Reviewerly/exHarmony huggingface dataset:
Important Citation Identification by Exploiting the Optimal In-text Citation Frequency

Research is always based on previously done work. To acknowledge the worthy work of the predecessors of the field, researchers do citations. Citations are factors that are used for measuring the impact factor of journals, to rank the researchers, to find out latest research topics, for allocating research grants etc. In current epoch the research community has turned their focus towards citations and is of the view that all citations are not equally important. To find out important citations, researchers used different approaches such as context based, cue word based, metadata based, frequency based, textual based etc. Among proposed methodologies, frequency based approach was extensively used. The citation with high frequency was considered as important, but it is yet unclear that what should be the frequency cut off value of citation for considering it important. This research explored the significance of applying Threshold value over Frequency count for binary classification. We identified optimal threshold value of frequency count and further applied this to classify the citations into important and non-important ones. To evaluate the proposed approach a benchmark data set annotated by two domain experts was used that consisted of 465 citation pairs. The results were compared with state of the art precision value of 0.72. While the experiment showed increased value of 0.75 in terms of precision.
Leveraging Blockchain in Monitoring SLA-Oriented Tourism Service Provisioning

A system for evaluating current services in the tourism industry is one of the sources of reference affecting tourists' decisions. However, such the system may not be trusted by users. The main reason may come from the evaluation result that has been tampered with via a third party or a network attack. Besides, the relationship between service providers and tourists is the service level agreements (SLAs), which is the provider's commitment. Nevertheless, the process of implementing the SLAs is manual, not automatic, so this leads to many disputes. In this study, we propose BlockSLA, an architecture for evaluating and enforcing agreements based on the blockchain. Our proposal uses the blockchain to ensure that the assessment process can be kept safe and reliable through the blockchain consensus processing mechanism. From these evaluations, smart contracts on the blockchain can automatically make compensation immediately and the parties (e.g., tourists, providers) can track these changes on their device. The architectural design is described in detail in this work.
The Adoption of Blockchain Technology in IoT: An Insight View

Internet of Things (IoT) growth is expected to reach phenomenal levels of diverse adoption and deployment amongst multiple industries. This ranges from financial, communication, transport, health and general consumer sectors. The expected burst of IoT deployment will introduce significant cyber security challenges, specifically in the area of configuration management. Blockchain technology is continuously evolving beyond the use as financial currency, and is proving to be a feasible solution for distributed record keeping and validation. The special characteristics of Blockchain technology introduces a new domain of IoT secure configuration and decentralized management, while taking into consideration the challenging constraints imposed by IoT devices efficient and low-power design. In this paper we try to identify an effective approach to manage and control security IoT device configurations by adopting a Blockchain technology.
Study on Automatic Defect Report Classification System with Self Attention Visualization

In recent years, software in devices such as smartphones and tablets has become increasingly multifunctional, and the use of OSS has become essential. In software development using large-scale OSS, it is important to report defects to appropriate personnel promptly. In this paper, we propose a method to classifying defect reports into appropriate categories using fine-tuned BERT and visualize self-attention information. In the evaluation, category classification was performed using defect reports of the actual OSS project. The F1 score was 0.87, which indicated that high-accuracy classification was possible. Also, the visualization results show that category-specific words can be extracted.
Using Augmented Reality Technology to Construct a Venue Navigation and Spatial Behavior Analysis System

Augmented reality (AR) technology has received much attention in recent years. Following Apple’s launch of ARKit and Google’s ARCore in 2017, AR has become more integrated into daily living via the smart phone. This paper focuses on guided tour services which utilize AR technology and presents an AR-based build for a spatial behavior analysis system. The system primarily services viewers, content providers, and researchers. The system’s functionalities include (1) allowing content providers to customize the guide’s target positioning and information, (2) offering an AR-guided tour mode for viewers, (3) recording and visualizations of the spatial behavior data of the viewer which is subsequently (4) uploaded to a cloud server for further research and analysis. The benefits of the system to future academic research include indoor direction indication methods, best route query, indoor foot traffic data analysis, optimization of directing viewers from one area to the next and others, all of which offers ways forward towards improving or even changing the visual presentation experience.
Reversible Data Hiding Based on Partitioning the Prediction Values

