Reviewerly/exHarmony huggingface dataset:
Bidirectional utilization of blockchain and privacy computing: Issues, progress, and challenges

Authors:
raw_author_name: Hongwei Zhang
raw_affiliation_string: National Engineering Laboratory for Computer Virus Prevention and Control Technology, Tianjin, 300457, China; School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China; Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin, 300384, China
raw_author_name: Wei Fan
raw_affiliation_string: National Engineering Laboratory for Computer Virus Prevention and Control Technology, Tianjin, 300457, China; School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China; Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin, 300384, China
raw_author_name: Jinsong Wang
raw_affiliation_string: National Engineering Laboratory for Computer Virus Prevention and Control Technology, Tianjin, 300457, China; School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China; Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin, 300384, China

Abstract: With the rapid development of information technology and the increasing popularity of personalized services, the flow of users' personal information in the Internet is inevitable between different platforms and applications, which greatly affects the trust relationship in the digital society. In recent years, an increasing number of researchers have been dedicated to exploring the applications of blockchain and privacy computing technologies in the process of data value release. Scholars have discovered that the combination of the two technologies can alleviate many pain points of each other. Specifically, privacy computing is a computational theory and method for protecting privacy of information throughout its lifecycle. Privacy computing enables the processing, analysis, and computation of data while preserving the confidentiality of the original information. Nevertheless, challenges related to data ownership and benefit distribution impede the establishment of a fair and effective collaborative environment among multiple parties, thereby restricting its further practical development. On the other hand, blockchain is a technology for establishing a trusted platform among mutually untrusted parties, characterized by decentralization, immutability, traceability, and incentive mechanisms. However, blockchain also faces security issues such as transparency of on-chain data and lack of protection in smart contract execution environments. Therefore, this paper aims to comprehensively analyze and explore the integration of blockchain and privacy computing from both perspectives, focusing on bidirectional utilization. We systematically categorize and summarize relevant literature in this field, employing problem-oriented approaches. Finally, we identify the existing challenges and propose potential improvement methods in this field.
Customer acceptance of frontline social robots—Human-robot interaction as boundary condition

Authors:
raw_author_name: Yameng Li
raw_affiliation_string: Newcastle Business School, The University of Newcastle, Newcastle 2300, Australia
raw_author_name: Shah Miah
raw_affiliation_string: Newcastle Business School, The University of Newcastle, Newcastle 2300, Australia

Abstract: From an interactionist perspective, we argue that what happens and how service is delivered during the human-robot interaction may alter the extent to which customers accept service robots. Extending previous research on customer acceptance of service robots and human-robot interaction, we treat the elements during the human-robot interaction process as boundary conditions for the link between service robots' functional and social-emotional capabilities. Specifically, we examine (1) contact frequency between customers and service robots, (2) interdependence among service robots and human service employees, and (3) service complexity, moderate the relationship between service robots' capabilities and customer acceptance. With data collected from 997 customers who have past experience with service robots, we found that the effect of functional and social-emotional capabilities of service robots on customer acceptance are more salient when contact frequency is low rather than high, interdependence among service robots and service employees is high rather than low, and service complexity is low rather than high. Our findings provide new insights into customer acceptance of social robots in the service settings.
Mineral exploration using trace element composition of pyrite grains from till: A case study from the Petäjäselkä Au occurrence, northern Finland

Authors:
raw_author_name: Jukka-Pekka Ranta
raw_affiliation_string: Oulu Mining School, University of Oulu, P.O. Box 3000, FI-90014 Oulu, Finland
raw_author_name: Pertti Sarala
raw_affiliation_string: Oulu Mining School, University of Oulu, P.O. Box 3000, FI-90014 Oulu, Finland
raw_author_name: Xuan Liu
raw_affiliation_string: Geological Survey of Finland, P.O. Box 96, FI-02151 Espoo, Finland
raw_author_name: Sabine Gilbricht
raw_affiliation_string: TU Bergakademie Freiberg, Germany
raw_author_name: Ferenc Molnár
raw_affiliation_string: Department of Mineralogy, Institute of Geography and Earth Sciences, Eötvös Lorand University, Hungary

