Reviewerly/exHarmony huggingface dataset:
A distributed <scp>identity</scp>‐based authentication scheme for internet of things devices using permissioned blockchain system

Abstract The Internet of Things (IoT) has become a significant technology on the internet with its widespread adoption in almost every place we could think of, like homes, hospitals, industries, companies, and so on. This adoption in virtually every device had made them smart, thereby reducing the human intervention to handle them. These devices become smart by gathering the sensed information and communicating with other devices or servers to take the appropriate decisions based on acquired data. However, these devices are deployed in batches with default usernames and passwords, making them vulnerable to attacks as seen in recent pasts like the Mirai botnet attack. Most of the attacks could have been avoided if these devices were equipped with a decent lightweight secure authentication scheme. One of the most common authentication procedures is using traditional public key infrastructure (PKI), which suffers from a single point of failure. Moreover, the complex procedures of PKI make them unfit for low‐powered IoT devices. Identity‐based cryptography (IBC), a lightweight cryptosystem, could be a good fit for these devices. But, even IBC suffers from a single point of failure and key escrow problem because of the private key generator (PKG). Blockchain has proved its mettle in eliminating a single point of failure with its robust distributed ledger technology. This article presents a novel authentication scheme for IoT devices based on identity‐based cryptography using a blockchain network. Blockchain is used as a distributed PKG, eliminating a single point of failure and key escrow problem of PKGs. Further, the proposed work is implemented in Hyperledger Fabric, which is an open‐source blockchain platform that efficiently performs the addition, updating, and deletions operation for effective authentication and communication of IoT devices.
Exploring factors influencing the acceptance of social robots among early adopters and mass market representatives

When designing social robots, it is crucial to understand the diverse expectations of different kinds of innovation adopters. Different factors influence early adopters of innovations and mass market representatives’ perceptions of the usefulness of social robots. The first aim of the study was to test how applicable the technology acceptance model 3 (TAM3) is in the context of social robots. Participants’ acceptance of social robotics in a workplace environment in the fuzzy front-end (FFE) innovation phase of a robot development project was examined. Based on the findings for the model, we developed a reduced version of the TAM3 that is more applicable for social robots. The second objective was to analyze how early adopters’ and mass market representatives’ acceptance of social robots differs. Quantitative research methods were used. For early adopters, result demonstrability has a significant influence on perceived usefulness of social robots, while for mass market representatives, perceived enjoyment has a more significant influence on perceived usefulness. The findings indicate that users’ innovation adoption style influences the factors that users consider important in the usefulness of social robots. Robot developers should take these into account during the FFE innovation phase.
ESSecA: An automated expert system for threat modelling and penetration testing for IoT ecosystems

Despite the growing spread of Internet of Things (IoT) ecosystems, their security assessment is still an open issue. Identifying threats, vulnerabilities, and attacks is a costly and time-consuming process, incompatible with their time-to-market. Undoubtedly, the introduction of automated security assessment techniques would increase the security level of many IoT products, while containing the costs. In this article, we introduce ESSecA, an Expert System for Security Assessment that guides penetration testers during the assessment of IoT systems, in a threat-intelligence-driven perspective. ESSecA bases its analysis on different knowledge-bases, some maintained by MITRE. Starting from the system model, ESSecA produces a Threat Model and a list of Attack Plans for each identified threat. This information can be used by penetration testers to perform a systematic security test of the target IoT infrastructure. We applied the technique to a typical home automation system, the Open Energy Monitor, providing some attack patterns for its security evaluation.
Immersive technologies for the training of operators in the process industry: A Systematic Literature Review

Immersive technologies aim to improve crucial process and safety training by increasing motivation, engagement and skills development. A Systematic Literature Review (SLR) was performed to identify immersive technologies applications that have been published in the past twenty years and aimed to enhance the training and learning of operators in the process industry, with special emphasis on the chemical industry. A set of 44 articles was obtained following the PRISMA framework with backward and forward snowballing. They were examined based on type of training, industry and technology. Only very few studies (10 out of 44) reported a comparison of immersive and traditional training. Six performance indicators (time; number of: mistakes, hints and instruction repetitions; events and equipment identification) were named to evaluate the immersive experience. To allow for a consistent analysis of the quality of immersive training in future studies, an effectiveness-efficiency model from the trainee viewpoint is proposed.
Eye fatigue estimation using blink detection based on Eye Aspect Ratio Mapping(EARM)

