Reviewerly/exHarmony huggingface dataset:
FuzzyDedup: Secure Fuzzy Deduplication for Cloud Storage

Data deduplication is of critical importance to reduce the storage cost for clients and to relieve the unnecessary storage pressure for cloud servers. While various techniques have been proposed for secure deduplication of identical files/blocks, the effective and secure deduplication solutions on fuzzy similar data (image, video, and others) which occupy a large portion in the real world across wide applications, remain open. In this article, we propose a novel deduplication system, named Fuzzy Deduplication (FuzzyDedup), to implement the secure deduplication of similar data (i.e., similar files, chunks, or blocks). In particular, we leverage the similarity-preserving hash, a fuzzy extractor based on error-correcting codes, and the encryption with customized design to construct a fuzzy-style deduplication encryption scheme (FuzzyMLE), achieving the ciphertext-based deduplication for similar data. Besides, to defend against data ownership cheating attack and duplicate-faking attack, a fuzzy-style proof of ownership scheme (FuzzyPoW) is designed for the cloud server to securely verify a client in possession of the similar data. To further enhance security and efficiency, we also propose both server-aided and random-tag FuzzyMLE to make FuzzyDedup robust against off-line brute-force attack and to support tag randomization, respectively. Then, we design Hamming distance reduction and tag cutting optimization algorithms to improve the tag query efficiency of FuzzyDedup. In the end, we formally prove the security of our solution and conduct experiments on real-world datasets for performance evaluation. Experimental results exhibit the efficiency of FuzzyDedup in terms of computation cost and communication overhead.
Situation-Aware Sensor-Based Wearable Computing Systems: A Reference Architecture-Driven Review

In the last fifteen years, there has been a widespread diffusion of wearable sensorized devices for a plethora of applications in heterogeneous domains. Wearable technology provides fundamental capabilities such as smart sensing, monitoring, data recording, and multi-modal interaction, in a seamless, pervasive, and easy-to-use way. An emerging research trend is the definition of situation-aware wearable computing systems, i.e., wearable devices able to perceive and understand what is happening in the environment in order to adapt their behavior and anticipate users’ needs, a capability known as situation awareness. Although the increasing interest of the research community in situation-aware wearable devices, there is a lack of studies, formal models, methodological approaches, and theoretical groundings on which these systems can be grounded. As a result, a very limited number of smart sensors (physical or virtual) capable of effectively and efficiently supporting Situation Awareness have been proposed so far. In this article, we provide a survey and a classification of state-of-the-art situation-aware wearable systems, outlining current research trends, shortcomings, and challenges, with an emphasis on the models, approaches, and computational techniques of situation awareness and wearable computing on which they are based. The survey has been performed using the PRISMA methodology for systematic reviews. The analysis has been conducted with respect to a reference architecture, namely SA-WCS, of a generic situation-aware wearable computing system that we propose in this article, grounded on Endsley’s model of Situation Awareness. Such reference architecture not only provides a systematic framework for the comparison and categorization of the works, it also aims to promote the development of the next generation WCS.
Open Set Recognition of Communication Signal Modulation Based on Deep Learning

This letter proposes a deep learning-based method for wireless communication signal modulation recognition. Modified generalized end-to-end (GE2E) loss is used to train the designed neural network, which can increase the similarity of the feature vectors of the same modulation type and reduce that of different types. The similarities between training samples and centroid vectors are calculated to set the adaptive thresholds for each known modulation type. The experimental results show that the proposed method can reject unknown signals while remaining the recognition quality of known signals, which has higher efficiency and lower complexity than other open set recognition (OSR) algorithms in modulation recognition.
An Analysis of E-Commerce Identification Using Sentimental Analysis: A Survey

Sentiment analysis is also known as opinion mining. It is one of the critical and rapidly growing fields in Natural Language Processing. This research is intended to address the most fundamental difficulties relating to sentiment analysis. Sentiment polarity classification. A generic technique for categorizing sentiment polarity, along with detailed processes, is presented. The input data for this project is taken from major e-commerce websites like Amazon's product ratings. Analyses on sentence-part classification and review-part categorization have shown promising results. At last, insights will be derived from preprocessed data for future execution and how much product structure and features impact execution. Created a significant impact on improving the business by producing a quality product, gadget, or accessory that customers expect and satisfying their expectations.
OAF-Net: An Occlusion-Aware Anchor-Free Network for Pedestrian Detection in a Crowd