Abstract: Geochemical anomalies and indicator mineral studies in glacial sediments are commonly used to define mineral exploration targets in glaciated areas. Trace element composition of sediment-hosted heavy minerals, such as pyrite and chalcopyrite, can be potentially used to fingerprint mineral deposits. This study presents results from trace element compositions of pyrite in the heavy mineral concentrates of till and mineralized bedrock samples from the orogenic gold ore occurrences in the Petäjäselkä area, Central Lapland belt, northern Finland. The trace element contents of pyrite grains were evaluated by using a robust compositional data analysis workflow, including symmetric pivot coordinates and robust principal component analysis (PCA). Four different pyrite textures (A-D) were identified from the gold ore occurrences. Compositional data analysis showed that the heavy mineral pyrite correlated with Pyrite D in the ore occurrence and confirmed the discrimination power of pyrite in the heavy mineral grains as a tool for source characterization of transported sedimentary material. The study also discusses the difficulties of finding suitable grains for the LA-ICP-MS, highlighting the importance of proper heavy mineral grain recovery procedures and the factors of oxidization and weathering processes influencing the geochemical compositions of the pyrite grains.
Parallel Acyclic Joins: Optimal Algorithms and Cyclicity Separation

Authors:
raw_author_name: Xiao Hu
raw_affiliation_string: University of Waterloo, Waterloo, Canada
raw_author_name: Yufei Tao
raw_affiliation_string: Chinese University of Hong Kong, Hong Kong, China

Abstract: We study equi-join computation in the massively parallel computation (MPC) model. Currently, a main open question under this topic is whether it is possible to design an algorithm that can process any join with load O(N polylog N/p 1/ρ* ) — measured in the number of words communicated per machine — where N is the total number of tuples in the input relations, ρ * is the join’s fractional edge covering number, and p is the number of machines. We settle the question in the negative for the class of tuple-based algorithms (all the known MPC join algorithms fall in this class) by proving the existence of a join query with ρ * = 2 that requires a load of Ω ( N/p 1/3 ) to evaluate. Our lower bound provides solid evidence that the “AGM bound” alone is not sufficient for characterizing the hardness of join evaluation in MPC (a phenomenon that does not exist in RAM). The hard join instance identified in our argument is cyclic, which leaves the question of whether O(N polylog N/p 1/ρ* ) is still possible for acyclic joins. We answer this question in the affirmative by showing that any acyclic join can be evaluated with load O(N / p 1/ρ* ), which is asymptotically optimal (there are no polylogarithmic factors in our bound). The separation between cyclic and acyclic joins is yet another phenomenon that is absent in RAM. Our algorithm owes to the discovery of a new mathematical structure — we call “canonical edge cover” — of acyclic hypergraphs, which has numerous non-trivial properties and makes an elegant addition to database theory.
Re-construction of original face images from plastic surgery images using harmony search algorithm and IRBFN model

Authors:
raw_author_name: Shiji S K
raw_affiliation_string: Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Tamilnadu, India
raw_author_name: S.H. Krishnaveni
raw_affiliation_string: Department of Computer Science and Engineering, Baselios Mathews II College of Engineering, Kollam, India
raw_author_name: L. Padma Suresh
raw_affiliation_string: Department of Electrical and Electronics Engineering, Baselios Mathews II College of Engineering, Kollam, India

Abstract: Face Reconstruction is the only biometric that allows you to perform passive identification in one to many environments. By changing the geometry and texture of the facial region, there can be an increased intraclass variability between images of an individual before and after surgery. Even so, the nonlinear changes caused by plastic surgery have a hard time being modelled by facial reconstruction systems. An algorithm for plastic surgery face reconstruction is presented here, that uses Harmony Search to confront the challenges involved. A non-disjoint algorithm generates the face granules, each representing different information with varying resolutions and sizes. A Scale-Invariant Feature Transform (SIFT) and Co-occurrence of Adjacent Local Binary Patterns were applied to extract discriminating information from granules of face images. As the concluding step, we used Harmony Search Method (HSM) to combine the various responses to attain the optimal solution. Finally Improved Radial Basis Function Network (IRBFN) algorithm is used for feature matching. The feature matching predict, where the face is recognized or not. The modified method, proposed in this work, has the better performance in classifying more images with higher accuracy. An obtained sensitivity of 100 % and specificity of 97.5 % was achieved by the proposed classifier. The results proved that it is possible to use the developed algorithms understand different pre and post surgical image conditions, evaluate their strength and future development trends, and provide all-facial features basis for detection of pre and post surgical images.
Reweighted robust and discriminative latent subspace projection for face recognition

Authors:
raw_author_name: Dongxu Cheng
raw_affiliation_string: School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China; School of Mathematics and Information Science, Zhongyuan University of Technology, Zhengzhou, Henan 450007, China
raw_author_name: Xinman Zhang
raw_affiliation_string: School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
raw_author_name: Xuebin Xu
raw_affiliation_string: Guangdong Xi'an Jiaotong University Academy, No. 3, Daliangshuxiang East Road, Foshan, Guangdong 528000, China

