Reviewerly/exHarmony huggingface dataset:
An Investigation on the Volatility of Cryptocurrencies by means of Heterogeneous Panel Data Analysis

Cryptocurrencies have emerged about ten years ago as a new form of currency and have attracted much attention since they depend on a fully decentralized system, and so their transactions are very fast and have zero transaction cost. Therefore, character of cryptocurrencies and their volatility have been discussed widely by investors, policymakers and economists in recent years. From this point of view, this study aims to explain the price volatility of cryptocurrencies with macro-financial indicators, and thereby, the effects of S&P 500 stock market index, gold price, oil price, 2-year benchmark US Bond interest rate and US Dollar index on the prices of four major cryptocurrencies, Bitcoin, Litecoin, Ethereum, and Ripple, are investigated. The study comprises a panel data analysis applied to daily data over the period of August 2016 – April 2019, and analysis results show that increases in gold price, oil price and S&P 500 index raise the prices of cryptocurrencies, while increases in 2-year benchmark US Bond interest rate and US Dollar index cause to a fall. This adverse effects of the US Dollar index and US Bond interest rate on the prices of cryptocurrencies indicates that when the value of US Dollar and US Bond yield decrease investors prefer to invest in cryptocurrencies as alternative investment instruments. On the other hand, cryptocurrencies move with a similar trend of stock market index, gold price and oil price which are overall market indicators. Thereby, findings of this study show that cryptocurrencies behave more like an investment instrument than a currency, and prices of these financial assets interact with significant macro-financial indicators.
An End-to-End Solution to Autonomous Driving Based on Xilinx FPGA

Nowadays, the autonomous driving topic is very hot, many people are trying to provide a solution to this problem. This time we build our own auto-driving car based on Xilinx Pynq-Z2, it provides an end-to-end solution which inputs images from camera and outputs control instructions directly. The platform also uses the power of Deep learning Processing Unit(DPU) to accelerate the inference process and provides a simulator for training and testing in virtual environment. If the car meets some situations which cannot be handled by AI model, it's easy to add extra traditional computer vision functions to our control system. So our platform can help people who want to try autonomous driving build their own model and test it efficiently. We hope that our platform can be easy to use and extend.
Privacy Preserving Computations over Healthcare Data

The healthcare platform has to face a massive amount of electronic health record today with the rapid development of cloud computing and internet of things. These big healthcare data should be analyzed to possess potential appliance value, or it will be meaningless. But the big healthcare data techniques can only be used if security and privacy issues are addressed, so it is essential to develop new techniques to suit for different privacy-preserving scenarios. The most promising technique to tackle such problems is homomorphic encryption, which can analyze the data in encrypted form. And another problem to be concerned is that the data are from different parties, usually encrypted with different public keys, so a secure multisource data computation scheme should be designed to suite for our cloud computing scenario. To make our scheme more flexible, we consider combining homomorphic encryption and proxy re-encryption techniques. Our scheme is efficient for computing encrypted data under multiple keys.
Deep Learning Side-Channel Attack Against Hardware Implementations of AES

In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside with the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled sets containing image and correct value pairs to be identified are input to a neural network, and repeatedly learning the set enables the neural network to identify objects with high accuracy. A new side-channel attack method, deep learning side-channel attack (DLSCA), utilizes the high identifying ability of the neural network to try and unveil a secret key of the cryptographic module by being trained with power waveforms and learning the leak model. However, at this stage, attacks on software implementations have been mainly investigated. In contrast, there are few studies about hardware implementations especially such as ASIC circuits. In this paper, we investigate the use of DL-SCA against hardware implementations of AES and demonstrate that it is able to unveil the secret key by applying a new technique named "mixed model dataset based on round-round XORed value." We also compare the attack performance and characteristics of DL-SCA with conventional analysis methods such as correlation power analysis and conventional template attack.
An Automatic Marker–Object Offset Calibration Method for Precise 3D Augmented Reality Registration in Industrial Applications

