Reviewerly/exHarmony huggingface dataset:
Universal Gate Set for Continuous-Variable Quantum Computation with Microwave Circuits

We provide an explicit construction of a universal gate set for continuous-variable quantum computation with microwave circuits. Such a universal set has been first proposed in quantum-optical setups, but its experimental implementation has remained elusive in that domain due to the difficulties in engineering strong nonlinearities. Here, we show that a realistic three-wave mixing microwave architecture based on the superconducting nonlinear asymmetric inductive element [Frattini et al., Appl. Phys. Lett. 110, 222603 (2017)APPLAB0003-695110.1063/1.4984142] allows us to overcome this difficulty. As an application, we show that this architecture allows for the generation of a cubic phase state with an experimentally feasible procedure. This work highlights a practical advantage of microwave circuits with respect to optical systems for the purpose of engineering non-Gaussian states and opens the quest for continuous-variable algorithms based on few repetitions of elementary gates from the continuous-variable universal set.
Improvement in Extended Object Tracking with the Vision-Based Algorithm

The main emphases on any object based tracking with vision algorithms are parametric state space based algorithms like a Bayesian filter and its family of algorithms or nonparametric algorithms like Mean Shift algorithms which are color sensitive. In this paper, We have considered vision based algorithm with Bayesian filter algorithms. We have seen more of the state space tracking algorithms uses point based object tracking approaches in which researchers did very well in the last decades, with the advent of faster computing devices available the tracking algorithm have improved a lot with extended object tracking where the object is tracked as an entire object instead of the point-based approaches. The proposed system of the algorithm is providing good results in terms of state estimation over its point based approach. So using this vision algorithm the complete object can be tracked using sensor system and this is the novelty of paper.
Bifröst: a Modular Blockchain Interoperability API

The blockchain (BC) world is rapidly becoming a universe of several ledgers designed for a specific purpose, holding data previously stored (i.e., siloed) in centralized databases.The use of different BCs for the same purpose could hamper the frictionless exchange of data or value.On one hand, it is natural that there are competing implementations exploring the benefits of BC.On the other hand, the problem of siloed data re-emerges, with respect to isolated chains.In this regard, BC interoperability is necessary to connect different BCs, exchanging information and assets.Moreover, to foster BC employment, developers must be able to interact with such different BCs without knowing the details of each implementation.This paper presents a novel solution, called Bifröst, to store and retrieve data on different BCs.Bifröst employs a notary scheme, which allows for connectivity to different BCs.The presented prototype is highly modular and currently implements seven adapters to popular BC implementations, including Bitcoin, Ethereum, and Stellar.The developed prototype was evaluated concerning performance, security, and data size to verify the feasibility of such an implementation and assess design decisions taken during its development.
Urban soil contamination in Salerno (Italy): Concentrations and patterns of major, minor, trace and ultra-trace elements in soils

This work discusses the results of a geochemical survey conducted in the Salerno urban area to determine the sources patterns of major, minor, trace and ultra-trace elements in soils. In particular, the study focused on elements that are potentially toxic and listed in the environmental Italian legislation (D.L. 152/06), in order to effectively monitoring an important aspect of environment health. A total of 151 topsoil samples were collected, air-dried and sieved (<2 mm). After aqua regia digestion the samples were analyzed for 42 elements by ICP-MS and ICP-AES. Geostatistical analyses were carried out in order to show the single element spatial distribution and the distribution of factor scores elemental associations from R-mode factor analysis. In performing factor analysis, the additive logratio (alr) transformation was applied to the whole dataset in order to deal with the closure effects of the investigated geochemical data, avoiding artefacts and spurious correlation. The use of alr-transformed data instead of the normal data in the factor analysis allowed for a better interpretation of the distribution patterns, since this produced four factor models which, once mapped, were easier to interpret. The study revealed that major and minor elements (Al, Ca, Fe, K, Mg, Na, P, S and Ti) have a perfectly natural distribution with no discernible association to any human activity or presence. In contrast, many trace and ultra-trace elements (Ag, As, Au, Ba, Be, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Pd, Pt, Rh, Sb, Sn, Tl, V and Zn) show anomalous concentration values located almost exclusively in highly inhabited areas, industrial sites and along high traffic roads. Other trace and ultra-trace elements (B, Bi, Ga, La, Sc, Se, Sr, Te, Th, Tl, U and W) show concentrations compatible with the natural background levels. Some potentially toxic elements (e.g. Pb and Zn) reach concentration levels tens of times higher than the legal limits in the busiest areas of the city. Exposure to high concentrations of these contaminants may cause health problems to people living in these zones.
Action Recognition in Manufacturing Assembly using Multimodal Sensor Fusion

Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers’ behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize those actions lacks reliability. The limitation can be surpassed by sensor fusion at data, feature, and decision levels. This paper presents a study that developed a multimodal sensor system and used sensor fusion methods to enhance the reliability of action recognition. One step in assembling a Bukito 3D printer, which composed of a sequence of 7 actions, was used to illustrate and assess the proposed method. Two wearable sensors namely Myo-armband captured both Inertial Measurement Unit (IMU) and electromyography (EMG) signals of assembly workers. Microsoft Kinect, a vision based sensor, simultaneously tracked predefined skeleton joints of them. The collected IMU, EMG, and skeleton data were respectively used to train five individual Convolutional Neural Network (CNN) models. Then, various fusion methods were implemented to integrate the prediction results of independent models to yield the final prediction. Reasons for achieving better performance using sensor fusion were identified from this study.
Comprehend Medical: A Named Entity Recognition and Relationship Extraction Web Service

Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models. Contrary to many existing open source tools, Comprehend Medical is scalable and does not require steep learning curve, dependencies, pipeline configurations, or installations. Currently, Comprehend Medical performs NER in five medical categories: Anatomy, Medical Condition, Medications, Protected Health Information (PHI) and Treatment, Test and Procedure (TTP). Additionally, the service provides relationship extraction for the detected entities as well as contextual information such as negation and temporality in the form of traits. Comprehend Medical provides two Application Programming Interfaces (API): 1) the NERe API which returns all the extracted named entities, their traits and the relationships between them and 2) the PHId API which returns just the protected health information contained in the text. Furthermore, Comprehend Medical is accessible through AWS Console, Java and Python Software Development Kit (SDK), making it easier for non-developers and developers to use.
Novel deep learning model for facial expression recognition based on maximum boosted CNN and LSTM

Deep learning has received increasing attention in all fields and has made considerable progress in facial expression recognition (FER). Mainly, the conventional FER methods are trained for constrained datasets, which may not operate well for real-time images. Such real-time image sequences limit the accuracy and efficacy of the traditional system. In this work, the authors present a novel deep learning framework which combines convolutional neural network (CNN) with long short-term memory (LSTM) cell for real-time FER. The novel framework has three main aspects: (i) two different pre-processing techniques are employed to handle illumination variances and to preserve subtle edge information of each image; (ii) correspondingly, the pre-processed images are inputted to the two individual CNN architecture which extracts the spatial features very effectively; (iii) spatial feature maps from two individual CNN layers are fused and integrated with an LSTM layer which extracts temporal relations between the successive frames. They experimented the authors’ proposed method on three publically available FER databases and also with self-created database. With pre-processing, their proposed model achieves comparable and better results to the state-of-the-art.
Parallel Societies: A Computing Perspective of Social Digital Twins and Virtual–Real Interactions

Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) of 2020, and Happy New Year to You! We would like to take this opportunity to express our sincere thanks to our editors, reviewers, authors, and readers for your great support and effort devoted to the TCSS, along with our best wish and hope that everyone has a happy, healthy, and fruitful 2020.
Mutual information-based label distribution feature selection for multi-label learning

Feature selection used for dimensionality reduction of the feature space plays an important role in multi-label learning where high-dimensional data are involved. Although most existing multi-label feature selection approaches can deal with the problem of label ambiguity which mainly focuses on the assumption of uniform distribution with logical labels, it cannot be applied to many practical applications where the significance of related label for every instance tends to be different. To deal with this issue, in this study, label distribution learning covered with a certain real number of labels is introduced to design a model for the labeling-significance. Nevertheless, multi-label feature selection is limited to handling only labels consisting of logical relations. In order to solve this problem, combining the random variable distribution with granular computing, we first propose a label enhancement algorithm to transform logical labels in multi-label data into label distribution with more supervised information, which can mine the hidden label significance from every instance. On this basis, to remove some redundant or irrelevant features in multi-label data, a label distribution feature selection algorithm using mutual information and label enhancement is developed. Finally, the experimental results show that the performance of the proposed method is superior to the other state-of-the-art approaches when dealing with multi-label data.
Salp Swarm Algorithm-Based Optimally Weighted Histogram Framework for Image Enhancement