With the advent of the information society, the eyes' health is threatened all over the world. Rules and systems have been proposed to avoid these problems, but most users do not use them due to the physical and time constraints and costs involved and the lack of awareness of eye health. In this paper, we estimate the eye fatigue sensitivity by detecting spontaneous blinks with high accuracy. The experimental results show that the proposed Eye Aspect Ratio Mapping can classify blinks with high accuracy at a low cost. We also found a strong correlation between the median SBR (Spontaneous Blink Rate) and the time between the objective estimation of eye fatigue and the subject's awareness of eye fatigue.
Toward Intelligent Design: An AI-Based Fashion Designer Using Generative Adversarial Networks Aided by Sketch and Rendering Generators

The traditional fashion industry is heavily dependent on designers whose talent and vision have a significant impact on their innovative designs. Through taking advantage of recent advances in image-to-image translation by generative adversarial networks (GANs), marked improvement in designers’ efficiency is now possible. Considering both randomness and controllability in the design process, this article presents a novel artificial intelligence (AI)-based framework for fashion design. Under this framework, a sketch-generation module which is based on latent space is firstly introduced for designing various sketches. Secondly, a rendering-generation module is proposed to learn mapping between textures and sketches to complete the task of fashion design. In order to achieve effectiveness in synthesizing semantic-aware textures on sketches, a multi-conditional feature interaction module is developed in the rendering-generation model. Moreover, two different training schemes are introduced to optimize both the sketch-generation module and the rendering-generation module. In order to evaluate the performance of our proposed models, we built a large-scale dataset which consists of 115,584 pairs of fashion item images. Experimental results demonstrate the effectiveness of our proposed method, and indicate that our model can facilitate designers’ design process by taking full advantage of the controllability of different conditions (e.g., sketch and texture) and the randomness of latent space.
A Secure Cloudlet-Based Charging Station Recommendation for Electric Vehicles Empowered by Federated Learning

The fast-growing electric vehicles (EVs) industry requires a well-designed recommendation system to locate charging stations while ensuring private data protection. This article proposes a secure cloudlet-based recommendation system for EVs. Unlike conventional methods where training a recommender model involves direct data sharing between data holders, our model utilizes a secure vertical federated learning technique, in which EVs data do not leave the platforms. To improve the efficiency of the model and to alleviate the communication-related concerns in our recommender model, cloudlet-based data aggregator(s) are used as a replacement for the existing centralized architectures. To enhance the security of our system, blockchain technology is incorporated to generate a trusted network of cloudlets that are responsible for transmitting the locally computed training parameters. The simulation results achieved from our proposed recommendation system show that the distribution of EVs over a designated area with charging stations is more optimal, and the proposed decentralized recommender with 10 cloudlets is 5.2 s quicker than a conventional centralized model.
A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation.
An analysis of why cuckoo search does not bring any novel ideas to optimization

It has been more than 10 years since the first version of cuckoo search was proposed by Yang and Deb and published in the proceedings of the World Congress on Nature & Biologically Inspired Computing, in 2009. The two main articles on cuckoo search have now been cited almost 8 700 times (according to Google scholar), there are books and chapters published about this algorithm, and a special issue has been organized to celebrate its first decade of existence. Given the popularity of the algorithm and its widespread use, it is quite surprising that no one has ever formally investigated its obvious resemblance to evolutionary algorithms. In this article, we conduct a rigorous analysis of cuckoo search in which we identify the concepts used in the algorithm and show that these are the exact same concepts proposed in the (μ+λ)–evolution strategy, a well-known evolutionary algorithm introduced originally in 1981, and in the classic differential evolution algorithm introduced in 1997. We analyze the “cuckoos’ parasitic behavior” metaphor that inspired the algorithm according to three criteria—usefulness, novelty and sound motivation—that allow to clarify whether the use of the metaphor is justified or not. The result is that cuckoo search does not comply with any of these criteria. Surprisingly, we found that the algorithm the authors proposed for cuckoo search does not match the publicly available implementation they provided; moreover, neither of them really follows the metaphor of the cuckoos that inspired the algorithm.
GMR-RRT*: Sampling-Based Path Planning Using Gaussian Mixture Regression

