Reviewerly/exHarmony huggingface dataset:
Implementation of Blockchain for Fair Polling System

Building an internet voting system that meets the legal criteria of the election commission of India has been a long-standing difficulty. In today’s world of information technology, distributed ledger systems are an exciting technological advancement. Blockchain technology enables an endless number of applications that make use of sharing economies. This article will investigate the application of blockchain as a service in the construction of distributed internet voting systems. The article discusses the criteria for establishing internet voting systems, as well as the legal and technological limits of implementing such systems utilizing blockchain as a service. The article begins by assessing several of the most well-known blockchain frameworks that provide blockchain as a service. Based on the constraints we observed, we suggest a unique electronic voting system based on blockchain. More widely, this study analyses the possibilities of distributed ledger technology by way of presentation of an internet voting system using blockchain technology, specifically the election process and the implementation of a blockchain-based application that enhances security and lowers the cost of holding a countrywide election. This paper proposes a new design of the voting system respecting the basic and most significant components such as transparency, secrecy, security, and decentralization, while simultaneously providing the benefit of mobility.
Monte Carlo Tree Search as an Offline Training Data Generator for Decision-Tree Based Game Agents

This article considers interactive simulated environments such as games with score functions that can be used to evaluate the performance of agents. For such environments, we present a novel approach which employs the Monte Carlo Tree Search (MCTS) algorithm as a teacher of tree-based machine learning models. This means that the teaching part – including the training data generation – can be done offline, e.g., during game production process. The motivation to do this is two-fold and it comes directly from our experience with deploying AI methods in the video game industry. Firstly, the game creation process often takes years and a lot of precomputation tasks can be done during this period. However, when the game is finally being played, the actions of AI agents must be calculated with as little CPU time as possible. Secondly, our approach is a practical alternative for computationally costly Reinforcement Learning (RL) techniques, whereby the learning process is inseparably combined with the model evaluation. Such an alternative is important because large-scale neural networks – used by most of the recent RL applications – are not preferred in the gamedev industry due to lack of interpretability and technical aspects related to both training and embedding them in shipped games. We present details of the training algorithm and investigate various properties of our approach in a corpus of empirical experiments. We show that as long as proper variety is preserved in the training scenarios, this approach can produce a strong decision tree player that requires 2-3 orders of magnitude less time than an MCTS player. We also show that it is possible to train models in a less complex environment, in the combinatorial aspect, and use them effectively in more complex ones as long as good strategies remain the same. MCTS and other purely search-based methods usually give worse results when the combinatorial complexity of a problem grows even if the same strategies are valid as before, i.e., in a simpler problem.
A Mutual Information-Based Assessment of Reverse Engineering on Rewards of Reinforcement Learning

Rewards are critical hyperparameters in reinforcement learning (RL), since in most cases different reward values will lead to greatly different performance. Due to their commercial value, RL rewards become the target of reverse engineering by the inverse reinforcement learning (IRL) algorithm family. Existing efforts typically utilize two metrics to measure the IRL performance: the expected value difference (EVD) and the mean reward loss (MRL). Unfortunately, in some cases, EVD and MRL can give completely opposite results, due to MRL focusing on whole state-space rewards, while EVD only considering partly sampled rewards. Such situation naturally rises to one fundamental question: whether current metrics and assessment are sufficient and accurate for more general use. Thus, in this article, based on the metric called normalized mutual information of reward clusters (C-NMI), we propose a novel IRL assessment; we aim to fill this research gap by considering a middle-granularity state space between the entire state space and the specific sampling space. We utilize the agglomerative nesting algorithm (AGNES) to control dynamical C-NMI computing via a fourth-order tensor model with injected manipulated trajectories. With such a model, we can uniformly capture different-dimension values of MRL, EVD, and C-NMI, and perform more comprehensive and accurate assessment and analyses. Extensive experiments on several mainstream IRLs are experimented in object world, hence revealing that the assessing accuracy of our method increases 110.13% and 116.59%, respectively, when compared with the EVD and MRL. Meanwhile, C-NMI is more robust than EVD and MRL under different demonstrations.
Deep Learning Applied to Cell Concentration Assessment Through Ultrasound Medical Images