In this paper, we present a new reversible data hiding (RDH) method. Instead of directly dividing the neighboring pixels into blocks, we sort the pixels according to their prediction values first, and then the sorted sequence is partitioned into non-overlapping blocks. That is, different from that in pixel value ordering (PVO) series only the neighboring pixels can be divided into the same block, in our new scheme the pixels far apart from each other in space may be divided into the same block in case they have the similar pixel values. Thus much redundancy in image can be exploited and a sharper prediction error histogram is easy to be obtained for data embedding. To achieve better performance, a new adaptive embedding strategy is also proposed. The experimental results show that our new method is superior to the traditional PVO based methods.
Improving Access to Science for Social Good

One of the major goals of science is to make the world socially a good place to live. The old paradigm of scholarly communication through publishing has generated enormous amount of heterogeneous data and metadata. However, most of the scientific results are not easily discoverable, in particular those results which benefit social good and are also targeted by non-scientists. In this paper, we showcase a knowledge graph embedding (KGE) based recommendation system to be used by students involved in activities aiming at social good. The proposed recommendation system has been trained on a scholarly knowledge graph constructed for this specific goal. The obtained results highlight that the KGEs successfully encoded the structure of the KG, and therefore, our system could provide valuable recommendations.
Learning a Partially-Known Discrete Event System

There are many cases in which our understanding of a system may be limited due to its complexity or lack of access into the entire system, leaving us with only partial system knowledge.This paper proposes a novel systematic active-learning method for realizing a partially-known Discrete Event System (DES).The proposed technique takes the available information about the system into account by tabularly capturing the known data from the system, and then, discovers the unknown part of the system via an active-learning procedure.For this purpose, a series of tables will be constructed to first infer the information about the system from the available data, and if unavailable, the developed algorithm collects the information through basic queries made to an oracle.It is proven that the developed technique returns a language-equivalent finite-state automaton model for the system under identification after a finite number of iterations.A real-world illustrative example is provided to explain the details of the proposed method.
BISEN: Efficient Boolean Searchable Symmetric Encryption with Verifiability and Minimal Leakage

The prevalence and availability of cloud infrastructures has made them the de facto solution for storing and archiving data, both for organizations and individual users. Nonetheless, the cloud's wide spread adoption is still hindered by dependability and security concerns, particularly in applications with large data collections where efficient search and retrieval services are also major requirements. This leads to an increased tension between security, efficiency, and search expressiveness, which current state of the art solutions try to balance through complex cryptographic protocols that tradeoff efficiency and expressiveness for near optimal security. In this paper we tackle this tension by proposing BISEN, a new provably-secure boolean searchable symmetric encryption scheme that improves these three complementary dimensions by exploring the design space of isolation guarantees offered by novel commodity hardware such as Intel SGX, abstracted as Isolated Execution Environments (IEEs). BISEN is the first scheme to enable highly expressive and arbitrarily complex boolean queries, with minimal information leakage regarding performed queries and accessed data, and verifiability regarding fully malicious adversaries. Furthermore, by exploiting trusted hardware and the IEE abstraction, BISEN reduces communication costs between the client and the cloud, boosting query execution performance. Experimental validation and comparison with the state of art shows that BISEN provides better performance with enriched search semantics and security properties.
An Improved Hybrid Clustering Algorithm Based on Particle Swarm Optimization and K-means

Abstract The most common algorithm in data mining is cluster analysis. Cluster analysis algorithm has been widely used in many fields, especially in data analysis, market research and pattern recognition. K-means clustering algorithm(KMCA), as one of the common algorithms in clustering analysis, occupies an important position in clustering analysis and has relatively more applications. However, in recent years, with the deepening of the application of clustering analysis algorithm, it is found that there are a series of problems in KMCA, which directly affect the accuracy of data analysis results. On this basis, a bionic optimization algorithm, particle swarm optimization algorithm, is generated, which makes up for the shortcomings of KMCA. The aim of this paper is to establish the optimal algorithm for data analysis by means of the research on the improved hybrid clustering algorithm of particle swarm optimization and k-means. The experimental results show that the algorithm can effectively integrate the advantages of particle swarm optimization algorithm and KMCA, which not only improves the speed of the algorithm, but also guarantees the accuracy of the results.
A User Authentication Enabled Piezoelectric Force Touch System for the Internet of Things

