Reviewerly/exHarmony huggingface dataset:
Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management

Along with the growing threat of cyberattacks, cybersecurity has become one of the most important areas of the Internet of Things (IoT). The purpose of IoT cybersecurity is to reduce cybersecurity risk for organizations and users through the protection of IoT assets and privacy. New cybersecurity technologies and tools provide potential for better IoT security management. However, there is a lack of effective IoT cyber risk management frameworks for managers. This paper reviews IoT cybersecurity technologies and cyber risk management frameworks. Then, this paper presents a four-layer IoT cyber risk management framework. This paper also applies a linear programming method for the allocation of financial resources to multiple IoT cybersecurity projects. An illustration is provided as a proof of concept.
Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

This paper suggests five new contributions with respect to the state-of-the-art.First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a fresh metaheuristic algorithm, namely the Slime Mould Algorithm (SMA), and a fuzzy controller tuning approach is offered.Second, a relatively easily understandable formulation of SMA is offered.Third, a real-world application of SMA is given, focusing on the optimal tuning of TSK PI-FCs for nonlinear servo systems in terms of optimization problems that target the minimization of discrete-time cost functions defined as the sum of time multiplied by squared control error.Fourth, using the concept of improving the performance of metaheuristic algorithms with information feedback models, proposed by Wang and Tan, Improving metaheuristic algorithms with information feedback models, IEEE Trans.Cybern.49 (2019), 542-555, Gu and Wang, Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Fut.Gen. Comput.Syst.107 (2020), 49-69, and Zhang et al., Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Inf.Sci.522 ( 2020), 1-16, new metaheuristic algorithms are introduced in terms of inserting the model F1 in SMA and other representative algorithms, namely Gravitational Search Algorithm (GSA), Charged System Search (CSS), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA).Fifth, the real-time validation of the cost-effective fuzzy controllers and their tuning approach is performed in the framework of angular position control of laboratory servo system.The comparison with other metaheuristic algorithms that solve the same optimization problem for optimal parameter tuning of cost-effective fuzzy controllers suggestively highlights the superiority of SMA.Experimental results are included.
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion

With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each other. This fact motivated a lot of research attention on fusing the multi-modal feature spaces to comprehensively characterize the data objects. Most of the existing state-of-the-arts focused on how to fuse the energy or information from multi-modal spaces to deliver a superior performance over their counterparts with single modal. Recently, deep neural networks have been exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. Substantial empirical studies are carried out to demonstrate its advantages that are benefited from deep multi-modal methods, which can essentially deepen the fusion from multi-modal deep feature spaces. In this article, we provide a substantial overview of the existing state-of-the-arts in the field of multi-modal data analytics from shallow to deep spaces. Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition, and fusion over multi-modal spaces. Finally, we share our viewpoints regarding some future directions in this field.
Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling

Joint entity and relation extraction is to detect entity and relation using a single model. In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former. To this end, we first design a tagging scheme to generate n tag sequences for an n-word sentence. Then a position-attention mechanism is introduced to produce different sentence representations for every query position to model these n tag sequences. In this way, our method can simultaneously extract all entities and their type, as well as all overlapping relations. Experiment results show that our framework performances significantly better on extracting overlapping relations as well as detecting long-range relation, and thus we achieve state-of-the-art performance on two public datasets.
Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.
Fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion

Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface. Although great progress has been made recently, fast and robust hand gesture recognition remains an open problem, since the existing methods have not well balanced the performance and the efficiency simultaneously. To bridge it, this work combines image entropy and density clustering to exploit the key frames from hand gesture video for further feature extraction, which can improve the efficiency of recognition. Moreover, a feature fusion strategy is also proposed to further improve feature representation, which elevates the performance of recognition. To validate our approach in a “wild” environment, we also introduce two new datasets called HandGesture and Action3D datasets. Experiments consistently demonstrate that our strategy achieves competitive results on Northwestern University, Cambridge, HandGesture and Action3D hand gesture datasets. Our code and datasets will release at https://github.com/Ha0Tang/HandGestureRecognition.
Meaningful Explanations of Black Box AI Decision Systems

Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.
Small and Practical BERT Models for Sequence Labeling

We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU.Starting from a public multilingual BERT checkpoint, our final model is 6x smaller and 27x faster, and has higher accuracy than a state-of-theart multilingual baseline.We show that our model especially outperforms on low-resource languages, and works on codemixed input text without being explicitly trained on codemixed examples.We showcase the effectiveness of our method by reporting on part-of-speech tagging and morphological prediction on 70 treebanks and 48 languages.
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing

