Reviewerly/exHarmony huggingface dataset:
On the Geometry of Fixed Points and Discontinuity

Authors:
raw_author_name: Nihal ÖZGÜR
raw_affiliation_string: BALIKESIR UNIVERSITY
raw_author_name: Mangey RAM
raw_affiliation_string: Graphic Era Deemed to be University

Abstract: Recently, there has been a considerable effort to obtain new solutions to the Rhoades' open problem on the existence of contractive mappings that admit discontinuity at the fixed point. An extended version of this problem is also stated using a geometric approach. In this paper, we obtain new solutions to this extended version of the Rhoades' open problem. A related problem, the fixed-circle problem (resp. fixed-disc problem) is also studied. Both of these problems are related to the geometric properties of the fixed point set of a self-mapping on a metric space. Furthermore, a new result about metric completeness and a short discussion on the activation functions used in the study of neural networks are given. By providing necessary examples, we show that our obtained results are effective.
Enabling Efficient Privacy-Preserving Spatiotemporal Location-Based Services for Smart Cities

Authors:
raw_author_name: Zhijun Li
raw_affiliation_string: School of Cyber Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Jianfeng Ma
raw_affiliation_string: School of Cyber Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Yinbin Miao
raw_affiliation_string: School of Cyber Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Xiangyu Wang
raw_affiliation_string: School of Cyber Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Jiayi Li
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Chao Xu
raw_affiliation_string: School of Guangzhou Institute of Technology, Xidian University, Guangzhou, China

Abstract: GPS-enabled Internet of Things devices, which can obtain the location and temporal information of installed objects to promote Location Based Service, are completely changing our lives. In recent years, the confidentiality and privacy of personal data have attracted widespread attention, especially when outsourcing to third-party providers. To achieve both the confidentiality and availability of outsourced data, various Dynamic Searchable Symmetric Encryption (DSSE) schemes have been proposed. However, existing solutions are limited either in terms of security or efficiency. To address this challenge, we propose a Secure and Efficient Search over Encrypted Spatio-Temporal Data (SES-ESTD) scheme that utilizes constrained pseudo-random function and enhanced asymmetric scalar-product preserving encryption. Our scheme not only achieves high retrieval efficiency but also ensures forward security and content privacy. We provide a formal security analysis to prove SES-ESTD is forward secure and content private. Furthermore, extensive experiments indicate that SES-ESTD incurs lower computation and storage overheads compared to other schemes. Most importantly, SES-ESTD achieves millisecond-level retrieval for millions of data points and provides a retrieval speed that is 2.85 times faster than existing forward secure spatio-temporal DSSE schemes.
ASO-DKELM: Alpine skiing optimization based deep kernel extreme learning machine for elderly stroke detection from EEG signal

Authors:
raw_author_name: P. Nancy
raw_affiliation_string: Computing Technologies, School of Computing, SRMIST, KTR Campus, Chennai, India
raw_author_name: J. Sathya Priya
raw_affiliation_string: Information Technology, Velammal Engineering College, Anna University, Chennai, India

Abstract: Stroke is considered the third leading cause of mortality followed by cancer and heart disease worldwide. Stroke detection in advance is the only procedure to avoid health risks while it necessitates continuous monitoring and observation of subjects. Many research works already exist in the medical field to detect diseases in advance using machine learning techniques. However, their detection results are limited by several biasing factors such as more training time and imprecise detection. So a novel stroke detection system using the Deep Kernel Extreme Learning Machine based Alpine Skiing (DKELM-AS) approach is proposed to predict the probability of subjects being affected by stroke disease. The electroencephalogram (EEG) signals of normal and stroke patients are collected and utilized to investigate the proposed approach. The raw data are preprocessed and the features are extracted based on frequency ranges using the Fast Hartley Transform (FHT) technique. Finally, the proposed DKELM-AS approach classifies them as stroke-affected EEG or normal EEG based on the precursor symptoms obtained from EEG signals. The experimental results reveal that the proposed DKELM-AS approach achieves an overall accuracy rate of about 95.2% in stroke detection based on the precursor symptoms of EEG signals.
Novel Robust Parcel-Size Classification Using mmWave Radar

Authors:
raw_author_name: Bo-Han Wu
raw_affiliation_string: Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
raw_author_name: Shih-Hau Fang
raw_affiliation_string: Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
raw_author_name: Hsiao-Chun Wu
raw_affiliation_string: School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, USA