In Internet of Things (IoT) applications, secure access to smart systems, e.g., smartphones, is important for protecting private information. Among various authentication techniques, keystroke authentication methods based on touch behavior of the user have received increasing attention. This is due to the unique benefits, such as no additional hardware component and the ease of use in most smart systems. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user's touch time and force information, which are obtained from a piezoelectric touch panel. After combining artificial neural networks with the user's touch features, an equal error rate (EER) of 1.09% is achieved, validating the feasibility of the proposed technique for achieving highly secure user authentication, hence advancing the development of security techniques potentially deployable in the field of IoT.
Agricultural Product Traceability System Based on Blockchain Technology

The quantity of agricultural products produced in China is huge. How to ensure the quality of agricultural products from farmland to table is a problem that has been studied all the time. From the perspective of decentralization, this paper uses the blockchain technology to combine the characteristics of agricultural products traceability, and the alliance chain hyperledge recology. As the underlying technology of the blockchain, the Fabric1.4.2 project under the application, the factory, quality inspection, logistics, distribution and supervision as the alliance nodes in the alliance chain, combined with the Internet of Things technology to automatically collect data to achieve agricultural products from planting, processing, quality Traceability of inspection, logistics, distribution and other processes. Through the scanning of the two-dimensional code, the matching of the traceable data and the data uplink are realized, and the products produced are guaranteed to be one yard and one code. Finally, the trace data of the production and circulation process of the product is realized through the scanning of the code of the e-commerce platform of the WeChat mini program.
A Survey on One-shot Neural Architecture Search

Abstract Complex deep neural network architecture such as AlexNet has great success in image classification, natural language processing and other applications. The choice of network architecture has proven to be critical. And researchers begin to design more and more complex networks in order to obtain better performance. However, designing complex networks manually often consumes a lot of computing resources and time, so the automated Neural Architecture Search(NAS) has attracted more and more attention in recent years. Currently, NAS have shown great potential in designing new architectures with high performance and high efficiency. Thus, first of all, this paper briefly introduces the development context and different directions of NAS. But compared to other NAS surveys, this paper focuses on One-shot-NAS methods. Among different methods of NAS, One-shot-NAS can achieve better performance in less time. Through comprehensive comparison and analysis, we apply a novel categorization on One-shot-NAS methods that are based on DAG, Hypernetwork, and network transformation respectively.
Smart healthcare systems on improving the efficiency of healthcare services

Healthcare has a critical position in human lives especially for those who have some health problems and need a practical solution for a better life. Recently, there is a rapid rise in e-health technologies such as Electronic Health Records (EHRs) and some emergency detection and response methods. One of the advances that can manage some of the challenges of smart healthcare in terms of security, sharing, integration and management, is cloud computing. The purpose of this article is to highlight the value of pervasive computing, especially cloud-based systems in healthcare sector. We review the significance and opportunities of using cloud computing in pervasive healthcare, and then look at the current as well as the future challenges it faces.
Cloud Technology: Opportunities for Cybercriminals and Security Challenges

Nowadays, there is a growing of interest about cloud technology to many companies around the world. That's why many companies trying and implementing cloud computing technologies in their business processes. This research will examine the security requirements that will apply for companies and organizations when they choose to move to a cloud service solution. The study is carried out because cloud services are very desirable in many industries today. Migrating to cloud services would often results in great benefits both financially and administratively. The concerns raised by the transition are how security should be handled. Many companies suffer from a lack of knowledge and it is seen as a big risk to make the transition. This leads to the question that the research strive to answer - which security demands will the transition to a cloud service implicate? In this paper we explain which security requirements are available both for local solutions and cloud solutions. We draw conclusions about what differences there are, what requirements are mutual, which ones are new and which ones are absent if a transition is made to cloud services. The result of this research is an evaluation that companies and organizations can use as a basis when they plan to implement this particular transition.
Attention-Based Sequence Learning Model for Arabic Diacritic Restoration

