Reviewerly/exHarmony huggingface dataset:
A Scalable IoT Protocol via an Efficient DAG-Based Distributed Ledger Consensus

The Internet of Things (IoT) suffers from various security vulnerabilities. The use of blockchain technology can help resolve these vulnerabilities, but some practical problems in terms of scalability continue to hinder the adaption of blockchain for application in the IoT. The directed acyclic graph (DAG)-based Tangle model proposed by the IOTA Foundation aims to avoid transaction fees by employing a different protocol from that used in the blockchain. This model uses the Markov chain Monte Carlo (MCMC) algorithm to update a distributed ledger. However, concerns about centralization by the coordinator nodes remain. Additionally, the economic incentive to choose the algorithm is insufficient. The present study proposes a light and efficient distributed ledger update algorithm that regards only the subtangle of each step by considering the Bayesian inference. Experimental results have confirmed that the performance of the proposed methodology is similar to that of the existing methodology, and the proposed methodology enables a faster computation time. It also provides the same resistance to possible attacks, and for the same reasons, as does the MCMC algorithm.
Smart Contract-Based Long-Term Auction for Mobile Blockchain Computation Offloading

In a mobile blockchain network, many mobile devices have insufficient computational capacity to execute computation-intensive tasks locally. To tackle this problem, blockchain tasks can be offloaded to edge servers with the aid of auction. However, most auction mechanisms on mobile blockchain ignore the automatic parallel execution and long-term performance. This paper aims to solve the problem of computation offloading in a mobile blockchain network. We transform this problem into a multi-choice multi-dimensional knapsack problem which is NP-hard. To improve the total utility of auction participants, this paper proposes a smart-contract-based double auction mechanism, named long-term auction for mobile blockchain (LAMB). The subtasks can be offloaded from one mobile device to heterogeneous edge servers. Also, LAMB satisfies the economic properties of an auction mechanism. Experimental results demonstrate that, the utility and utilization ratio can be achieved by 130.55% higher and 138.64% higher, respectively, in comparison to the existing auction algorithm WBD. Furthermore, the proposed LAMB can guarantee long-term performance for task offloading, and it can achieve automatic execution in an autonomous and secure environment.
Learning Hierarchical Representations for Expressive Speaking Style in End-to-End Speech Synthesis

Although Global Style Tokens (GSTs) are a recently-proposed method to uncover expressive factors of variation in speaking style, they are a mixture of style attributes without explicitly considering the factorization of multiple-level speaking styles. In this work, we introduce a hierarchical GST architecture with residuals to Tacotron, which learns multiple-level disentangled representations to model and control different style granularities in synthesized speech. We make hierarchical evaluations conditioned on individual tokens from different GST layers. As the number of layers increases, we tend to observe a coarse to fine style decomposition. For example, the first GST layer learns a good representation of speaker IDs while finer speaking style or emotion variations can be found in higher-level layers. Meanwhile, the proposed model shows good performance of style transfer.
Subword Attentive Model for Arabic Sentiment Analysis

Social media data is unstructured data where these big data are exponentially increasing day to day in many different disciplines. Analysis and understanding the semantics of these data are a big challenge due to its variety and huge volume. To address this gap, unstructured Arabic texts have been studied in this work owing to their abundant appearance in social media Web sites. This work addresses the difficulty of handling unstructured social media texts, particularly when the data at hand is very limited. This intelligent data augmentation technique that handles the problem of less availability of data are used. This article has proposed a novel architecture for hand Arabic words classification and understands based on convolutional neural networks (CNNs) and recurrent neural networks. Moreover, the CNN technique is the most powerful for the analysis of Arabic tweets and social network analysis. The main technique used in this work is character-level CNN and a recurrent neural network stacked on top of one another as the classification architecture. These two techniques give 95% accuracy in the Arabic texts dataset.
Dynamic Graphs on the GPU

We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one hash table per vertex to store adjacency lists and achieves 3.4-14.8x faster insertion rates over the state of the art across a diverse set of large datasets, as well as deletion speedups up to 7.8x. The data structure supports queries and dynamic updates through both edge and vertex insertion and deletion. In addition, we define a comprehensive evaluation strategy based on operations, workloads, and applications that we believe better characterize and evaluate dynamic graph data structures.
Mutual Information Based Sensor Tasking with Applications to Space Situational Awareness