Abstract: A novel robust package-height estimation and parcel-size classification scheme is proposed in this work. A pertinent prototype system consisting of a millimeter-wave radar, a shipment track, and a stepping motor has been built for proof of concept. Three new schemes using the range-profile data produced by the mmWave radar, namely, the detection based-on cumulative distribution (DBCD) scheme, the detection based-on peak-value clustering (DBPC) scheme, and the DBCD–DBPC hybrid scheme are introduced here. For benchmark evaluation of the proposed new system, two practical scenarios, namely, the undisturbed scenario (no people is close to the system) and the disturbed scenario (some people is close to the system), are tested. This new system can overcome the serious drawback of the existing camera-assisted systems, which have to rely on sufficient lighting conditions, and operate normally even in complete darkness. Meanwhile, the proposed new system can accommodate parcels/packages in arbitrary orientations, which cannot be allowed by the current camera-assisted systems, during the parcel-size measurements. According to the test experiments, the proposed DBCD–DBPC hybrid scheme can reach up to a very high average classification accuracy of 96.4% for both undisturbed and disturbed scenarios. The proposed novel package-height estimation and parcel-size classification technique in this work can be very useful for intelligent inventory management and smart logistics in the future.
Anchor-Free Multi-UAV Detection and Classification Using Spectrogram

Authors:
raw_author_name: Tao Li
raw_affiliation_string: School of Telecommunications Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Yongzhao Li
raw_affiliation_string: School of Telecommunications Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Yuhan Ruan
raw_affiliation_string: School of Telecommunications Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Rui Zhang
raw_affiliation_string: School of Telecommunications Engineering, Xidian University, Xi&#x2019;an, China

Abstract: The advancements in unmanned aerial vehicle (UAV) technology have brought immense convenience to society. However, unauthorized UAVs pose a serious threat to personal privacy, public safety, and aviation security. Therefore, accurate UAV detection and classification are crucial. Moreover, with the increased popularity of UAVs, the likelihood of multiple UAVs appearing in the same area simultaneously has also dramatically increased. Recent studies demonstrate that object detectors, such as FasterRCNN and YOLO, can be used to detect and classify multiple UAVs based on spectrograms. To our best knowledge, the object detectors are directly used to classify UAV without considering the characteristics of the UAV signal spectrogram, which results in a decrease in recognition performance. In this paper, we analyze the characteristics of the UAV signal spectrogram in detail and conclude two problems, i.e., prior anchor mismatch and cross-domain detection, hindering the implementation of object detector for UAV recognition. To solve prior anchor mismatch, we propose an anchor-free detector based on keypoint and design a novel keypoints matching algorithm to improve recognition performance. To solve cross-domain detection, we propose an adversarial learning based data adaptation method, which can generate domain-independent and domain-aligned features. Finally, the experiments adopt practical spectrogram and synthetic spectrogram to verify the superiority of the proposed anchor-free detector and the effectiveness of the proposed data adaptation method.
Recommendations with minimum exposure guarantees: A post-processing framework

Authors:
raw_author_name: Rodrigo Alves
raw_affiliation_string: Czech Technical University in Prague, Prague, Czech Republic
raw_author_name: Antoine Ledent
raw_affiliation_string: Singapore Management University, Singapore
raw_author_name: Rodrygo L.T. Santos
raw_affiliation_string: Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
raw_author_name: Marius Kloft
raw_affiliation_string: TU Kaiserslautern, Kaiserslautern, Rlp, Germany

Abstract: Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous work and thus can be deployed to larger datasets and allows the organization to define a minimum level of exposure for groups of items. We conduct an extensive empirical evaluation indicating that our new framework can increase the exposure of items from disadvantaged groups at a small cost of recommendation accuracy.
EnsMulHateCyb: Multilingual hate speech and cyberbully detection in online social media

Authors:
raw_author_name: Hemaank Mahajan
raw_affiliation_string: Department of Applied Mathematics, Delhi Technological University, Main Bawana Road, New Delhi 110042, India
raw_author_name: Sanjay Kumar
raw_affiliation_string: Department of Computer Science and Engineering, Delhi Technological University, Main Bawana Road, New Delhi 110042, India