Abstract: Subspace projection has been widely studied to implement the feature extraction for face recognition. However, it scarcely facilitates exploring the label information in the subspace projection procedure. The feature extraction and classification are usually divided into two separate procedures. This fails to explore the discriminative information from the training data, and thus degrades the discrimination of the extracted features and lowers the face recognition accuracy. To address this issue, we present a novel reweighted robust and discriminative latent subspace projection (ReRDLSP) method which organically integrates the reweighted latent low-rank representation model and discriminative ridge regression into a union framework. We employ a reweighted nuclear norm minimization to realize the low-rank regularization, which provides adaptive weighted values assignment, regularizes the representation matrix adaptively, and thus boosts the capability and flexibility of the method. Moreover, a discriminative ridge regression method is presented by employing a nonnegative relaxation, which enlarges the margins of the face samples from different classes as much as possible and boosts the freedom of fitting the label information. The computational complexity and convergence analysis are also discussed in detail. Extensive experiments are demonstrated and very promising results are achieved compared with some state-of-the-art face recognition methods.
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents

Authors:
raw_author_name: Jeongeun Park
raw_affiliation_string: Department of Artificial Intelligence, Korea University, Seoul, South Korea
raw_author_name: Joonhyung Lee
raw_affiliation_string: Department of Artificial Intelligence, Korea University, Seoul, South Korea
raw_author_name: Sangbeom Park
raw_affiliation_string: Department of Artificial Intelligence, Korea University, Seoul, South Korea
raw_author_name: Minsuk Chang
raw_affiliation_string: Google Research, Seattle, WA, USA
raw_author_name: Youngjae Yu
raw_affiliation_string: Department of Artificial Intelligence, Yonsei University, Seoul, South Korea
raw_author_name: Sungjoon Choi
raw_affiliation_string: Department of Artificial Intelligence, Korea University, Seoul, South Korea

Abstract: In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs). To tackle this problem, we first present an uncertainty estimation method for LLMs to classify whether the command is certain (i.e., clear) or not (i.e., ambiguous or infeasible). Once the command is classified as uncertain, we further distinguish it between ambiguous or infeasible commands leveraging LLMs with situational aware context prompts. For ambiguous commands, we disambiguate the command by interacting with users via question generation with LLMs. We believe that proper recognition of the given commands could lead to a decrease in malfunction and undesired actions of the robot, enhancing the reliability of interactive robot agents. We present a dataset for robotic situational awareness consisting of pairs of high-level commands, scene descriptions, and labels of command type (i.e., clear, ambiguous, or infeasible). We validate the proposed method on the collected dataset and pick-and-place tabletop simulation environment. Finally, we demonstrate the proposed approach in real-world human-robot interaction experiments.
PASTS: Progress-aware spatio-temporal transformer speaker for vision-and-language navigation

Authors:
raw_author_name: Liuyi Wang
raw_affiliation_string: Department of Control Science and Engineering, Tongji University, Shanghai, China
raw_author_name: Chengju Liu
raw_affiliation_string: Department of Control Science and Engineering, Tongji University, Shanghai, China; Tongji Artificial Intelligence (Suzhou) Research Institute, Suzhou, China
raw_author_name: Zongtao He
raw_affiliation_string: Department of Control Science and Engineering, Tongji University, Shanghai, China
raw_author_name: Shu Li
raw_affiliation_string: Department of Control Science and Engineering, Tongji University, Shanghai, China
raw_author_name: Qingqing Yan
raw_affiliation_string: Department of Control Science and Engineering, Tongji University, Shanghai, China
raw_author_name: Huiyi Chen
raw_affiliation_string: Rutgers University, New Brunswic, USA
raw_author_name: Qijun Chen
raw_affiliation_string: Department of Control Science and Engineering, Tongji University, Shanghai, China

Abstract: Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of an independent speaker model to provide pseudo instructions for data augmentation. However, current speaker models based on Long-Short Term Memory (LSTM) lack the ability to attend to features relevant at different locations and time steps. To address this, we propose a novel progress-aware spatio-temporal transformer speaker (PASTS) model that uses the transformer as the core of the network. PASTS uses a spatio-temporal encoder to fuse panoramic representations and encode intermediate connections through steps. Besides, to avoid the misalignment problem that could result in incorrect supervision, a speaker progress monitor (SPM) is proposed to enable the model to estimate the progress of instruction generation and facilitate more fine-grained caption results. Additionally, a multifeature dropout (MFD) strategy is introduced to alleviate overfitting. The proposed PASTS is flexible to be combined with existing VLN models. The experimental results demonstrate that PASTS outperforms previous speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset.
Reactive mobile manipulation based on dynamic dual-trajectory tracking