Although pedestrian detection has achieved promising performance with the development of deep learning techniques, it remains a great challenge to detect heavily occluded pedestrians in crowd scenes. Therefore, to make the anchor-free network pay more attention to learning the hard examples of occluded pedestrians, we propose a simple but effective Occlusion-aware Anchor-Free Network (namely OAF-Net) for pedestrian detection in crowd scenes. Specifically, we first design a novel occlusion-aware detection head, which includes three separate center prediction branches combining with the scale and offset prediction branches. In the detection head of OAF-Net, occluded pedestrian instances are assigned to the most suitable center prediction branch according to the occlusion level of human body. To optimize the center prediction, we accordingly propose a novel weighted Focal Loss where pedestrian instances are assigned with different weights according to their visibility ratios, so that the occluded pedestrians are up-weighted during the training process. Our OAF-Net is able to model different occlusion levels of pedestrian instances effectively, and can be optimized towards catching a high-level understanding of the hard training samples of occluded pedestrians. Experiments on the challenging CityPersons, Caltech, and CrowdHuman benchmarks sufficiently validate the efficacy of our OAF-Net for pedestrian detection in crowd scenes.
Defending Against Membership Inference Attacks With High Utility by GAN

The success of machine learning (ML) depends on the availability of large-scale datasets. However, recent studies have shown that models trained on such datasets are vulnerable to privacy attacks, among which membership inference attack (MIA) brings serious privacy risk. MIA allows an adversary to infer whether a sample belongs to the training dataset of the target model or not. Though a variety of defenses against MIA have been proposed such as differential privacy and adversarial regularization, they also result in lower model accuracy and thus make the models less unusable. In this article, aiming at maintaining the accuracy while protecting the privacy against MIA, we propose a new defense against membership inference attacks by generative adversarial network (GAN). Specifically, sensitive data is used to train a GAN, then the GAN generate the data for training the actual model. To ensure that the model trained with GAN on small datasets can has high utility, two different GAN structures with special training techniques are utilized to deal with the image data and table data, respectively. Experiment results show that the defense is more effective on different data sets against the existing attack schemes, and is more efficient compared with most advanced MIA defenses.
Double Deep Q-Learning With Prioritized Experience Replay for Anomaly Detection in Smart Environments

Anomaly detection in smart environments is important when dealing with rare events, which can be safety-critical to individuals or infrastructure.Safety-critical means in this case, that these events can be a threat to the safety of individuals (e.g. a person falling to the ground) or to the security of infrastructure (e.g.unauthorized access to protected facilities).However, recognizing abnormal events in smart environments is challenging, because of the complex and volatile nature of the data recorded by monitoring sensors.Methodologies proposed in the literature are frequently domain-specific and are subject to biased assumptions about the underlying data.In this work, we propose the adaption of a deep reinforcement learning algorithm, namely double deep q-learning (DDQN), for anomaly detection in smart environments.Our proposed anomaly detector directly learns a decision-making function, which can classify rare events based on multivariate sequential time series data.With an emphasis on improving the performance in rare event classification tasks, we extended the algorithm with a prioritized experience replay (PER) strategy, and showed that the PER extension yields an increase in detection performance.The adaption of the improved version of the DDQN reinforcement learning algorithm for anomaly detection in smart environments is the major contribution of this work.Empirical studies on publicly available real-world datasets demonstrate the effectiveness of our proposed solution.Here specifically, we use a dataset for fall and for occupancy detection to evaluate the solution proposed in this work.Our solution yields comparable detection performance to previous work, and has the additional advantages of being adaptable to different environments and capable of online learning.
An Online Time-Optimal Trajectory Planning Method for Constrained Multi-Axis Trajectory With Guaranteed Feasibility

Online time-optimal trajectory planning exists in a wide range of applications such as computer numerical control (CNC) manufacturing, robotics and autonomous vehicles. Generally, the methods to generate such time-parameterized trajectory can be categorized as offline methods and online methods. Offline methods tend to generate time-optimal trajectory while it is computationally prohibitive for high frequency real-time applications. Current online methods are computationally efficient but either deal with the sparse waypoint trajectories or sacrifice the time optimality a lot. This letter innovatively proposed a time-optimal switching trajectory index coordination (TOS-TIC) strategy to solve the online time-optimal planning problem for continuous multi-axis trajectories. The proposed method is able to generate time-optimal trajectory for continuous geometric paths while considering high order kinematic constraints. The time-optimality of the trajectory planned by TOS-TIC is nearly the same as the offline planned optimal results. Meanwhile, the proposed method is computationally efficient for online applications. The proposed TOS-TIC planning strategy is an universal approach and firstly able to solve the online time-optimal trajectory planning problem for constrained continuous multi-axis trajectories, which has excellent application prospect in modern automatic industry and intelligent robots/vehicles.
$$\mathsf {Rubato}$$: Noisy Ciphers for Approximate Homomorphic Encryption

