Reviewerly/exHarmony huggingface dataset:
SECAUCTEE: Securing Auction Smart Contracts using Trusted Execution Environments

Smart contracts running on blockchains have emerged as an indispensable mechanism to enhance trust, security, transparency and traceability of data shared among critical distributed applications. Unfortunately, a smart contract deployed on a blockchain by itself is usually inadequate in maintaining data security and privacy because the data are replicated to all the nodes on the network. There has been some recent work that tries to tackle this privacy leakage issue in smart contract execution by integrating blockchains with hardware supported trusted execution environments(TEEs). Although TEEs ensure privacy to some extent, the smart contract execution can still be compromised if the developed code does not use the TEEs' capabilities correctly. One important security issue for leveraging TEEs in practice is the memory access pattern disclosure. Even though the TEEs encrypt all the memory content during the program execution, the memory access sequence can be observed by the malicious operating system, and can be used to infer sensitive information such as “who submitted the second highest bid to the auction?”. Hence, for enhanced security for TEE based applications, the memory access pattern leakage need to be addressed. Given these observations, an apparent question that comes to light is, how can we use TEEs correctly to enable efficient, privacy enhancing and secure applications? In this work, we address this challenge in the context of digital auctions. We develop a novel generic and secure framework that allow an auction smart contract to run inside secure enclaves over Intel SGX based TEEs on a blockchain. To our knowledge, this is the first work that provides access pattern leakage free TEE based secure auction smart contract deployment. We achieve this by implementing oblivious execution (i.e., no memory access pattern leakage) of both first price and second price sealed bid auctions as templates. Furthermore, we implement an end-to-end encryption service to keep the bids secure. Our empirical results and privacy analysis show that this architecture does not cause a significant impact to efficiency given the level of security achieved.
Research on the Influence of Sports and Nutrition Matching on Improving Students’ Physique Based on Intelligent Sensor

With the continuous development of social economy, students’ learning is paid more and more attention, but at the same time, their physical condition also deserves attention. In view of these problems and limitations, this paper is based on intelligent sensors, which is different from the traditional physical education teaching, adopts the corresponding prescription for sports teaching, combines teaching with fun for the corresponding health intervention, and realizes the two-way dimensional analysis of compound sports prescription through comprehensive nutrition collocation, so as to improve students’ physical and mental health, in order to improve students’ physical fitness. The simulation results show that the intelligent sensor is effective, which can improve the systematicness and scientificity of students’ participation in exercise in sports activities and improve students’ learning and physical fitness.
Insulator Detection Based on Deep Learning Method in Aerial Images for Power Line Patrol

Insulators play an important role of supporting wires and insulating in power lines, and insulator detection using aerial images has became a trend in power line patrol. However, traditional visual detection methods are usually less accurate and less robust due to the complex image background. To solve this problem, this paper propose a novel and efficient deep learning based method to detect insulator in aerial image. First, the ResNet network pre-trained on ImageNet dataset is adopted as the backbone network of the proposed network. Then, a cascaded convolution module is designed to extract composite insulator features corresponding to multiple receptive fields. To make the proposed network to learn the most powerful insulator features, SPP module and SE module are introduced into the detection branches of the proposed network. Experimental results verify that the proposed network achieves higher accuracy than five existing networks.
Scientific and Technical Productivity of African Countries: What Scopus and WIPO Patentscope Data Tell Us?

This study maps the scientific publication and patenting activity of the member countries of the African Union (AU) using different Scientometrics indicators. This study uses the Scopus data of Elsevier science. Scopus extensively covers global as well as African scholarly literature. This study traces the literature growth, subject wise productivity and citation pattern of AU member countries. Besides the literature data, this study also maps the patenting activity using data from the World Intellectual Property Organization (WIPO) PATENTSCOPE database. The study observed a visible growth in publication since the last decade. The major activity is in Medicine related subject areas followed by Agriculture and Biological Science. The publications from African countries are wellcited (9.08 per paper) and above the average of Asian and Latin American countries but below the world average. The patenting activity as reflected from WIPO data, is limited and South Africa is the only country with quite a visible number of patents. The outcome and the recommendations of this study will undoubtedly be useful for scholars, policy, and decision-makers in the governments of the member states of AU.
Forecasting the Collapse-Induced Ground Vibration Using a GWO-ELM Model