This paper focuses on the problem of managing or tasking a network of sensors to accurately track a number of objects while using information theoretic sensor performance metrics. The mathematical formulation of optimally tasking a group of sensors using the mutual information as a sensor utility measure is discussed along with the relative merits of maximizing mutual information. The resulting sensor-tasking optimization problem is shown to be combinatorial in nature, for which the computational complexity increases with an increase in the number of objects as well as the number of sensors. Depending upon the number of objects and available sensors, appropriate suboptimal approximations are presented to alleviate the computational complexity of the tasking problem. The submodular property of the mutual information measure is used to provide guarantees on the optimality of different approximations. Numerical simulations involving tracking ground objects with moving unmanned aerial vehicles and tracking resident space objects with ground-based sensors are considered to show the efficacy of the developed methods.
Decentralized learning for self-adaptive QoS-aware service assembly

The highly dynamic nature of future computing systems, where applications dynamically emerge as opportunistic aggregation of autonomous and independent resources available at any given time, requires a radical shift in the adopted computing paradigms. Indeed, they should fully reflect the decentralized perspective of the execution environment and consider QoS, scalability and resilience as key objectives. In this context, the everything-as-a-service (XaaS) paradigm, which envisions the creation of new services as an assembly of independent services available within the environment, can greatly help in tackling the challenges of developing future applications. However, in order to be effective, XaaS paradigm requires self-adaptive service assembly solutions able to cope with the unpredictable variability and scalability of the execution environment, the lack of global knowledge, and the QoS requirements of services to be built. We contribute in this direction by designing a fully decentralized and collective self-adaptive service assembly framework whose main features are: (i) self-assembly, i.e., the ability to operate autonomously, (ii) online-learning, i.e., the ability to dynamically learn from experience, (iii) QoS-awareness, i.e., the inclusion of QoS requirements as driving forces for self-assembly, (iv) scalability, i.e., the ability to cope with a large number of services, and (v) resilience, i.e., the ability to maintain the persistence of service delivery when facing unexpected changes (e.g., in the number and/or QoS of services). Simulation experiments show that our solution makes the system able to quickly converge to viable assemblies that improve and maintain over time the social welfare of the system, despite the local perspective of each participating service.
SIMPATICO 3D Mobile for Diagnostic Procedures

Correct interpretation of images may be crucial for early disease detection. A growing number of medical instruments are image-oriented and produce a large quantity of image data, typically in the DICOM format, which contain spatio-temporal features together with alpha-numeric information regarding patients. Dealing with this high-dimensional datasets is a complex and time-consuming task. In addition the diffusion of smartphones and tablets requires the development of technological features enabling the medical team to check on helthcare processes on-the-go and freely access and send image and data for case analysis and collaborative diagnostic. This paper presents SIMPATICO 3D (Sistema Informativo Medico PATologIe COmplesse) a system supporting scientists and physicians by providing facilities for case studies analysis and diagnostic imaging in a shared virtual environment and details the features of SIMPATICO 3D Mobile (standing for Evolution Imaging System 3D for Mobile), that extends SIMPATICO 3D with dedicated functions for the mobile environment.
Blockchain in Education

Every individual undergoes a series of educational programs and acquires skills and pedagogical certifications throughout his/her life from various educational and skill development organisations across the world, including the companies they work for. It is imperative that there is a comprehensive record of these certifications that can be authentically verified by those wanting to employ the individual for these respective skills accredited through certifications. In this chapter, the authors explore the utility of blockchain technology-led digitization, automation of trust, and disintermediation in education sector. They examine some of the prominent use cases and challenges faced by blockchain technology. They also look at the current state of blockchain technology-enabled applications in related domains and its implications for the education sector in India along with a real-life illustration with implementation using AuxCert on Auxledger, a permissioned blockchain platform from Auxesis group.
Directional adversarial training for cost sensitive deep learning classification applications

In many real-world applications of Machine Learning it is of paramount importance not only to provide accurate predictions, but also to ensure certain levels of robustness. Adversarial Training is a training procedure aiming at providing models that are robust to worst-case perturbations around predefined points. Unfortunately, one of the main issues in adversarial training is that robustness w.r.t. gradient-based attackers is always achieved at the cost of prediction accuracy. In this paper, a new algorithm, called Wasserstein Projected Gradient Descent (WPGD), for adversarial training is proposed. WPGD provides a simple way to obtain cost-sensitive robustness, resulting in a finer control of the robustness-accuracy trade-off. Moreover, WPGD solves an optimal transport problem on the output space of the network and it can efficiently discover directions where robustness is required, allowing to control the directional trade-off between accuracy and robustness. The proposed WPGD is validated in this work on image recognition tasks with different benchmark datasets and architectures. Moreover, real world-like datasets are often imbalanced: this paper shows that when dealing with such type of datasets, the performance of adversarial training are mainly affected in term of standard accuracy.
Individual differences in susceptibility to cybercrime victimization and its psychological aftermath