Mobile robot autonomous path planning is an essential factor for its wide deployment in real-world applications. Conventional sampling-based algorithms have gained tremendous success in the path planning field, but they usually take much time to find the optimal solution so that the planning quality (evaluated with time cost and path length) cannot be guaranteed. In this paper, based on Gaussian Mixture Regression (GMR) and the family of Rapidly-exploring Random Tree (RRT) schemes, we propose the GMR-RRT* algorithm to achieve fast path planning for mobile robots. The proposed GMR-RRT* consists of learning navigation behaviors from human demonstrations and planning a high-quality path for the robot. Using the GMR, the key features of human demonstrations are captured to form a probability density distribution of the human trajectory in the current environment. This distribution is further utilized to guide the RRT scheme’s sampling process to generate a feasible path in the current environment quickly. We test the proposed GMR-RRT* in different environments, comparing it with three state-of-the-art sampling-based algorithms. The experimental results demonstrate that the GMR-RRT* algorithm can achieve better performance in terms of time cost, memory usage, and path length.
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization

Abstract The unsupervised detection and localization of anomalies in natural images is an intriguing and challenging problem. Anomalies manifest themselves in very different ways and an ideal benchmark dataset for this task should contain representative examples for all of them. We find that existing datasets are biased towards local structural anomalies such as scratches, dents, or contaminations. In particular, they lack anomalies in the form of violations of logical constraints, e.g., permissible objects occurring in invalid locations. We contribute a new dataset based on industrial inspection scenarios that evenly covers both types of anomalies. We provide pixel-precise ground truth data for each anomalous region and define a generalized evaluation metric that addresses localization ambiguities that can arise for logical anomalies. Furthermore, we propose a novel algorithm that improves over the state of the art in the joint detection of structural and logical anomalies. It consists of a local and a global network branch. The first one inspects confined regions independent of their spatial locations in the input image and is primarily responsible for the detection of entirely new local structures. The second one learns a globally consistent representation of the training data through a bottleneck that enables the detection of violations of long-range dependencies, a key characteristic of many logical anomalies. We perform extensive evaluations on our new dataset to corroborate our claims.
Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems (RS) touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim at solving a constrained optimization problem by imposing a constraint on the level of fairness while optimizing the main recommendation objective, e.g., click through rate (CTR). While this alleviates the impact of unfair recommendations, the expected return of an approach may significantly compromise the recommendation accuracy due to the inherent trade-off between fairness and utility. This motivates us to deal with these conflicting objectives and explore the optimal trade-off between them in recommendation. One conspicuous approach is to seek aPareto efficient/optimal solution to guarantee optimal compromises between utility and fairness. Moreover, considering the needs of real-world e-commerce platforms, it would be more desirable if we can generalize the wholePareto Frontier, so that the decision-makers can specify any preference of one objective over another based on their current business needs. Therefore, in this work, we propose a fairness-aware recommendation framework usingmulti-objective reinforcement learning (MORL), called MoFIR (pronounced "more fair ''), which is able to learn a single parametric representation for optimal recommendation policies over the space of all possible preferences. Specially, we modify traditional Deep Deterministic Policy Gradient (DDPG) by introducingconditioned network (CN) into it, which conditions the networks directly on these preferences and outputs Q-value-vectors. Experiments on several real-world recommendation datasets verify the superiority of our framework on both fairness metrics and recommendation measures when compared with all other baselines. We also extract the approximate Pareto Frontier on real-world datasets generated by MoFIR and compare to state-of-the-art fairness methods.
AStitch: enabling a new multi-dimensional optimization space for memory-intensive ML training and inference on modern SIMT architectures