Blasting demolition is a popular method in the area of building demolishing. Due to the complex process of the building components’ collapse, it is difficult to predict the collapse-induced ground vibrations. As the accuracy of the empirical equation in predicting the collapse-induced ground vibration is not high, there is a significant risk of damage to the surrounding structures. To mitigate this risk, it is necessary to control and predict the peak particle velocity (PPV) and dominant frequency of ground vibration with higher accuracy. In this study, the parameters on the PPV and frequency of collapse-induced ground vibration are analyzed based on the Hertz theory. Then, fall tests are performed to simulate the collapse process of structural components and to investigate the characteristics of influential parameters on PPV and frequency. Using kernel density estimation (KDE) and Pearson correlation, the PPV and frequency are correlated with the distance from the falling point to the monitored point (R) and the mass of the falling structural component (M). Using recorded ground vibration data, the PPV and frequency are predicted using an extreme learning machine in combination with gray wolf optimization. The efficiency of the proposed algorithm is compared with other predictive models. The results indicate that the accuracy pre-diction of the proposed algorithm is better than those of plain extreme learning machines and the empirical equations, which indicates that the approach can be applied for PPV and frequency prediction of collapse-induced ground vibrations during blasting demolition.
A Deep Learning Approach for M2M Traffic Classification Using Call Detail Records

With the rapid progress of the fifth-generation mobile communication technology (5G) and its applications in vertical industries, the number of Machine to Machine (M2M) connections and network traffic generated by M2M devices have been greatly increased. However, on the other hand, the insufficient analysis of the increasing M2M traffic may lead to lack of network service quality management and potential cyber security risk. In this paper, a novel Call Detail Records (CDR) based deep learning approach is introduced to perform traffic analysis for M2M applications. With the datasets collected from actual scenarios, the provided approach achieves a training accuracy of 95% and a prediction accuracy of 77% in practical M2M application classification tasks.
Multiple Cues-Based Robust Visual Object Tracking Method

Visual object tracking is still considered a challenging task in computer vision research society. The object of interest undergoes significant appearance changes because of illumination variation, deformation, motion blur, background clutter, and occlusion. Kernelized correlation filter- (KCF) based tracking schemes have shown good performance in recent years. The accuracy and robustness of these trackers can be further enhanced by incorporating multiple cues from the response map. Response map computation is the complementary step in KCF-based tracking schemes, and it contains a bundle of information. The majority of the tracking methods based on KCF estimate the target location by fetching a single cue-like peak correlation value from the response map. This paper proposes to mine the response map in-depth to fetch multiple cues about the target model. Furthermore, a new criterion based on the hybridization of multiple cues i.e., average peak correlation energy (APCE) and confidence of squared response map (CSRM), is presented to enhance the tracking efficiency. We update the following tracking modules based on hybridized criterion: (i) occlusion detection, (ii) adaptive learning rate adjustment, (iii) drift handling using adaptive learning rate, (iv) handling, and (v) scale estimation. We integrate all these modules to propose a new tracking scheme. The proposed tracker is evaluated on challenging videos selected from three standard datasets, i.e., OTB-50, OTB-100, and TC-128. A comparison of the proposed tracking scheme with other state-of-the-art methods is also presented in this paper. Our method improved considerably by achieving a center location error of 16.06, distance precision of 0.889, and overlap success rate of 0.824.
Assume a Quantum Dataset

Data-processing algorithms often require that the data is prepared in appropriate structures that are readily accessible or can be prepared on demand.Quantum computers derive their power from storing and manipulating quantum superpositions and could potentially speed up data science tasks.However, they often require input in the form of a quantum state that encodes a nonquantum data set.Here we describe some of the challenges of encoding nonquantum data for use by quantum computers.
Optimization of BIPV based on electrical energy generated and return of investment

The availability of non-renewable resources to generate electrical energy has decreased. The National Energy Council says that Indonesia has huge potential renewable energy resources. The largest amount coming from solar energy. However, only 5% is utilized. The number of building owners who want to install the technology related to renewable energy on/in a building in Indonesia is still very small. This happens due to the high initial cost of installation. In case of an existing building, the boundaries added with the limitation of structure and limitation of potential-available integrated area. This study proposed a BIPV model in an existing building to contribute the maximum use of renewable energy in a relatively limited potential-available integrated area. The experimental method was applied to several models. Amount of electrical energy generated, and ROI were used as optimization parameters. ROI will give more comprehensive review to a building owner, to show that the time period is the most critical determinant of investment. The study shows that installation of PV on an opaque wall in the west orientation is the optimum one. The model generated 777741 kWh/year of electrical energy, exceeding the existing consumption. ROI calculated in 4.36 years, relatively short compared to 25 years of PV life guarantee. The optimization on this model is supported by its largest number of integrated area and high annual radiation received. This study shows significant results to the building owner. Electrical energy and ROI should be fully taken as a consideration of when to decide to apply PV.
CBIR : Color Feature Extraction using CIELAB Color Space with Compact Color Signature

