Reviewerly/exHarmony huggingface dataset:
SoliAudit: Smart Contract Vulnerability Assessment Based on Machine Learning and Fuzz Testing

Blockchain has flourished in recent years. As a decentralized system architecture, smart contracts give the blockchain a user-defined logical concept. The smart contract is an executable program that can be used for automatic transactions on the Ethereum blockchain. In 2016, the DAO attack resulted in the theft of 60M USD due to unsafe smart contracts. Smart contracts are vulnerable to hacking because they are difficult to patch and there is a lack of assessment standards for ensuring their quality. Hackers can exploit the vulnerabilities in smart contracts when they have been published on Ethereum. Thus, this study presents SoliAudit (Solidity Audit), which uses machine learning and fuzz testing for smart contract vulnerability assessment. SoliAudit employs machine learning technology using Solidity machine code as learning features to verify 13 kinds of vulnerabilities, which have been listed as Top 10 threats by an open security organization. We also created a gray-box fuzz testing mechanism, which consists of a fuzzer contract and a simulated blockchain environment for on-line transaction verification. Different from previous research systems, SoliAudit can detect vulnerabilities without expert knowledge or predefined patterns. We subjected SoliAudit to real-world evaluation by using near 18k smart contracts from the Ethereum blockchain and Capture-the-Flag samples. The results show that the accuracy of SoliAudit can reach to 90% and the fuzzing can help identify potential weaknesses, including reentrancy and arithmetic overflow problems.
Economic development, financial development, and income inequality nexus

Income inequality has gained prominence by exacerbating the economic stability of both developed and developing countries over the past few decades. The intensity of this issue is non-trivial with economies witnessing failure in policies, indecorous economic governance, and the challenging economic ideologies. The present study examines the interconnection between economic development and financial development on income inequality, considering Kuznets hypothesis, Greenwood and Jovanovic hypothesis and the new specification by Baiardi and Morana, using unbalanced dynamic panel GMM estimation models. Following revised country classification given by International Monetary Fund (IMF) in 2016, the study includes an unbalanced panel of 85 countries consisting of 28 high-income, 41 middle-income, and 16 low-income countries for the period ranging from 1984 to 2014. The findings of the study reveal that there is no clear-cut evidence to support the proposition of economic development along with financial growth, which would reduce the problem of income inequality. Moreover, in most advanced or highly developed high-income countries also do not enjoy the benefits of financial development.
A Simultaneous Scrambling and Diffusion Color Image Encryption Algorithm Based on Hopfield Chaotic Neural Network

The classic scrambling-diffusion structure for image encryption is vulnerable to be attacked. The scrambling and diffusion process can be cracked separately due to the two processes are independent. In this paper, we proposed a novel color image encryption algorithm which can overcome the problem. In the proposed algorithm, firstly, the first diffusion is performed simultaneously with the first scrambling which can resist the attacker to separately crack the scrambling or diffusion. Then, the second diffusion matrix generated from the Hopfield chaotic neural network (HCNN) which is related to the plaintext is performed which can not only resist common chosen-plaintext attack, but also improved the key sensitivity. Finally, a second scrambling is performed on some special pixels to further improve the security. Experiments and security analysis indicate that the proposed algorithm has an excellent performance in image encryption and various attacks.
Benchmarking Post-quantum Cryptography in TLS

Post-quantum cryptographic primitives have a range of trade-offs compared to traditional public key algorithms, either having slower computation or larger public keys and ciphertexts/signatures, or both. While the performance of these algorithms in isolation is easy to measure and has been a focus of optimization techniques, performance in realistic network conditions has been less studied. Google and Cloudflare have reported results from running experiments with post-quantum key exchange algorithms in the Transport Layer Security (TLS) protocol with real users’ network traffic. Such experiments are highly realistic, but cannot be replicated without access to Internet-scale infrastructure, and do not allow for isolating the effect of individual network characteristics. In this work, we develop and make use of a framework for running such experiments in TLS cheaply by emulating network conditions using the networking features of the Linux kernel. Our testbed allows us to independently control variables such as link latency and packet loss rate, and then examine the performance impact of various post-quantum-primitives on TLS connection establishment, specifically hybrid elliptic curve/post-quantum key exchange and post-quantum digital signatures, based on implementations from the Open Quantum Safe project. Among our key results, we observe that packet loss rates above 3–5% start to have a significant impact on post-quantum algorithms that fragment across many packets, such as those based on unstructured lattices. The results from this emulation framework are also complemented by results on the latency of loading entire web pages over TLS in real network conditions, which show that network latency hides most of the impact from algorithms with slower computations (such as supersingular isogenies).
Opportunities and Challenges in Using AI Chatbots in Higher Education