This work is motivated for the interest of developing quantitative algorithms for ultrasound imaging systems. The performance of a deep learning algorithm to assess cell concentration from simulated ultrasound images is analyzed and presented in this paper. The algorithm was adapted from a convolutional neural network previously used to the analysis of optical images. Two different datasets obtained by simulation of ultrasound imaging of cells floating in a fluid were made for the present work, corresponding to different cell concentration ranges: 0-100 cells/μl and 0-2000 cells/μl. Datasets were used in their original form and also modified to incorporate noise and amplitude changes, in order to resemble experimental ultrasound images. Different training strategies of the network were analyzed. The neural network outperformed conventional numerical methods providing more accurate results and lower data dispersion.
Eye Centre Localisation with Convolutional Neural Networks in High- and Low-Resolution Images

Eye centre localisation is critical to eye tracking systems of various forms and with applications in variety of disciplines. An active eye tracking approach can achieve a high accuracy by leveraging active illumination to gain an enhanced contrast of the pupil to its neighbourhood area. While this approach is commonly adopted by commercial eye trackers, a dependency on IR lights can drastically increase system complexity and cost, and can limit its range of tracking, while reducing system usability. This paper investigates into a passive eye centre localisation approach, based on a single camera, utilising convolutional neural networks. A number of model architectures were experimented with, including the Inception-v3, NASNet, MobileNetV2, and EfficientNetV2. An accuracy of 99.34% with a 0.05 normalised error was achieved on the BioID dataset, which outperformed four other state-of-the-art methods in comparison. A means to further improve this performance on high-resolution data was proposed; and it was validated on a high-resolution dataset containing 12,381 one-megapixel images. When assessed in a typical eye tracking scenario, an average eye tracking error of 0.87% was reported, comparable to that of a much more expensive commercial eye tracker.
A Speculative Computation Approach for Energy-Efficient Deep Neural Network

Deep neural networks (DNNs) have been widely used for data processing and analysis nowadays. Many computational techniques have been proposed to improve the energy efficiency of executing DNNs, which is critical for emerging smart edge applications. This article presents a speculative computation approach to improving the energy efficiency of DNN computations. The proposed approach employs the techniques of input channel partitioning and threshold-based negative masking to predict and eliminate unnecessary computations. Moreover, a systematic procedure of threshold optimization is proposed to achieve the best tradeoff between the energy and accuracy performance. Finally, an energy-efficient DNN processor architecture was designed and implemented to support the proposed speculative computation approach. The experimental results show that the proposed DNN processor with speculative computation can enhance the energy efficiency of the processor by 22.8%, with only 0.96% accuracy degradation and 1% implementation overhead.
IPAM: An Improved Penalty Aware VM Migration Technique

In today’s era, more and more businesses are moving on the cloud. The customer needs to be assured of the quality-of-service they desire from the cloud service provider. Service level agreement (SLA) is a significant medium for building this confidence. The services are often running on virtual machines (VMs) provided by service provider. Various cloud management activities require transferring of VMs among the servers. They make use of live migration to achieve these objectives. With multiple VMs being migrated, it is essential to assign the available bandwidth among the VMs to minimize the penalty incurred due to SLA violation during migration. However, depending upon the assigned transfer rates, some VMs in the group might complete the migration process earlier than the others. In this paper, we propose an algorithm and a controller to assign freed bandwidth to the remaining VMs in the group. With extensive simulations, it is found that the penalty, migration time, and downtime for VMs is significantly reduced compared to the state-of-the-art approaches.
Face Detection Based Marking Attendance through Local Binary Pattern Histogram

In the past decades, Automatic Face Recognition (AFR) methods have advanced considerably in accuracy, and they've been widely used for both safety and commercial applications. It is an intelligent process for student’s face recognition in actual time that can be used by a college to track their student’s attendance. As a result, real-time system face recognition has been a realistic approach for dealing with different activities on a daily basis. Because proper background reduction in an image is indeed a difficulty, the work is extremely demanding. A simple rapid Structural Equation Analysis was utilized to recognize the faces identified with a reasonable degree of certainty to recognize genuine human faces. To identify an authentic individual, the matching face is employed. Our technology saves the participant's facial traits as datasets and uses these as validation.
Effectively and Efficiently Supporting Visual Big Data Analytics over Big Sequential Data: An Innovative Data Science Approach

