Reviewerly/exHarmony huggingface dataset:
An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.
Collision Avoidance of Redundant Robotic Manipulators Using Newton’s Method

This study investigates the application of Newton method to the problems of collision avoidance and path planning for robotic manipulators, especially robots with high Degrees of Freedom (DOF). The proposed algorithm applies to the potential fields method, where the Newton technique is used for performing the optimization. As compared to classical gradient descent method this implementation is mathematically elegant, enhances the performance of motion generation, eliminates oscillations, does not require gains tuning, and gives a faster convergence to the solution. In addition, the paper presents a computationally efficient symbolic formula for calculating the Hessian with respect to joint angles, which is essential for achieving realtime performance of the algorithm in high DOF configuration spaces. The method is validated successfully in simulation environment. Results for different methods (Newton, gradient descent and gradient descent with momentum) are compared in terms of quality of the path generated, oscillations, minimum distance to obstacles and convergence rate.
Reservoir-Computing Based Equalization With Optical Pre-Processing for Short-Reach Optical Transmission

Chromatic dispersion is one of the key limitations to increasing the transmission distance-rate product for short-reach communication systems relying on intensity modulation and direct detection. The available optical dispersion-compensation techniques have lost favor due to their high impact on the link loss budget. Alternative digital techniques are usually power-hungry and introduce latency. In this work, we compare different digital, optical and joint hybrid approaches to provide equalization and dispersion compensation for short-reach optical transmission links. Reservoir computing is reviewed as a promising technique to provide equalization with memory in an easily trainable fashion, and the properties of the reservoir network are directly linked to system performance. Furthermore, we propose a new hybrid method relying on reservoir computing combined with a simple signal pre-conditioning stage directly in the optical domain. The optical pre-processing uses an arrayed waveguide grating to split the received signal into narrow sub-bands. The performance of the proposed scheme is thoroughly characterized both in terms of reservoir properties and appropriate pre-processing. The benefits are numerically demonstrated for 32-GBd on-off keying signal transmission, and show an increase in reach from 10 km to 40 km, corresponding to 400%, compared with more complex digital-only techniques.
Metals Coprecipitation with Barite: Nano-XRF Observation of Enhanced Strontium Incorporation

Coprecipitation can be an effective treatment method for the removal of environmentally relevant metals from industrial wastewaters such as produced waters from the oil and gas industry. The precipitation of barite, BaSO4, through the addition of sulfate removes barium while coprecipitating strontium and other alkaline earth metals even when these are present at concentrations below their solubility limit. Among other analytical methods, X-ray fluorescence (XRF) nanospectroscopy at the Hard X-ray Nanoprobe (HXN) beamline at the National Synchrotron Light Source II (NSLS-II) was used to quantify Sr incorporation into barite. Thermodynamic modeling of (Ba,Sr)SO4 solid solutions was done using solid solution—aqueous solution (SS-AS) theory. The quantitative, high-resolution nano-XRF data show clearly that the Sr content in (Ba,Sr)SO4 solid solutions varies widely among particles and even within a single particle. We observed substantial Sr incorporation that is far larger than thermodynamic models predict, likely indicating the formation of metastable solid solutions. We also observed that increasing barite supersaturation of the aqueous phase led to increased Sr incorporation, as predicted by available kinetic models. These results suggest that coprecipitation offers significant potential for designing treatment systems for aqueous metals' removal in desired metastable compositions. Solution conditions may be optimized to enhance the incorporation of Sr by increasing sulfate addition such that the barite saturation index remains above ∼3 or by increasing the aqueous Sr to Ba ratio.
QCOR

Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing programming models for QC were designed with fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their classical counterparts in a heterogeneous fashion. This modus operandi calls out for a programming model and a high-level programming language that natively and seamlessly supports heterogeneous quantum-classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR , which enables single-source quantum-classical programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. QCOR approaches quantum-classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with classical high-performance computing.
Enhancing Differential Evolution With Novel Parameter Control

In this paper, we proposed a novel DE variant named DE-NPC for real parameter single objective optimization. In DE-NPC algorithm, a novel adaptation scheme for the scale factor F is first proposed, which is based on the location information of the population rather than the fitness difference. The adaptation scheme of crossover rate CR in our DE-NPC is based on its success probability. Furthermore, a novel population size reduction scheme is also employed in DE-NPC, which can get a better perception of the landscape of objectives and consequently obtain an overall better performance. The algorithm validation is conducted under our test suite containing 88 benchmarks from CEC2013, CEC2014 and CEC2017 in comparison with several state-of-the-art DE variants. The experiment results show that our novel DE-NPC algorithm is competitive with these state-of-the-art DE variants.
Studying social media sentiment using human validated analysis