Abstract: Nowadays, users across the globe interact with one another for information exchange, communication, and association on various online social media. However, some individuals exploit these venues for malicious practices like hate speech and cyberbully. In this paper, we present an improved multilingual hate speech and cyberbully detection model using bagging-stacking based hybrid ensemble deep learning techniques. The proposed model utilizes Bi-directional Long Short-Term Memory (BiLSTM), Bi-directional Gated Recurrent Unit (Bi-GRU), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) techniques to enhance the overall performance. We first preprocess the multilingual data streams followed by adoption of Global vectors for word Representation (GloVe) embeddings to convert words to a vector representation in parallel enabling the data streams for binary classification task. In order to construct an architecture for the detection of hate speech and cyberbully, we introduce a heterogeneous fusion of multiple effective models in a unique approach such that CNN-LSTM utilizes a stacking approach with stochastic gradient descent to achieve optimal weights, whereas all the base learners used bagging ensemble approach with cross-validation to reach optimal weights. The final output layer of the proposed ensemble deep learning architecture is achieved using a super learner approach on base learners. To show the efficacy of the proposed model, we conduct the simulation on a total of nine real-world social media datasets in different languages and compared the results with other contemporary hate speech and cyberbully detection methods. The collected findings show that the proposed model outperforms other models on considered datasets and shows an improvement of at least 4.44% in F1 scores.
Enabling secure image transmission in unmanned aerial vehicle using digital image watermarking with H-Grey optimization

Authors:
raw_author_name: K. Jyothsna Devi
raw_affiliation_string: SRM University AP., Neerukonda Mandal, Guntur, 522 240, India
raw_author_name: Priyanka Singh
raw_affiliation_string: SRM University AP., Neerukonda Mandal, Guntur, 522 240, India
raw_author_name: Muhammad Bilal
raw_affiliation_string: Department of Computer Engineering, Hankuk University of Foreign Studies, Yongin-Si, 17035, South Korea; School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, United Kingdom
raw_author_name: Anand Nayyar
raw_affiliation_string: Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet Nam

Abstract: Drone technology, also known as Unmanned Aerial Vehicles (UAVs), has advanced rapidly in the last decade owing to the huge number of users. This approach has an immense opportunity in areas like healthcare, agriculture, and forestry. However, the transmission of digital images via drone technology is associated with vulnerabilities and risks, due to a lack of efficient security solutions. Digital Image Watermarking (DIW) has emerged as a viable solution for digital image transmission in UAV applications. However, achieving robustness, security, and imperceptibility in a watermarking system at the same time is a difficult task. To address the aforementioned concerns, this paper proposes a secure digital image watermarking scheme in the hybrid DWT-SVD domain. The scaling factor is chosen adaptively from the hybrid Harmony-Grey Wolf (H-Grey) optimization algorithm to maintain a balance between imperceptibility and robustness. In addition, a novel symmetric cryptographic-based four-level encryption approach PSMD (Partitioning, Substituting, Merging, Division) is proposed to address UAV image security concerns by using Fibonacci and prime number series to generate two random keys. The proposed encryption scheme provides high security at low computational cost. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.27 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0008 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed encryption scheme is highly secure and the computational cost is also low. The average embedding and extraction time is 0.25 s and 0.09 s It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme's effectiveness. The presented DIW can be used for copyright protection, UAV image transmission applications, military applications, and other purposes.
Galaxy: A Scalable BFT and Privacy-Preserving Pub/Sub IoT Data Sharing Framework Based on Blockchain

Authors:
raw_author_name: Yuchao Zhang
raw_affiliation_string: School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunication, Beijing, China
raw_author_name: Xiaotian Wang
raw_affiliation_string: School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunication, Beijing, China
raw_author_name: Xiaofeng He
raw_affiliation_string: School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunication, Beijing, China
raw_author_name: Ning Zhang
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada
raw_author_name: Zibin Zheng
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Ke Xu
raw_affiliation_string: Department of Computer Science and Technology, Tsinghua University, Beijing, China

