Reviewerly/exHarmony huggingface dataset:
A behaviour biometrics dataset for user identification and authentication

As e-Commerce continues to shift our shopping preference from the physical to online marketplace, we leave behind digital traces of our personally identifiable details. For example, the merchant keeps record of your name and address; the payment processor stores your transaction details including account or card information, and every website you visit stores other information such as your device address and type. Cybercriminals constantly steal and use some of this information to commit identity fraud, ultimately leading to devastating consequences to the victims; but also, to the card issuers and payment processors with whom the financial liability most often lies. To this end, we recognise that data is generally compromised in this digital age, and personal data such as card number, password, personal identification number and account details can be easily stolen and used by someone else. However, there is a plethora of data relating to a person's behaviour biometrics that are almost impossible to steal, such as the way they type on a keyboard, move the cursor, or whether they normally do so via a mouse, touchpad or trackball. This data, commonly called keystroke, mouse and touchscreen dynamics, can be used to create a unique profile for the legitimate card owner, that can be utilised as an additional layer of user authentication during online card payments. Machine learning is a powerful technique for analysing such data to gain knowledge; and has been widely used successfully in many sectors for profiling e.g., genome classification in molecular biology and genetics where predictions are made for one or more forms of biochemical activity along the genome. Similar techniques are applicable in the financial sector to detect anomaly in user keyboard and mouse behaviour when entering card details online, such that they can be used to distinguish between a legitimate and an illegitimate card owner. In this article, a behaviour biometrics (i.e., keystroke and mouse dynamics) dataset, collected from 88 individuals, is presented. The dataset holds a total of 1760 instances categorised into two classes (i.e., legitimate and illegitimate card owners' behaviour). The data was collected to facilitate an academic start-up project (called CyberSignature1) which received funding from Innovate UK, under the Cyber Security Academic Startup Accelerator Programme. The dataset could be helpful to researchers who apply machine learning to develop applications using keystroke and mouse dynamics e.g., in cybersecurity to prevent identity theft. The dataset, entitled 'Behaviour Biometrics Dataset', is freely available on the Mendeley Data repository.
Non-Newtonian fluid simulation and reconstruction from monocular videos

In fluid simulation, the parameters of the constitutive model are difficult to predict accurately, which leads to the inconsistency between the simulation results and the visual effects of monocular videos. To solve this problem, a monocular video-guided non-Newtonian fluid reconstruction method is proposed. The model takes non-Newtonian fluid simulation videos as input for training and learns the best low-dimensional latent space representation of a single frame of fluid simulation images. Inter-frame prediction is then performed in this latent domain, employing a convolutional long–short-term memory network to predict latent vector representations of future frames. Finally, the reconstruction parameters of the constitutive model are predicted based on the frame-by-frame latent representation encoding and inter-frame temporal features. In the model verification stage, the real video of the non-Newtonian fluid is used as the input to predict the parameters of the fluid constitutive model, and realize the simulation and reconstruction of the non-Newtonian fluid based on the Cross model. Experimental results showed that the video-guided simulation reconstruction method could obtain fluid flow phenomena that were more consistent with those in the real video than the reconstruction method based on rheometer measurements. Furthermore, it provided higher pixel accuracy and precision at different times and visual effects that were more consistent with the actual fluid flow.
Blockchain Technology and Decentralized Applications Using Blockchain

A blockchain is a collection of blocks, each of which contains a set of transactions that are verified using an encryption algorithm. The blocks are constantly adding to each other, forming a chain of blocks. Blockchain technology is widely used for security and smart contracts, financial and non-financial spheres, a large variety of blockchain systems are used. Decentralized applications (DApps) are in their early stages of development, and several critical issues which services, the Internet of Things (IoT), healthcare services, and supply chain to name a few. This paper throws light on several aspects of blockchain technology, the linkage between blockchain and DApps. It also illustrates working of blockchain transaction process and Ethereum transaction. It also elaborates on the working of smart contracts. Furthermore, this paper also discusses the making of decentralized application in detail and enlighten the architecture of DApps. Moving ahead this paper also discusses the implementation of DApps in various sectors in industry like health care, banking and stock market. It also puts light on the challenges of implementing decentralized application in the present centralized market and its scalability as well. We have also discussed the future of decentralized application and its growing market demand due to its various unique features. The actual user interaction in the projects is exceedingly small: in fact, even the five major applications in any sector, be it games or exchanges, have roughly over 1 k users. However, the marketplace for DApps is expected to increase substantially in the near future.
Improved Utilization of Solar Energy Using Estimated Optimal Tilt Factor and Trackers