This study explicated individual differences in susceptibility to cybercrime victimization by predicting that frequent use of information technology (IT) devices is related to greater likelihood of cybercrime victimization (exposure hypothesis), and testing whether individuals more vulnerable to cybercrimes are characterized by higher (overconfidence hypothesis) or lower (negligence hypothesis) levels of IT self-efficacy. Our study also investigated whether cybercrime victimization would influence one's perception of interpersonal relations and subjective well-being. Data were drawn from a representative sample of 1018 Hong Kong participants (46% men; mean age = 42.18). The results from correlation analysis supported the exposure hypothesis by revealing a positive association between IT use and cybercrime victimization. Consistent with both the exposure and overconfidence hypotheses, the mediation analysis showed that IT self-efficacy was positively related to IT use, which in turn was positively related to cybercrime victimization. The correlation analysis further revealed that cybercrime victimization was inversely associated with trusting people online, perceived control, perceived fairness, life satisfaction, and happiness. Our novel findings imply escalating risks of cybercrime victimization as both the amount of IT use and users' IT proficiency are growing, and evidence-based programs are needed to increase users' awareness of the prevalence of cybercrimes and build resilience to combat cyber-attacks.
LCBM: A Multi-View Probabilistic Model for Multi-Label Classification

Multi-label classification is an important research topic in machine learning, for which exploiting label dependencies is an effective modeling principle. Recently, probabilistic models have shown great potential in discovering dependencies among labels. In this paper, motivated by the recent success of multi-view learning to improve the generalization performance, we propose a novel multi-view probabilistic model named latent conditional Bernoulli mixture (LCBM) for multi-label classification. LCBM is a generative model taking features from different views as inputs, and conditional on the latent subspace shared by the views a Bernoulli mixture model is adopted to build label dependencies. Inside each component of the mixture, the labels have a weak correlation which facilitates computational convenience. The mean field variational inference framework is used to carry out approximate posterior inference in the probabilistic model, where we propose a Gaussian mixture variational autoencoder (GMVAE) for effective posterior approximation. We further develop a scalable stochastic training algorithm for efficiently optimizing the model parameters and variational parameters, and derive an efficient prediction procedure based on greedy search. Experimental results on multiple benchmark datasets show that our approach outperforms other state-of-the-art methods under various metrics.
Tacotron-Based Acoustic Model Using Phoneme Alignment for Practical Neural Text-to-Speech Systems

Although sequence-to-sequence (seq2seq) models with attention mechanism in neural text-to-speech (TTS) systems, such as Tacotron 2, can jointly optimize duration and acoustic models, and realize high-fidelity synthesis compared with conventional duration-acoustic pipeline models, these involve a risk that speech samples cannot be sometimes successfully synthesized due to the attention prediction errors. Therefore, these seq2seq models cannot be directly introduced in practical TTS systems. On the other hand, the conventional pipeline models are broadly used in practical TTS systems since there are few crucial prediction errors in the duration model. For realizing high-quality practical TTS systems without attention prediction errors, this paper investigates Tacotron-based acoustic models with phoneme alignment instead of attention. The phoneme durations are first obtained from HMM-based forced alignment and the duration model is a simple bidirectional LSTM-based network. Then, a seq2seq model with forced alignment instead of attention is investigated and an alternative model with Tacotron decoder and phoneme duration is proposed. The results of experiments with full-context label input using WaveGlow vocoder indicate that the proposed model can realize a high-fidelity TTS system for Japanese with a real-time factor of 0.13 using a GPU without attention prediction errors compared with the seq2seq models.
Handwriting Analysis based on Histogram of Oriented Gradient for Predicting Personality traits using SVM

Handwriting Analysis is a method to understand and predict the characteristic traits of a person based on his handwriting style. Graphology is the scientific term used for handwriting analysis. Professional handwriting examiners, called graphologists, manually study and understand the handwriting of an individual to classify the writers personality. Nevertheless, the manual process of handwriting analysis is time-consuming, costly and depends majorly on the skills of the graphologists. To make this process computerized we extracted several features of handwriting samples and classified the writer into 5 personality traits namely Energetic, Extrovert, Introvert, Sloppy and Optimistic. Histogram of oriented gradient(HOG) extracts the features from the handwriting sample of the writer which serves as an input for the Support Vector Machine model to give output as the personality trait of the person. For this paper, digital handwriting sample data of 50 different users were collected. The proposed system predicts the personality trait of a person with 80% correctness using the Polynomial kernel. In this paper, we propose a computerized method for personality trait prediction based on the users handwriting. Two different methods are applied to the same handwriting sample data to measure and compare the performance of the proposed system.
FilterK: A new outlier detection method for k-means clustering of physical activity