Abstract: The emergence of the Internet of Things (IoT) technology in recent years has led to a considerable amount of data to be shared across different organizations. The publish and subscribe (Pub/Sub) paradigm, with its asynchronous, one-to-many and decoupling characteristics, is considered to be a promising communication model in IoT. However, designing a Pub/Sub framework for IoT data sharing confronts two challenges: Byzantine faults and privacy concerns. Byzantine nodes that are subjectively malicious or hacked by attackers may discard or forge data in the broker network composed of untrusted IoT organizations. Unauthorized brokers or clients may try to obtain the content of publications or subscriptions, thus violating the IoT data privacy. Existing works have limitations in terms of relatively low scalability and high overhead in tackling these two challenges. In this paper, we propose Galaxy, a blockchain based Pub/Sub IoT data sharing framework. To achieve Byzantine fault-tolerant (BFT) Pub/Sub, Galaxy adopts sharding to improve scalability and achieve efficient BFT Pub/Sub workflow within each shard with a novel leader rotation scheme. In attaining privacy-preserving Pub/Sub, a secret key sharing and encrypted Pub/Sub scheme is designed in Galaxy to achieve low overhead without breaking the decoupling of the system. We implemented a prototype of Galaxy and deployed it on Alibaba Cloud for experimental evaluation. The experiment results show the feasibility and efficiency of Galaxy.
Multi-Label Feature Selection via Positive or Negative Correlation

Authors:
raw_author_name: Yaojin Lin
raw_affiliation_string: School of Computer Science, Minnan Normal University, Fujian, China
raw_author_name: Zhuoxin He
raw_affiliation_string: School of Computer Science, Minnan Normal University, Fujian, China
raw_author_name: Lei Guo
raw_affiliation_string: Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Nanping, China
raw_author_name: Weiping Ding
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong, China

Abstract: Feature selection, a meaningful preprocessing technique in machine learning, plays a key role in multi-label learning to select more discriminative features. Recently, multi-label feature selection algorithms based on sparse regression model have received extensive attention. However, in the existing sparse regression model, feature-label correlations are obtained from a single viewpoint via a loss function. With the aim of learning richer information, in this article, the sparse regression model is integrated with a new feature-label correlation, which allows different viewpoints to be considered simultaneously. For this reason, a new correlation, called positive or negative correlation, is proposed. Furthermore, in many existing multi-label feature selection algorithms, each feature is associated with all of the labels, while in many scenarios, each feature is associated with only some labels. Aiming at the above two shortcomings, a new multi-label feature selection algorithm is proposed in four stages. First, a pseudo-label space is built and the global label correlation is embedded into this space. Second, the positive or negative correlation between features and labels is calculated. Third, a new objective function is established. The weight matrix and feature ranking results are obtained through the optimization process. Final, using the weight matrix as prior knowledge, the proposed algorithm exploits label-specific and label-group-specific features techniques to further improve performance. Extensive experiments on over than 20 multi-label datasets show its superior performance.
Revisiting Nakamoto Consensus in Asynchronous Networks: A Comprehensive Analysis of Bitcoin Safety and Chain Quality

Authors:
raw_author_name: Muhammad Saad
raw_affiliation_string: Department of Computer Science, University of Central Florida, Orlando, FL, USA
raw_author_name: Afsah Anwar
raw_affiliation_string: Department of Computer Science, University of Central Florida, Orlando, FL, USA
raw_author_name: Srivatsan Ravi
raw_affiliation_string: Department of Computer Science, University of Southern California, Los Angeles, CA, USA
raw_author_name: David Mohaisen
raw_affiliation_string: Department of Computer Science, University of Central Florida, Orlando, FL, USA

Abstract: The Bitcoin blockchain safety relies on strong network synchrony. Therefore, violating the blockchain safety requires strong adversaries that control a mining pool with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx$</tex-math> </inline-formula> 51% hash rate. In this paper, we show that the network synchrony does not hold in the real world Bitcoin network which can be exploited to feasibly violate the blockchain safety and chain quality. Towards that, first we construct the Bitcoin ideal functionality to formally specify its ideal execution model in a synchronous network. We then develop a large-scale data collection system through which we connect with more than 103K IP addresses of the Bitcoin nodes and identify 871 mining nodes. We contrast the ideal functionality against the real world measurements to expose the network anomalies that can be exploited to optimize the existing attacks. Particularly, we observe a non-uniform block propagation pattern among the mining nodes showing that the Bitcoin network is asynchronous in practice. To realize the threat of an asynchronous network, we present the attack that allows an adversary to orchestrate concurrent mining on multiple branches of the blockchain to violate common prefix and chain quality properties. We also propose the attack countermeasures by tweaking Bitcoin Core to model the Bitcoin ideal functionality. Our measurements, theoretical modeling, proposed attack, and countermeasures open new directions in the security evaluation of Bitcoin and similar blockchain systems.