Reviewerly/exHarmony huggingface dataset:
A Blockchain Oriented Software Application in the Revised Payments Service Directive context

The new European Payments Service Directive (Directive (EU) 2015/2366) introduces a novelty for users of online accounts: the possibility of accessing their own bank statements or making payment transactions directly through software created by Third Party Providers. The new players authorized by the directive represent the real novelty with respect to the previous one (Directive 2007/64/CE), and introduce for the first time a strong risk of disintegration between the Bank and its customers. New authorized parties include the Account Servicing Payment Service Provider, the Payment Initiation Service Provider and the Account Information Service Provider. This new mechanism for accessing information on personal bank statements or for the payment will stimulate a remodeling of the offers for customers. In this work a first attempt to implement a service of account information and a service of account storing through a blockchain oriented software application is presented.
An Exploration on College English Writing Teaching Innovation in the Age of Big Data

We have entered into the age of big data. Driven by social progress and economic growth, information technology grows more and more mature. The deep integration between informatization and education asks for more development for college English teaching in China, while it is to becoming increasingly difficult to cultivate intellectuals with high skills and abilities in the traditional English teaching model. Big data, as one of the most prominent features of the information age, has been widely applied in college English teaching. This paper analyzes the dilemma of college English writing teaching and makes an exploration on the innovation from the perspective of the era of big data.
Big Data Technologies: A Comprehensive Survey

In the last decade, the digitization in every aspect of life has resulted in the explosive generation of data. Therefore, the term Big Data had drawn the attention of researchers and the corporate world. This survey paper presents the concept and definition of Big data followed by its characteristics. We provide a brief overview of the challenges of big data, its technologies, and tools that play a significant role in storing and management of big data. We also highlight the work done by various researchers in the storage and analysis of big data. A comparison of storage technologies is also presented that will help the researchers to have a fair idea to address the different challenges.
Techniques for Compressing Deep Convolutional Neural Network

Deep Convolutional Neural Network (CNNs) has evolved to be a progressive technique for both artificial intelligence and computer vision applications. However, these models are also known for being large, computationally expensive and requires lot of memory therefore its hard to implement them on various embedded systems or mobile phones as they have limited hardware resources, low power budget and strict latency requirements. This has motivated researchers to develop various techniques to achieve the goal of model compression of deep CNNs and also to simultaneously optimize its performance. In this paper, recent bench marking advancements in the techniques of pruning, quantization and coding are systematically explored which are mostly implemented on the top of convolutional neural networks with convolutional layer and fully connected layer to provide a compressed network. Also the challenges and future directions are discussed to reduce the model size.
Adapting In-Vehicle Voice Output:A User- and Situation-Adaptive Approach

There is a current trend towards natural, adaptive in-vehicle spoken dialogue systems which react flexibly to the individual requirements of a driver or the driving situation. They aim to provide the driver with the most efficient form of interaction and to thereby reduce his cognitive load. Studies show that even the syntactic form of system output has an influence on the driver and his driving performance. Against this background, in this paper we present our user-centered approach for a user- and situation-adaptive strategy for the syntactic design of voice output. Based on the collected data of two user studies, we combine the two aspects of speech production and perception and compare actual language behaviour with syntactic preferences. The resulting strategy will be evaluated and elaborate further in future user studies.
Quantum Fourier Transform Simulation on Sunway TaihuLight

Quantum Fourier Transform is a key part of many quantum computing, and it involves phase estimation, ordering and factoring. Especially in large number decomposition, periodic data can be transformed into a normal distribution of probability amplitudes. If Quantum Fourier Transform can be implemented on a large scale, it will be a threat to the security of the current RSA cryptosystem. However, the physical implementation of quantum computers currently faces many difficulties, and it is still far away from quantum computers that can exert huge computing power. Therefore, it can only be simulated by classical computers. This article uses the supercomputer independently developed by China, Sunway TaihuLight, to simulate the Quantum Fourier Transform. Based on the heterogeneous and parallel characteristics of SW26010 processor, 46 qubits Quantum Fourier Transform are simulated using MPI, the acceleration thread library, calculation and communication hiding strategy, with the acceleration ratio reaching 6.45 times.
18‐3: Impact Study of Windshield Geometry on the Subjective Customer Perception for Augmented Reality Head‐up Displays (AR HUD)

New Generations of Head‐up Displays (HUD) are expected to grow in size and projection distance. For these reasons, even smallest deviations in the windshield geometry may cause new optical image defects. These image defects need to be specified, measured, and correlated with subjective customer perceptions. Based on a customer study (n=54), this paper gives first indications that heterogeneous image quality disturb customers.
One-stage Vehicle Engine Number Recognition System