This work contributes toward improving the utilization of solar energy. The problem with some of the existing tracking techniques for solar photovoltaic panels is that they have not been put to their best use. In this study, the objective is to optimally use tracking techniques to improve solar energy utilization. The study begins with the solar irradiance data and then proceeds with the definition and mathematical calculations related to the tilt factor. Monthly optimized tilt angles that maximize tilt factor for beam radiation are estimated through mathematical modeling. The net tilt factor is assessed to calculate the tilted global irradiance on tilted PV panels. Different solar tracking techniques like horizontal axis (continuous, daily, weekly and monthly) adjustment, vertical axis continuous adjustment and two-axis are considered to evaluate the response of the 1 kW PV array. The response is evaluated in terms of energy yield using Hybrid Optimization of Multiple Energy Resources (HOMER). The results show that the two-axis tracking system yields the best electrical energy production (2100 kWh/yr), which is 21.28% more than the yield of horizontal axis monthly adjustment (1653 kWh/yr). The extended results further show that if the horizontal axis monthly adjustments are being made using the estimated optimal angles, the energy yield increased from 1653 kWh/yr to 1792 kWh/yr, an increase of 8.40% compared to the HOMER-based calculations. The optimal angles and tilt factor calculations were performed using MATLAB. An additional advantage associated with horizontal axis monthly adjustment is that its implementation is easy, and the associated cost is less than the other tracking techniques. The results obtained can be used further to design PV-based microgrids efficiently.
Analyzing structural change and causality between energy consumption and Bitcoin’s activity

This study purposes to examine a Granger causality, in the presence of the structural change, between Bitcoin's activity and fossil and renewable energy consumptions. To achieve this goal, the two-regime Markov Switching Granger Causality test-based Vector Autoregressive is employed. The empirical results show that there is a significant structural change in the causality between both energy consumption and Bitcoin's activity as the lower Akaike, and Bayesian information criterion are revealed. Regarding the causality results, our findings reveal a significant bidirectional causality between renewable energy consumption and Bitcoin's activity in both high and low energy consumption regimes. However, in terms of fossil consumption and Bitcoin's activity nexus, it is solely strong evidence of bidirectional causality between them in a low consumption regime. Our results indicate a strong predictive power of Bitcoin's activity in energy consumptions in both peak and non-peak periods, and it is also highlighted that Bitcoin's activity could not be neglected in preparing the energy policy.
Optimizing Random Forest-Based Inference on RISC-V MCUs at the Extreme Edge

Random forests (RFs) use a collection of decision trees (DTs) to perform the classification or regression. RFs are adopted in a wide variety of machine learning (ML) applications, and they are finding increasing use also in scenarios at the extreme edge of the Internet of Things (TinyML) where memory constraints are particularly tight. This article addresses the optimization of the computational and storage costs for running DTs on the microcontroller units (MCUs) typically deployed in TinyML scenarios. We introduce three alternative DT kernels optimized for memory- and compute-limited MCUs, providing insight into the key memory-latency tradeoffs on an open-source RISC-V platform. We identify key bottlenecks and demonstrate that SW optimizations enable up to significant memory footprint and latency decrease. Experimental results show that the optimized kernels achieve up to 4.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> latency, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.8\times $ </tex-math></inline-formula> speedup, and 45% storage reduction against the widely-adopted naive DT design. We carry out a detailed performance and energy cost analysis of various optimized DT variants: the best approach requires just 8 instructions and 0.155 pJ per decision.
Wavelength-tuning phase-shifting interferometry of transparent plates using sub-signal frequency correction

The difficulty of measurements on transparent flats comes from that the measured surfaces contribute corresponding sub-signals to the captured interferograms. As the cornerstone of phase demodulation, the frequency solution of the inter-harmonics is significant. The frequency solution accuracy is not satisfactory in traditional algorithms since the unavoidable frequency leakage. To overcome the problem above, a Fourier transform-based frequency correction algorithm with a narrower spectrum interval (FT-FCA) is proposed. By the developed algorithm, reliable multi-surface measurements can be ensured by the obtained sub-signal frequencies and the designed sampling weight functions. In the comparative studies, the robustness of the proposed FT-FCA to the random wavelength-tuning error and phase-shifting error is verified. In the experiments, two transparent flats with thicknesses of 20 mm and 43 mm were measured using a Fizeau wavelength-shifting interferometer, and the wavefronts were reconstructed by the developed algorithm. The comparative experimental analysis verifies the effectiveness of the FT-FCA.
Efficiently generating sentence-level textual adversarial examples with Seq2seq Stacked Auto-Encoder

In spite deep learning has advanced numerous successes, recent research has shown increasing concern on its vulnerability over adversarial attacks. In Natural Language Processing, crafting high-quality adversarial text examples is much more challenging due to the discrete nature of texts. Recent studies perform transformations on characters or words, which are generally formulated as combinatorial optimization problems. However, these approaches suffer from inefficiency due to the high dimensional search space. To address this issue, in this paper, we propose an end-to-end Seq2seq Stacked Auto-Encoder (SSAE) neural network, which generates adversarial text examples efficiently via direct network inference. SSAE has two salient features. The outer auto-encoder preserves syntactic and semantic information to the original examples. The inner auto-encoder projects sentence embedding into a high-level semantic representation, on which constrained perturbations are superimposed to increase adversarial ability. Experimental results suggest that SSAE has a higher attack success rate than existing word-level attack methods, and is 100x to 700x faster at attack speed on IMDB dataset. We further find out that the adversarial examples generated by SSAE have strong transferability to attack different victim models.
A reliable image compression algorithm based on block luminance adopting deep learning for video surveillance application

Abstract Video surveillance systems are essential as other application domain. Handling efficient and reliable for underground projects as well surveillance image is so significant to ensure security and safety. The wireless channels are efficient as data transferring media. On the other hand, the bandwidth may be limited for some environmental conditions. Hence, the image compression algorithm is very important to be conducted and applied to save the transmission bandwidth. This paper presents an image compression algorithm for video surveillance. The method is based on the concept of luminance variation of image. The image compression method is expected to achieve a reasonable compression ratio with acceptable quality. With another meaning, the compressed image size is decreased and consumes a smaller transmission bandwidth via the wireless channel compared with the original image size. The method adopts a deep learning approach to improve the quality with limited bandwidth. The proposed method is abbreviated as DLBL (deep learning block luminance). DLBL implemented and tested on some tested bed images. The performance of the proposed method is compared with some ones considering the same conditions. Some measurable criteria are taken into consideration for performance evaluation. The criteria are the compression ratio (CR), peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). From the experiments results, the proposed method showed significant and efficient performance compared with some other related ones. This is clear from the values of CR, PSNR and SSIM.
Effects of ICTs on the Terms of Trade of Sub-Saharan African Economies

Abstract This paper argues that the increasing adoption of information and communication technologies (ICTs) is a factor that improves the terms of trade of sub-Saharan African (SSA) economies. According to new theories of international trade, ICTs can change the terms of trade by increasing productivity, reducing costs, and increasing human capital endowment and specialization. Here, we use World Development Indicators (WDI) and United Nations Conference on Trade and Development (UNCTAD) data over the period from 2005 to 2017 and a vector autoregression (VAR) model on a panel of 39 SSA countries to illustrate the importance of ICTs in this regard. Our results show that the change in the terms-of-trade index is positively affected by the change in the number of internet users as a percentage of mobile phone subscribers in SSA. Furthermore, an impulse response function shows that a shock that would increase the number of internet users by 10 percent of mobile phone subscribers would result in the terms-of-trade index gains of more than 5% within about a year.
Application of Retinex and histogram equalisation techniques for the restoration of faded and distorted artworks: A comparative analysis

Images and artworks form an integral part of research and intangible heritage. However the images are not always available in a good condition. So restoration and enhancement of the degraded and faded images are very much essential to obtain accurate information from them. In this paper a comparative performance analysis of different Histogram Equalization techniques and Retinex based methods for the restoration of old and almost damaged artworks is presented.
Evaluating classifiers in SE research: the ECSER pipeline and two replication studies

