Reviewerly/exHarmony huggingface dataset:
Future of the Internet of Things Emerging with Blockchain and Smart Contracts

The Internet of Things (IoT) has the potential to change the way the world works from home automation to smart cities, from improved healthcare to an efficient management system in supply chains to industry 4.0 revolution.IoT is increasingly becoming an essential part of the homes and industrial automation; nevertheless, there are still many challenges that need to fix.IoT solutions are costly and complicated, while issues regarding security and privacy must be addressed with a sustainable plan.Support the growing number of connected devices; the IoT is in dire need of a reboot.Blockchain technology might be the answer.Starting as a decentralized financial solution in the form of Bitcoin, Blockchain technology has expanded to diverse areas and Information Technology applications.Blockchain technology and Smart Contracts can address the outstanding security and privacy issues that impede further development of the IoT.Blockchain is a decentralized system with no central governance, facilitates interactions, promotes new and improved transaction models, and allows autonomous coordination of the devices using enhanced encryption techniques.The primary reason for this paper is to showcase the challenges and problems we are facing with the current internet of things solutions and analyze how the use of Blockchain and Smart Contracts can help achieve a new, more robust internet of things system.Finally, we examine some of the many projects using the Internet of Things together with Blockchain and Smart Contracts, to create new solutions that are only possible by integrating these technologies.
Multi-View Pose Generator Based on Deep Learning for Monocular 3D Human Pose Estimation

In this paper, we study the problem of monocular 3D human pose estimation based on deep learning. Due to single view limitations, the monocular human pose estimation cannot avoid the inherent occlusion problem. The common methods use the multi-view based 3D pose estimation method to solve this problem. However, single-view images cannot be used directly in multi-view methods, which greatly limits practical applications. To address the above-mentioned issues, we propose a novel end-to-end 3D pose estimation network for monocular 3D human pose estimation. First, we propose a multi-view pose generator to predict multi-view 2D poses from the 2D poses in a single view. Secondly, we propose a simple but effective data augmentation method for generating multi-view 2D pose annotations, on account of the existing datasets (e.g., Human3.6M, etc.) not containing a large number of 2D pose annotations in different views. Thirdly, we employ graph convolutional network to infer a 3D pose from multi-view 2D poses. From experiments conducted on public datasets, the results have verified the effectiveness of our method. Furthermore, the ablation studies show that our method improved the performance of existing 3D pose estimation networks.
ON CONVOLUTION OF BOAS TRANSFORM OF WAVELETS

In this paper, we show that the Boas transform of Convolution (crosscorrelation) of two wavelets satisfies the required admissibility and regularity conditions.These resulting new wavelets are then employed to analyze Boas transform of convolved (cross-correlated) signals by means of Boas transform wavelet convolution and cross-correlation theorems.Analogously to Bedrosian theorem, Boas transform product theorem is given.
Privacy Protection in Smart Cities by a Personal Data Management Protocol in Blockchain

Due to the increase of cybercrime and security risks in computer networks as well as violations of user privacy, it is essential to upgrade the existing protection models and provide practical solutions to meet these challenges.An example of these risks is the presence of a third party between users and various services, which leads to the collection and control of large amounts of users' personal information and the possibility of their databases being misused or hacked.Blockchain technology and encrypted currencies have so far shown that a decentralized network of peer-to-peer users, along with a general ledger, can do reliable computing.So, in this article, we are going to introduce a protocol that converts the blockchain network to an automated access control manager without the presence of a third party.To this end, we designed a mutual authentication protocol to create a secure channel between the user and the service and then demonstrate its accuracy and completeness using the Gong-Nidham-Yahalom belief logic [1].The results of our evaluations show that our proposed protocol is secure enough to be used on the blockchain network and attackers are unable to penetrate, track, impersonate, inject, misrepresent or distort information using the common attacks.
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification

Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small. To ensure good generalizability of deep networks under small sample sizes, learning discriminative features is crucial. To this end, several loss functions have been proposed to encourage large intra-class compactness and inter-class separability. In this paper, we propose to enhance the discriminative power of features from a new perspective by introducing a novel neural network termed Relation-and-Margin learning Network (ReMarNet). Our method assembles two networks of different backbones so as to learn the features that can perform excellently in both of the aforementioned two classification mechanisms. Specifically, a relation network is used to learn the features that can support classification based on the similarity between a sample and a class prototype; at the meantime, a fully connected network with the cross entropy loss is used for classification via the decision boundary. Experiments on four image datasets demonstrate that our approach is effective in learning discriminative features from a small set of labeled samples and achieves competitive performance against state-of-the-art methods. Code is available at https://github.com/liyunyu08/ReMarNet.
Real-Time Analysis of Online Sources for Supporting Business Intelligence Illustrated with Bitcoin Investments and IoT Smart-Meter Sensors in Smart Cities

Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%).
FSD-10: A fine-grained classification dataset for figure skating

Action recognition is an important and challenging problem in video analysis. Although the past decade has witnessed progress in action recognition with the development of deep learning, such process has been slow in competitive sports content analysis. To promote the research on action recognition from competitive sports video clips, we introduce a Figure Skating Dataset (FSD-10) for fine-grained sports content analysis. To this end, we collect 1484 clips from the worldwide figure skating championships in 2017–2018, which consist of 10 different actions in men/ladies programs. Each clip is at a rate of 30 frames per second with resolution 1080 × 720, which are annotated by experts. To build a baseline for action recognition in figure skating, we evaluate state-of-the-art action recognition methods on FSD-10. Motivated by the idea that domain knowledge is of great concern in sports field, we propose a keyframe based temporal segment network (KTSN) for classification and achieve remarkable performance. Experimental results demonstrate that FSD-10 is an ideal dataset for benchmarking action recognition algorithms, as it requires to accurately extract action motions rather than action poses. We hope FSD-10, which is designed to have a large collection of finegrained actions, can serve as a new challenge to develop more robust and advanced action recognition models.
Estimation of the Monthly Global, Direct, and Diffuse Solar Radiation in Japan Using Artificial Neural Network

In order to obtain the optimal design of solar energy system, the data of solar radiation should be provided.In this study, an estimation model of monthly solar radiation in Japan is developed using artificial neural network (ANN).The purpose of this study is to provide an accurate model to estimate the solar radiation, especially for the location where measured data is not available.The structure of ANN is constructed using geographical and 6 years-meteorological data between 2011-2016.The model has been validated by comparing the estimation results with measured solar radiation data on five different stations in 2017.Considering relatively small mean absolute percentage error (MAPE) and root mean square error (RMSE), it is believed that the proposed model could accurately predict the monthly solar radiation, which further could be used to obtain optimal design of solar energy system in Japan.
Unsupervised nonlinear adaptive manifold learning for global and local information

In this paper, we propose an Unsupervised Nonlinear Adaptive Manifold Learning method (UNAML) that considers both global and local information. In this approach, we apply unlabeled training samples to study nonlinear manifold features, while considering global pairwise distances and maintaining local topology structure. Our method aims at minimizing global pairwise data distance errors as well as local structural errors. In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data, we add a feature approximate error that can be used to learn a linear extractor. Also, we add a feature approximate error that can be used to learn a linear extractor. In addition, we use a method of adaptive neighbor selection to calculate local structural errors. This paper uses the kernel matrix method to optimize the original algorithm. Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.
Challenges towards Building an effective Cyber Security Operations Centre

The increasing dependency of modern society on IT systems and infrastructures for essential services (e.g.internet banking, vehicular network, health-IT, etc.) coupled with the growing number of cyber incidents and security vulnerabilities have made Cyber Security Operations Centre (CSOC) undoubtedly vital.As such security operations monitoring is now an integral part of most business operations.SOCs (used interchangeably as CSOCs) are responsible for continuously and protectively monitoring business services, IT systems and infrastructures to identify vulnerabilities, detect cyber-attacks, security breaches, policy violations, and to respond to cyber incidents swiftly.They must also ensure that security events and alerts are triaged and analysed, while coordinating and managing cyber incidents to resolution.Because SOCs are vital, it is also necessary that SOCs are effective.But unfortunately, the effectiveness of SOCs are a widespread concern and a focus of boundless debate.In this paper, we identify and discuss some of the pertinent challenges to building an effective SOC.We investigate some of the factors contributing to the inefficiencies in SOCs and explain some of the challenges they face.Further, we provide and prioritise recommendations to addressing the identified issues.
On the Relationship between Self-Admitted Technical Debt Removals and Technical Debt Measures