This study proposes a one-stage vehicle engine number recognition system which avoids using the traditional three-stage recognition procedures of positioning, segmentation, and then character recognition, without the needs of image preprocessing procedures, we directly locate and recognizes the text targets in the vehicle engine image. The experiment using 926 labeled images via transfer learning to train our prediction model and then using this prediction model to test another 2310 unlabeled images, the overall accuracy achieved 99.48% and the execution time for recognize a single image is 234ms.
Distributed Coding Based Multiple Descriptions for Robust Video Transmission over Error-Prone Networks

In this paper, we propose a novel multiple description coding (MDC) method to enhance the robustness of video transmission over error-prone networks. The proposed MDC method provides benefits of both distributed video coding (DVC) and multiple description coding techniques, which can offer not only higher performance compared to the conventional MDC methods but also effective scheme for the error resilience. In the proposed MDC method, the input video sequence is split into odd and even group of pictures (GOPs) subsequences, which are independently encoded using the new H.265/High efficiency video coding (H.265/HEVC) based DVC technique. Though the codec itself is not the core novelty of this paper, our proposed codec is the first MDC codec in literature employing H.265/HEVC based DVC approaches, thus all results presented in this paper are new. Experimental results show that the proposed method can achieve a wide range of tradeoffs between coding efficiency and error resilience, and provide much better peak signal-to-noise ratio (PSNR) performance than other conventional MDC methods.
A Novel Pooling Method for Regularization of Deep Neural networks

Dropout has been introduced as a powerful regularization approach to preventing overfitting problem in deep neural networks, particularly in deep convolutional neural networks(DCNNs). A number of methods have been designed recently to improve and generalize the dropout technique. These methods include spectral dropout which achieves improved generalization and avoids overfitting by eliminating noisy and weak Fourier domain coefficients of the neural network activations. On the other hand, a pooling process plays a crucial role in deep convolutional neural networks, which serves to reduce the dimensionality of processed data for decreasing computational cost as well as for avoiding overfitting and enhancing the generalization capability of the network. For this reason, we focus on the pooling layer, and we propose a new pooling method called Spectral Dropout Pooling, by applying the Spectral dropout technique in the pooling region, in order to avoid overfitting problem, as well as to enhance the generalization ability of DCNNs. Experimental results on several image benchmarks show that Spectral Dropout Pooling outperforms the existing pooling methods in classification performance as well as is effective for improving the generalization ability of DCNNs. Moreover, we show that Spectral Dropout Pooling combined with other regularization methods, such as batch normalization, is competitive with other existing methods in classification performance.
Prevention of Phishing Attack in Internet-of-Things based Cyber-Physical Human System

In Internet-of-Things enabled Cyber-Physical Human System (CPHS), the controller control the destination systems. The challenger or hacker can perform a number of attacks on this network to threaten the identity and vulnerability of the system, by consuming the networked resources. One of the issue that possesses threat on identities and user credentials is the phishing. The mechanisms for phishing detection in IoT based CPHS should be light-weight and not much complicated in order to meet the CPHS requirement. In CPHS, the credentials can be compromised from the user by showing very similar electronic pages or messages, and encouraging user to provide their secured financial data. These issues need to be resolved in order to get the right output and get all the functionalities to work properly. CPHS has mainly two major components, the first one is controller and second one is destination system. Commands are sent from the sensor to the destination via sensor nodes on the network and the destination system communicates with the controller about what actions to perform or how to deal with the information that controller has sent.
Application of Mobile Learning in College English Teaching Under the Background of “Internet +”

The application of “Internet +” technology has accelerated the reform of college English teaching in China and provided new ideas for the English teaching model. Therefore, when conducting relevant teaching activities, college English teachers should make good use of mobile learning methods to guide college students to master more learning skills and improve their English application ability. This work first analyzed the meaning of “Internet +” and mobile learning, then discussed the challenges brought by “Internet +” to college mobile English teaching and finally introduced the application of mobile learning in college English teaching to better improve the quality of college English teaching.
Research on Vehicle Routing Branch Pricing Algorithm for Multi-Model Electric Vehicles Based on Board Testing