This paper investigates the problem of learning cross-lingual representations in a contextual space.We propose Cross-Lingual BERT Transformation (CLBT), a simple and efficient approach to generate cross-lingual contextualized word embeddings based on publicly available pre-trained BERT models (Devlin et al., 2018).In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages.We demonstrate the effectiveness of this approach on zero-shot cross-lingual transfer parsing.Experiments show that our embeddings substantially outperform the previous state-of-the-art that uses static embeddings.We further compare our approach with XLM (Lample and Conneau, 2019), a recently proposed cross-lingual language model trained with massive parallel data, and achieve highly competitive results. 1* Email corresponding 1 Our code is released at https://github.com/WangYuxuan93/CLBT 2 In this paper, we refer to these embeddings as static as opposed to contextualized ones.
Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.
Certificateless-Signcryption-Based Three-Factor User Access Control Scheme for IoT Environment

User access control is a crucial requirement in any Internet of Things (IoT) deployment, as it allows one to provide authorization, authentication, and revocation of a registered legitimate user to access real-time information and/or service directly from the IoT devices. To complement the existing literature, we design a new three-factor certificateless-signcryption-based user access control for the IoT environment (CSUAC-IoT). Specifically, in our scheme, a user U's password, personal biometrics, and mobile device are used as the three authentication factors. By executing the login and access control phase of CSUAC-IoT, a registered user (U) and a designated smart device (Si) can authorize and authenticate mutually via the trusted gateway node (GN) in a particular cell of the IoT environment. In our setting, the environment is partitioned into disjoint cells, and each cell will contain a certain number of IoT devices along with a GN. With the established session key between U and Si, both entities can then communicate securely. In addition, CSUAC-IoT supports new IoT devices deployment, user revocation, and password/biometric update functionality features. We prove the security of CSUAC-IoT under the real-or-random (ROR) model, and demonstrate that it can resist several common attacks found in a typical IoT environment using the AVISPA tool. A comparative analysis also reveals that CSUAC-IoT achieves better tradeoff for security and functionality, and computational and communication costs, in comparison to five other competing approaches.
Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder

With growing dependence of industrial robots, a failure of an industrial robot may interrupt current operation or even overall manufacturing workflows in the entire production line, which can cause significant economic losses. Hence, it is very essential to maintain industrial robots to ensure high-level performance. It is widely desired to have a real-time technique to constantly monitor robots by collecting time series data from robots, which can automatically detect incipient failures before robots totally shut down. Model-based methods are typically used in anomaly detection for robots, yet explicit domain knowledge and accurate mathematical models are required. Data-driven techniques can overcome these limitations. However, a major difficulty for them is the lack of sufficient fault data of industrial robots. Besides, the used technique for anomaly detection of robots should be required to not only capture the temporal dependency in collected time series data, but also the inter-correlations between different metrics. In this paper, we introduce an unsupervised anomaly detection for industrial robots, sliding-window convolutional variational autoencoder (SWCVAE), which can realize real-time anomaly detection spatially and temporally by coping with multivariate time series data. This method has been verified by a KUKA KR6R 900SIXX industrial robot, and the results prove that the proposed model can successfully detect anomaly in the robot. Thus, this work presents a promising tool for condition-based maintenance of industrial robots.
Blockchain-Based Incentives for Secure and Collaborative Data Sharing in Multiple Clouds

The prosperity of cloud computing has driven an increasing number of enterprises and organizations to store their data on private or public cloud platforms. Due to the limitation of individual data owners in terms of data volume and diversity, data sharing over different cloud platforms would enable third parties to take advantage of big data analysis techniques to provide value-added services, such as providing healthcare services for customers by gathering medical data from multiple hospitals. However, it remains a challenging task to design effective incentives that encourage secure and collaborative data sharing in multiple clouds. In this paper, we propose a reliable collaboration model consisting of three types of participants, which include data owners, miners, and third parties, where the data is shared via blockchain and recorded by a smart contract. In general, these participants may acquire and store the sharing of data using their private or public clouds. We analyze the topological relationships between the participants and develop some Shapley value models from simple to complicate in the process of revenue distribution. We also discuss the incentive effect of sharing security data and rationality of the designed solution through analysis towards distribution rules.
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning

Abstract We propose F alcon , an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. F alcon presents four main advantages – (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNet (iii) F alcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, F alcon presents new theoretical insights for protocol design that make it highly efficient and allow it to outperform existing secure deep learning solutions. Compared to prior art for private inference, we are about 8× faster than SecureNN (PETS’19) on average and comparable to ABY 3 (CCS’18). We are about 16 − 200× more communication efficient than either of these. For private training, we are about 6× faster than SecureNN, 4.4× faster than ABY 3 and about 2−60× more communication efficient. Our experiments in the WAN setting show that over large networks and datasets, compute operations dominate the overall latency of MPC, as opposed to the communication.
Fractional-order double-ring erbium-doped fiber laser chaotic system and its application on image encryption

Laser chaotic systems have been extensively studied and applied in the past 20 years. However, the fractional form of the chaotic system of erbium-doped fiber laser has not been studied yet. Therefore, this paper proposes a fractional-order double-ring erbium-doped fiber laser chaotic system. The dynamic characteristics of the system are studied by phase diagram, bifurcation diagram, Lyapunov exponent spectrum, Spectral Entropy (SE) complexity, and coexisting attractors. At the same time, the digital circuit of the new system is implemented on the digital signal processing (DSP) platform. Compared with integer-order double-ring erbium-doped fiber laser chaotic systems, this fractional-order chaotic system has more complex dynamics and higher sensitivity and randomness. Finally, the fractional-order double-ring erbium-doped fiber laser chaotic system is applied to the image encryption algorithm with the improved gravitation model. In this encryption algorithm, the diffusion part uses an improved universal gravitation model, and the Zigzag algorithm scrambles the pixels. The security of the algorithm is analyzed. The analyses of the experimental results show that the image encryption scheme proposed in this paper has a good encryption effect and good security performance. The research in this paper provides theoretical guidance and experimental basis for the research of fractional-order laser chaotic secure communication.
Sapphire: A Configurable Crypto-Processor for Post-Quantum Lattice-based Protocols

Public key cryptography protocols, such as RSA and elliptic curve cryptography, will be rendered insecure by Shor’s algorithm when large-scale quantum computers are built. Cryptographers are working on quantum-resistant algorithms, and lattice-based cryptography has emerged as a prime candidate. However, high computational complexity of these algorithms makes it challenging to implement lattice-based protocols on low-power embedded devices. To address this challenge, we present Sapphire – a lattice cryptography processor with configurable parameters. Efficient sampling, with a SHA-3-based PRNG, provides two orders of magnitude energy savings; a single-port RAM-based number theoretic transform memory architecture is proposed, which provides 124k-gate area savings; while a low-power modular arithmetic unit accelerates polynomial computations. Our test chip was fabricated in TSMC 40nm low-power CMOS process, with the Sapphire cryptographic core occupying 0.28 mm2 area consisting of 106k logic gates and 40.25 KB SRAM. Sapphire can be programmed with custom instructions for polynomial arithmetic and sampling, and it is coupled with a low-power RISC-V micro-processor to demonstrate NIST Round 2 lattice-based CCA-secure key encapsulation and signature protocols Frodo, NewHope, qTESLA, CRYSTALS-Kyber and CRYSTALS-Dilithium, achieving up to an order of magnitude improvement in performance and energy-efficiency compared to state-of-the-art hardware implementations. All key building blocks of Sapphire are constant-time and secure against timing and simple power analysis side-channel attacks. We also discuss how masking-based DPA countermeasures can be implemented on the Sapphire core without any changes to the hardware.
Subradiant states of quantum bits coupled to a one-dimensional waveguide

The properties of coupled emitters can differ dramatically from those of their individual constituents. Canonical examples include sub- and super-radiance, wherein the decay rate of a collective excitation is reduced or enhanced due to correlated interactions with the environment. Here, we systematically study the properties of collective excitations for regularly spaced arrays of quantum emitters coupled to a one-dimensional (1D) waveguide. We find that, for low excitation numbers, the modal properties are well-characterized by spin waves with a definite wavevector. Moreover, the decay rate of the most subradiant modes obeys a universal scaling with a cubic suppression in the number of emitters. Multi-excitation subradiant eigenstates can be built from fermionic combinations of single excitation eigenstates; such "fermionization" results in multiple excitations that spatially repel one another. We put forward a method to efficiently create and measure such subradiant states, which can be realized with superconducting qubits. These measurement protocols probe both real-space correlations (using on-site dispersive readout) and temporal correlations in the emitted field (using photon correlation techniques).