Reviewerly/exHarmony huggingface dataset:
Improving Transaction Speed and Scalability in Blockchain Systems

This paper presents a parallel mining architecture model intended to be used in blockchain systems to improve transaction speed and network scalability while maintaining decentralization. Typical blockchain validation times are significantly slower than traditional digital transaction systems. The model proposed is intended to allow devices with limited computational power to make meaningful contributions to the blockchain system by introducing parallel proof of work, managed by automated manager nodes. This will allow blockchain systems to be integrated into cloud environments and the internet of things. The proposed model is also intended to address and reduce power consumption problems current blockchain systems face, by allowing the network to validate transactions without the need of high-powered specialty mining machines. Automation and virtualization of network nodes is intended to utilize hardware already online to preform parallel proof of work together in contrast to nodes all competing against each other and wasting electrical power.
Pushing the Limits: Searching for Implementations with the Smallest Area for Lightweight S-Boxes

The area is one of the most important criteria for an S-box in hardware implementation when designing lightweight cryptography primitives. The area can be well estimated by the number of gate equivalent (GE). However, to our best knowledge, there is no efficient method to search for an S-box implementation with the least GE. Previous approaches can be classified into two categories, one is a heuristic that aims at finding an implementation with a satisfying but not necessarily the smallest GE number; the other one is SAT-based focusing on only the smallest number of gates while it ignored that the areas of different gates vary. Implementation with the least gates would usually not lead to the smallest number of GE.
Sifting scheme for continuous-variable quantum key distribution with short samples

Preventing the secret key from being stolen is an important issue in practical quantum key distribution systems. In the sifting step, the legitimate parties discard the useless portion of the raw data to form the sifted key. This step is executed at high speed to support the high repetition frequency of the systems without guaranteeing the security of the raw data. In practical systems, useless data contain abnormal data and the key measured by the legitimate party on different bases. Here we propose a sifting scheme based on machine learning that can monitor anomaly quantum signal disturbances in practical continuous-variable quantum key distribution systems. It randomly samples small amounts of data from the data block and uses short samples to preliminarily sift the abnormal one. The results show that the model can quickly distinguish normal communication from most common attacks with the cost of a small part of the raw keys and improve system performance under attacks. In principle, the model can also be generalized and applied to discrete-variable quantum key distribution systems and further enhance the security of quantum key distribution.
FRUGAL: Unlocking Semi-Supervised Learning for Software Analytics

Standard software analytics often involves having a large amount of data with labels in order to commission models with acceptable performance. However, prior work has shown that such requirements can be expensive, taking several weeks to label thousands of commits, and not always available when traversing new research problems and domains. Unsupervised Learning is a promising direction to learn hidden patterns within unlabelled data, which has only been extensively studied in defect prediction. Nevertheless, unsupervised learning can be ineffective by itself and has not been explored in other domains (e.g., static analysis and issue close time).Motivated by this literature gap and technical limitations, we present FRUGAL, a tuned semi-supervised method that builds on a simple optimization scheme that does not require sophisticated (e.g., deep learners) and expensive (e.g., 100% manually labelled data) methods. FRUGAL optimizes the unsupervised learner’s configurations (via a simple grid search) while validating our design decision of labelling just 2.5% of the data before prediction.As shown by the experiments of this paper FRUGAL outperforms the state-of-the-art adoptable static code warning recognizer and issue closed time predictor, while reducing the cost of labelling by a factor of 40 (from 100% to 2.5%). Hence we assert that FRUGAL can save considerable effort in data labelling especially in validating prior work or researching new problems.Based on this work, we suggest that proponents of complex and expensive methods should always baseline such methods against simpler and cheaper alternatives. For instance, a semi-supervised learner like FRUGAL can serve as a baseline to the state-of-theart software analytics.
Multi-Depth-of-Field 3-D Profilometry for a Microscopic System With Telecentric Lens