In this paper, a new algorithm denoted as FilterK is proposed for improving the purity of k-means derived physical activity clusters by reducing outlier influence. We applied it to physical activity data obtained with body-worn accelerometers and clustered using k-means. We compared its performance with three existing outlier detection methods: Local Outlier Factor, Isolation Forests and KNN using the ground truth (class labels), average cluster and event purity (ACEP). FilterK provided comparable gains in ACEP (0.581 → 0.596 compared to 0.580–0.617) whilst removing a lower number of outliers than the other methods (4% total dataset size vs 10% to achieve this ACEP). The main focus of our new outlier detection method is to improve the cluster purities of physical activity accelerometer data, but we also suggest it may be potentially applied to other types of dataset captured by k-means clustering. We demonstrate our method using a k-means model trained on two independent accelerometer datasets (training n = 90) and re-applied to an independent dataset (test n = 41). Labelled physical activities include lying down, sitting, standing, household chores, walking (laboratory and non-laboratory based), stairs and running. This type of clustering algorithm could be used to assist with identifying optimal physical activity patterns for health.
Development of a generative-adversarial-network-based signal reconstruction method for nuclear power plants

In nuclear power plants (NPPs), the reliability of instrumentation signals is crucial for making appropriate decisions. Multiple signals can become faulty simultaneously and become unavailable under harsh conditions, and this could lead to improper decisions being made, which could result in catastrophic failure. The authors proposed a new signal reconstruction method based on a generative adversarial network (GAN) that can be applied to reconstruct multiple missing signals. In the method, a GAN model is first trained to generate realistic signal sets from random latent vectors and then used to find the optimal latent vector for the reconstruction of a damaged signal set. The signal set is reconstructed by replacing its missing parts with the corresponding parts of the signal set generated from the optimal latent vector. Experiments were conducted to verify the applicability of the proposed method to the reconstruction of multiple missing signals under various NPP emergency situations.
Windowed Bundle Adjustment Framework for Unsupervised Learning of Monocular Depth Estimation With U-Net Extension and Clip Loss

This letter presents a self-supervised framework for learning depth from monocular videos. In particular, the main contributions of this letter include: (1) We present a windowed bundle adjustment framework to train the network. Compared to most previous works that only consider constraints from consecutive frames, our framework increases the camera baseline and introduces more constraints to avoid overfitting. (2) We extend the widely used U-Net architecture by applying a Spatial Pyramid Net (SPN) and a Super Resolution Net (SRN). The SPN fuses information from an image spatial pyramid for the depth estimation, which addresses the context information attenuation problem of the original U-Net. The SRN learns to estimate a high resolution depth map from a low resolution image, which can benefit the recovery of details. (3) We adopt a clip loss function to handle moving objects and occlusions that were solved by designing complicated network or requiring extra information (such as segmentation mask [1]) in previous works. Experimental results show that our algorithm provides state-of-the-art results on the KITTI benchmark.
Laying the foundation for smart contract development: an integrated engineering process model

Abstract Smart contracts are seen as the major building blocks for future autonomous blockchain- and Distributed Ledger Technology (DLT)-based applications. Engineering such contracts for trustless, append-only, and decentralized digital ledgers allows mutually distrustful parties to transform legal requirements into immutable and formalized rules. Previous experience shows this to be a challenging task due to demanding socio-technical ecosystems and the specificities of decentralized ledger technology. In this paper, we therefore develop an integrated process model for engineering DLT-based smart contracts that accounts for the specificities of DLT. This model was iteratively refined with the support of industry experts. The model explicitly accounts for the immutability of the trustless, append-only, and decentralized DLT ecosystem, and thereby overcomes certain limitations of traditional software engineering process models. More specifically, it consists of five successive and closely intertwined phases: conceptualization, implementation, approval, execution, and finalization. For each phase, the respective activities, roles, and artifacts are identified and discussed in detail. Applying such a model when engineering smart contracts will help software engineers and developers to better understand and streamline the engineering process of DLTs in general and blockchain in particular. Furthermore, this model serves as a generic framework which will support application development in all fields in which DLT can be applied.