Reviewerly/exHarmony huggingface dataset:
Textural-compositional evolution of pyrite and metal remobilization during low-grade metamorphism of metapelite: Contribution to gold mineralization in the Luk Ulo Complex, Central Java, Indonesia

Authors:
raw_author_name: Ryohei Takahashi
raw_affiliation_string: Graduate School of International Resource Sciences, Akita University, Japan
raw_author_name: Andrea Agangi
raw_affiliation_string: Department of Geology, University of Johannesburg, South Africa; Graduate School of International Resource Sciences, Akita University, Japan
raw_author_name: Akira Imai
raw_affiliation_string: Department of Earth Resources Engineering, Faculty of Engineering, Kyushu University, Japan
raw_author_name: Hinako Sato
raw_affiliation_string: Graduate School of International Resource Sciences, Akita University, Japan

Abstract: The Cretaceous Luk Ulo Complex (LUC) in Central Java, Indonesia, hosts orogenic gold-type ores with S derived from metapelite. The host rock consists of accreted metapelite, metamorphosed under temperature up to 510 ± 30 °C. We investigated the textural and chemical evolution of pyrite and carbonaceous matter (CM), and analyzed the hosts phases for trace elements (TEs) such as As, Pb, Cu, Sb, Te, Ag, Zn, Bi, Se, Hg, and Au, with the aim of documenting the behavior of these elements during prograde metamorphism and evaluate the implications on gold mineralization. With increasing metamorphic grade, the texture and chemical composition of pyrite evolved through different stages: 1) framboidal pyrite and pyrite nodules in the sedimentary–diagenetic facies, 2) anhedral–subhedral pyrite in sub-greenschist facies (SGSF), 3) euhedral pyrite in lower greenschist facies (LGSF), 4) elongated pyrite in middle greenschist facies (MGSF), and 5) replacement of pyrite by pyrrhotite in upper greenschist facies (UGSF). The framboidal pyrite and pyrite nodules contain measurable TEs. Recrystallization of such pyrite into euhedral pyrite in LGSF resulted in significant depletion of TEs, except for Se, by pressure-solution. With increasing metamorphic grade, the recrystallization of organic matter to “cooked” CM in LGSF was accompanied by significant depletion in most TEs, with only Fe and S being retained. The transformation of cooked CM into graphite in MGSF, and of elongated pyrite into pyrrhotite in UGSF resulted in further removal of remaining TEs. These observations and the absence of para-amphibolite in the LUC suggest that remobilization of TEs and S from sedimentary pyrite and CM might have occurred in the LGSF, much earlier than previously proposed (UGSF-amphibolite transition).
A preliminary study on few-shot knowledge reasoning mechanism based on three-way partial order structure

Authors:
raw_author_name: Enliang Yan
raw_affiliation_string: School of Artificial Intelligence, South China Normal University, Foshan 528225, China
raw_author_name: Tao Zhang
raw_affiliation_string: School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
raw_author_name: Jianping Yu
raw_affiliation_string: School of Foreign Studies, Yanshan University, Qinhuangdao 066004, China
raw_author_name: Tianyong Hao
raw_affiliation_string: School of Artificial Intelligence, South China Normal University, Foshan 528225, China; School of Computer Science, South China Normal University, Guangzhou 510631, China
raw_author_name: Qiliang Chen
raw_affiliation_string: School of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510006, China

Abstract: Few-shot knowledge reasoning, as a novel theory that attempts to combine few-shot learning with knowledge reasoning, is a challenging subject. This article intends to explore few-shot knowledge reasoning mechanism based on three-way partial order structure. First, from the perspective of granular computing, three knowledge reasoning operators are defined: granule splitting, granule combination, and granule isomorphism; then, based on the knowledge reasoning operators, the data augmentation operation and knowledge reasoning algorithm are presented; finally, some specific experiments on five datasets are conducted to further illustrate the few-shot knowledge reasoning algorithm. Experiment results show that the method proposed in this article can effectively realize knowledge reasoning based on small sample size data. It can be concluded that the proposed few-shot knowledge reasoning method is effective at some extent, and is expected to bring inspiration to the development of other concept-based knowledge reasoning models.
Revocable certificateless Provable Data Possession with identity privacy in cloud storage

Authors:
raw_author_name: Kai Zhang
raw_affiliation_string: Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu, China; College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
raw_author_name: Zirui Guo
raw_affiliation_string: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
raw_author_name: Liangliang Wang
raw_affiliation_string: Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu, China; College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
raw_author_name: Lei Zhang
raw_affiliation_string: College of Information Technology, Shanghai Ocean University, Shanghai, China
raw_author_name: Lifei Wei
raw_affiliation_string: College of Information Engineering, Shanghai Maritime University, Shanghai, China