Microscopic profilometry based on fringe projection is notable for its noncontact and robust nature and is widely used in industrial electronics. However, its ability to observe electronic packages is limited by the depth of field (DOF) of the lens. This article proposes a novel reconstruction scheme based on telecentric 3-D profilometry to overcome this limitation. The advantage of profilometry based on phase-shifting fringe projection is utilized through defocus detection to achieve high-precision contour measurement with a single DOF. After completing such local reconstruction, a complete 3-D model is registered from partial fragments in different DOFs, making it simple for the microscope system to measure the geometric structure of objects whose thickness exceeds three times the DOF. To accommodate occlusion in local reconstruction, a specific detection algorithm is designed based on intensity variance. Beyond that, we present a novel projection system calibration scheme to improve the calibration robustness and accuracy. Instead of directly using the detected phase to calculate the coordinates of the corners, we first model the relationship between the phase and pixel coordinates and then leverage the estimated parameters to recover the phase. Our proposed method reduces the influence of calibration board spraying errors on the calibration results. Experiments show the effectiveness of this new approach.
Arabic Fake News and Spam Handling: Methods, Resources and Opportunities

Fake news detection and elimination is an emergent challenge with the growth development in social media and especially by the user-generated content UGC. Works in handling such content are more and more important worldwide, however, those interested in the Arabic language are still very limited when compared to the use of this language on the web. In this paper, we give the main used methods in last years to handle Arabic spam content and fake news in social media. Our findings are twice, the first is that Twitter is the most investigated platform in this area, and the second is the dominance of Arabic dialectal content by spammers. We recommend more works in handling Arabic dialects, also the orientation to other social media platforms such as Facebook, which are the most used in some Arab regions.
Harnessing Physical Reservoir Computing in Nonlinear Mechanical Metastructures

Mechanical metamaterials and metastructures have gained increasing interests due to their unique properties and promising engineering applications. With the recent advances in new technologies, researchers are attempting to embed more intelligence directly within the mechanical domain of functional metastructures, enabling them to perform intelligent tasks such as sensing the environment, changing properties and geometries, and making decisions in an even more proactive and autonomous manner, as compared to traditional systems with add-on electronics-based intelligence. However, the existing efforts mainly rely on ad-hoc designs to achieve limited tasks within pre-defined configurations, and there is lack of a systematic foundation for constructing and integrating the different aspects of mechano-intelligence, such as observation, learning, decision-making and execution. As a first step toward the systematic design of mechanical metastructures with mechano-intelligence, this research proposes to advance the state of art by exploiting the versatile computing capability embedded in nonlinear metastructures through an efficient machine learning framework called Physical Reservoir Computing. We show that the nonlinear and high-dimensional dynamics of the mechanical metastructure can be rich computational resources to perform various benchmark tasks without re-designing the structure, indicating the versatile computing potential of the metastructure. Further, we investigate the relationship between the parameters and the computational performance of the proposed system, revealing how the structural dynamics may affect the computing power and how to maximize the performance of the metastructure reservoir. Overall, this research examines a novel and effective approach to achieve mechano-computing capability in mechanical metastructures, which could form the basis towards creating future systems with more integrated mechano-intelligence.
Training a Reinforcement Learning Agent based on XCS in a Competitive Snake Environment

In contrast to neural networks, learning classifier systems are no “black box” algorithm. They provide rule-based artificial intelligence, which can easily be analysed, interpreted and even adapted by humans. We constructed an agent based on such a learning classifier system and trained it to play in a competitive snake environment by utilizing reinforcement learning and self-play methods. Our preliminary experiments show promising results that we plan to extend on in the future.
Human Activity Recognition Based on Wavelet-Based Features along with Feature Prioritization

Activity recognition from human action data is quite a challenging task in the biomedical data science community. The main challenge in dealing with human activity recognition (HAR) datasets is their high cardinality. Therefore, reducing cardinality is a cardinal area of research in the HAR field. In this research, reducing the data dimensionality by utilizing future selection methods has been used. This research work has extracted features using wavelet packet transform (WPT) and the cardinality of the feature set has been reduced by using the Genetic Algorithm (GA) technique. The selected features also have been ranked according to their importance based on their SHAP values. In the venture, an interesting inspection has been found. That is in HAR datasets, signal values lay into lower frequency regions mostly. The highest accuracy and f1-score which have been got are 94.74&#x0025;, 94.73&#x0025;, and 89.98&#x0025;, 89.67&#x0025; for the feature extracted and feature selected dataset respectively.
Narraport: Narrative-Based Interactions and Report Generation with Large Datasets