With the increasing awareness of the environment and the gradual popularization of electric vehicles, it is considered that different types of electric vehicles in logistics enterprises have different battery maximum capacity, battery charging rate, unit consumption rate of electricity, maximum load capacity, fixed cost and variable cost. In this paper, considering the factors such as mileage, charging time and delivery time of electric vehicles in practical application, the vehicle routing problem with time windows is studied, the corresponding mixed integer programming model is established, and then the branch pricing algorithm is improved to obtain the optimal solution. In the search process, the penalty factor is added to allow the existence of infeasible solutions and reduce the possibility of local optimization. Then, the accuracy of the model and the results of the algorithm are verified by several sets of examples, and it is also proved that the acceleration process proposed in this paper can effectively improve the solution speed of the algorithm. Finally, the influence of the change of vehicle variable cost on the results is analyzed by examples of different scales.
Correlation Impact by Random Forest Towards Prediction of Phishing Website

Abstract Phishing is an online identity theft that lure unsuspecting victims into phishing website that asks for their personal information such as financial credentials or online access. The most obvious phishing attacks use spam emails to lure victims into visiting its phishing website. The less obvious attack is when a phisher spoofes legitimate sites to bring the victims to the phishing website by presenting a visually similar websites. To address this issue, this paper focuses on the effectiveness of the Random Forest algorithm in predicting a website whether a phishing or lieitimate website. The prediction model is developed using the classfication methodology and the results revealed that the prediction accuracy of Random Forest in only 66.66%. This is due to potential high correlation among the features when training the dataset since each Random Tree in the forest should protect each other from individual errors. In the future, this project is hoped to investigate features selection of the same dataset because the phishing website prediction model requires features that at least some predictive power.
Revisiting Practical and Usable Coercion-Resistant Remote E-Voting

In this paper we revisit the seminal coercion-resistant e-voting protocol by Juels, Catalano and Jakobsson (JCJ) and in particular the attempts to make it usable and practical. In JCJ the user needs to handle cryptographic credentials and be able to fake these in case of coercion. In a series of three papers Neumann et al. analysed the usability of JCJ, and constructed and implemented a practical credential handling system using a smart card which unlock the true credential via a PIN code, respectively fake the credential via faking the PIN. We present several attacks and problems with the security of this protocol, especially an attack on coercion-resistance due to information leakage from the removal of duplicate ballots.Another problem, already stressed but not solved by Neumann et al, is that PIN typos happen frequently and would invalidate the cast vote without the voter being able to detect this. We construct different protocols which repair these problems. Further, the smart card is a trusted component which can invalidate cast votes without detection and can be removed by a coercer to force abstention, i.e. presenting a single point of failure. Hence we choose to make the protocols hardware-flexible i.e. also allowing the credentials to be store by ordinary means, but still being PIN based and providing PIN error resilience. Finally, one of the protocols has a linear tally complexity to ensure an efficient scheme also with many voters .
Searchable Symmetric Encryption with Tunable Leakage Using Multiple Servers

Searchable symmetric encryption has been a promising primitive as it enables a cloud user to search over outsourced encrypted data efficiently by only leaking small amount of controllable leakage. However, recent leakage-abuse attacks demonstrate that those stand leakage profiles can be exploited to perform severe attacks – the attacker can recover query or document with high probability. Ideal defending methods by leveraging heavy cryptographic primitives, e.g. Oblivious RAM, Multiparty Computation, are still too inefficient for practice nowadays.In this paper, we investigate another approach for countering leakage-abuse attacks. Our idea is to design SSE with tunable leakage, which provides a configurable way for trade-off between privacy and efficiency. Another idea is to share the leakage among multiple non-collude servers, thus a single server can only learn partial, rather than the whole leakage. Following the ideas, we proposed two SSE schemes. The first scheme uses two servers and is static, which serves as the first step to emphasize our design methodology. Then we propose a dynamic SSE scheme, by additionally use a third server to hold dynamic updates. We demonstrate that the leakage for the third server is only partial update history, a newly defined leakage notion that leaks limited information rather than the whole update history. Our schemes provide stronger security that hides search/access pattern in a tunable way as well as maintains forward and backward privacy. We also report the performance of our constructions, which shows that both schemes are efficient.
Optimized Two Dimensional Wavelet Filter from BAT Algorithm

Due to the effect of a quantization error, it is not possible to fully restore the original image in the lossy wavelet-based image compression. However, the quantization error can be minimized by optimizing or evolving the filter bank. In this work, the coefficients of the standard wavelet filter and its inverse filter were optimized by evolution of Bat algorithm. The optimized wavelet filters were used with the SPIHT encoder/decoder for image compression. The performance of the optimized filters in reconstruction during the decompression process was investigated using the metrics PSNR, MSE and SSIM. The results obtained show that the proposed filter outperforms standard wavelet filters by minimizing the error between the original and the decompressed image.
Trusted Cooperative E-Learning Service Deployment Model in Multi-Cloud Environment