Abstract: Provable Data Possession (PDP) has gained widespread adoption for ensuring the integrity of data in remote cloud storage, where a data owner can delegate a third party auditor (TPA) to perform data auditing. To eliminate key escrow problem or complicated certificate management in classic solutions, numerous certificateless PDP schemes have been proposed while they failed to achieve efficient user revocation and protect user identity privacy. Therefore, we propose ReCIP, a revocable certificateless PDP scheme with identity privacy, where a TPA can perform public data integrity batch verification for a user while learning no useful knowledge about user identity privacy. Technically, we introduce a new user revocation strategy that directly revokes users’ secret keys, with no correlation to the number of data blocks in place for revocation time cost. To further boost the efficiency of ReCIP, we employ a semi-generic online-offline strategy to obtain an online-offline ReCIP (ReCIPoo) to reduce the time cost of tag generation. Moreover, we conduct a formal security proof of ReCIP, where the security is reduced to simple computational Diffie–Hellman problem and discrete logistic problem. Compared to state-of-the-art solutions, our ReCIPoo achieves comparable computation and communication cost while still achieving user revocation and protecting user identity privacy.
Occlusion-Aware Visual Object Tracking Based on Multi-template Updating Siamese Network

Authors:
raw_author_name: Lifan Sun
raw_affiliation_string: Longmen Laboratory, Luoyang, 471023, China; School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China
raw_author_name: Jiayi Zhang
raw_affiliation_string: School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China
raw_author_name: Dan Gao
raw_affiliation_string: School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China
raw_author_name: Bo Fan
raw_affiliation_string: School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China
raw_author_name: Zhumu Fu
raw_affiliation_string: School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

Abstract: Visual object tracking is a crucial area of computer vision research. It aims to accurately track objects in videos with challenges such as occlusion, deformation, and lighting variations. Existing algorithms face difficulties when objects leave the camera or reappear after being occluded, and they also struggle to track objects with significant appearance changes. To address these issues, this study proposed a novel tracking algorithm. It combines tracking and detection, enabling global searching when objects are occluded or disappear and redetecting them when they reappear. A multi-template updating mechanism was used to adapt to changes in appearance. The study proposed the OTB100-AB and UAV123-AB datasets to evaluate the tracker's ability to handle target disappearance, along with the TNR metric. The proposed algorithm was evaluated on these datasets, as were OTB50 and UAV20L, which outperformed state-of-the-art algorithms and significantly improved tracking performance.
The influence of spatial dimensions of virtual environments on attitudes and nonverbal behaviors during social interactions

Authors:
raw_author_name: Eugy Han
raw_affiliation_string: Department of Communication, Stanford University, California, USA
raw_author_name: Cyan DeVeaux
raw_affiliation_string: Department of Communication, Stanford University, California, USA
raw_author_name: Jeffrey T. Hancock
raw_affiliation_string: Department of Communication, Stanford University, California, USA
raw_author_name: Nilam Ram
raw_affiliation_string: Department of Communication, Stanford University, California, USA; Department of Psychology, Stanford University, California, USA
raw_author_name: Gabriella M. Harari
raw_affiliation_string: Department of Communication, Stanford University, California, USA
raw_author_name: Jeremy N. Bailenson
raw_affiliation_string: Department of Communication, Stanford University, California, USA

Abstract: Research on physical-world environments has shown that the spatial properties of built worlds are consequential for shaping psychological states and social behavior. However, it has been difficult to empirically test this in natural settings in the physical world. This study uses immersive virtual reality (VR) environments, which have shown to have comparable effects to physical-world environments, to investigate the influence of two spatial dimensions (ceiling height and floor area) on individuals’ attitudes and nonverbal behaviors during social interactions. In the present study, groups of three to four physically remote participants wore VR headsets (n = 110) and took part in discussions every week for four weeks in one of four virtual environments that varied in their spatial dimensions (low or high ceilings, small or large floor areas). Results showed that, when in a virtual environment with a high ceiling, participants reported feeling greater perceived restorativeness, awe, and momentary affective well-being, compared to when they were in virtual environments with low ceilings. Participants also paid more social attention (i.e., looked at other group members), when they were in virtual environments with high ceilings. When in a virtual environment with a large floor area, participants reported having a greater sense of awe, compared to environments with small floor areas. Furthermore, when in a large environment with a high ceiling, participants physically moved their heads more slowly and virtually stood further apart from their group members, compared to the other three conditions. We discuss implications for theoretical work on context and behaviors as well as design of social VR environments.
Federated learning with a Balanced Heterogeneous-Yoke and Loose restriction