Diacritic restoration is a fundamental issue in many languages such as Arabic, Greek and others. The bulk of online texts lack these markings. This makes these languages difficult for a human to interpret and computer process. In this work, we will automatically restore the diacritics for the Arabic language. Previous sequence-to-sequence based approaches such as Abandah et al., [1], and Belinkov and Glass [2] have been used. These use Recurrent Neural Network (RNN) involving Long- Short Term Memory (LSTM) models. Interestingly, these are language-independent, avoiding any morphological, syntactical analysis or any type of external resources. This approach leads to making this model agnostic to new languages. In this work, we extend the previous work with an alternate RNN architecture involving Gated-Recurrent Units (GRU). We augment the basic RNN model with attention-based mechanisms. Our GRU-attentive model improved the Diacritic Error Rate (DER) by 0.19% and by 2.32% compared to the state of the art.
A Fourier-Based Approach to Generalization and Optimization in Deep Learning

The success of deep neural networks stems from their ability to generalize well on real data; however, et al. have observed that neural networks can easily overfit randomly-generated labels. This observation highlights the following question: why do gradient methods succeed in finding generalizable solutions for neural networks while there exist solutions with poor generalization behavior? In this work, we use a Fourier-based approach to study the generalization properties of gradient-based methods over 2-layer neural networks with band-limited activation functions. Our results indicate that in such settings if the underlying distribution of data enjoys nice Fourier properties including bandlimitedness and bounded Fourier norm, then the gradient descent method can converge to local minima with nice generalization behavior. We also establish a Fourier-based generalization error bound for band-limited function spaces, applicable to 2-layer neural networks with general activation functions. This generalization bound motivates a grouped version of path norms for measuring the complexity of 2-layer neural networks with ReLU-type activation functions. We empirically demonstrate that regularization of the group path norms results in neural network solutions that can fit true labels without losing test accuracy while not overfitting random labels.
Color Image Encryption Technique Using 4D Logistic Map

Due to the increased use of data transmission over the public channel, data security is highly required. Chaotic maps are performing better in speed and robustness than the cryptographic techniques for data security. The 2D logistic and 3D logistic maps are the commonly used chaotic maps. But, in case of sensitive information, the extension of 3D logistic map is used. Here, a 4D logistic map has been proposed for the data encryption. It is performing better than the 3D logistic map; which is being verified by the NPCR, UACI parameters as well as the encryption and decryption timings.
Security Transmission and Storage of Internet of Things Information Based on Blockchain

Abstract The Internet of Things is developing rapidly. Many devices around us are connected through the Internet, and humans have entered the era of the Internet of Things. However, while many devices are connected to the Internet, it also provides more attack surfaces for hackers, so the security issues of the Internet of Things are becoming increasingly prominent. This paper uses asymmetric encryption algorithms and blockchain technology to build a key-based security architecture. In terms of information security transmission, this article uses ECC (Elliptic Curves Cryptography, Elliptic Curve Cryptography) and DH (Diffie-Heilman key exchange, Diffie-Heilman key exchange) as encryption tools for the entire architecture, not just for Encrypted communication between IoT devices is also used for encrypted communication between IoT Internet of Things (IoT) devices and Raft service clusters, thereby ensuring the privacy of sensitive information and the efficiency of data communication. In terms of information security storage, this article uses the blockchain data structure and consensus algorithm to achieve reliable storage. Considering the real-time nature of the Internet of Things, the Raft consensus algorithm is adopted. This consensus algorithm has strong consistency in the existing blockchain consensus algorithms, which is more in line with the requirements of the final consensus consensus algorithm of public chains such as POW. The blockchain data structure is used to store the public key data content submitted by each IoT device. The hash pointer in the blockchain data structure guarantees the integrity and difficulty of change of the stored data.
Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems

In this article, a novel self-evolving data cloud-based proportional integral derivative (PID) (SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using fuzzy rules with nonparametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameter concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like subcontrollers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems.