Reviewerly/exHarmony huggingface dataset:
Generalizable control for quantum parameter estimation through reinforcement learning

Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic schemes that can achieve high precision. While conventional schemes for quantum parameter estimation focus on the optimization of the probe states and measurements, it has been recently realized that control during the evolution can significantly improve the precision. The identification of optimal controls, however, is often computationally demanding, as typically the optimal controls depend on the value of the parameter which then needs to be re-calculated after the update of the estimation in each iteration. Here we show that reinforcement learning provides an efficient way to identify the controls that can be employed to improve the precision. We also demonstrate that reinforcement learning is highly generalizable, namely the neural network trained under one particular value of the parameter can work for different values within a broad range. These desired features make reinforcement learning an efficient alternative to conventional optimal quantum control methods.
Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

The widespread growth of Big Data and the evolution of Internet of Things (IoT) technologies enable cities to obtain valuable intelligence from a large amount of real-time produced data. In a Smart City, various IoT devices generate streams of data continuously which need to be analyzed within a short period of time; using some Big Data technique. Distributed stream processing frameworks (DSPFs) have the capacity to handle real-time data processing for Smart Cities. In this paper, we examine the applicability of employing distributed stream processing frameworks at the data processing layer of Smart City and appraising the current state of their adoption and maturity among the IoT applications. Our experiments focus on evaluating the performance of three DSPFs, namely Apache Storm, Apache Spark Streaming, and Apache Flink. According to our obtained results, choosing a proper framework at the data analytics layer of a Smart City requires enough knowledge about the characteristics of target applications. Finally, we conclude each of the frameworks studied here have their advantages and disadvantages. Our experiments show Storm and Flink have very similar performance, and Spark Streaming, has much higher latency, while it provides higher throughput.
A quantum network stack and protocols for reliable entanglement-based networks

We present a stack model for breaking down the complexity of entanglement-based quantum networks. More specifically, we focus on the structures and architectures of quantum networks and not on concrete physical implementations of network elements. We construct the quantum network stack in a hierarchical manner comprising several layers, similar to the classical network stack, and identify quantum networking devices operating on each of these layers. The layers responsibilities range from establishing point-to-point connectivity, over intra-network graph state generation, to inter-network routing of entanglement. In addition we propose several protocols operating on these layers. In particular, we extend the existing intra-network protocols for generating arbitrary graph states to ensure reliability inside a quantum network, where here reliability refers to the capability to compensate for devices failures. Furthermore, we propose a routing protocol for quantum routers which enables to generate arbitrary graph states across network boundaries. This protocol, in correspondence with classical routing protocols, can compensate dynamically for failures of routers, or even complete networks, by simply re-routing the given entanglement over alternative paths. We also consider how to connect quantum routers in a hierarchical manner to reduce complexity, as well as reliability issues arising in connecting these quantum networking devices.
A new approach for query expansion using Wikipedia and WordNet

Query expansion (QE) is a well-known technique used to enhance the effectiveness of information retrieval. QE reformulates the initial query by adding similar terms that help in retrieving more relevant results. Several approaches have been proposed in literature producing quite favorable results, but they are not evenly favorable for all types of queries (individual and phrase queries). One of the main reasons for this is the use of the same kind of data sources and weighting scheme while expanding both the individual and the phrase query terms. As a result, the holistic relationship among the query terms is not well captured or scored. To address this issue, we have presented a new approach for QE using Wikipedia and WordNet as data sources. Specifically, Wikipedia gives rich expansion terms for phrase terms, while WordNet does the same for individual terms. We have also proposed novel weighting schemes for expansion terms: in-link score (for terms extracted from Wikipedia) and a tf-idf based scheme (for terms extracted from WordNet). In the proposed Wikipedia-WordNet-based QE technique (WWQE), we weigh the expansion terms twice: first, they are scored by the weighting scheme individually, and then, the weighting scheme scores the selected expansion terms concerning the entire query using correlation score. The proposed approach gains improvements of 24% on the MAP score and 48% on the GMAP score over unexpanded queries on the FIRE dataset. Experimental results achieve a significant improvement over individual expansion and other related state-of-the-art approaches. We also analyzed the effect on retrieval effectiveness of the proposed technique by varying the number of expansion terms.
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

Non-parallel many-to-many voice conversion, as well as zero-shot voice conversion, remain under-explored areas. Deep style transfer algorithms, such as generative adversarial networks (GAN) and conditional variational autoencoder (CVAE), are being applied as new solutions in this field. However, GAN training is sophisticated and difficult, and there is no strong evidence that its generated speech is of good perceptual quality. On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN. In this paper, we propose a new style transfer scheme that involves only an autoencoder with a carefully designed bottleneck. We formally show that this scheme can achieve distribution-matching style transfer by training only on a self-reconstruction loss. Based on this scheme, we proposed AUTOVC, which achieves state-of-the-art results in many-to-many voice conversion with non-parallel data, and which is the first to perform zero-shot voice conversion.
Implementation of cloud service models in training of future information technology specialists

