Reviewerly/exHarmony huggingface dataset:
Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Abstract Research into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive. To tackle these two issues, this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation (SAFE‐PSO) approach. Firstly, the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN. Secondly, the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates. Thirdly, a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost. The SAFE‐PSO algorithm is tested on two widely used datasets: The 10‐category (i.e., CIFAR10) and the 100−category (i.e., CIFAR100). The experimental results show that SAFE‐PSO is very effective and efficient, which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.
Blockchain assisted clustering with Intrusion Detection System for Industrial Internet of Things environment

Industry 4.0 has the potential to create new industrial revolution by offering intelligent, secure, independent, and self-adaptive Industrial Internet of Things (IIoT) networks. These IIoT nodes can be grouped via the clustering process into clusters to enhance networks’ efficiency, lifetime, and scalability. However, it offers several benefits but still security is a primary concern, which can be resolved by Intrusion Detection Systems (IDS) and blockchain technology. Therefore, an effective Blockchain-Assisted Cluster-based Intrusion Detection System for IIoT, called the BAC-IDS technique has been devised. The proposed BAC-IDS model aims to cluster the IIoT devices for detecting the intrusions and enabling blockchain-based secure data transmission. BAC-IDS technique involves Harris Hawks Optimization (HHO)-based clustering technique to choose the Cluster Heads (CH) effectively and construct clusters accordingly. In addition, Chicken Swarm Optimization with Gated Recurrent Unit-based intrusion detection is also followed to determine the presence of intrusions in IIoT environment. Moreover, blockchain technology enables secure data transmission from Cluster Members (CMs) to CHs. The design of HHO clustering and CSO-based hyperparameter optimization for the IIoT environment represents the novelty of the proposed work. A wide range of simulations are performed under different aspects. From experimental results, it has been observed that the BAC-IDS approach has gained a TNR (99.97%), AUC (99.96%), precision (99.99%), recall (99.97%), accuracy (99.98%), F-score (99.98%) and error rate (0.03) on NSL-KDD2015 dataset. Further, on CICIDS 2017 dataset, it has obtained TNR (99.96%), AUC (99.98%), precision (99.96%), recall (99.98%), accuracy (99.97%), F-score (99.96%), and error rate (0.04). The experimental results highlight the supremacy of the proposed BAC-IDS technique over current state-of-art techniques.
A Blockchain-Assisted Privacy-Aware Authentication Scheme for Internet of Medical Things

Benefiting from the progress of Internet of Things (IoT) technology, medical devices, wearables, sensors, and users can be connected with each other to form an Internet of Medical Things (IoMT) ecosystem. IoMT improves efficiency, increases accuracy, and reduces the costs of the traditional healthcare system. However, since IoMT involves different entities and heterogeneous networks and carries a large amount of private information, it is a challenging task to ensure data security and protect privacy in the IoMT ecosystem. In this article, we focus on the issue of privacy-aware authentication between entities. We first propose a blockchain-assisted authentication framework for IoMT applications in the fog computing paradigm. Furthermore, we present two privacy-preserving authentication protocols based on elliptic curve cryptography (ECC) and physically unclonable functions (PUFs), respectively, in terms of the capacity of involved entities. Security analysis and performance evaluation demonstrate that compared with several previous protocols, the proposed protocols have competitive computation and communication costs while achieving expected security requirements.
SAILFISH: Vetting Smart Contract State-Inconsistency Bugs in Seconds

This paper presents SAILFISH, a scalable system for automatically finding state-inconsistency bugs in smart contracts. To make the analysis tractable, we introduce a hybrid approach that includes (i) a light-weight exploration phase that dramatically reduces the number of instructions to analyze, and (ii) a precise refinement phase based on symbolic evaluation guided by our novel value-summary analysis, which generates extra constraints to over-approximate the side effects of whole-program execution, thereby ensuring the precision of the symbolic evaluation. We developed a prototype of SAILFISH and evaluated its ability to detect two state-inconsistency flaws, viz., reentrancy and transaction order dependence (TOD) in Ethereum smart contracts. Our experiments demonstrate the efficiency of our hybrid approach as well as the benefit of the value summary analysis. In particular, we show that SAILFISH outperforms five state-of the-art smart contract analyzers (SECURIFY, MYTHRIL, OYENTE, SEREUM and VANDAL) in terms of performance, and precision. In total, SAILFISH discovered 47 previously unknown vulnerable smart contracts out of 89,853 smart contracts from ETHERSCAN.
Static hand gesture recognition in sign language based on convolutional neural network with feature extraction method using ORB descriptor and Gabor filter