Industrial augmented reality (AR) applications demand high on the visual consistency of virtual-real registration. To present, the marker-based registration method is most popular because it is fast, robust, and convenient to obtain the registration matrix. In practice, the registration matrix should multiply an offset matrix that describes the transformation between the attaching position and the initial position of the marker relative to the object. However, the offset matrix is usually measured, calculated, and set manually, which is not accurate and convenient. This paper proposes an accurate and automatic marker–object offset matrix calibration method. First, the normal direction of the target object is obtained by searching and matching the top surface of the CAD model. Then, the spatial translation is estimated by aligning the projected and the imaged top surface. Finally, all six parameters of the offset matrix are iteratively optimized using a 3D image alignment framework. Experiments were performed on the publicity monocular rigid 3D tracking dataset and an automobile gearbox. The average translation and rotation errors of the optimized offset matrix are 2.10 mm and 1.56 degree respectively. The results validate that the proposed method is accurate and automatic, which contributes to a universal offset matrix calibration tool for marker-based industrial AR applications.
QuickXsort: A Fast Sorting Scheme in Theory and Practice

QuickXsort is a highly efficient in-place sequential sorting scheme that mixes Hoare's Quicksort algorithm with X, where X can be chosen from a wider range of other known sorting algorithms, like Heapsort, Insertionsort and Mergesort. Its major advantage is that QuickXsort can be in-place even if X is not. In this work we provide general transfer theorems expressing the number of comparisons of QuickXsort in terms of the number of comparisons of X. More specifically, if pivots are chosen as medians of (not too fast) growing size samples, the average number of comparisons of QuickXsort and X differ only by o(n)-terms. For median-of-k pivot selection for some constant k, the difference is a linear term whose coefficient we compute precisely. For instance, median-of-three QuickMergesort uses at most $$n \lg n - 0.8358n + {\mathcal {O}}(\log n)$$ comparisons. Furthermore, we examine the possibility of sorting base cases with some other algorithm using even less comparisons. By doing so the average-case number of comparisons can be reduced down to $$n \lg n - 1.4112n + o(n)$$ for a remaining gap of only 0.0315n comparisons to the known lower bound (while using only $${\mathcal {O}}(\log n)$$ additional space and $${\mathcal {O}}(n\log n)$$ time overall). Implementations of these sorting strategies show that the algorithms challenge well-established library implementations like Musser's Introsort.
An Approach for Printing Tissue-mimicking Abnormalities Dedicated to Applications in Breast Imaging

The development of new techniques for detection of breast cancer often relies on virtual clinical trials. These trials however require vast number of x-ray images of the breast containing realistically looking pathologies. For this purpose, as many as possible and as realistic as possible physical models of breast lesions are needed. The goal of this study is to create a methodology for manufacturing various breast abnormalities, with a detail description of the stages needed for their development. These models are to be used for development of new breast cancer detection technologies.
On the Effectiveness of Deep Representation Learning: The Atrial Fibrillation Case

The automatic, unsupervised analysis of biomedical time series is vital for diagnostic and preventive medicine, enabling fast and reliable data processing that reveals clinical insights without human intervention. This article explores and quantifies the benefits of modern deeplearning architectures of varying degrees of complexity.
Foreign exchange rate forecasting by artificial neural networks

Forecasting exchange rates are an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative technique for a forecasting task because of several distinguishing features. Neural networks were originally developed in cognitive science and later were used in engineering for pattern recognition and classification. Neural networks are used because they can model nonlinear behavior in financial markets, in contrast to traditional linear models which are more restrictive. Neural networks can approximate any nonlinear function and are capable of dealing with “noisy” data. In this work, we present an approach of forecasting the exchange rate of the Euro against the US dollar by Nonlinear Autoregressive with Exogenous Input (NARX) Neural Network. It was used the Neural network toolbox of Matlab 2016 software. Different kinds of algorithms and network structures are tested to find the best model for the prediction of foreign currency exchange rates. It is shown how to receive closed price one step ahead.
A Partial Ligt-weight Image Encryption Scheme

Due to greater network capacity and faster data speed, fifth generation (5G) technology is expected to provide a huge improvement in Internet of Things (IoTs) applications, Augmented & Virtual Reality (AR/VR) technologies, and Machine Type Communications (MTC). Consumer will be able to send/receive high quality multimedia data. For the protection of sensitive multimedia data, a large number of encryption algorithms are available, however, these encryption schemes does not provide light-weight encryption solution for real-time application requirements. This paper proposes a new multi-chaos computational efficient encryption for digital images. In the proposed scheme, plaintext image is transformed using Lifting Wavelet Transform (LWT) and only one-fourth part of the transformed image is encrypted using light-weight Chebyshev and Intertwining maps. Both chaotic maps were chaotically coupled for the confusion and diffusion processes which further enhances the image security. Encryption/decryption speed and other security measures such as correlation coefficient, entropy, Number of Pixels Change Rate (NPCR), contrast, energy, homogeneity confirm the superiority of the proposed light-weight encryption scheme.
Automating SLA-Driven API Development with SLA4OAI