In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the willingness of many government, researchers, and organizations are led by the initiates of open data to share their data and make them publicly accessible. Healthcare, disease, and epidemiological data, such as privacy-preserving statistics on patients who suffered from epidemic diseases such as Coronavirus disease 2019 (COVID-19), are examples of open big data. Analyzing these open big data can be for social good. For instance, people get a better understanding of the disease by analyzing and mining the disease statistics, which may inspire them to take part in preventing, detecting, controlling and combating the disease. Having a pictorial representation further enhances the understanding of the data and corresponding results for analysis and mining because a picture is worth a thousand words. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. The visualization and visual analytics of sequences of real-life COVID-19 epidemiological data illustrate the ideas. Through our solution, we enable users to visualize the COVID-19 epidemiological data over time. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. The effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data are demonstrated by evaluation of these real-life sequential COVID-19 epidemiological data.
Hidden Markov Models and their Application for Predicting Failure Events

We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.
On Account Association With Assistance From Mobile Networks

In this paper, we draw attention to the problem of account association attacks designed to determine whether a target mobile phone number is associated with a particular online account. In the case of 4G/LTE, the adversary launches an account association attack by sending SMS messages to the target phone number and analyzing patterns in traffic related to the online account. We evaluate the proposed attacks in both a local 4G/LTE testbed and a major commercial 4G/LTE network. Our extensive experiments show that the proposed attacks can successfully identify account association with near-perfect accuracy. Our experiments also illustrate that the proposed attacks can be launched in a way that the victim receives no indication of being under attack.
Object Detection and Tracking Based on Artificial Vision for a Single Board Computer (SBC)

In the field of computer vision, detecting and tracking objects is an area on demand.-For this reason, algorithms specialized in tracking any object have been developed. However, those algorithms are unable to initiate the detecting process automatically since users are required to manually draw a bounding box. A potential solution is to merge an object detection neural network with a tracking vision-based algorithm. Therefore, this research proposes an algorithm developed to enhance the capabilities of KCF, an object tracking algorithm, by combining it with SSD MobileNet V2, a neural network for object identification. The proposed algorithm is developed so it can be executed on a Single Board Computer (SBC). Hence, it is possible to run and process real-time video on a Raspberry Pi 4.
Automatically Discovering Visual Product Characteristics

Marketing models typically focus on how structured product characteristics impact consumer preferences. However, visual characteristics of products present in unstructured image data play an important role in impacting preferences for many categories. We seek to automatically discover and quantify visual characteristics (attributes) from image data using a disentanglement-based approach. While the deep learning literature has shown that supervision is required to obtain unique disentangled representations, ground truth visual characteristics are typically unknown. We develop a method that does not require such supervision, and instead uses readily available structured product characteristics as supervisory signals to enable disentanglement. Our method does not need prior knowledge of characteristics, yet we are able to discover semantically interpretable and statistically independent characteristics. Moreover, the method quantifies the levels of each discovered product characteristic, necessary for managerial tasks such as demand modeling and conjoint analysis. We apply this method to automatically discover visual product characteristics of watches, and discover 6 semantically interpretable visual characteristics providing a disentangled representation. Our results find the supervisory signal `brand' best promotes disentanglement relative to an unsupervised approach. We lastly demonstrate how consumers preferences may be assessed over these discovered visual characteristics using a choice-based conjoint analysis.
Proposal for Information Security Risk Mitigation Practices Based on a Regulatory Approach

Business objectives could be affected by the materialization of several types of risks, including operational, legal, or contractual, technological and information security risks. As part of the treatment and mitigation of the threats, specifically those related to regulatory and information security, the following research work offers proposed mitigation actions - adjusted to the organizational context of a company - that are required to treat the aforementioned risks. This is done by using the ISO 27002 reference guide as a tool, and two specific input variables: a) organizational risk profile and b) a regulatory environment restricted to the nature of the organization. The study is conducted for a Costa Rican financial institution dedicated to pension fund management. As a result of the analysis, mitigation practices are established, which contemplate an outline of the organizational activities and responsibilities related to regulatory compliance with the general regulation of information technology management and the law of personal data protection, providing guidelines that enrich the internal management regarding the treatment of failure modes identified during the study period.
Hybrid Optimization-Based Structural Design of Deep Q Network With Feature Selection Algorithm for Medical Data Classification