A transciphering framework converts a symmetric ciphertext into a homomorphic ciphertext on the server-side, reducing computational and communication overload on the client-side. In Asiacrypt 2021, Cho et al. proposed the $$\mathsf {RtF}$$ framework that supports approximate computation. In this paper, we propose a family of noisy ciphers, dubbed $$\mathsf {Rubato}$$ , with a novel design strategy of introducing noise to a symmetric cipher of a low algebraic degree. With this strategy, the multiplicative complexity of the cipher is significantly reduced, compared to existing HE-friendly ciphers, without degrading the overall security. More precisely, given a moderate block size (16 to 64), $$\mathsf {Rubato}$$ enjoys a low multiplicative depth (2 to 5) and a small number of multiplications per encrypted word (2.1 to 6.25) at the cost of slightly larger ciphertext expansion (1.26 to 1.31). The security of $$\mathsf {Rubato}$$ is supported by comprehensive analysis including symmetric and LWE cryptanalysis. Compared to $$\mathsf {HERA}$$ within the $$\mathsf {RtF}$$ framework, client-side and server-side throughput is improved by 22.9% and 32.2%, respectively, at the cost of only 1.6% larger ciphertext expansion.
Practical Post-Quantum Signature Schemes from Isomorphism Problems of Trilinear Forms

In this paper, we propose a practical signature scheme based on the alternating trilinear form equivalence problem. Our scheme is inspired by the Goldreich-Micali-Wigderson's zero-knowledge protocol for graph isomorphism, and can be served as an alternative candidate for the NIST's post-quantum digital signatures. First, we present theoretical evidences to support its security, especially in the post-quantum cryptography context. The evidences are drawn from several research lines, including hidden subgroup problems, multivariate cryptography, cryptography based on group actions, the quantum random oracle model, and recent advances on isomorphism problems for algebraic structures in algorithms and complexity. Second, we demonstrate its potential for practical uses. Based on algorithm studies, we propose concrete parameter choices, and then implement a prototype. One concrete scheme achieves 128 bit security with public key size $$\approx 4100$$ bytes, signature size $$\approx 6800$$ bytes, and running times (key generation, sign, verify) $$\approx 0.8$$ ms on a common laptop computer.
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension

Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text.Conventional neural models are insufficient for logical reasoning, while symbolic reasoners cannot directly apply to text.To meet the challenge, we present a neural-symbolic approach which, to predict an answer, passes messages over a graph representing logical relations between text units.It incorporates an adaptive logic graph network (AdaLoGN) which adaptively infers logical relations to extend the graph and, essentially, realizes mutual and iterative reinforcement between neural and symbolic reasoning.We also implement a novel subgraph-to-node message passing mechanism to enhance context-option interaction for answering multiple-choice questions.Our approach shows promising results on ReClor and LogiQA.Context: If the company gets project A, product B can be put on the market on schedule.Product B is put on schedule if and only if the company's fund can be normally turned over.If the company's fund cannot be turned over normally, the development of product C cannot be carried out as scheduled.The fact is that the development of product C is carried out as scheduled.Question: This shows: Options: A. The company gets project A and product B is put on the market on schedule.B. The company does not get project A and product B is not put on the market on schedule.C. Product B is put on the market on schedule and the company's fund is turned over normally.D. Product B is not put on the market on
SymmeProof: Compact Zero-Knowledge Argument for Blockchain Confidential Transactions

To reduce the transmission cost of blockchain confidential transactions, we propose SymmeProof, a novel communication efficient non-interactive zero-knowledge range proof protocol without a trusted setup. We design and integrate two new techniques in SymmeProof, namely vector compression and inner-product range proof. The proposed vector compression is able to reduce the communication cost to log(n) for n-size vectors. The proposed inner-product range proof converts a range proof relation into an inner-product form, which can further reduce the range proof size with the vector compression technique. Based on these two techniques, SymmeProof can eventually achieve a log(n)-size range proof. The proposed SymmeProof can be used in many important applications such as blockchain confidential transactions as well as arguments for arithmetic circuits satisfiability. We evaluate the performance of SymmeProof. The results show that SymmeProof substantially outperforms representative methods such as Bulletproofs in the proof size without a trusted setup.
UAVs-Aided Delay-Tolerant Blockchain Secure Offline Transactions in Post-Disaster Vehicular Networks