There is an increasing demand for rapid content filtering in relation to topics like digital forensics for legal cases, cybersecurity, and social media conduct monitoring. While there have been significant advances in algorithms and frameworks for media processing, this task requires an ensemble of tools and algorithms that are not well-understood by human analysts, thereby reducing their trustworthiness. In this paper, we present a novel perspective on this problem through the development of an intelligent system that generates reports from large email datasets in the form of short stories. The stories generated by the system are based on identifiable plot structures in popular media. These structures are used as semantic sensemaking templates to organize data for further filtering and triage. The end-to-end system, accessible through an interactive dashboard, incorporates unsupervised annotation modules (such as speech acts and sentiment), topic discovery, communication network analysis, character personality profiles, and automated text and visualization generators. This emerging application prototype is developed and internally deployed in collaboration with analysts and researchers actively working in this area.
Stereo Waterdrop Removal with Row-wise Dilated Attention

Existing vision systems for autonomous driving or robots are sensitive to waterdrops adhered to windows or camera lenses. Most recent waterdrop removal approaches take a single image as input and often fail to recover the missing content behind waterdrops faithfully. Thus, we propose a learning-based model for waterdrop removal with stereo images. To better detect and remove waterdrops from stereo images, we propose a novel row-wise dilated attention module to enlarge attention’s receptive field for effective information propagation between the two stereo images. In addition, we propose an attention consistency loss between the ground-truth disparity map and attention scores to enhance the left-right consistency in stereo images. Because of related datasets’ unavailability, we collect a real-world dataset that contains stereo images with and without waterdrops. Extensive experiments on our dataset suggest that our model outperforms state-of-the-art methods both quantitatively and qualitatively. Our source code and the stereo waterdrop dataset are available at https://github.com/VivianSZF/Stereo-Waterdrop-Removal.
Multilingual Neural Machine Translation for Low Resourced Languages: Ometo-English

Unlike technologically favored languages, under-resourced languages highly suffer from the lack of language resources for machine translation. In this paper, we present a new approach to overcome the problem of language resources that share significant amount of linguistic resource in the Ometo language family. The dataset for the experiment are collected from religious domain consisting of four Ometo (Wolaita, Gamo, Gofa, and Dawuro) languages paired with English sentence and automatically extracted from the web. The collected corpus were used for conducting neural machine translation experiments from Ometo to English. Among the experiments, the Wolaita, Dawuro and Gamo paired with English sentence combination for training and Gofa for testing gives highest BLEU score 4.5 than the other combinations while the Wolaita, Gamo, and Gofa with Dawuro testing provide the lowest result. The BLEU score of the machine translation system shows a promising result despite the language differences, the morphological richness, and complexity of the Ometo languages which has high impact on the performance of the Ometo-English machine translation. Further, we are now working towards developing a translation system that significantly reduces the effect of morphological richness and complexity of the Ometo languages through different linguistic processing.
Impact of data synchronization methods on the quality of engineering NIS data from several integrated applications in GIS architecture

This study examines the impact of data synchronization techniques on the quality of network information system (NIS) engineering data from multiple integrated applications in a geospatial information system (GIS) architecture. As a case study has been chosen a grid power infrastructure to examine data synchronization impact on the data storage quality during NIS Plan and the mobile solution Plan interaction. Three different interaction scenarios have been considered and optimal solutions were proposed in accordance with business priorities.
Augmenting Digital Sheet Music through Visual Analytics

Abstract Music analysis tasks, such as structure identification and modulation detection, are tedious when performed manually due to the complexity of the common music notation (CMN). Fully automated analysis instead misses human intuition about relevance. Existing approaches use abstract data‐driven visualizations to assist music analysis but lack a suitable connection to the CMN. Therefore, music analysts often prefer to remain in their familiar context. Our approach enhances the traditional analysis workflow by complementing CMN with interactive visualization entities as minimally intrusive augmentations. Gradual step‐wise transitions empower analysts to retrace and comprehend the relationship between the CMN and abstract data representations. We leverage glyph‐based visualizations for harmony, rhythm and melody to demonstrate our technique's applicability. Design‐driven visual query filters enable analysts to investigate statistical and semantic patterns on various abstraction levels. We conducted pair analytics sessions with 16 participants of different proficiency levels to gather qualitative feedback about the intuitiveness, traceability and understandability of our approach. The results show that MusicVis supports music analysts in getting new insights about feature characteristics while increasing their engagement and willingness to explore.
UNIVERSITY BRAND MANAGEMENT IN SOCIAL MEDIA