Leading research directions are defined on the basis of self-analysis of the study results on the use of cloud technologies in training by employees of joint research laboratory “Сloud technologies in education” of Kryvyi Rih National University and Institute of Information Technology and Learning Aids of the NAES of Ukraine in 2009-2018: cloud learning technologies, cloud technologies of blended learning, cloud-oriented learning environments, cloud-oriented methodological systems of training, the provision of cloud-based educational services. The ways of implementation SaaS, PaaS, IaaS cloud services models which are appropriate to use in the process of studying the academic disciplines of the cycles of mathematical, natural science and professional and practical training of future specialists in information technology are shown, based on the example of software engineering, computer science and computer engineering. The most significant advantages of using cloud technologies in training of future information technology specialists are definite, namely, the possibility of using modern parallel programming tools as the basis of cloud technologies. Conclusions are drawn; the direction of further research is indicated: designing a cloud-oriented learning environment for future specialists in computer engineering, identifying trends in the development of cloud technologies in the professional training and retraining of information technology specialists, developing a methodology for building the research competencies of future software engineering specialists by using cloud technologies.
A multi-layered blockchain framework for smart mobility data-markets

Blockchain has the potential to render the transaction of information more secure and transparent. Nowadays, transportation data are shared across multiple entities using heterogeneous mediums, from paper collected data to smartphone. Most of this data are stored in central servers that are susceptible to hacks. In some cases shady actors who may have access to such sources, share the mobility data with unwanted third parties. A multi-layered Blockchain framework for Smart Mobility Data-market (BSMD) is presented for addressing the associated privacy, security, management, and scalability challenges. Each participant shares their encrypted data to the blockchain network and can transact information with other participants as long as both parties agree to the transaction rules issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility data-market. In a case study of real-time mobility data sharing, we demonstrate the performance of BSMD on a 370 nodes blockchain running on heterogeneous and geographically-separated devices communicating on a physical network. We also demonstrate how BSMD ensures the cybersecurity and privacy of individual by safeguarding against spoofing and message interception attacks and providing information access management control.
A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was preprocessed using the henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks.
Blockchain and Trust in a Smart City

Trust in a smart city is fundamental to its transparency, the participation of its people in governance, entrepreneurial initiatives, trade, commerce and hence the growth of its economy. A city gets smart by transforming itself to a digital city, and a digital city runs on data, analytics, internet of things, artificial intelligence and machine learning. This inevitable transformation creates the fundamental need for trust. How does data remain sacrosanct and verifiable? How do people trust institutions? How do institutions trust each other? How do devices trust each other? This article explores blockchain as an essential layer of trust in a smart city. It explains the technology by drawing real-life examples to the ‘memory game’ that operates in an ecosystem of ‘trust and consensus.’ The article provides further insight into institutions that can be governed on blockchain through ‘smart contracts’ in a sovereign and human independent manner. The use cases of blockchain have been corroborated with examples of successful blockchain implementation. The value for blockchain, in general, and smart cities, in particular, has been presented across four categories: (a) the network effect on trust on society, governments and industries; (b) empowering the individual and strengthening the economy; (c) the liquid economy and (d) the shareable economy. Given the current topology of technology innovations, there is no solution better than blockchain that embodies trust. It is a hope and expectation that this article will help smart city planners, developers, architects and thinkers implement blockchain as the embodiment of trust in smart cities that are increasingly becoming digital.
Token-Weighted Crowdsourcing

Blockchain-based platforms often rely on token-weighted voting (“τ-weighting”) to efficiently crowdsource information from their users for a wide range of applications, including content curation and on-chain governance. We examine the effectiveness of such decentralized platforms for harnessing the wisdom and effort of the crowd. We find that τ-weighting generally discourages truthful voting and erodes the platform’s predictive power unless users are “strategic enough” to unravel the underlying aggregation mechanism. Platform accuracy decreases with the number of truthful users and the dispersion in their token holdings, and in many cases, platforms would be better off with a “flat” 1/n mechanism. When, prior to voting, strategic users can exert effort to endogenously improve their signals, users with more tokens generally exert more effort—a feature often touted in marketing materials as a core advantage of τ-weighting—however, this feature is not attributable to the mechanism itself, and more importantly, the ensuing equilibrium fails to achieve the first-best accuracy of a centralized platform. The optimality gap decreases as the distribution of tokens across users approaches a theoretical optimum, which we derive, but tends to increase with the dispersion in users’ token holdings. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images