Authors:
raw_author_name: Gongcheng Wang
raw_affiliation_string: Key State Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China
raw_author_name: Haofei Ma
raw_affiliation_string: Key State Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China
raw_author_name: Han Wang
raw_affiliation_string: Key State Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China
raw_author_name: Pengchao Ding
raw_affiliation_string: Key State Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China
raw_author_name: Wenda Xu
raw_affiliation_string: Key State Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China
raw_author_name: Weidong Wang
raw_affiliation_string: Key State Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China
raw_author_name: Zhijiang Du
raw_affiliation_string: Key State Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China

Abstract: The letter presents an online obstacle avoidance strategy for a nonholonomic mobile manipulator, including dual-trajectory generation for the mobile base(MB) and end effector(EE), as well as whole-body reactive obstacle avoidance. The feasible region of the manipulator's workspace is generated by traversing the path of the MB, and the spatio-temporal trajectory of the EE is generated by quadratically constrained quadratic programming(QCQP) planning, satisfying the physical constraints and obstacle avoidance constraints. The reactive obstacle avoidance strategy is based on quadratic programming(QP), using singularity avoidance and EE trajectory tracking tasks as soft constraints with different priorities. Considering the nonholonomic constraints and motion differences of MB, we employ a dynamic tracking approach to avoid obstacles of MB. Additionally, an adaptive weight method is utilized to adjust the priority of MB path tracking task, effectively preventing joint velocity jitter during the insertion process. Our strategy has been proven to generate obstacle avoidance trajectories for EE in real-time and ensure safe joint velocities for MB and the manipulator through simulation and real-world scenarios.
Few-shot Incremental Event Detection

Authors:
raw_author_name: Hao Wang
raw_affiliation_string: School of Information Scienceand Technology, North China University of Technology, and CNONIX National Standard Applicationand Promotion Lab
raw_author_name: Hanwen Shi
raw_affiliation_string: School of Information Scienceand Technology, North China University of Technology, and CNONIX National Standard Applicationand Promotion Lab
raw_author_name: JianYong Duan
raw_affiliation_string: School of Information Scienceand Technology, North China University of Technology, and CNONIX National Standard Applicationand Promotion Lab

Abstract: Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively solve this problem, but it requires abundant data of new classes. In practice, however, the lack of high-quality labeled data of new event classes makes it difficult to obtain enough data for model training. To address the above mentioned issues, we define a new task, few-shot incremental event detection, which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K and IFSED-KP. Experimental results show that our approach has a higher F1-score than baseline methods and is more stable.
Analyzing transparency and malicious insiders prevention for cloud computing environment

Authors:
raw_author_name: Syed Rizvi
raw_affiliation_string: College of Information Sciences and Technology, Pennsylvania State University, PA USA
raw_author_name: Iyonna Williams
raw_affiliation_string: College of Information Sciences and Technology, Pennsylvania State University, PA USA

Abstract: The emerging cloud technology and its integration with cloud services from an enterprise standpoint have provided a gateway for Cloud Service Providers (CSPs) to garner consumers and their data. Due to the growing increase of Cloud Service Users (CSUs), it is pertinent to provide an adequate amount of cloud security to prevent detrimental impacts to businesses. Two large challenges CSPs face for cloud security include transparency and malicious insiders. The primary objective of this work is to provide a framework that analyzes CSPs based on evaluation metrics such as data breaches, data loss, account hijacking, insecure APIs, DoS, malicious insiders, abuse of cloud services, insufficient due diligence, and shared technology concerns. The security index of a CSP is then computed based on the listed evaluation metrics and given a score that translates to a linguistic rating of the CSP. Factors within transparency and malicious insiders are further broken down within the framework to emphasize various components. Transparency factors include published audit certifications, consumer base, membership of cloud organizations, and published incidents. Malicious insiders are sub-sectioned into personnel-related prevention, policy-related prevention, third-party CSP prevention, technical-related prevention, and hypervisor-related prevention. With this scoring contribution, analysis is able to be done on a CSP's potential threats within the network, which can then be visualized into recommended security controls or preventative measures. Finally, we conducted case studies to demonstrate the application of the scoring analysis using our proposed framework and to evaluate the security levels of two extensively employed CSPs.