Vehicular networks which are paralyzed by natural disasters is faced with communication dilemma. Through building a decentralized communication network, unmanned aerial vehicles (UAVs) with high mobility and flexibility are expected to be the solution to the post-disaster vehicular networks. The blockchain technology has been widely used in UAV networks to provision the prompt security of distributed communications. However, existing works ignore the dynamics of the network in that due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blockchain transactions. The delay of synchronization can result in severe security issues. On addressing the issues, this paper proposes UAVs-aided blockchain offline transactions to ensure the security and effectiveness of delay-tolerant blockchain transactions when UAVs are offline. In specific, we consider vehicle-to-vehicle (V2V) charging transactions in post-disaster vehicular networks. By establishing offline channel between charging and discharging electric vehicles (EVs) by hashed time locked contract (HTLC), we design a UAVs aided penalty algorithm with accountable assertions to prevent deposit forging attacks and double-spending attacks. In addition, considering the selfishness of EVs, a Stackelberg game based incentive scheme is developed to encourage EVs to participate the offline transactions and to improve their trust values. By suing the mechanisms above, our proposal addresses the security of offline EV charging, as well as the selfishness of participant. Using extensive simulations, we demonstrate that the proposed scheme can realize secure transactions among EVs and can effectively improve the utilities of EVs through the comparison with conventional schemes.
Evolutionary Sampling Agent for Expensive Problems

Data-driven evolutionary algorithms are widely studied for their ability to solve expensive optimization problems in engineering and science. This article introduces a novel optimization framework to solve costly optimization problems, called the evolutionary sampling agent (ESA). ESA considers the optimization algorithm as an agent, which operates on four different characteristics of evolutionary sampling strategies to search the global optimum. Among these four evolutionary sampling strategies, the first strategy prefers exploration, the second and the fourth strategies use different local search methods preferring exploitation, and the third strategy integrates good genes from historical solutions. ESA consists of two layers of learning mechanisms. On the one hand, the evolutionary sampling strategies use historical data to construct surrogate models to efficiently sample a candidate solution. On the other hand, the agent adjusts the probability of selecting different sampling strategies through the feedback information received in the optimization process. Seven benchmark functions with 30, 50, and 100 dimensions were adopted. Compared with the other state-of-the-art methods, the results show that ESA yields a promising performance for expensive problems.
Efficient Hardware Arithmetic for Inverted Binary Ring-LWE Based Post-Quantum Cryptography

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ring learning-with-errors</i> (RLWE)-based encryption scheme is a lattice-based cryptographic algorithm that constitutes one of the most promising candidates for Post-Quantum Cryptography (PQC) standardization due to its efficient implementation and low computational complexity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Binary Ring</i> -LWE (BRLWE) is a new optimized variant of RLWE, which achieves smaller computational complexity and higher efficient hardware implementations. In this paper, two efficient architectures based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Linear-Feedback Shift Register</i> (LFSR) for the arithmetic used in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inverted Binary Ring</i> -LWE ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inv</i> BRLWE)-based encryption scheme are presented, namely the operation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A\cdot B+C$ </tex-math></inline-formula> over the polynomial ring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{q}/(x^{n}+1)$ </tex-math></inline-formula> . The first architecture optimizes the resource usage for major computation and has a novel input processing setup to speed up the overall processing latency with minimized input loading cycles. The second architecture deploys an innovative serial-in serial-out processing format to reduce the involved area usage further yet maintains a regular input loading time-complexity. Experimental results show that the architectures presented here improve the complexities obtained by competing schemes found in the literature, e.g., involving 71.23% less area-delay product than recent designs. Both architectures are highly efficient in terms of area-time complexities and can be extended for deploying in different lightweight application environments.
Deep-learning Hopping Capture Model for Automatic Modulation Classification of Wireless Communication Signals

Recent years have witnessed a surge of developments in deep learning (DL) motivated by a variety of contemporary applications. The conventional DL-based automatic modulation classification (AMC) methods are always relying on a great quan- tity of data. In this paper, we propose a DL-based AMC model with short data for spectrum sensing of wireless communication signals. First, a hopping transform unit is proposed to represent the transient variation occurred either by frequency, amplitude or phase modulations. Second, a bidirectional long short-term memory (Bi-LSTM) based hopping feature perception model, namely deep-learning hopping capture model (DHCM), is built for the AMC. A comprehensive comparison of the DHCM with other existing methods is then provided under various signal-to- noise ratios (SNRs). The experimental results demonstrate the superiority of the proposed method under short data.