In this paper, a new structure of deep learning neural network is introduced to identify the static hand gesture in the sign language. The proposed structure includes the convolutional neural network (CNN) and the classical non-intelligent feature extraction method. In the proposed structure, the hand gesture image, after preprocessing and removing its background, passes through three different streams of feature extraction, to well extract of effective features and determine the hand gesture class. These three streams, that independently extract their own specific features, consist of three widely used methods in the hand gesture classification named CNN, Gabor filter and ORB feature descriptor. Then these features are merged and formed the final feature vector. By combining these efficient methods, in addition to achieving a very high accuracy in hand gestures classifying, the proposed structure becomes more resistant to uncertainties such as rotation and ambiguity in the hand gestures. Another prominent feature of the proposed structure is its comprehensiveness on different image databases, compared to the similar methods. The transfer learning technique demonstrates that the proposed structure has the ability to be used as a pre-trained structure for any type of image database. Finally, the proposed structure is applied to the three different databases of Massey, ASL Alphabet and ASL, which have 2520, 87,000 and 23,400 of hand gesture images, respectively. The results show the mean accuracy of the proposed structure for the Massey test set of 758 images, ASL with 7020 test images, and ASL Alphabet with 26,100 test images, at 99.92%, 99.8%, and 99.80% respectively.
Information-Theory-based Nondominated Sorting Ant Colony Optimization for Multiobjective Feature Selection in Classification

Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA&#x2019;s performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.
Linguistic Perspectives on Register

Language users change their written and spoken language according to the situational characteristics and communicative purpose of production—that is, according to the register being produced. Research on registers has focused on register description or patterns of register variation, on detailed analysis of individual linguistic features or an account for the use of a broad range of linguistic features, and on the distinction between written and spoken registers. In this review, we survey register studies according to the register being investigated: spoken, written, electronic/online, literary, or historical. This survey also shows that recent register studies have focused on more specialized written and spoken domains and that the use of corpus linguistics tools and advanced statistical methods such as multidimensional analysis has allowed for broad analyses of the language used in different registers. Finally, we point to areas of register research that need further investigation.
A Division-Free Toom–Cook Multiplication-Based Montgomery Modular Multiplication

Toom-Cook multiplication is a theoretically more efficient multiplication algorithm than traditionally used Karatsuba and schoolbook multiplication but is rarely used in practical hardware designs due to its inherent exact divisions, which are time-consuming and difficult for parallel and serial acceleration. This brief proposes a method of division-free Toom-Cook multiplication based Montgomery modular multiplication, which makes it possible for Toom-Cook multiplication to be applied in practical and efficient hardware implementations. We also provide a hardware implementation of modular multipliers of 256 bits and 1024 bits with advantages on area-time-product over previous researches.
Ageing (dis)gracefully: Enabling designers to understand material change

From the moment of purchase, pristine objects are subjected to an array of stimuli including wear, impact, heat, light, water and air which alter their tactile and aesthetic properties. Material change is often regarded as ‘damage’ or ‘degradation’ and contributes to premature obsolescence but has potential to be used as a tool to engender emotional engagement with an object and extend product lifetimes. However, materials resources for designers rarely provide information about how materials will change in use. In this paper we draw on a combination of literature and user studies to elucidate the complex web of factors which contribute to changes in material surfaces, which we present in a ‘framework for understanding material change’. We go on to explore the role that changes to product material surfaces, and the design of objects to change in particular ways with use, could have on the transition to circular modes of consumption. A range of resources which aim to increase designers' understanding of material change are presented, and the challenges of creating, utilising and developing these resources are discussed.
Direct adaptive model-free control of a class of uncertain nonlinear systems using Legendre polynomials