The OpenAPI Specification (OAS) is the de facto standard to describe RESTful APIs from a functional perspective. OAS has been a success due to its simple model and the wide ecosystem of tools supporting the SLA-Driven API development lifecycle. Unfortunately, the current OAS scope ignores crucial information for an API such as its Service Level Agreement (SLA). Therefore, in terms of description and management of non-functional information, the disadvantages of not having a standard include the vendor lock-in and prevent the ecosystem to grow and handle extra functional aspects. In this paper, we present SLA4OAI, pioneering in extending OAS not only allowing the specification of SLAs, but also supporting some stages of the SLA-Driven API lifecycle with an open-source ecosystem. Finally, we validate our proposal having modeled 5488 limitations in 148 plans of 35 real-world APIs and show an initial interest from the industry with 600 and 1900 downloads and installs of the SLA Instrumentation Library and the SLA Engine.
Iot Intelligent Restaurant System Design

In recent years, Chinese people's pursuit of living standards has undergone a huge change, and people's consumption capacity has also made a qualitative leap, leading to increasingly fierce competition in the catering market. Coupled with the arrival of 5G era, in the next ten years, the high-tech wisdom of the restaurant that combines the Internet of things thinking with 5G technology will become the industry leader. In the past, due to the complex work and the need to employ a large number of waiters, the noisy environment is not good for dining experience, and the waiters may make mistakes due to long working hours. During the peak period of dining, customers need to wait for a table in the queue outside the store, and they cannot leave the line or miss the number in the process of queuing. In this way, it is easy to make customers feel anxious and affect the consumption experience. But now we can integrate the Internet of things thinking into the construction of the restaurant, which can improve the sense of technology of the restaurant, improve the dining experience of customers, increase the working efficiency of the restaurant, reduce the operating cost of the restaurant, etc..
A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management

Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.
4-Connected Shift Residual Networks

The shift operation was recently introduced as an alternative to spatial convolutions. The operation moves subsets of activations horizontally and/or vertically. Spatial convolutions are then replaced with shift operations followed by point-wise convolutions, significantly reducing computational costs. In this work, we investigate how shifts should best be applied to high accuracy CNNs. We apply shifts of two different neighbourhood groups to ResNet on ImageNet: the originally introduced 8-connected (8C) neighbourhood shift and the less well studied 4-connected (4C) neighbourhood shift. We find that when replacing ResNet's spatial convolutions with shifts, both shift neighbourhoods give equal ImageNet accuracy, showing the sufficiency of small neighbourhoods for large images. Interestingly, when incorporating shifts to all point-wise convolutions in residual networks, 4-connected shifts outperform 8-connected shifts. Such a 4-connected shift setup gives the same accuracy as full residual networks while reducing the number of parameters and FLOPs by over 40%. We then highlight that without spatial convolutions, ResNet's downsampling/upsampling bottleneck channel structure is no longer needed. We show a new, 4C shift-based residual network, much shorter than the original ResNet yet with a higher accuracy for the same computational cost. This network is the highest accuracy shift-based network yet shown, demonstrating the potential of shifting in deep neural networks.
The Implementation of Decentralised Ledger Technologies for Public Procurement

In the context of the digital transformation of the European economy and society, new technologies have emerged bringing new opportunities for the benefit of citizens, public administrations and businesses. One of these is the Blockchain-based smart contracts rooted in what is known as Distributed Ledger Technologies (DLTs). This article aims to provide an overview of how DLTs could be successfully implemented in the governance of public procurement, as well as discussing some legal challenges that might appear. Taking into account the proof of concept of all the existing projects in this field, DLTs promise to provide procuring with a high level of transparency, integrity, autonomy, and overall speed-up procurement cycles. While the architecture of a Blockchain-based public procurement scheme is constrained by the immaturity of the technology, it is observed that the establishment of private ledgers (or public-permissioned ledgers) in tender procedures might be more suitable to public procurement. Although the process of implementation might be costly and less feasible in comparison with other fields in the public sector, initiatives focusing on this area should be endorsed. Ultimately, the successful implementation of DLTs in the governance of public procurement is inconceivable without a far-reaching professionalisation of procurement practitioners. Keywords: Blockchain; Smart Public Contracts; Artificial Intelligence; Digital Governance; Distributed Ledger Technology; Digital Procurement.
Effective hybrid genetic algorithm for removing salt and pepper noise