The purpose of the article is to present the results of an applied analysis of the characteristics of brand management by leading Russian universities in social media. Research methodology: based on the international subject ranking QS World University Rankings by Subject 2021: Social Sciences &amp; Management 2021, six leading Russian universities were selected for the study: Higher School of Economics National Research University, Russian University of Economics. Plekhanov, Financial University under the Government of the Russian Federation, Russian Academy of National Economy and Public Administration under the President of the Russian Federation, Moscow State Institute of International Relations University, Peoples' Friendship University of Russia. The results of a comparative analysis of the weight and dynamics of information flows of data of universities, the specifics of their work with target audiences, the characteristics of managing the digital audience of universities are presented. Based on the data of empirical research, recommendations have been developed for managing a university brand in an online network environment. The article was prepared based on the results of research carried out at the expense of budgetary funds on a state assignment for the Financial University under the Government of the Russian Federation.
Deep Multiinstance Mammogram Classification With Region Label Assignment Strategy and Metric-Based Optimization

Mammography is one of the most widely used and effective ways to screen early breast cancer. Convolutional-neural-network-based methods have obtained promising results for automatic mammography diagnosis. However, most of those approaches ignore the relationship between global and local characteristics of mammograms and lose sight of the relationship between different views of a patient. This study designs a novel region label assignment strategy, which takes advantage of all regions in each mammogram from a patient by assigning different labels to different regions and calculating the loss for each region separately. This approach enables the classifier to distinguish variable and tiny lesions in complex global conditions better. Only one case-level classification label is needed for diagnosing one patient (case). Moreover, the categories of mammogram data sets are always imbalanced. To address this problem, this study designs an area under the receiver operating characteristic curve (AUC)-based optimization method on minibatch strategy. Experimental results on a constructed data set and two publicly available data sets demonstrate that the proposed method performs satisfactorily compared with state-of-the-art mammogram classifiers. Visualization results show the proposed method can find out mammograms containing malignant lesions and illustrate the rough location of lesions.
A New Approach to Remote Health Monitoring using Augmented Reality with WebRTC and WebXR

The domain of Augmented Reality (AR) focus with the realm of conveying interactive experience of the objects in the real-world environment with the enhancement towards system generated information. It also conveys different forms of modality, visual perception, auditory and haptic information about the object which is considered for analysis. The system is used to formulate significant collaborative experiences with superimposed experiences among the users. This research work focus on the remote assistance for diagnosis and collaboration among the people who are involved as workers in product confirmation and analysis. The mechanism also decreases the communication issues with the intention of making the users to have a good enough environment to have a complete set of interaction. Therefore, to give a remote diagnostic of the work and guidance for the site workers from an expert we propose WebRTC (AppRTC) for Real-Time Video Communication and Google ARCore/ WebAR/ WebXR for augmenting the Video call, will be used and integrated to enhance the proposed system. Thereby the assembly process in improved for service quality with timely communication among the workers of a specified group..
Fast and Robust Differential Relative Pose Estimation With Radial Distortion

In this letter, we address the differential two-view geometry problem of estimating the relative pose between two consecutive frames in the presence of radial distortion. This problem is of both theoretical and practical interests and has not been solved. We derive its parameterization and present an effective and robust generalized eigenvalue solver based on the hidden variable technique. Furthermore, we propose a nonlinear refinement scheme within the maximum likelihood criterion to produce more accurate estimates of the relative pose and radial distortion. Compared with the standard differential solutions without modeling the radial distortion, our approach can recover more geometrically correct point correspondences for a pair of radially distorted images. Moreover, our differential solution runs an order of magnitude faster than the discrete solution in terms of recovering the full camera motion. Experiment results on both synthetic and real data demonstrate the effectiveness of our model and method in dealing with the radial distortion.
Two-Layers Workspace: A New Approach to Cooperative Object Transportation With Obstacle Avoidance for Multi-Robot System

In this paper we present a new approach to cooperative overhead object transportation with obstacle avoidance for multi-robot systems.This approach considers situations in which there are obstacles that only pose a risk to the robots involved in the transport and not necessarily to the group of robots with the load.Thus, we propose to separate the workspace into two layers in a way that the obstacles which only pose risk of collision to the robots can be transposed by a local planner.This new approach to the load transportation problem allows the system to find shorter paths or even find a solution in situations which would not be possible with the classic approach.Furthermore, the developed method has a configuration space independent of the number of robots and it is robust to environmental variation, not reducing its performance in more complex environments as in narrow passages or long corridors.The proposed approach was tested by simulation and the results are compared to the classic one.