The immense progress of new technology we have been created an enormous number of digital images by using such devices as a digital camera, scanner, and mobile phones so on. All the images which are taken by the devices to keep in Image Database. For retrieving the desire images which were given in an input image has compared with the large database according to the visual content used by the technique as referred to as the Content Based Image Retrieval (CBIR) system. There are two phases for retrieving images in the CBIR system, as the first one is feature extraction and the second one is similarity size. Thus, the feature extraction consists of every image has produced symbolic content in the form of the function. The visual contents of an image in the CBIR system contain the features which have represented as shape, texture, spatial region and color of the images. In our paper tries to design the images’ color features as in the steps to focus color representation in the k-d tree, CIELAB color space of color signature compression along with categories of Human’s color for Content-based image retrieval and also acquire the results using MATLAB.
Blind Face Restoration via Multi-Prior Collaboration and Adaptive Feature Fusion

Blind face restoration (BFR) from severely degraded face images is important in face image processing and has attracted increasing attention due to its wide applications. However, due to the complex unknown degradations in real-world scenarios, existing priors-based methods tend to restore faces with unstable quality. In this article, we propose a multi-prior collaboration network (MPCNet) to seamlessly integrate the advantages of generative priors and face-specific geometry priors. Specifically, we pretrain a high-quality (HQ) face synthesis generative adversarial network (GAN) and a parsing mask prediction network, and then embed them into a U-shaped deep neural network (DNN) as decoder priors to guide face restoration, during which the generative priors can provide adequate details and the parsing map priors provide geometry and semantic information. Furthermore, we design adaptive priors feature fusion (APFF) blocks to incorporate the prior features from pretrained face synthesis GAN and face parsing network in an adaptive and progressive manner, making our MPCNet exhibits good generalization in a real-world application. Experiments demonstrate the superiority of our MPCNet in comparison to state-of-the-arts and also show its potential in handling real-world low-quality (LQ) images from several practical applications.
Open Access Monographs

The UK Research and Innovation funding council announced its latest Open Access Policy on August 6, 2021. This policy applies to all UKRI funded research, and thus constitutes a significant move towards OA as an academic standard. For the first time in the UK, OA is to be mandated for academic books – this means that both monographs and edited chaptered books must be published Open Access from January 2024, though a 1 year embargo is permissible. As the infrastructures, business models and workflows supporting OA book publishing are currently lagging behind journals, especially in the Arts and Humanities, many researchers and institutions have responded to the policy with some consternation, even whilst supporting the aims and ethics of OA publishing. This article explores some of these apprehensions and questions raised by institutions, academics and by librarians regarding OA book publishing in a UK context, especially regarding funding and sustainability. It aims to dispel certain myths around OA book publishing in general, particularly the notion that Book Processing Charges are a necessary or even desirable element. The article then presents some of the varied models and systems currently in use and development, particularly the work of the UKRI/Research England funded COPIM project (Community- Led Open Access Infrastructures for Monographs), one of the aims of which is to build ways of delivering more sustainable revenue sources to OA publishers. It focuses in particular a key and soon to be launched output of the project: the Open Book Collective.
Blockchain-based solutions for smart mobility sustainability assurance

Abstract Smart mobility represents the future for many of today’s cities and is part of the smart city concept. Besides all its characteristics, the smart city concept aims to use the IoT (Internet of Things) technology to improve the citizens quality of life through a secure and efficient administration of city resources. The exponential growth of population and urbanization creates many challenges, especially from a mobility perspective. The main challenge in this direction is to ensure a sustainable transportation system. The objective of this paper is to present a survey on the sustainability assurance for smart mobility systems by using the blockchain technology. Moreover, this study proposes a conceptual model for a sustainable smart mobility system that implies the usage of blockchain technology. Widely known through its connection with bitcoin and other cryptocurrencies, blockchain can serve the safety and security requirements that are specific to intelligent transportation systems (ITS). The main advantage of this technology is the capability to face against several security threats by storing securely the information. This approach will streamline and secure the sensors networks data handling by the TMCs (Traffic Monitoring Centers). The process of these data handling is challenging because of possible exposure to cyber-attacks that can lead for example to erroneous traffic lights phases and green-interval settings in crossroads. The conclusions will be issued based on a comparison with the current IoT approaches for ensuring the secure storage of traffic data retrieved from sensors networks.
A Blockchain-Based Approach to Track Traffic Messages in Vehicular Networks

Sharing traffic information on the vehicular network is essential to implement an efficient transportation system. Earlier communication of road closure warnings, car accident notices, and driver route changes is crucial to reduce traffic congestion. Unfortunately, tracking the exchanged traffic data and ensuring their correctness are not easy tasks. Vehicles can act maliciously by sending erroneous information or being subject to cyber-attacks, causing incorrect messages in the system. To efficiently identify the incorrect traffic information and track the exchanged data, we use a blockchain due to its decentralization, data immutability, security, and transparency features. We consider both the well-secured proof of work (PoW) and the practical Byzantine fault tolerance (PBFT) consensus algorithms. We study the adaptation of those two algorithms to enable an efficient, secure, and transparent exchange of data in vehicular networks. We also discuss their empirical performance under real-world vehicular network settings.
Automatic Generation of Medical Report with Knowledge Graph

As an important part of medical diagnosis, medical images are widely used in the diagnosis and treatment of diseases. Radiologists need to write reports for a large number of medical images every day, which usually occupies most of the radiologists’ work time. Automatic medical report generation becomes an urgent problem to be solved. Most of the existing works in the domain of medical report generation have some problems. The first is abnormalities are not well represented and the connection between them is usually ignored. Second, there is no or no good fusion between image and semantic information. We present to establish abnormality graph with prior knowledge to represent abnormalities and their relationships. And we use a multimodal interaction module to fuse image and semantic information. Experimental results on two datasets, a public English medical reports dataset and a Chinese medical reports dataset conducted by ourselves, show that our method outperforms the state-of-art methods.
AULA-Caps: Lifecycle-Aware Capsule Networks for Spatio-Temporal Analysis of Facial Actions

Most state-of-the-art approaches for Facial Action Unit (AU) detection rely on evaluating static frames, encoding a snapshot of heightened facial activity. In real-world interactions, however, facial expressions are more subtle and evolve over time requiring AU detection models to learn spatial as well as temporal information. In this work, we focus on both spatial and spatio-temporal features encoding the temporal evolution of facial AU activation. We propose the Action Unit Lifecycle-Aware Capsule Network (AULA-Caps) for AU detection using both frame and sequence-level features. While, at the frame-level, the capsule layers of AULA-Caps learn spatial feature primitives to determine AU activations, at the sequence-level, it learns temporal dependencies between contiguous frames by focusing on relevant spatio-temporal segments in the sequence. The learnt feature capsules are routed together such that the model learns to selectively focus on spatial or spatio-temporal information depending upon the AU lifecycle. The proposed model is evaluated on popular benchmarks, namely BP4D and GFT datasets, obtaining state-of-the-art results for both.
Analysis of the influence of solar cycles on meteorological variables in the central inter-Andean valley of Peru in the period 1986–2019

Solar activity transmitted in energy reaches the Earth, affecting atmospheric temperature and other global weather variables. This research aims to analyze the relationship between solar cycles and meteorological variables in the Central Inter-Andean Valley during the period 1986–2019. Sunspot data were obtained from the Royal Observatory of Belgium for solar cycles 22, 23 and 24, and the meteorological variables of the National Service of Meteorology and Hydrology of Peru from five stations that are distributed in the Valley. For the correlation of the variables, the nonparametric Spearman statistical test with a third-degree polynomial regression was applied. The results show that there is a decrease in the number of sunspots in cycle 24, and that the temperature varies by 3.85 °C in the study period. Of the five stations analyzed, four of them show that there is no relation between the variables and only in the station Viques there is an inverse relation for α<0.05 referring to the variables of maximum temperature and sunspots, obtaining a coefficient of determination of R2=0.619. Therefore, solar activity does not influence the climate variability of the study valley, and this is due to climate change due to the effects of anthropogenic activities.
An Introduction to Generative Adversarial Learning: Architectures and Applications

Generative Adversarial Networks (GANs) continue to be one of the most popular deep learning approaches for synthetic data generation. Moreover, GANs are finding their way in other application domains, such as speech and audio synthesis, object detection and segmentation, text-to-image translation, and policy learning in deep reinforcement learning, among many others. This book presents a collection of chapters on the latest advancements on GANs and state-of-the-art applications.