Authors:
raw_author_name: Boyuan Li
raw_affiliation_string: School of Computer and Artificial Intelligence, Zhengzhou University, Henan, China

Abstract: The most critical challenges of federated learning for joint modeling via model sharing are the problem of model drift due to the heterogeneous distribution of local data, and the cooperation of a subset of random devices due to communication constraints. To address these dilemmas, we propose a novel Federated Learning algorithm with a Balanced Heterogeneous-Yoke and Loose restriction(FedByL), which chooses to exploit more historical gradients from the client to accelerate model convergence; in addition, a more relaxed local restriction is chosen in local updates, and an incentive clause is proposed to encourage cooperative clients to find the global optimum. Experiments and analyses on 95 sets of five real-world datasets and one synthetic dataset show that FedByL achieves better in terms of convergence speed and accuracy compared to superior algorithms (e.g. FedDyn, SCAFFOLD). In particular, FedByL communication overhead is the same as FedAvg, being lightweight and requiring only one additional storage variable locally, which greatly reduces the communication burden and the risk of information being hijacked.
SelectE: Multi-scale adaptive selection network for knowledge graph representation learning

Authors:
raw_author_name: Lizheng Zu
raw_affiliation_string: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Lin Lin
raw_affiliation_string: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Song Fu
raw_affiliation_string: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Feng Guo
raw_affiliation_string: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Jinlei Wu
raw_affiliation_string: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China

Abstract: Most knowledge graphs in the real world suffer from incompleteness which can be addressed through knowledge graph representation learning (KGRL) techniques that use known facts to infer missing links. In this paper, a novel multi-scale adaptive selection network for KGRL, namely SelectE, is developed to learn richer multi-scale interactive features and automatically select important features, thereby achieving promising KGRL performance. Specifically, first, the input feature matrix is redesigned to better cooperate with multi-scale convolution to improve the interaction of entities and relations. Second, a multi-scale learning module is designed to learn richer multi-scale features from the input matrix using multiple branches with different kernel sizes. Third, to automatically strengthen the important features and suppress the useless features, a multi-scale adaptive selection mechanism is proposed to dynamically allocate the weights of the obtained features based on their contained information. The core of SelectE is to maximize interactions while also considering how to better utilize features. Finally, the outstanding performance of SelectE is validated by a series of comparison experiments on seven benchmark datasets (FB15k-237, WN18RR, FB15k, WN18, YAGO3-10, KINSHIP, UMLS). The experimental results show that SelectE outperforms other state-of-the-art models, demonstrating its remarkable performance and generalization.
An extended proximity relation and quantified aggregation for designing robust fuzzy query engine

Authors:
raw_author_name: Miroslav Hudec
raw_affiliation_string: Faculty of Economic Informatics, University of Economics in Bratislava, Bratislava, Slovakia; Faculty of Economics, VSB – Technical University of Ostrava, Ostrava, Czech Republic
raw_author_name: Miljan Vučetić
raw_affiliation_string: Artificial Intelligence Department, Vlatacom Institute of High Technology, Belgrade, Serbia; Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia

Abstract: In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users’ requirements. It aims to assist users in recommending similar products or services by retrieving the most suitable entities when the limitations of queries and recommendation approaches are recognized. The proposed fuzzy engine model considers various complex aspects, including imprecise preferences explained by linguistic terms, uncertain data in datasets, connections among elementary requirements, and the lack of historical data necessary for personalized recommendations. To achieve this goal, we propose a similarity matching based on the extended proximity relation and an adapted conformance measure for elementary requirements. In this direction, a new monotonicity property for proximity relation is introduced to ensure consistency in similarities among ordinal categorical data (including binary data) expressed by crisp or fuzzy numbers. Therefore, the conformance measure used to evaluate the similarity between user requirements and attribute values is expressed as a fuzzy number. Next, we propose a quantified aggregation of elementary requirements by strictly monotone fuzzy relative quantifiers. The flexibility is further extended by a convex combination of possibility and necessity measures. A hotel selection experiment is being carried out to explore the potential of the proposed fuzzy query engine. Finally, the limitations and usefulness of the proposed approach are addressed.
CFAuditChain: Audit BlockChain Based On Cuckoo Filter