Accurately identifying and categorizing cancer structures/sub-types in histological images is an important clinical task involving a considerable workload and a specific subspecialty of pathologists. Digitizing pathology is a current trend that provides large amounts of visual data allowing a faster and more precise diagnosis through the development of automatic image analysis techniques. Recent studies have shown promising results for the automatic analysis of cancer tissue by using deep learning strategies that automatically extract and organize the discriminative information from the data. This paper explores deep learning methods for the automatic analysis of Hematoxylin and Eosin stained histological images of breast cancer and lymphoma. In particular, a deep learning approach is proposed for two different use cases: the detection of invasive ductal carcinoma in breast histological images and the classification of lymphoma sub-types. Both use cases have been addressed by adopting a residual convolutional neural network that is part of a convolutional autoencoder network (i.e., FusionNet). The performances have been evaluated on the public datasets of digital histological images and have been compared with those obtained by using different deep neural networks (UNet and ResNet). Additionally, comparisons with the state of the art have been considered, in accordance with different deep learning approaches. The experimental results show an improvement of 5.06% in F-measure score for the detection task and an improvement of 1.09% in the accuracy measure for the classification task.
Deep Learning-Based Signal Modulation Identification in OFDM Systems

Signal modulation identification (SMI) plays a very important role in orthogonal frequency-division multiplexing (OFDM) systems. Currently, SMI methods are often implemented via feature extraction based on machine learning. However, the traditional methods encounter a bottleneck where the probability of correct classification (PCC) is very limited and hence it is hard to implement in practical OFDM systems due to the fact that traditional methods are difficult to extract feature of the OFDM signals. In order solve these problems, we propose a deep learning (DL) based SMI method for identifying OFDM signals. Specifically, convolutional neural network (CNN) is adopted to train in-phase and quadrature (IQ) samples for OFDM signals. Then we choose dropout layer to prevent overfitting and improve its identification accuracy. In addition, datasets with different modulation modes are adopted to verify our trained CNN. Experiments are conducted to show that our proposed method achieves higher accuracy and better consistency than traditional methods. Moreover, extensive results confirm that the proposed method performs robustly in different datasets.
A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.
Quantum computing models for artificial neural networks

Neural networks are computing models that have been leading progress in Machine Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small scale quantum computing devices have become available in recent years, paving the way for the development of a new paradigm in information processing. Here we give an overview of the most recent proposals aimed at bringing together these ongoing revolutions, and particularly at implementing the key functionalities of artificial neural networks on quantum architectures. We highlight the exciting perspectives in this context and discuss the potential role of near term quantum hardware in the quest for quantum machine learning advantage.
Predicting susceptibility to cyber-fraud victimhood

Purpose This paper aims to develop a theoretical framework to predict susceptibility to cyber-fraud victimhood. Design/methodology/approach A survey was constructed to examine whether personality, socio-demographic characteristics and online routine activities predicted one-off and repeat victimhood of cyber-fraud. Overall, 11,780 participants completed a survey (one-off victims, N = 728; repeat victims = 329). Findings The final saturated model revealed that psychological and socio-demographic characteristics and online routine activities should be considered when predicting victimhood. Consistent with the hypotheses, victims of cyber-frauds were more likely to be older, score high on impulsivity measures of urgency and sensation seeking, score high on addictive measures and engage in more frequent routine activities that place them at great risk of becoming scammed. There was little distinction between one-off and repeat victims of cyber-frauds. Originality/value This work uniquely combines psychological, socio-demographic and online behaviours to develop a comprehensive theoretical framework to predict susceptibility to cyber-frauds. Importantly, the work here challenges the current utility of government websites to protect users from becoming scammed and provides insights into methods that might be used to protect users from becoming scammed.
Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian's stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).
Lightweight Security Architecture Based on Embedded Virtualization and Trust Mechanisms for IoT Edge Devices

Security issues represent the greatest obstacle to the growth of edge computing and the Internet of Things (IoT). In this paradigm, IoT applications are migrating to edge devices. As a result, potential security risks are arising, and unauthorized access to data from IoT edge devices is becoming a real concern. Thus, there is a need for a comprehensive, end-toend security approach since the system's more vulnerable point determines its overall security level. An edge device security system has to be built with a root of trust (i.e., something that is unclonable) and a chain of trust. Additionally, a security layer is necessary to ensure that different IoT applications execute isolated from each other in the device. In this sense, this article defines a security architecture that integrates trust mechanisms with embedded virtualization, providing security from hardware to applications. Our experiments show that the proposed architecture can be implemented with a smaller overhead and memory footprint compared to other proposed approaches in the literature, which makes it highly suitable for resource-constrained edge devices.