Abstract Context Automated classifiers, often based on machine learning (ML), are increasingly used in software engineering (SE) for labelling previously unseen SE data. Researchers have proposed automated classifiers that predict if a code chunk is a clone, if a requirement is functional or non-functional, if the outcome of a test case is non-deterministic, etc. Objective The lack of guidelines for applying and reporting classification techniques for SE research leads to studies in which important research steps may be skipped, key findings might not be identified and shared, and the readers may find reported results (e.g., precision or recall above 90%) that are not a credible representation of the performance in operational contexts. The goal of this paper is to advance ML4SE research by proposing rigorous ways of conducting and reporting research. Results We introduce the ECSER (Evaluating Classifiers in Software Engineering Research) pipeline, which includes a series of steps for conducting and evaluating automated classification research in SE. Then, we conduct two replication studies where we apply ECSER to recent research in requirements engineering and in software testing. Conclusions In addition to demonstrating the applicability of the pipeline, the replication studies demonstrate ECSER ’s usefulness: not only do we confirm and strengthen some findings identified by the original authors, but we also discover additional ones. Some of these findings contradict the original ones.
Cryptocurrencies vs Central Banks' Digital Currencies: The Role of Financial Literacy

A bifurcation point has arisen in the transformation of the global monetary and financial system, associated with its further digital transformation: will it be based on private digital currencies like Bitcoin, or on the basis of central bank digital currencies (CBDC)? To a large extent, this depends on the willingness of economic agents to use virtual currencies. The purpose of the study is to explore the factors determining the attitude of economic agents to digital currencies and the impact of financial literacy on using these instruments as an investment object and means of payment. The authors use the following research methods : content analysis, retrospective analysis, methods of comparative cross-country analysis, and empirical research in the form of an online survey of graduate financial students. This study is one of the first to reveal differences in the assessment of their knowledge and readiness to use digital currencies of financial and non-financial students, as well as to confirm an adequate assessment of the risks and opportunities of different types of virtual currencies if students have financial knowledge. The research shows that the situation with the decisionmaking of economic agents on the use of cryptocurrencies and the CBDC differs: in the first case, the initiative comes from the economic agents themselves, who make decisions at their own peril and risk; in the second case, economic agents are confronted with the fact of the existence of the CBDC and the need to use them. The authors conclude that the population’s low financial and digital literacy can create a mental barrier to the use of CBDC, complicating their implementation in national monetary systems. The lack of financial literacy leads to an exaggeration of their knowledge by participants in the cryptocurrency market.
Research on Data Transaction Security Based on Blockchain

With the increasing value of various kinds of data in the era of big data, the demand of different subjects for data transactions has become more and more urgent. In this paper, a blockchain-based data transaction protection scheme is proposed to realize the secure transaction sharing among data. This paper carries out the following work: by analyzing the existing data transaction models, we find the data security and transaction protection problems, establish a third-party-free data transaction platform using blockchain, protect users’ data security by combining AES and improved homomorphic encryption technology, and upload the encrypted data to the Interplanetary File System (IPFS) for distributed storage. Finally, we use the powerful functions of the IPFS, combined with inadvertent transmission protocol, two-way authentication, zero-knowledge proof, and other security verification for data transactions. The security analysis proves that this scheme has higher security despite the time overhead, and we will continue to optimize the scheme to improve efficiency in the future.
HAPPY: Hip Arthroscopy Portal Placement Using Augmented Reality

Correct positioning of the endoscope is crucial for successful hip arthroscopy. Only with adequate alignment can the anatomical target area be visualized and the procedure be successfully performed. Conventionally, surgeons rely on anatomical landmarks such as bone structure, and on intraoperative X-ray imaging, to correctly place the surgical trocar and insert the endoscope to gain access to the surgical site. One factor complicating the placement is deformable soft tissue, as it can obscure important anatomical landmarks. In addition, the commonly used endoscopes with an angled camera complicate hand–eye coordination and, thus, navigation to the target area. Adjusting for an incorrectly positioned endoscope prolongs surgery time, requires a further incision and increases the radiation exposure as well as the risk of infection. In this work, we propose an augmented reality system to support endoscope placement during arthroscopy. Our method comprises the augmentation of a tracked endoscope with a virtual augmented frustum to indicate the reachable working volume. This is further combined with an in situ visualization of the patient anatomy to improve perception of the target area. For this purpose, we highlight the anatomy that is visible in the endoscopic camera frustum and use an automatic colorization method to improve spatial perception. Our system was implemented and visualized on a head-mounted display. The results of our user study indicate the benefit of the proposed system compared to baseline positioning without additional support, such as an increased alignment speed, improved positioning error and reduced mental effort. The proposed approach might aid in the positioning of an angled endoscope, and may result in better access to the surgical area, reduced surgery time, less patient trauma, and less X-ray exposure during surgery.