This study presents a new approach for recovering an image perturbed by salt and pepper noise (SPN) using a hybrid genetic algorithm (HGA) at all densities, called effective HGA (EHGA). The main contribution of the proposed algorithm is combining the genetic algorithm with image denoising methods that are integrated into the population to achieve rapid convergence. The idea is to evolve a group of individuals into a number of iterations using crossover and mutation operators. This approach evolves a set of images rather than a set of parameters from the filters. Experimental results of simulation on different images using peak signal-to-noise ratio, structural similarity index metric, image enhancement factor and Universal Quality Index show that the proposed algorithm outperforms other methods in removing the SPN qualitatively and quantitatively if the noise density is moderate and high. EHGA also preserves important features such as texture and corners of the image.
Constructing a Comprehensive Events Database from the Web

In this paper, we consider the problem of constructing a comprehensive database of events taking place around the world. Events include small hyper-local events like farmer's markets, neighborhood garage sales, as well as larger concerts and festivals. Designing a high-precision and high-recall event extractor from unstructured pages across the whole web is a challenging problem. We cannot resort overly to domain-specific strategies since it needs to work on all web pages, including on new domains; we need to account for variations in page layouts and structure across websites. Further, we need to deal with low-quality pages on the web with limited structure. We have built an ML-powered extraction system to solve this problem, using schema.org annotations as training data. Our extraction system operates in two phases. In the first phase, we generate raw event information from individual web pages. To do this, an \em event page classifier predicts if a web page contains any event information; this is then followed by a \em single/multiple classifier that decides if the page contains a single event or multiple events; the first phase concludes by applying \em event extractors that extract the key fields of a public event (the title, the date/time information, and the location information). In the second phase, we further improve the extraction quality via three novel algorithms, \em repeated patterns, \em event consolidation and \em wrapper induction, which are designed to use the raw event extractions as input and generate events whose quality is significantly higher. We evaluate our extraction models on two large scale publicly available web corpus, Common Crawl and ClueWeb12. Experimental analysis shows that our methodology achieves over 95% extraction precision and recall on both datasets.
Self-supervised Representation Learning Using 360° Data

The amount of 360-degree panoramas shared online has been rapidly increasing due to the availability of affordable and compact omnidirectional cameras, which offers huge amount of new information unavailable before. In this paper, we present the first work to exploit unlabeled 360-degree data for image representation learning. We propose middle-out, a new self-supervised learning task, which leverages the spatial configuration of normal field-of-view images sampled from a 360-degree image as supervisory signal. We train a Siamese ConvNet model to identify the middle image among three shuffled images sampled from a panorama by perspective projection. Compared to previous self-supervised methods that train models using image patches or video frames with limited field-of-view, our method leverages the rich semantic information contained in 360-degree images and enforces the model to not only learn about objects, but also develop a higher-level understanding about object relationships and scene structures. We quantitatively demonstrate that the feature representation learned using the proposed task is useful for a wide range of vision tasks including object classification, object detection, scene classification, semantic segmentation, and geometry estimation. We also qualitatively show that the proposed method can enforce the ConvNet to extract high-level semantic concepts, an ability which previous self-supervised learning methods have not acquired.
Data Classification Based on the Features Reduction and Piecewise Linear Separation

The article discusses information technology for classifying data using space reduction methods to the level of data visualization. The components of the steps of using information technology are discussed in detail. The basis is the reduction of the multidimensional feature space into a space that can be visualized. The next step is the construction of piecewise linear class separators by the user on the training set. Further, the borders are projected into the original space of multidimensional objects. Thus, the boundaries of classes are determined, which is in the proposed technology a trained classification system. The advantage is the construction of a flexible non-linear classification system using piecewise linear separators. The proposed method is based on the use of visualization data. A distinctive feature of information technology is that the construction of class delimiters is done by user. Based on visual data analysis, user determines the location of class boundaries and spaces. Thus, technology provides user with convenient tools for data analyzing and classifying.