This article introduces a novel optimally selected plateau limit (PL)-based histogram modification framework. This approach preserves the brightness and improves the contrast of an image effectively without introducing absurd visual deterioration, unnatural contrast effects, and structural artifacts. It also enhances the weak illumination situations, such as backlighting effect and the nonuniform illumination of images without introducing any undesirable artifacts. The proposed method based on the subhistogram and clipping operations utilizes the PLs to modify the histogram of the image before applying the histogram equalization approach. The salp swarm algorithm (SSA)-based optimization technique is incorporated to compute the optimal PLs or adaptive weighted limits. To prove the efficiency of the proposed algorithm, a comparative study is done with the well-known histogram-based processing techniques and state-of-art methods in the literature. Furthermore, well-recognized different evaluation parameters are considered to compare the proposed framework with other existing methods.
Novel fractional-order generic Jacobi-Fourier moments for image analysis

Orthogonal moments were successfully used to extract features from gray-scale and color images. Recently, scientists show that orthogonal moments of fractional-orders have better capabilities to extract the fine features. In this work, novel orthogonal generic fractional-order Jacobi-Fourier moments are proposed for image processing, pattern recognition and computer vision applications. Novel orthogonal Jacobi-Fourier polynomials of fractional-order were derived and defined in polar coordinates. The mathematical equation for orthogonality was formulated and a three-term recurrence relation was derived for easier computation of these polynomials. The proposed orthogonal fractional-order Jacobi-Fourier moments are generic where other orthogonal fractional-order moments are derived as special cases by choosing different values of the controlling parameters. The invariance to geometric transformations, rotation, scaling and translations, is proved where the required mathematical formulae for these invariances are presented. The proposed new fractional-order moments were tested using different datasets of gray-scale and color images in terms of image reconstruction, invariance to geometric transformations, robustness to noise, image recognition, and computational times where their performance were compared with the recent existing orthogonal integer- and fractional-order moments. The proposed generic fractional-order Jacobi-Fourier moments outperformed all existing orthogonal moments.
Postdigital We-Learn

This paper examines relationships between learning and technological change and argues that we urgently need new ways to approach what it means to learn in the context of a global Fourth Industrial Revolution. It briefly introduces the postdigital perspective, which considers the digital ‘revolution’ as something that has already happened and focuses to its reconfiguration. It claims that what we access, how we access it, what we do with it, and who then accesses what we have done, are important elements of a postdigital world worthy of closer examination. Focusing to recent debates about postdigital collective intelligence, we develop the concept of postdigital we-learn by showing that it might help us, amongst other things, to counter the idea of a lone human accessing education primarily for future individual, economic profit, as prescribed by the neoliberal learning economy. Building on new schools of thought emerging in response to the expansion of non-human (algorithmic) agency, we refine the concept of postdigital we-learn as a gathering between humans and machines. The consequences of this gathering are uncomfortable, as they imply unlearning elements of both capitalism and critical pedagogy. However, such unlearning is inherent to ‘a critical pedagogy of becoming’ and positions postdigital we-learn as a suitable framework for understanding and development of emancipatory, critical learning in our postdigital reality.
Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices

Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type but with slightly different dynamics. For such multiple IoT devices, there is no guarantee that an agent who interacts only with one IoT device and learns the optimal control policy will also control another IoT device well. Therefore, we may need to apply independent reinforcement learning to each IoT device individually, which requires a costly or time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent IoT device shares their learning experience (i.e., the gradient of loss function) with each other, and transfers a mature policy model parameters into other agents. They accelerate its learning process by using mature parameters. We incorporate the actor-critic proximal policy optimization (Actor-Critic PPO) algorithm into each agent in the proposed collaborative architecture and propose an efficient procedure for the gradient sharing and the model transfer. Using multiple rotary inverted pendulum devices interconnected via a network switch, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices and that the learning speed can be faster if more agents are involved.
MiniNet: An Efficient Semantic Segmentation ConvNet for Real-Time Robotic Applications

