Reviewerly/exHarmony huggingface dataset:
Optimizing Blockchain Networks with Artificial Intelligence: Towards Efficient and Reliable IoT Applications

Blockchain is one of the new tools which is still experiencing a serious lack of understanding and awareness in general public. Besides, to say that blockchain is a versatile technology with many applications would be an understatement. The several features of blockchain technology have led to an immense amount of research interest in the blockchain systems, especially from the perspective of enabling the Internet-of-things (IoT). It is expected that its adoption will be gradual, starting with researchers and leading up to startups and companies who will discover its potential for change. Then the general public who will demand changes would use it as an everyday technology. Finally, organizations that had until then resisted change would adopt it at a large scale, thus, paving the way for globalization of blockchain. In this regard, it is important to understand that the blockchain is not a single object, a single trend or a particular feature, but a composition and collection of several autonomous as well as manually operated entities. Because of these modular features, the blockchain offers an infinite range of application choices. Another paradigm which is emerging in parallel is the so-called artificial intelligence. The evolution of artificial intelligence is an incredible tale which started with neural networks in the last century and continued its way to the development of complex deep learning techniques. From the perspective of IoT networks, it has the potential to provide services like ultra-reliable and low-latency communications (uRLLC), long-distance and high-mobility communications (LDHMC), and ultra-massive machine-type communications (umMTC) which can be the key to realizing tactile Internet for next generation of wireless networks. Moreover, artificial intelligence techniques do not require a mathematically tractable model to optimize the performance of the devices. This means that they can be used for a number of environments that includes both indoor and outdoor communication scenarios. Although the applications of blockchain in IoT networks have much potential, there are several open issues and research challenges that need to be addressed first. In this context, one of the main issues in blockchain networks is branching, i.e., forking event. The forking not only introduces excessive overhead in the network but can also lead to potential security attacks. Thus, to overcome this interesting problem, we use deep neural networks in a distributed IoT setup. More specifically, the communication delay between the IoT miner and communication point is directly related to the rate of the link. We aim to maximize the rate of the communication link from IoT miner to the communication point with the help of neural networks. The numerical results show a significant improvement in the rate which can lead to a reduction in transmission delays. We anticipate that this work on artificial intelligence-enabled blockchain system would pave the way for future studies and research.
Does informality help entrepreneurs achieve firm growth? evidence from a post-conflict economy

This article evaluates the impact of starting-up unregistered and operating informally on small firm growth in Kosovo, the country with the highest levels of informality in Western Balkans. The study uses mixed research methods as an inquiry to combining both qualitative and quantitative forms. Reporting data from 487 business owners extracted from a 2017 nationally representative survey of 8,533 households in Kosovo, 47.7 percent had started-up unregistered and were operating informally at the time of the survey. Using an instrumental variable (IV) estimation with a binary endogenous regressor to estimate the impact of informality (a treatment group) on small firm growth (control group), while controlling for other entrepreneur and firm determinants, a strong positive effect of the informality on firm growth is identified. Entrepreneurs operating informally had an 11.6 percentage points higher probability of achieving their firm growth objectives compared with entrepreneurs operating formally. Yet, the qualitative interviews revealed that growth objectives of small firms were limited, so the unregistered firms outperformed registered firms only under the modest growth objectives. Taking into account the limitations of the study, the implications for theory and policy are proposed to tackle the informality such as incentives of government small business support programs.
Hybrid-task learning for robust automatic speech recognition