In this paper, a simple model-free controller for a class of uncertain nonlinear systems is presented using Legendre polynomials. According to the orthogonal functions theorem, Legendre polynomials are universal approximators. From this point of view, they are similar to fuzzy systems and can be used as a controller to approximate the ideal control law. Legendre coefficients are estimated online using the adaptation rule obtained from stability analysis. In comparison with fuzzy systems and neural networks, Legendre polynomials are simpler and less computational. Moreover, there are very few tuning parameters in Legendre polynomials. The case studies are an inverted pendulum and an articulated robot manipulator. Simulation results verify the effectiveness of the proposed controller and its robustness against large uncertainties. Comparisons with model-free controller using extended state observer and adaptive fuzzy systems show the superiority of proposed controller in disturbance rejection.
Mobile terminal gesture recognition based on improved FAST corner detection

Mobile terminal gesture recognition is an extreme challenge, not only because its limited computing resource make it complicated to identify feature points but also the complex background can easily affect the recognition result. This study proposes a gesture recognition method based on improved features from accelerated segment test (FAST) corner detection. First, in order to eliminate the effects of complex background and light, the intersection of the two frame images is obtained through background subtraction and the multi-colour space to realise the detection of the hand. Second, in order to improve the performance of the algorithm, an improved FAST corner detection method combined with the back propagation neural network (BPNN) is proposed in accordance with the characteristics of fingertips. Subsequently, the feature points are screened by method of non-maximum suppression. Finally, gesture recognition is realised by matching feature points. Experimental results illustrate that this method has strong anti-interference ability in complex background, and it is good at performance.
BI-RADS density categorization using deep neural networks

The Breast Imaging and Reporting Data System (BI-RADS) density score is a qualitative measure and thus subject to inter- and intra-radiologist variability. In this study we investigated the possibility of fine-tuning a state-of-the-art deep neural networks for (i) distinguishing fatty breasts (BI-RADS I and II) from dense ones (BI-RADS III and IV), (ii) classifying the low risk group into BIRADS I and II, and (iii) classifying the high risk group into BIRADS III and IV. To do so 3813 images acquired from nine mammography units and three manufacturers were used to train an Inception- V3 network architecture. The network was pre-trained on the ImageNet data set and we trained it on our dataset using transfer learning. Before feeding the images into the input layer of Inception- V3, the breast tissue was segmented from the background and the pectoral muscle was excluded from the image in the mediolateral oblique view. Images were then cropped by using the breast bounding box and resized to make the images compatible with the input layer of the network. The performance of the network was evaluated on a blinded test set of 150 mammograms acquired from 14 mammography units provided by six manufacturers. The reference density value for these images was obtained based on the consensus of three radiologists. The network achieved an accuracy of 92.0% in high versus low risk classification. For the second and third classification tasks, the overall accuracy was 85.9% and 86.1%. When results from all three classifications combined, the networks achieved an accuracy of 83.33% and a Cohen’s kappa of 0.775 (95% CI: 0.694-0.856) for four-point density categorization. The obtained results suggest that a deep learning-based computerized tool can be used for providing BI-RADS density scores.
Design of Intelligent Classroom Attendance System Based on Face Recognition

It is time-consuming and laborious for classroom attendance methods in Chinese universities, and the attendance costs are too high. In this paper, we use the deep learning related ideas to improve the AlexNet convolutional neural network, and use the WebFace data set to improve the network training and test. The Top-5 error rate is only 6.73%. We applied this model to face recognition and combined with RFID card reading technology, which developed a smart classroom attendance system based on face recognition. Research shows that the system is efficient and stable, which effectively reduce classroom attendance costs.
Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning

Abstract This work presents self-adaptive multiobjective real-code population-based incremental learning hybridised with differential evolution (MRPBIL-DE) for solving a 6D robot trajectory planning multiobjective optimisation problem. The objective functions are assigned to minimise travelling time and minimise maximum jerk taking place during motion while the constraints are velocity, acceleration and jerk constraints. A five order polynomial function is used to represent a motion equation while the motion path is divided into two sub-paths; from initial to intermediate positions and from intermediate to final positions. The optimiser is used to find a set of design variables including joint positions, velocities and accelerations at intermediate positions, moving time from the initial to intermediate positions, and that from the intermediate to final positions. Several multiobjective meta-heuristics (MOMHs) along with the proposed algorithm are used to solve the trajectory optimisation problem of robot manipulators while their performances are investigated. The results indicated that the proposed algorithm is effective and efficient for multiobjective robot trajectory planning optimisation problem. The results obtained from such a method are set as the baseline for further study of robot trajectory planning optimisation.
Cryptographically significant mds matrices over finite fields: A brief survey and some generalized results