The technical debt (TD) in a software project refers to the adoption of an inadequate solution from its design to the source code. When developers admit the presence of technical debt in the source code, through comments or commit messages, it is called self-admitted technical debt (SATD). This aspect of TD has been the subject of numerous research studies, which have investigated its distribution, the impact on software quality, and removal. Therefore, this work focuses on the relationship between SATD and TD values. In particular, the study aims to compare the admitted technical debt with respect to its objective measure. In fact, the trends of TD values during SATD removals have been studied. This was done thanks to the use of an SATD dataset and their related removals in four open source projects. Instead, the SonarQube tool was used to measure TD values. Thanks to this work, it turned out that SATD removals in a few cases correspond to an effective reduction of TD values, while in numerous cases, the classes indicated are removed.
Fault Diagnosis Method of Power Electronic Converter Based on Broad Learning

In order to realize the unsupervised extraction and identification of fault features in power electronic circuits, we proposed a fault diagnosis method based on sparse autoencoder (SAE) and broad learning system (BLS). Firstly, the feature is extracted by the sparse autoencoder, and the fault samples and feature vectors are combined as the input of the broad learning system. The broad learning system is trained based on the error precision step update method, and the system is used to the fault type identification. The simulation results of the thyristor fault diagnosis of the three-phase bridge rectifier circuit show that the method is effective and has better performance than other traditional methods.
Reasoning About Inconsistent Formulas

The analysis of inconsistent formulas finds an ever-increasing range of applications, that include axiom pinpointing in description logics, fault localization in software, model-based diagnosis, optimization problems, but also explainability of machine learning models. This paper overviews approaches for analyzing inconsistent formulas, focusing on finding and enumerating explanations of and corrections for inconsistency, but also on solving optimization problems modeled as inconsistent formulas.
Differentially private release of medical microdata: an efficient and practical approach for preserving informative attribute values

Abstract Background Various methods based on k -anonymity have been proposed for publishing medical data while preserving privacy. However, the k -anonymity property assumes that adversaries possess fixed background knowledge. Although differential privacy overcomes this limitation, it is specialized for aggregated results. Thus, it is difficult to obtain high-quality microdata. To address this issue, we propose a differentially private medical microdata release method featuring high utility. Methods We propose a method of anonymizing medical data under differential privacy. To improve data utility, especially by preserving informative attribute values, the proposed method adopts three data perturbation approaches: (1) generalization, (2) suppression, and (3) insertion. The proposed method produces an anonymized dataset that is nearly optimal with regard to utility, while preserving privacy. Results The proposed method achieves lower information loss than existing methods. Based on a real-world case study, we prove that the results of data analyses using the original dataset and those obtained using a dataset anonymized via the proposed method are considerably similar. Conclusions We propose a novel differentially private anonymization method that preserves informative values for the release of medical data. Through experiments, we show that the utility of medical data that has been anonymized via the proposed method is significantly better than that of existing methods.
Making Adversarial Examples More Transferable and Indistinguishable

Fast gradient sign attack series are popular methods that are used to generate adversarial examples. However, most of the approaches based on fast gradient sign attack series cannot balance the indistinguishability and transferability due to the limitations of the basic sign structure. To address this problem, we propose a method, called Adam Iterative Fast Gradient Tanh Method (AI-FGTM), to generate indistinguishable adversarial examples with high transferability. Besides, smaller kernels and dynamic step size are also applied to generate adversarial examples for further increasing the attack success rates. Extensive experiments on an ImageNet-compatible dataset show that our method generates more indistinguishable adversarial examples and achieves higher attack success rates without extra running time and resource. Our best transfer-based attack NI-TI-DI-AITM can fool six classic defense models with an average success rate of 89.3% and three advanced defense models with an average success rate of 82.7%, which are higher than the state-of-the-art gradient-based attacks. Additionally, our method can also reduce nearly 20% mean perturbation. We expect that our method will serve as a new baseline for generating adversarial examples with better transferability and indistinguishability.
Complete hyperentangled state analysis using weak cross-Kerr nonlinearity and auxiliary entanglement

Abstract We present a new method for the complete analysis of hyperentangled Bell state and Greenberger–Horne–Zeilinger state in polarization and spatial-mode degrees of freedom, resorting to weak cross-Kerr nonlinearity and auxiliary frequency entanglement. The weak cross-Kerr nonlinearity with small phase shift is used to construct quantum nondestructive detector, and it is realizable with the current technology. Compared with the previous schemes, our scheme largely reduces the requirement on nonlinearity with the help of auxiliary entanglement in the third degree of freedom. Our method provides an efficient avenue for the hyperentangled state analysis, and will be useful for high-capacity quantum information processing.
Automatic Attendance System using Face Recognition