In order to properly train an automatic speech recognition system, speech with its annotated transcriptions is most often required. The amount of real annotated data recorded in noisy and reverberant conditions is extremely limited, especially compared to the amount of data than can be simulated by adding noise to clean annotated speech. Thus, using both real and simulated data is important in order to improve robust speech recognition, as this increases the amount and diversity of training data (thanks to the simulated data) while also benefiting from a reduced mismatch between training and operation of the system (thanks to the real data). Another promising method applied to speech recognition in noisy and reverberant conditions is multi-task learning. The idea is to train one acoustic model to solve simultaneously at least two tasks that are different but related, with speech recognition being the main task. A successful auxiliary task consists of generating clean speech features using a regression loss (as a denoising auto-encoder). This auxiliary task though uses as targets clean speech, which implies that real data cannot be used. In order to tackle this problem a Hybrid-Task Learning system is proposed. This system switches frequently between multi and single-task learning depending on whether the input is real or simulated data respectively. Having a hybrid architecture allows us to benefit from both real and simulated data while using a denoising auto-encoder as auxiliary task of a multi-task setup. We show that the relative improvement brought by the proposed hybrid-task learning architecture can reach up to 4.4% compared to the traditional single-task learning approach on the CHiME4 database. We also demonstrate the benefits of the hybrid approach compared to multi-task learning or adaptation.
Designing and deploying insurance recommender systems using machine learning

Abstract Recommender systems have become extremely important to various types of industries where customer interaction and feedback is paramount to the success of the business. For companies that face changes that arise with ever‐growing markets, providing product recommendations to new and existing customers is a challenge. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in order to make sure they are adequately covered for their needs. Our system uses customer characteristics in addition to customer portfolio data. Since the number of possible recommendable products is relatively small, compared to other recommender domains, and missing data is relatively frequent, we chose to use Bayesian Networks for modeling our systems. We also present a deep‐learning‐based approach to provide recommendations to prospects (potential customers) where only external marketing data is available at the time of prediction. This article is categorized under: Application Areas &gt; Industry Specific Applications Algorithmic Development &gt; Structure Discovery Algorithmic Development &gt; Bayesian Models Technologies &gt; Machine Learning
Almost exact state transfer in a spin chain via pulse control

Quantum communication through spin chains has been extensively investigated. In this scenario, state transfer through linearly arranged spins connected by uniform nearest-neighbor couplings qualifies as a natural choice, with minimal control requirements. However, quantum states usually cannot be perfectly transferred through a uniformly coupled chain due to the dispersion of the chain. Here, we propose an effective quantum control technique to realize almost exact state transfer (AEST) in a quantum spin chain. The strategy is to add a leakage elimination operator (LEO) Hamiltonian to the evolution, which implements a sequence of pulse control acting on a perfect state transfer subspace. By using the one-component Feshbach PQ partitioning technique, we obtain the conditions over the required pulses. AEST through a spin chain can then be obtained under a suitable pulse intensity and duration.
A T–S fuzzy model identification approach based on evolving MIT2-FCRM and WOS-ELM algorithm

Inter type-2 fuzzy model has been confirmed to be more effective in Takagi–Sugeno (T–S) fuzzy model identification compared to type-1 fuzzy model. It is indisputable that some algorithms based on inter type-2 fuzzy model have already been developed and shown remarkable modeling performance. To further improve the modeling accuracy, the optimization methods and the neural network are taken into consideration. In this paper, an evolving modified inter type-2 fuzzy c-regression model (MIT2-FCRM) algorithm based on gravitational search algorithm (GSA) and a consequent parameter identification method based on extreme learning machine algorithm with forgetting factor for processing online sequences (namely WOS-ELM) were proposed. Then a novel approach for T–S fuzzy modeling was presented, in which, the coefficients of the upper and lower hyperplanes were obtained by evolving MIT2-FCRM algorithm based on GSA, a hyper-plane-shaped membership function (MF) was utilized to identify the antecedent parameters of the T–S fuzzy model, and WOS-ELM was employed to identify the consequent parameters. The modeling results of six examples indicate that the proposed approach is superior to other studies in terms of identification accuracy, compact fuzzy rules and noise resistance ability.
Dynamics of quantum correlations in quantum teleportation

A scheme of quantum teleportation using Jaynes–Cummings model is proposed. It consists of using a partial entangled state generated from the interaction between a two-level atom and single mode of radiation field as a quantum channel. By controlling the number of photons inside cavity and detuning parameter, the dynamical evolution of quantum correlation and entanglement degree in terms of quantum discord and concurrence, respectively, in the nonresonance case are investigated. Moreover, a comparative study between the teleported entanglement, teleported quantum correlation and average fidelity shows a good similarity between their dynamical evolution. Consequently, we conclude that quantum discord may be considered as a good information source in quantum teleportation process.
Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.
Modeling Hypoxia-Induced Neuropathies Using a Fast and Scalable Human Motor Neuron Differentiation System