A matrix is MDS or super-regular if and only if every square submatrices of it are nonsingular. MDS matrices provide perfect diffusion in block ciphers and hash functions. In this paper we provide a brief survey on cryptographically significant MDS matrices - a first to the best of our knowledge. In addition to providing a summary of existing results, we make several contributions. We exhibit some deep and nontrivial interconnections between different constructions of MDS matrices. For example, we prove that all known Vandermonde constructions are basically equivalent to Cauchy constructions. We prove some folklore results which are used in MDS matrix literature. Wherever possible, we provide some simpler alternative proofs. We do not discuss efficiency issues or hardware implementations; however, the theory accumulated and discussed here should provide an easy guide towards efficient implementations.
Sensorstation

We present Sensorstation, a research product to explore the effect of smart sensors and services on the communal life within a shared apartment. Sensorstation utilizes wireless sensors and a shared output device displaying a steady data stream of sensor based notifications. It was deployed on the kitchen table in a shared apartment for 19 days to enable communal residents to co-design and to co-speculate on smart sensors and services in the context of their shared apartment. We synthesize and interpret findings to illustrate how residents created positive connections between each other, while simultaneously exercising self-monitoring, control over others, and contemplating reward systems and penalties. Our work contributes to a nuanced understanding of smart technology for shared apartments. We argue that design has an obligation to consider smart technology that acknowledges boundaries and to provide negotiation spaces to configure agency.
A conceptual perspective on interoperability in context-aware software systems

Context-aware software systems can interact with different devices to complete their tasks and act according to the context, regardless of their development and organizational differences. Interoperability is a big challenge in the engineering of such systems. To discuss how interoperability has been addressed in context-aware software systems, strengthening the scientific basis for its understanding and conceptualization. A quasi-systematic literature review was undertaken to observe interoperability in such context-aware software systems to support the discussions. Its dataset includes 17 from 408 papers identified in the technical literature. The extracted information was qualitatively analyzed by following the principles of Grounded Theory. The analysis allowed to identify ten interoperability concepts, organized into a Theoretical Framework according to structural and behavioral perspectives, which deals with interoperability as the ability of things (an object, a place, an application or anything that can engage interaction with a system) to interact for a particular purpose, once their differences (development platforms, data formats, culture, legal issues) have been overcome. Once the interoperability is established from structural concepts (context, perspective, purpose, the level of provided support and system attributes), it can be measured, improved and observed from the behavioral concepts (evaluation method, challenges, issues, and benefits). The Interoperability Theoretical Framework provides relevant information to organize the knowledge related to interoperability, considering context, and can be used to guide the evolution of software systems regarding changes focused on interoperability.
Reference Based Face Super-Resolution

Despite the great progress of image super-resolution in recent years, face super-resolution has still much room to explore good visual quality while preserving original facial attributes for larger up-scaling factors. This paper investigates a new research direction in face super-resolution, called Reference based face Super-Resolution (RefSR), in which a reference facial image containing genuine attributes is provided in addition to the low-resolution images for super-resolution. We focus on transferring the key information extracted from reference facial images to the super-resolution process to guarantee the content similarity between the reference and super-resolution image. We propose a novel Conditional Variational AutoEncoder model for this Reference based Face Super-Resolution (RefSR-VAE). By using the encoder to map the reference image to the joint latent space, we can then use the decoder to sample the encoder results to super-resolve low-resolution facial images to generate super-resolution images with good visual quality. We create a benchmark dataset on reference based face super-resolution (RefSR-Face) for general research use, which contains reference images paired with low-resolution images of various pose, emotions, ages and appearance. Both objective and subjective evaluations were conducted, which demonstrate the great potential of using reference images for face super-resolution. By comparing it with state-of-the-art super-resolution approaches, our proposed approach also achieves superior performance.