Efficient models for semantic segmentation, in terms of memory, speed, and computation, could boost many robotic applications with strong computational and temporal restrictions. This article presents a detailed analysis of different techniques for efficient semantic segmentation. Following this analysis, we have developed a novel architecture, MiniNet-v2, an enhanced version of MiniNet. MiniNet-v2 is built considering the best option depending on CPU or GPU availability. It reaches comparable accuracy to the state-of-the-art models but uses less memory and computational resources. We validate and analyze the details of our architecture through a comprehensive set of experiments on public benchmarks (Cityscapes, Camvid, and COCO-Text datasets), showing its benefits over relevant prior work. Our experiments include a sample application where these models can boost existing robotic applications.
Access to Finance for Artificial Intelligence Regulation in the Financial Services Industry

Abstract This paper discusses the design of the legal and regulatory framework for using artificial intelligence (AI) in the financial services markets to enhance access to finance (financial inclusion). The author argues that the development of AI should continue to adhere to the regulatory objectives of market safety, consumer protection, and market integrity. However, to ensure equality and fairness, access to finance should be made a clear policy choice. In the first part, the author discusses how AI can lead to systemic risks and market manipulation on trading platforms. For example, by examining the use of algorithms for trading on the capital market, the author discerns the regulatory objectives and the possible methods of regulation for peer-to-peer platforms. In the second part, the author discusses how the use of AI to provide consumers with investment advice, such as financial advice provided from robo-advisers, can close the investment advisory gap and provide consumers with access to finance. The current regime does not provide adequate protection to financial consumers in this regard. In the third part, the author discusses how AI can be used as a form of RegTech to streamline compliance processes, thereby increasing competition in financial markets and providing a benefit to consumers. However, this use may be in conflict with privacy, data protection, and ethical concerns. The author makes policy recommendations and suggests some directions for governance in the use of AI in financial services to enhance access to finance. The findings of this paper are relevant to research on the future governance of AI in financial services, public policy innovation, and urban development.
A Language Identification System using Hybrid Features and Back-Propagation Neural Network

Language Identification (LID) is accurate identification of the unknown language by comparison of speech biometrics of test speech sample and language models accumulated beforehand. This paper presents and encourages the use of hybrid robust feature extraction techniques for spoken language identification (LID) system. In the feature extraction stage, different techniques are applied individually such as Mel frequency cepstral coefficients (MFCCs), perceptual linear prediction features (PLP), relative perceptual linear prediction features (RASTA-PLP). Later, performance of our LID system based on several combinations of the different features (hybrid features) are investigated such as MFCC, PLP, combined with their 1st order derivatives, MFCC + RASTA-PLP, MFCC + SDC (Shifted delta cepstral coefficients). Language identification phase or classification utilizes feed forward back-propagation neural network (FFBPNN) and comparison is based on two learning algorithms: the Levenberg–Marquardt “trainlm” and the scaled conjugate gradient “trainscg”. A comparative analysis in terms of performance is done between different hybrid feature extraction techniques and their individual counterparts. Results clearly indicates that improved performance is obtained with hybrid features with “trainlm” learning algorithm as compared to their individual counterparts. The results are very promising with MFCC-RASTA-PLP hybrid feature extraction technique in comparison to the other hybrid feature extraction techniques with overall accuracy of 94.6% and a minimum test error rate of 0.10. The efficiency of proposed hybrid approaches is determined by simulating several experiments on a user defined language database of speech signals in the working platform of MATLAB.
Sentiment Analysis Using Gated Recurrent Neural Networks

Text sentiment analysis is an important and challenging task. Sentiment analysis of customer reviews is a common problem faced by companies. It is a machine learning problem made demanding due to the varying nature of sentences, different lengths of the paragraphs of text, contextual understanding, sentiment ambiguity and the use of sarcasm and comparatives. Traditional approaches to sentiment analysis use the tally or recurrence of words in a text which are allotted sentiment values by some expert. These strategies overlook the order of words and the complex different meanings they can communicate. Hence, RNNs were introduced that are effective yet challenging to train. Bi-GRUs and Bi-LSTM architectures are a recent form of RNNs which can store information about long-term dependencies in sequential data. In this work, we attempted a survey of different deep learning techniques that have been applied to sentiment classification and analysis. We have implemented the baseline models for LSTM, GRU and Bi-LSTM and Bi-GRU on an Amazon review dataset.