Human motor neuron (MN) diseases encompass a spectrum of disorders. A critical barrier to dissecting disease mechanisms is the lack of appropriate human MN models. Here, we describe a scalable, suspension-based differentiation system to generate functional human MN diseases in 3 weeks. Using this model, we translated recent findings that mRNA mis-localization plays a role in disease development to the human context by establishing a membrane-based system that allows efficient fractionation of MN cell soma and neurites. In response to hypoxia, used to mimic diabetic neuropathies, MNs upregulated mitochondrial transcripts in neurites; however, mitochondria were decreased. These data suggest that hypoxia may disrupt translation of mitochondrial mRNA, potentially leading to neurite damage and development of neuropathies. We report the development of a novel human MN model system to investigate mechanisms of disease affecting soma and/or neurites that facilitates the rapid generation and testing of patient-specific MN diseases.
Video Anomaly Detection using Convolutional Spatiotemporal Autoencoder

A convolutional spatiotemporal autoencoder is used for video anomaly detection. The proposed model architecture comprises of three major sections, such as spatial encoder, temporal encoder-decoder, and spatial decoder. The spatial encoder is implemented using three layers of the convolutional layers. Then, the temporal encoder-decoder is realized with the help of Convolutional Long Short Term Memory (ConvLSTM), gated with the tanh and sigmoid activation functions. Finally, the spatial decoder is implemented using three layers of deconvolutional layers. The proposed model is trained only on the dataset comprises the normal classes by minimizing the reconstruction error. Later, when the trained model is tested using the test dataset susceptible to contain anomalous activities, then high reconstruction error has resulted. Subsequently, a high anomaly score and low regularity score has resulted. When the regularity score of the frames falls below the set threshold level, then the corresponding frames are treated as anomalous ones. The proposed model is trained and tested on UCSD Ped1 and Ped2 dataset successfully. The results of the performance evaluation are found to be promising.
Local–Global Memory Neural Network for Medication Prediction

Electronic medical records (EMRs) play an important role in medical data mining and sequential data learning. In this article, we propose to use a sequential neural network with dynamic content-based memories to predict future medications, given EMRs. The local-global memory neural network contains two layers of memories: the local memory and the global memory. Particularly, our method learns the hidden knowledge within EMRs by locally remembering individual patterns of a patient (via local memory) and globally remembering group evidence of disease (via global memory). In addition, we show how our model can be modified to classify the hidden states of EMRs from different patients at each time step into different phases that indicate the progressions of medications in terms of a specific disease, in an unsupervised manner. Experimental results on real EMRs data sets show that, by learning EMRs with external local and global memories, with regard to a given disease, our model improves the prediction performance compared with several alternative methods.
Toward a Public-Key Management Framework for Virtual Assets and Virtual Asset Service Providers

The recent Financial Action Task Force (FATF) Recommendations define virtual assets and virtual asset service providers (VASPs), and require under the Travel Rule that originating VASPs obtain and hold the required and accurate originator information and the required beneficiary information on virtual asset transfers. In this paper, we discuss the notion of key ownership evidence as a core part of originator and beneficiary information required by the FATF Recommendations. We discuss the approaches to securely communicate the originator and beneficiary information between VASPs, and review the existing standards for public-key certificates as applied to VASPs and virtual asset transfers. We propose the notion of a trust network of VASPs in which originator and beneficiary information, including key ownership information, can be exchanged securely off-chain while observing the individual privacy requirements.
Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features

Automatic modulation classification (AMC) becomes more and more important in the electronic reconnaissance. Recently, lots of researchers focus on deep learning architecture based AMC approach but the recognition rate of WBFM and QAM is less than desirable. In this paper, we proposed a joint AMC model of two expert features and CNN-LSTM networks. Before entering the deep learning network, the un-classified signal is first detected whether WBFM or not by the maximum of zero-center normalization amplitude spectrum density. Then the signal which is not WBFM will be inputted to the CNN-LSTM network, while QAM16 and QAM64 are regarded as the same class here. Finally, Haar-wavelet transform crest searching is used to classify QAM16 and QAM64. Compared with former CNN-LSTM architecture, the results of the experiment and deduction show the average recognition rate of the proposed model is increased by 11.5% at 10 dB SNR.
A Survey on Machine Learning Approaches and Its Techniques:

With the data and information is available at a tremendous rate, there is a need for machine learning approaches. Machine learning, it analyses the study and constructs the algorithms by making prediction on data. It builds model from the inputs to make the decisions or predictions. Machine learning algorithms it assists in bridging the gap of understanding. In this literature we investigate different machine learning approaches and its techniques.
Camera Placement Meeting Restrictions of Computer Vision

In the blooming era of smart edge devices, surveillance cameras have been deployed in many locations. Surveillance cameras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cameras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a significant restriction of computer vision: in order to track a moving object, the object must occupy enough pixels. The number of pixels depends on many factors (How far away is the object? What is the camera resolution? What is the focal length?). In this study, we propose a camera placement method that identifies effective camera placement in arbitrary spaces and can account for different camera types as well. Our strategy represents spaces as polygons, then uses a greedy algorithm to partition the polygons and determine the cameras' locations to provide the desired coverage. Our solution also makes it possible to perform object tracking via overlapping camera placement. Our method is evaluated against complex shapes and real-world museum floor plans, achieving up to 85% coverage and 25% overlap.
Modified Cuckoo Search Algorithm using a New Selection Scheme for Unconstrained Optimization Problems

Background: Cuckoo Search Algorithm (CSA) was introduced by Yang and Deb in 2009. It considers as one of the most successful in various fields compared with the metaheuristic algorithms. However, random selection is used in the original CSA which means there is no high chance for the best solution to select, also, losing the diversity. Methods: In this paper, the Modified Cuckoo Search Algorithm (MCSA) is proposed to enhance the performance of CSA for unconstrained optimization problems. MCSA is focused on the default selection scheme of CSA (i.e. random selection) which is replaced with tournament selection. So, MCSA will increase the probability of better results and avoid the premature convergence. A set of benchmark functions is used to evaluate the performance of MCSA. Results: The experimental results showed that the performance of MCSA outperformed standard CSA and the existing literature methods. Conclusion: The MCSA provides the diversity by using the tournament selection scheme because it gives the opportunity to all solutions to participate in the selection process.
Collective design anticipation

Design and anticipation are two closely related concepts. While previous research has focussed on exploring this relationship, less attention has been paid on uncovering the collective nature of design anticipation. The paper offers a theoretical framework to account for collective design anticipation and the conditions that make it possible. In specific, the paper discusses the notion of a design anticipation space, which embodies temporal and semantic dimensions, and discusses conditions for collective design anticipation through the concept of boundary objects. The results are derived through an abductive methodology that combines theoretical investigations with reflections on empirical work in the wild.
Visual navigation algorithm based on line geomorphic feature matching for Mars landing

In order to satisfy the requirement of precise pin-point landing mission in Mars exploration, this paper proposes an innovative visual navigation algorithm based on feature lines extraction and matching to estimate the attitude and position of the lander during descent phase. Currently, common navigation landmarks are feature points and craters. But, the number of feature points extracted from landing image is so large that the image process is complex and time-consuming, and they do not usually have absolute coordinate information to absolute navigation. Meanwhile, crater is matched difficultly. Hence, ridges, fissures and other surface textures similar to lines are most common visual features of Mars and can be used as navigation landmarks for visual absolute and relative navigation. Firstly, that feature lines matching based on the EDLine algorithm remains invariant to scaling, rotation, and illumination, etc. provides the basis for visual navigation by using feature line. Secondly, feature lines selected as navigation landmarks can be used to absolute navigation and relative navigation respectively, which can implement integrated navigation. In this paper, a novel integrated visual navigation has been designed to finish the precise pin-point landing and overcome the accumulated errors of relative navigation and the limited application range of absolute navigation. Experimental simulation results show that the integrated navigation algorithm solves the limitation of single navigation mode and improves the effectiveness and stability of the algorithm.