Artificial intelligence (AI) conversational chatbots have gained popularity over time, and have been widely used in the fields of e-commerce, online banking, and digital healthcare and well-being, among others. The technology has the potential to provide personalised service to a range of consumers. However, the use of chatbots within educational settings is still limited. In this paper, we present three chatbot prototypes, the Warwick Manufacturing Group, University of Warwick, are currently developing, and discuss the potential opportunities and technical challenges we face when considering AI chatbots to support our daily activities within the department. Three AI virtual agents are under development: 1) to support the delivery of a taught Master's course simulation game; 2) to support the training and use of a newly introduced educational application; 3) to improve the processing of helpdesk requests within a university department. We hope this paper is informative to those interested in using chatbots in the educational domain. We also aim to improve awareness among those within the chatbot development industry, in particular the chatbot engine providers, about the educational and operational needs within educational institutes, which may differ from those in other domains.
Secure Quantum Communication Technologies and Systems: From Labs to Markets

We provide a broad overview of current quantum communication by analyzing the recent discoveries on the topic and by identifying the potential bottlenecks requiring further investigation. The analysis follows an industrial perspective, first identifying the state or the art in terms of protocols, systems, and devices for quantum communication. Next, we classify the applicative fields where short- and medium-term impact is expected by emphasizing the potential and challenges of different approaches. The direction and the methodology with which the scientific community is proceeding are discussed. Finally, with reference to the European guidelines within the Quantum Flagship initiative, we suggest a roadmap to match the effort community-wise, with the objective of maximizing the impact that quantum communication may have on our society.
Detecting Motion Blurred Vehicle Logo in IoV Using Filter-DeblurGAN and VL-YOLO

Extracting vehicle information is of great significance to the construction of the Internet of Vehicles (IoV). Vehicle logo detection (VLD) technology can effectively extract vehicle information. Due to the complex traffic environment, existing methods have difficulty to accurately detect the vehicle logo, especially when the vehicle logo has motion blur. To alleviate these problems, a new approach is proposed to detect vehicle logo under motion blur with the combination of Filter-DeblurGAN and VL-YOLO. Filter-DeblurGAN possesses a judgment mechanism, which can determine whether the image needs to be deblurred based on the degree of blur of each image. It can also deblur images with arbitrary resolution. Filter-DeblurGAN solves the defect that DeblurGAN lacks the judgment mechanism and is too harsh on resolution. It can be directly applied to the deblurring process of VLD. We also propose a method using VL-YOLO and Eliminate Outliers Cluster (EOC) algorithm to further improve the detection accuracy. VL-YOLO achieves accurate detection of the vehicle logo by constructing a deeper multi-scale detection network and using the initial candidate boxes provided by EOC algorithm. The EOC algorithm is not affected by outliers. In order to further promote the research of VLD, we have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results demonstrate that the proposed method achieves good detection accuracy in the environment of motion blur, and outperforms existing methods.
Measuring the levels of 21st-century digital skills among professionals working within the creative industries: A performance-based approach