In the area of medical informatics, the medical data classification is considered a complicated job. However, accurate classification of medical data is a complex task. Therefore, a robust and effective hybrid optimization-based deep learning method for classifying the medical data is developed in this research. The input data is pre-processed using data normalization method. Then, the features are selected using the proposed Henry Sea Lion Optimization (HSLnO), which is the combination of Henry Gas Solubility Optimization (HGSO) and Sea Lion Optimization (SLnO). The classification process is achieved using an optimized Deep Q Network (DQN). The DQN is optimized using the proposed Shuffled Shepherd Whale optimization Algorithm (SSWOA). The proposed SSWOA is developed by the integration of the Shuffled Shepherd Optimization Algorithm (SSOA) and Whale Optimization Algorithm (WOA). The developed technique achieves better performance of testing accuracy, sensitivity, and specificity with values of 95.413%, 95.645%, and 95.364%, respectively.
An Estimation of Distribution Algorithm for Puzzle Reconstruction Problem

Puzzle is a game to have a very long history for training the human logic thinking. In addition, the puzzle-solving methods can be used in various practical applications. In this paper, we proposed an EDA-based edge-matching puzzle solver. The proposed approach is based on the probability model. We have presented how to build the suitable probability model for puzzle solving. And we also provide the sampling method to construct a candidate solution. The experimental results show the proposed approach is available and effective.
Adaptive Multiview Graph Difference Analysis for Video Summarization

Adapting detection to different shot types is a significant challenge for video summarization methods based on shot boundary detection. In our recent work, a new graph model was introduced in the feature modelling of frames and analysed for changes in graph structure to improve the detection of shot boundaries. In this paper, we further explore the potential of graph models and propose a more general framework for online, real-time automatic video summarization. The framework develops a novel adaptive multiview graph difference analysis method to improve the algorithm’s robustness in detecting different shot transitions. Previous fusion methods typically used a priori knowledge to assign weights to the various feature differences from videos. In contrast, our framework can weigh and fuse the resulting differences by learning the importance of various video features from the structural changes of the corresponding multiview graphs. Additionally, we propose a new threshold-based adaptive decision method which can dynamically select the most accurate shot boundary decision threshold by analysing a small number of historical frames and learning the tolerance factor in the current shot. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of precision and F-score on the VSUMM and YouTube datasets.
A 2022 Primer for Crypto-Trading

This note is a short introduction to the main institutions in the world of crypto-asset trading. I review the different forms of centralized and decentralized trading to provide clarity on the tools and processes available and to highlight the risks and opportunities in this space. This note is non-technical and its main audience is practitioners and academics who seek a plain-vanilla overview, and it was developed for the Global Risk Institute.
Performance Investigation of Image Quality improvement approaches for Brain MR Images of Infant

MRI (Magnetic Resonance Imaging) is a medical diagnostic method that is currently used to provide high- quality imaging of a newborn's brain. The skull, heel, bladder, and knee are all commonly treated with MRI. In Neuroimaging (MRI) images, noises including Gaussian noise, salt and additive noise, and speckle noise are common. Noise removal MRI images is essential for enhancing image quality and the accuracy of diagnosis and management quantitative analyses. Several noise removal strategies for babies' MR images are investigated in this paper, including Total Variation (TV), Non-Local Means (NLM), Anisotropic Diffusion (AD), Bilateral Filtering, Wavelet Based, and Shift-Invariant Wavelet. Benchmarks such as MSE, RMSE, EGRAS, REF, and others were used to evaluate quantitative data. This study demonstrated that it surpasses the presently available noise removal filters.
Broad Learning System for Tackling Emerging Challenges in Face Recognition

Face recognition has been rapidly developed and widely used.However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding.Emerging challenges for face recognition are resulted from information loss.This study aims to tackle these challenges with a broad learning system (BLS).We integrated two models, IR 3 C with BLS and IR 3 C with a triplet loss, to control the learning process.In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models.In the model of IR 3 C with BLS, the recognition rates for the four challenging strategies are all 100%.In the model of IR 3 C with a triplet loss, the recognition rates are 94.61%, 94.61%, 96.95%, 96.23%, respectively.The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.