Authors:
raw_author_name: Kang Liu
raw_affiliation_string: Big Data and Artificial Intelligence Research Institute, China Telecom Corporation Limited Beijing Research Institute , Beijing 102200 , China
raw_author_name: Yang Lu
raw_affiliation_string: Peking University, School of software and microelectronic , Beijing 100871 , China
raw_author_name: Shiyi Tan
raw_affiliation_string: Peking University, School of software and microelectronic , Beijing 100871 , China
raw_author_name: Wei Liang
raw_affiliation_string: Big Data and Artificial Intelligence Research Institute, China Telecom Corporation Limited Beijing Research Institute , Beijing 102200 , China
raw_author_name: Huiping Sun
raw_affiliation_string: Peking University, School of software and microelectronic , Beijing 100871 , China
raw_author_name: Zhong Chen
raw_affiliation_string: Peking University, School of Computer Science , Beijing 100871 , China

Abstract: Abstract Log blockchain can be used to ensure the integrity of log data. However, current methods are facing the problems of throughput mismatch and rough audit granularity. Packaging multiple logs to generate integrity proofs improves throughput but reduces audit granularity, and the auditor can only locate the tampering log packet instead of specific records. This paper proposes CFAuditChain, an audit blockchain based on a cuckoo filter, where the proof of existence (PoE) of the single log is calculated while calculating the integrity proof of packet logs. The PoE is saved in the cuckoo filter and stored using blockchain for immutability. Therefore, the auditor can verify the credibility of each log based on the filter when the integrity proof of a log packet does not match. The theoretical analysis and experimental results show that CFAuditChain provides the granularity of audit logs down to the records level at an acceptable cost.
Identity-Based Encryption with (Almost) Tight Security in the Multi-instance, Multi-ciphertext Setting

Authors:
raw_author_name: Dennis Hofheinz
raw_affiliation_string: ETH Zurich, Zurich, Switzerland
raw_author_name: Christoph Striecks
raw_affiliation_string: AIT Austrian Institute of Technology, Vienna, Austria

Abstract: Abstract We construct an identity-based encryption (IBE) scheme that is tightly secure in a very strong sense. Specifically, we consider a setting with many instances of the scheme and many encryptions per instance. In this setting, we reduce the security of our scheme to a variant of a simple assumption used for a similar purpose by Chen and Wee (CRYPTO 2013, Springer, 2013). The security loss of our reduction is $$\textbf{O} (k)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> (where $$k $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> is the security parameter). Our scheme is the first IBE scheme to achieve this strong flavor of tightness under a simple assumption. Technically, our scheme is a variation of the IBE scheme by Chen and Wee. However, in order to “lift” their results to the multi-instance, multi-ciphertext case, we need to develop new ideas. In particular, while we build on (and extend) their high-level proof strategy, we deviate significantly in the low-level proof steps.
Line segment detection algorithm in image extraction improvement study

Authors:
raw_author_name: Yuemei Ren
raw_author_name: Lei Li

Abstract: In recent years, image processing technology has been developing and maturing, but due to the influence of many interfering factors in the acquisition process, there is a large amount of redundant information in the images obtained. The line segment detection algorithm in image extraction needs to be improved. This study utilizes computer technology to improve the line segment detection technology, and designs a line segment detection algorithm based on the linear detection improvement. Firstly, based on the basic principle of straight line detection algorithm, for the problems of line segment breakage and missing in straight line detection, RGB three-channel grayscale map is applied to detect line segments. Then the detected line segments are connected, merged and deleted. The test results show that the line segment detection algorithm improved based on straight line detection has the highest accuracy rate of 94.50 %, and the average processing time per image is also the lowest at 0.2 s. The algorithm runs faster at 0.25 s and has a higher F-value. It is able to detect the boundaries of a variety of rectangular targets, using the improved line segment detection algorithm has a wide range of applicability, lower error rate, and strong anti-interference ability. The improved line segment detection algorithm has a greater advantage in rectangular target extraction for document, text and book type images.
A penalized criterion for selecting the number of clusters for K-medians

Authors:
raw_author_name: Antoine Godichon-Baggioni
raw_affiliation_string: Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne-Université, 75005 Paris, France

Abstract: Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i.e in the case where K-medians algorithm should be preferred to K-means because of its robustness. More precisely, we concentrate on a common question in clustering: how to chose the number of clusters? The answer proposed here is to consider the choice of the optimal number of clusters as the minimization of a penalized criterion. In this paper, we obtain a suitable penalty shape for our criterion and derive an associated oracle-type inequality. Finally, the performance of this approach with different types of K-medians algorithms is compared on a simulation study with other popular techniques. All studied algorithms are available in the R package Kmedians on CRAN.