The measurement of online sentiment is a developing field in social science and big data research. The methodology from this study provides an analysis of online sentiment using a unique combination of NLP and human validation techniques in order to create net sentiment scores and categorise topics of online conversation. The study focused on measuring the online sentiment of South Africa's major banks (covering almost the entire retail banking industry) over a 12-month period. Through this methodology, firms are able to track shifts in online sentiment (including extreme firestorms) as well as to monitor relevant conversation topics. To date, no published methodology combines the use of big data NLP and human validation in such a structured way. Microsampling for manual validation of sentiment analysis (both qualitative and quantitative approaches in order to obtain the most accurate results) Sentiment measurement Sentiment map
Cumulative link models for deep ordinal classification

This paper proposes a deep convolutional neural network model for ordinal regression by considering a family of probabilistic ordinal link functions in the output layer. The link functions are those used for cumulative link models, which are traditional statistical linear models based on projecting each pattern into a 1-dimensional space. A set of ordered thresholds splits this space into the different classes of the problem. In our case, the projections are estimated by a non-linear deep neural network. To further improve the results, we combine these ordinal models with a loss function that takes into account the distance between the categories, based on the weighted Kappa index. Three different link functions are studied in the experimental study, and the results are contrasted with a statistical analysis. The experiments run over two different ordinal classification problems and the statistical tests confirm that these models improve the results of a nominal model and outperform other robust proposals considered in the literature.
Cyber-Physical Trust Systems Driven by Blockchain

Cyber Physical Trust Systems (CPTS) are Cyber Physical Systems and Internet of Things enriched with trust as an explicit, measurable, testable and verifiable system component. In this paper, we propose to use blockchain, a distributed ledger technology, as the trust enabling system component for CPTS. We propose two schemes for CPTSs driven by blockchain in relation to two typical network model cases. We show that our proposed approach achieves the security properties, such as device identification, authentication, integrity, and non-repudiation, and provides protection against popular attacks, such as replay and spoofing. We provide formal proofs of those properties using the Tamarin Prover tool. We describe results of a proof-of-concept which implements a CPTS driven by blockchain for physical asset management and present a performance analysis of our implementation. We identify use cases in which CPTSs driven by blockchain find applications.
Mitigating Drift in Time Series Data with Noise Augmentation

Machine leaning (ML) models must be accurate to produce quality AI solutions. There must be high accuracy in the data and with the model that is built using the data. Online machine learning algorithms fits naturally with use cases that involves time series data. In online environments the data distribution can change over time producing what is known as concept drift. Real-life, real-time, machine learning algorithms operating in dynamic environments must be able to detect any drift or changes in the data distribution and adapt and update the ML model in the face of data that changes over time. In this paper we present the work of a simulated drift added to time series ML models. We simulate drift on Multiplayer perceptron (MLP), Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Gated Recurrent Unit (GRU). Results show ML models with flavors of recurrent neural network (RNN) are less sensitive to drift compared to other models. By adding noise to the training set, we can recover accuracy of the model in the face of drift.
Rendering Optimization for Mobile Web 3D Based on Animation Data Separation and On-Demand Loading

Based on advances in image processing technology and Web-enabling technologies for mobile devices, mobile Augmented Reality (AR) and Virtual Reality (VR) has developed rapidly. The rendering and interaction of 3D models is an important part of AR and VR applications and is closely related to user experience. However, since the existing WebGL 3D JavaScript libraries for Web-based mobile 3D (represented by three.js and babylon.js) load the entire model file at once, large-size 3D models with complex interactions cannot be rendered smoothly due to limited data transmission, the weak computation capabilities of mobile Web browsers, and the latency of 3D model rendering. In this paper, we first propose model-animation data separation and an on-demand loading mechanism to improve the data request and loading process of Web 3D models. The main mechanisms are the following: (1) The model data are segmented into topological data and animation data sequences, and only the necessary data of the model are loaded when the Web-based mobile 3D model is first rendered. (2) The 3D model animation data sequence is semantically decomposed, and a multigranular model animation data service is established to provide continuous animation data support. (3) An asynchronous request-response mechanism is used to optimize the loading method of the model data. The model rendering mechanism uses an on-demand request and rendering method to transform the centralized loading process of the 3D model into a decentralized process. According to the testing and verification results, this optimization method can reduce the latency of mobile Web 3D in model data transmission and rendering by 24.72% for the experiment models. The interaction experience of Web-based mobile AR and VR is substantially improved relative to existing Web 3D rendering engines and rendering mechanisms, especially in complex interactive service scenarios.
Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.

Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This depends on critical knowledge to understand the risk of opioid overdose of patients. In this paper, we present our work on building machine learning based prediction models to predict opioid overdose of patients based on the history of patients' electronic health records (EHR). We performed two studies using New York State claims data (SPARCS) with 440,000 patients and Cerner's Health Facts database with 110,000 patients. Our experiments demonstrated that EHR based prediction can achieve best recall with random forest method (precision: 95.3%, recall: 85.7%, F1 score: 90.3%), best precision with deep learning (precision: 99.2%, recall: 77.8%, F1 score: 87.2%). We also discovered that clinical events are among critical features for the predictions.
Protecting against Website Fingerprinting with Multihoming

Abstract Anonymous communication tools, such as Tor, are extensively employed by users who want to keep their web activity private. But recent works have shown that when a local, passive adversary observes nothing more than the timestamp, size and direction (incoming or outgoing) of the packets, it can still identify with high accuracy the website accessed by a user. Several defenses against these website fingerprinting attacks have been proposed but they come at the cost of a significant overhead in traffic and/or website loading time. We propose a defense against website fingerprinting which exploits multihoming, where a user can access the Internet by sending the traffic through multiple networks. With multihoming, it is possible to protect against website fingerprinting by splitting traffic among the networks, i.e., by removing packets from one network and sending them through another, whereas current defenses can only add packets. This enables us to design a defense with no traffic overhead that, as we show through extensive experimentation against state-of-the-art attacks, reaches the same level of privacy as the best existing practical defenses. We describe and evaluate a proof-ofconcept implementation of our defense and show that is does not add significant loading-time overhead. Our solution is compatible with other state-of-the-art defenses, and we show that combining it with another defense further improves privacy.
Advance hybrid medical watermarking algorithm using speeded up robust features and discrete cosine transform

In the continuous development of computer network technology, multimedia technology and information technology, digitization has become the main means of displaying information, thus facilitating the storage, copying and dissemination of digital multimedia information. In this context, there are no restrictions on arbitrary editing, copying, modification, and dissemination of digital images, music, etc., which leads to various social problems such as information security, copyright disputes, and piracy. With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Experiments show that the algorithm has good invisibility and strong robustness against conventional and geometric attacks and can effectively protect the security of images with NC value more than 90%.
Transformation GAN for Unsupervised Image Synthesis and Representation Learning

Generative Adversarial Networks (GAN) have shown promising performance in image synthesis and unsupervised learning (USL). In most cases, however, the representations extracted from unsupervised GAN are usually unsatisfactory in other computer vision tasks. By using conditional GAN (CGAN), this problem could be solved to some extent, but the main drawback of such models is the necessity for labeled data. To improve both image synthesis quality and representation learning performance under the unsupervised setting, in this paper, we propose a simple yet effective Transformation Generative Adversarial Networks (TrGAN). In our approach, instead of capturing the joint distribution of image-label pairs p(x,y) as in conditional GAN, we try to estimate the joint distribution of transformed image t(x) and transformation t. Specifically, given a randomly sampled transformation t, we train the discriminator to give an estimate of input transformation, while following the adversarial training scheme of the original GAN. In addition, intermediate feature matching as well as feature-transform matching methods are introduced to strengthen the regularization on the generated features. To evaluate the quality of both generated samples and extracted representations, extensive experiments are conducted on four public datasets. The experimental results on the quality of both the synthesized images and the extracted representations demonstrate the effectiveness of our method.
Symboleo: Towards a Specification Language for Legal Contracts

Legal contracts specify the terms and conditions (in essence, requirements) that apply to business transactions. Smart contracts are software systems that monitor and control the execution of contracts to ensure compliance. This paper proposes a formal specification language for contracts, called Symboleo, where contracts consist of collections of obligations and powers that define the legal contract's compliant executions. The formal semantics of Symboleo is based on an extension of an ontology for Law and is described in terms of logical axioms on statecharts that describe the lifetimes of contracts, obligations and powers. Our proposal includes a preliminary evaluation through the specification of a real life-inspired Sale-of-Goods contract, with a prototype execution engine. We envision this language to enable formally verifying contracts to detect requirements-level issues and to generate executable smart contracts (e.g., on blockchain technology).
Digital Transformation Enablers and Barriers in the Economy of Kazakhstan

This study aims to analyze the digital performance of Kazakhstan through dynamic analysis of national statistics and international indices as Global Innovation Index and ICT Development Index.The research combines three stages: analysis of digital transformation policies performance, review of ICT industry development, and comparative analysis of the positions of Kazakhstan, Turkey and South Korea as a benchmark in the international indices.This research findings show that despite great efforts of Kazakhstan in digitalization, the ICT industry contribution does not increase, it even falls.The international indices demonstrate that the reason is the weakness of the country in skills, venture capital, and innovation linkages.This leads to low knowledge, technology outputs, and creative outputs.The enablers of digital and overall innovation advancement of economy are identified.According to the international rankings the country has been doing its best in Access and Use areas.Another enablers are good business environment, ease of starting a business, protecting minority investors, and FDI inflows.The findings help to draw recommendations for strategic directions in order to improve the digital performance in Kazakhstan.The main limitation of this study is a lack of dynamic information on positions of Kazakhstan in other international indices related to digitalization.