The main aim of this study was to measure the actual levels of digital skills among professionals in the creative industries. Therefore, the levels of the following four types of digital skills were examined by means of a performance test: information, critical-thinking, creativity, and problem-solving skills. Authentic tasks were developed for each skill and were performed by professionals working within the creative industries (N = 87). As a result, one observational test directly measured a refined set of indices for each skill. The results reveal that the participants have most difficulties with digital information evaluation and problem-solving skills. For example, the participants rarely checked their answers on another website or provided multiple solutions with an explanation. The results contribute to determining detailed skill indices related to the differences in working professionals' digital skill levels.
Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources. We design novel scheduling policies, that decide on the subset of devices to transmit at each round not only based on their channel conditions, but also on the significance of their local model updates. Numerical results show that the proposed scheduling policy provides a better long-term performance than scheduling policies based only on either of the two metrics individually. We also observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., more devices should be scheduled.
Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the best effect. At the same time, the language model has achieved excellent results in more and more natural language processing tasks. In this paper, we present a focused attention model for the joint entity and relation extraction task. Our model integrates well-known BERT language model into joint learning through dynamic range attention mechanism, thus improving the feature representation ability of shared parameter layer. Experimental results on coronary angiography texts collected from Shuguang Hospital show that the F1-scores of named entity recognition and relation classification tasks reach 96.89% and 88.51%, which outperform state-of-the-art methods by 1.65% and 1.22%, respectively.
Incentive Mechanism for Edge-Computing-Based Blockchain

Blockchain has been gradually applied to different Internet-of-Things platforms. As the efficiency of the blockchain mainly depends on the network computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this article, we focus on investigating incentive mechanism for rational miners to purchase the computational resources. An edge-computing-based blockchain network is considered, where the edge service provider (ESP) can provide computational resources for the miners. Accordingly, we formulate a two-stage Stackelberg game between the miners and ESP. The aim is to investigate SE of the optimal mining strategy under the two different mining schemes, in order to find the optimal incentive for the ESP and miners to choose autofit strategies. Through theoretical analysis and numerical simulations, we can demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the blockchain.
Fraud Detection using Machine Learning and Deep Learning

Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. transactions. This paper aims to benchmark multiple machine learning methods such as k-nearest neighbor (KNN), random forest and support vector machines (SVM), while the deep learning methods such as autoencoders, convolutional neural networks (CNN), restricted boltzmann machine (RBM) and deep belief networks (DBN). The datasets which will be used are the European (EU) Australian and German dataset. The Area Under the ROC Curve (AUC), Matthews Correlation Coefficient (MCC) and Cost of failure are the 3-evaluation metrics that would be used.
The effect of urban morphology on the solar capacity of three-dimensional cities

As a clean and renewable resource, solar energy is increasingly being used to relieve the pressures on environmental protection and the exhaustion of conventional energy. Although photovoltaic modules have been installed in many cities, the lack of quantitative mapping of the annual solar energy potential of urban surfaces hinders the effective utilization of solar energy. Herein, we provide a solar irradiation estimation solution for three-dimensional (3D) cities to quantify annual irradiations on urban envelopes and to investigate the effect of urban morphology on the resulting solar capacity. By modelling urban surfaces as 3D point clouds, annual irradiations of the point clouds were estimated. An empirical investigation across ten cities suggests that urban areas at lower latitudes tend to have larger values of annual irradiation; moreover, an area having greater building heights consistently has the largest third quartile of irradiation compared with lower buildings in the same city. Conversely, areas with many low buildings have a larger proportion of useable areas; in this arrangement, façades can optimally utilize solar energy, meaning that large irradiations are concentrated on certain façades. The Pearson correlation coefficients between solar capacity and urban morphology indices suggest that urban morphology has an important effect on solar capacity.
Flexible Multi-modal Hashing for Scalable Multimedia Retrieval

Multi-modal hashing methods could support efficient multimedia retrieval by combining multi-modal features for binary hash learning at the both offline training and online query stages. However, existing multi-modal methods cannot binarize the queries, when only one or part of modalities are provided. In this article, we propose a novel Flexible Multi-modal Hashing (FMH) method to address this problem. FMH learns multiple modality-specific hash codes and multi-modal collaborative hash codes simultaneously within a single model. The hash codes are flexibly generated according to the newly coming queries, which provide any one or combination of modality features. Besides, the hashing learning procedure is efficiently supervised by the pair-wise semantic matrix to enhance the discriminative capability. It could successfully avoid the challenging symmetric semantic matrix factorization and O ( n 2 ) storage cost of semantic matrix. Finally, we design a fast discrete optimization to learn hash codes directly with simple operations. Experiments validate the superiority of the proposed approach.
Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets

We are concerned with the vulnerability of computer vision models to distributional shifts. We formulate a combinatorial optimization problem that allows evaluating the regions in the image space where a given model is more vulnerable, in terms of image transformations applied to the input, and face it with standard search algorithms. We further embed this idea in a training procedure, where we define new data augmentation rules according to the image transformations that the current model is most vulnerable to, over iterations. An empirical evaluation on classification and semantic segmentation problems suggests that the devised algorithm allows to train models that are more robust against content-preserving image manipulations and, in general, against distributional shifts.
Graph Networks for Multiple Object Tracking

Multiple object tracking (MOT) task requires reasoning the states of all targets and associating these targets in a global way. However, existing MOT methods mostly focus on the local relationship among objects and ignore the global relationship. Some methods formulate the MOT problem as a graph optimization problem. However, these methods are based on static graphs, which are seldom updated. To solve these problems, we design a new near-online MOT method with an end-to-end graph network. Specifically, we design an appearance graph network and a motion graph network to capture the appearance and the motion similarity separately. The updating mechanism is carefully designed in our graph network, which means that nodes, edges and the global variable in the graph can be updated. The global variable can capture the global relationship to help tracking. Finally, a strategy to handle missing detections is proposed to remedy the defect of the detectors. Our method is evaluated on both the MOT16 and the MOT17 benchmarks, and experimental results show the encouraging performance of our method.
The Impact of Transformational Leadership on Employee Retention: Mediation and Moderation Through Organizational Citizenship Behavior and Communication

This study investigates the impact of transformational leadership on employee retention in small- and medium-sized enterprises (SMEs) and probes the mediating role of organizational citizenship behavior (OCB) and the moderating role of communication. Data were collected using convenience sampling from 505 employees of SMEs. A Smart PLS structural equation modeling (PLS-SEM) was used to estimate the various relationships. The findings of the study reveal a positive and significant relationship between transformational leadership and OCB. Similarly, this study finds a positive and significant relationship in OCB and employee retention. In addition, OCB had a positive mediating effect on the relationship between transformational leadership and employee retention. Furthermore, communication positively moderates the transformational leadership- OCB and OCB-employee retention relationships. Leaders at SMEs should implement the traits of transformational leadership such as developing a compelling vision for employees, focusing on goal achievement, having problem-solving techniques, having a sense of purpose, and spending time on the training and development of the team to enhance OCB and employee retention.
Spam Review Detection Using the Linguistic and Spammer Behavioral Methods

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profits or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, the spam review detection problem has gained much attention from communities and researchers, but still there is a need to perform experiments on real-world large-scale review datasets. This can help to analyze the impact of widespread opinion spam in online reviews. In this work, two different spam review detection methods have been proposed: (1) Spam Review Detection using Behavioral Method (SRD-BM) utilizes thirteen different spammer's behavioral features to calculate the review spam score which is then used to identify spammers and spam reviews, and (2) Spam Review Detection using Linguistic Method (SRD-LM) works on the content of the reviews and utilizes transformation, feature selection and classification to identify the spam reviews. Experimental evaluations are conducted on a real-world Amazon review dataset which analyze 26.7 million reviews and 15.4 million reviewers. The evaluations show that both proposed models have significantly improved the detection process of spam reviews. Specifically, SRD-BM achieved 93.1% accuracy whereas SRD-LM achieved 88.5% accuracy in spam review detection. Comparatively, SRD-BM achieved better accuracy because it works on utilizing rich set of spammers behavioral features of review dataset which provides in-depth analysis of spammer behaviour. Moreover, both proposed models outperformed existing approaches when compared in terms of accurate identification of spam reviews. To the best of our knowledge, this is the first study of its kind which uses large-scale review dataset to analyze different spammers' behavioral features and linguistic method utilizing different available classifiers.