Automatic face recognition technologies have made many improvements within the changing world.We have find a real world solution for automatic student attendance system in a classroom using Real-Time Face Recognition.In order to reduce the human computation, our system are going to be recognizing human faces fast and precisely in images or videos that are being used for further face recognition.Here we use the opencv library dlib for face recognition.Further the label corresponding to the recognized image, which is the student name, is provided with attendance, else the system marks as absent.
Automatic pupil detection and gaze estimation using the vestibulo-ocular reflex in a low-cost eye-tracking setup

Automatic eye tracking is of interest for interaction with people suffering from amyotrophic lateral sclerosis, for using the eyes to control a computer mouse, and for controlled radiotherapy of uveal melanoma. It has been speculated that gaze estimation accuracy might be improved by using the vestibulo-ocular reflex. This involuntary reflex results in slow, compensatory eye movements, opposing the direction of head motion. We therefore hypothesised that leaving the head to move freely during eye tracking must produce more accurate results than keeping the head fixed, only allowing the eyes to move. The purpose of this study was to create a low-cost eye tracking system that incorporates the vestibulo-ocular reflex in gaze estimation, by keeping the head freely moving. The instrument used comprised a low-cost head-mounted webcam which recorded a single eye. Pupil detection was fully automatic and in real time with a straightforward hybrid colour-based and model-based algorithm, despite the lower-end webcam used for recording and despite the absence of direct illumination. A model-based algorithm and an interpolation-based algorithm were tested in this study. Based on mean absolute angle difference in the gaze estimation results, we conclude that the model-based algorithm performed better when the head was not moving and equally well when the head was moving. With most deviations of the points of gaze from the target points being less than 1° using either algorithm when the head is moving freely, it can be concluded that our setup performs fully within the 2° benchmark from literature, whereas deviations when the head was not moving exceeded 2°. The algorithms used were not previously tested under passive illumination. This was the first study of a low-cost eye-tracking setup taking into account the vestibulo-ocular reflex.
Efficient Searchable Symmetric Encryption Supporting Dynamic Multikeyword Ranked Search

Searchable symmetric encryption that supports dynamic multikeyword ranked search (SSE-DMKRS) has been intensively studied during recent years. Such a scheme allows data users to dynamically update documents and retrieve the most wanted documents efficiently. Previous schemes suffer from high computational costs since the time and space complexities of these schemes are linear with the size of the dictionary generated from the dataset. In this paper, by utilizing a shallow neural network model called “Word2vec” together with a balanced binary tree structure, we propose a highly efficient SSE-DMKRS scheme. The “Word2vec” tool can effectively convert the documents and queries into a group of vectors whose dimensions are much smaller than the size of the dictionary. As a result, we can significantly reduce the related space and time cost. Moreover, with the use of the tree-based index, our scheme can achieve a sublinear search time and support dynamic operations like insertion and deletion. Both theoretical and experimental analyses demonstrate that the efficiency of our scheme surpasses any other schemes of the same kind, so that it has a wide application prospect in the real world.
Audience Attention and Emotion in News Filmed with Drones: A Neuromarketing Research

Emotional journalism is being driven by audiovisual technology such as drones, also known as unmanned aerial vehicles, which have demonstrated their usefulness in transforming objective news into news stories from a new visual perspective, facilitating access to dangerous or difficult places. They also allow for greater immersion by an audience that has become an active participant in the news, and they contribute to the storytelling of communication despite the risk to privacy and security that their misuse might entail. The aim of this research is to determine the differences in attention and intensity of the emotions experienced when viewing two pieces of audiovisual news: One was filmed with the technological support of a drone, and the other was produced in the conventional way. The techniques of eye tracking and galvanic skin response were used in 30 Spanish university students. The results suggest that attention was focused on the most spectacular visual elements, although the images filmed with a drone received a higher concentration of attention from the subjects, and this attention was spread throughout the entire image, which demonstrates that drones enhance the effectiveness of panoramic images with natural landscapes. The greatest emotion generated by viewing the images recorded with drones was statistically significant, but it was limited exclusively to these particular scenes, and not to the entire recording of the news.