This work reveals that memory-intensive computation is a rising performance-critical factor in recent machine learning models. Due to a unique set of new challenges, existing ML optimizing compilers cannot perform efficient fusion under complex two-level dependencies combined with just-in-time demand. They face the dilemma of either performing costly fusion due to heavy redundant computation, or skipping fusion which results in massive number of kernels. Furthermore, they often suffer from low parallelism due to the lack of support for real-world production workloads with irregular tensor shapes. To address these rising challenges, we propose AStitch, a machine learning optimizing compiler that opens a new multi-dimensional optimization space for memory-intensive ML computations. It systematically abstracts four operator-stitching schemes while considering multi-dimensional optimization objectives, tackles complex computation graph dependencies with novel hierarchical data reuse, and efficiently processes various tensor shapes via adaptive thread mapping. Finally, AStitch provides just-in-time support incorporating our proposed optimizations for both ML training and inference. Although AStitch serves as a stand-alone compiler engine that is portable to any version of TensorFlow, its basic ideas can be generally applied to other ML frameworks and optimization compilers. Experimental results show that AStitch can achieve an average of 1.84x speedup (up to 2.73x) over the state-of-the-art Google's XLA solution across five production workloads. We also deploy AStitch onto a production cluster for ML workloads with thousands of GPUs. The system has been in operation for more than 10 months and saves about 20,000 GPU hours for 70,000 tasks per week.
Dependency syntax guided BERT-BiLSTM-GAM-CRF for Chinese NER

Traditional named entity recognition (NER) methods derived from deep learning (DL) often ignore the long-distance syntactic dependence relationships between words. In such methods, the representation of Chinese word vectors is too simple to solve the problem of polysemy. To address these shortcomings, this paper proposes a syntactic dependency guided BERT-BiLSTM-GAM-CRF model for Chinese NER. First, the self-attention method guided by the dependency syntactic parsing tree is embedded in the transformer computing framework of the BERT model. This can not only obtain the deep two-way linguistic representation of a word according to the context information of the word, but it can also better express the long-distance syntactic dependency relationships between words; Second, the trained word vector sequence is input into the BiLSTM layer embedded in the global attention mechanism (GAM), and then the most important whole situation semantic information in the sentence is obtained. Finally, the CRF is employed to learn the dependence relationships between adjacent labels to obtain the best sentence level label sequence. A large number of experiments on the CLUENER-2020 corpus, MSRA corpus, Weibo corpus and OntoNotes4 corpus prove that the constructed model has good results for the Chinese NER task, and the F1 values are 81.08%, 94.97%, 63.60% and 75.64% respectively.
An Improved A-Star Algorithm for Complete Coverage Path Planning of Unmanned Ships

Aiming at the low efficiency and high energy consumption of unmanned ships traversing the entire area, a complete coverage path planning algorithm based on the improved A-star algorithm is proposed. The positioning and vision systems of unmanned ships are used to digitize the actual water information, and the grid method is used to convert the information into an environmental map that can be planned. Compared to the trapezoidal partition of unity method and the short-side reciprocating traversal algorithm in the traversal process, experiments show that path planning is more efficient with the boustrophedon partition of unity method and the long-side reciprocating traversal algorithm. Aiming at the “dead zone”, an improved A-star algorithm is proposed on the basis of the traditional A-star algorithm, that it can shorten about 1/4 path using the proposed algorithm. Simulation shows that the improved A-star algorithm can shorten the traversal path to 40 steps but the traditional A-star algorithm needs 54 steps. Navigation test shows that the proposed algorithm can shorten the traversal path and improve traversal efficiency while ensuring the coverage of unmanned ships.
CDS: Cross-Domain Self-supervised Pre-training

We present a two-stage pre-training approach that improves the generalization ability of standard single-domain pre-training. While standard pre-training on a single large dataset (such as ImageNet) can provide a good initial representation for transfer learning tasks, this approach may result in biased representations that impact the success of learning with new multi-domain data (e.g., different artistic styles) via methods like domain adaptation. We propose a novel pre-training approach called Cross-Domain Self-supervision (CDS), which directly employs unlabeled multi-domain data for downstream domain transfer tasks. Our approach uses self-supervision not only within a single domain but also across domains. In-domain instance discrimination is used to learn discriminative features on new data in a domain-adaptive manner, while cross-domain matching is used to learn domain-invariant features. We apply our method as a second pre-training step (after ImageNet pre-training), resulting in a significant target accuracy boost to diverse domain transfer tasks compared to standard one-stage pre-training.