) into succinctly-encoded and efficiently-searchable trie macro-nodes with a possibly large fan-out. Then, we observe that the choice of the subtries to compress depends on the trie structure and its edge labels. Hence, we develop a data-aware optimisation approach that selects the best subtries to compress via the above pool of succinct encodings, with the overall goal of minimising the total space occupancy and still achieving efficient query time. We also investigate some variants of this approach that induce interesting space–time trade-offs in the CoCo-trie design. Our experimental evaluation on six diverse and large datasets (representing URLs, XML data, DNA and protein sequences, database records, and search-engine dictionaries) shows that the space–time performance of well-established and highly-engineered data structures solving this problem is very input-sensitive. Conversely, our CoCo-trie provides a robust and uniform improvement over all competitors for half of the datasets, and it results on the Pareto space–time frontier for the others, thus offering new competitive trade-offs.