The objective of the research work is to propose an adaptive trust model applicable to cooperative software services deployed in a cloud environment towards e-learning. The cooperation among all the participating services in terms of their compliance with the e-learning standards, risk on the unavailability of specific services, and governance in the case of disputes were not considered. The reputations of such cloud-based classrooms can be evaluated in terms of the trust between domain-specific services like registration, assessment, accounting, and results from announcement services deployed as infrastructure and platform services in a multi-cloud environment. The trust relationship is also to be ascertained between the provider and client of various virtual services in the case of emergent de-provisioning of any resource for a particular course of study.
The Development of Emerging Technological Applications for Not-for-Profit Organizations in Capstone Projects: A Case in Scout Association of Hong Kong

There is a growing interest in emerging technologies such as VR among practitioners from different sectors, including education and social welfare, given these technologies may enhance the experience of their service targets, and may be easily adopted by ordinary users. Additionally, the barriers of users' adoption are also lowering. Nevertheless, not-for-profit organizations may find significant barriers for developing their emerging technological applications, which are largely due to the resources and capabilities required. As a result, the adoption of these emerging technologies by not-for-profit organizations (NPO) is yet to be seen. Through a case-study in Scout Association of Hong Kong, we have learnt that NPOs might develop and adopt different emerging technological applications to fit their needs by collaborating with the universities, possibly with students' capstone project as a medium. These collaborations will be beneficial to not only the NPOs but also to the students and the university. Based on our findings, we also highlighted a few lessons for these collaborations.
MemTimes: Temporal Scoping of Facts with Memory Network

This paper works on temporal scoping, i.e., adding time interval to facts in Knowledge Bases (KBs). The existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we propose a novel neural model based on Memory Network to do temporal reasoning among sentences for the purpose of temporal scoping. We design proper ways to encode both semantic and temporal information contained in the mention set of each fact, which enables temporal reasoning with Memory Network. We also find ways to remove the effect brought by noisy sentences, which can further improve the robustness of our approach. The experiments show that this solution is highly effective for detecting temporal scope of facts.
Bridge Segmentation Performance Gap Via Evolving Shape Prior

Deep neural networks are very compelling for medical image segmentation. However, deep models often suffer from notable performance drops in real clinical settings due to the complex appearance shift in daily scannings. Domain adaptation partially addresses the problem between imaging domains. However, it heavily depends on the expensive re-collection and re-training for domain-specific datasets and thus is not applicable to domain-agnostic images. In this paper, we propose a case adaptation strategy aiming to bridge the segmentation performance gap on domain-agnostic images. Our contribution is three-fold. First, we design a general self-supervised learning framework for case adaptation, which exploits its predictions as supervision to drive the adaptation. Without extra annotations and any burden on model complexity, the framework enables trained deep models at-hand to directly segment domain-agnostic testing images. Second, we propose a novel Evolving Shape Prior (ESP) which recursively introduces strong shape knowledge into networks and evolves with the adaptation procedure to provide adaptive supervision. ESP can stabilize self-supervised learning and guide it to move towards model convergence. Third, we perform extensive experiments on 10 datasets with different levels of difficulty and typical appearance shifts blended, proving our framework is a promising solution in reducing segmentation performance degradation. Through this work, we investigate the feasibility of case adaptation as a general strategy in enhancing the robustness of deep segmentation networks, with comprehensive analyses proving its efficacy and efficiency.
A Certificate Storage Method for Confirming Video Copyright and Tracing the Source Based on Alliance Chain

Abstract In recent years, with the development and update of business models and emerging technologies, there have been more and more forms of piracy and infringement of online video works. It is difficult for the department with copyright to confirm the copyright of the work and trace the source of piracy. Alliance blockchain technology can be partially decentralized, highly controllable, and data is not public by default, providing new ideas and methods for solving the above problems. The thesis proposes a multi-factor confirmation and traceability depositing method (MCTA) for video works of alliance blockchain. First, the alliance member nodes store the signature information of the person in charge in each step in the private chain during video production; second, the smart contract pre-processes the source video, implants watermarks and calculates video fingerprints, and creates a video ID for each video; Finally, double-factor authentication for the suspected infringement of pirated video to confirm the right. Carry on simulation experiment through Truffle+MiniNet+SimBlock . The results show that the method proposed in the thesis is effective and feasible for depositing video works.