Reviewerly/exHarmony huggingface dataset:
Informative Path Planning for Mobile Sensing with Reinforcement Learning

Large-scale spatial data such as air quality, thermal conditions and location signatures play a vital role in a variety of applications. Collecting such data manually can be tedious and labour intensive. With the advancement of robotic technologies, it is feasible to automate such tasks using mobile robots with sensing and navigation capabilities. However, due to limited battery lifetime and scarcity of charging stations, it is important to plan paths for the robots that maximize the utility of data collection, also known as the informative path planning (IPP) problem. In this paper, we propose a novel IPP algorithm using reinforcement learning (RL). A constrained exploration and exploitation strategy is designed to address the unique challenges of IPP, and is shown to have fast convergence and better optimality than a classical reinforcement learning approach. Extensive experiments using real-world measurement data demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in most test cases. Interestingly, unlike existing solutions that have to be re-executed when any input parameter changes, our RL-based solution allows a degree of transferability across different problem instances.
Student engagement with mobile‐based assessment systems: A survival analysis

Abstract Research on mobile‐based assessment systems is still an emerging topic in the mobile learning field. Current research has demonstrated that the use of mobile‐based assessment systems seems to have a positive impact on students' learning outcomes and motivation. The paper identifies some factors that influence student engagement with mobile‐based formative assessment in a language learning course. Survival analysis of the interaction of N = 86 students from eight English as a Foreign Language (EFL) courses over 5 weeks showed that students with higher levels of self‐reported effort and perceived importance engaged for longer periods of time. Perceived ease of use, perceived usefulness and behavioural intention to use were all predictors of the longer use of mobile‐based assessment systems. We found that within the first 25–50 min of use, about half of the students might disengage from using a mobile‐based assessment application.
ConfigCrusher: towards white-box performance analysis for configurable systems

Stakeholders of configurable systems are often interested in knowing how configuration options influence the performance of a system to facilitate, for example, the debugging and optimization processes of these systems. Several black-box approaches can be used to obtain this information, but they either sample a large number of configurations to make accurate predictions or miss important performance-influencing interactions when sampling few configurations. Furthermore, black-box approaches cannot pinpoint the parts of a system that are responsible for performance differences among configurations. This article proposes ConfigCrusher, a white-box performance analysis that inspects the implementation of a system to guide the performance analysis, exploiting several insights of configurable systems in the process. ConfigCrusher employs a static data-flow analysis to identify how configuration options may influence control-flow statements and instruments code regions, corresponding to these statements, to dynamically analyze the influence of configuration options on the regions' performance. Our evaluation on 10 configurable systems shows the feasibility of our white-box approach to more efficiently build performance-influence models that are similar to or more accurate than current state of the art approaches. Overall, we showcase the benefits of white-box performance analyses and their potential to outperform black-box approaches and provide additional information for analyzing configurable systems.
DI-Mondrian: Distributed improved Mondrian for satisfaction of the L-diversity privacy model using Apache Spark

For the extraction of useful patterns, the collected data should be distributed to and shared with analyzers. This, however, creates problems and challenges for the individual with respect to their privacy and identity. In this paper, the Mondrian multidimensional anonymization method was developed and improved for satisfaction of the l-diversity privacy model, and it has been presented in a distributed fashion within the Apache Spark framework. Since one of the major challenges in data privacy is the tradeoff between privacy and data utility, the presented method focuses on information loss and classifier evaluation criteria. Therefore, the cut dimension was selected using the coefficient of variation and information gain criteria, and the cut points were chosen dynamically, which led to a decrease in the information loss parameter and an improvement in the classifier performance evaluation criteria such as accuracy and FMeasure compared to the previous algorithms in the literature. The processing speed is 100 times higher in Spark than in the Hadoop framework. Consequently, the proposed method was presented in a distributed fashion based on RDDs programming within Apache Spark framework. This will resolve the problem of speed in large-scale data anonymization as it exists in the previous Hadoop-based algorithms. The results of the experiments performed on the numerical datasets demonstrate the improvements made by the proposed method.
Rethinking Fast and Friendly Transport in Data Center Networks

The sustainable growth of bandwidth has been an inevitable tendency in current Data Center Networks (DCN). However, the dramatic expansion of link capacity offers a remarkable challenge to the transport layer protocols of DCN, i.e., how to converge fast and enable data flow to utilize the high bandwidth effectively. Meanwhile, the new protocol should be compatible to the traditional TCP because the applications with old TCP versions are still widely deployed. Therefore, it is important to achieve a trade-off between the aggressiveness and TCP-friendliness in protocol design. In this article, we first empirically investigate why the existing typical data center TCP variants naturally fail to guarantee both fast convergence and TCP friendliness. Then, we design a new transport protocol for DCN, namely Fast and Friendly Converging (FFC), which makes independent decisions and self-adjustment through retrieving the two-dimensional congestion notification from both RTT and ECN. We further present a mathematic model to analyze its competing behavior and converging process. The results from simulation experiments and real implementation show that FFC can achieve fast convergence, thus benefiting the flow completion time. Moreover, when coexisting with the traditional TCP, FFC also presents a moderate behavior, while introducing trivial deployment overhead only at the end-hosts.
Medical speciality classification system based on binary particle swarms and ensemble of one vs. rest support vector machines

Nowadays, artificial intelligence plays an integral role in medical and healthcare informatics. Developing an automatic question classification and answering system is essential for coping with constant advancements in science and technology. However, efficient online medical services are required to promote offline medical services. This article proposes a system that automatically classifies medical questions of patients into medical specialities and supports the Arabic language in the MENA region. Text classification is not trivial, especially when dealing with a highly morphologically complex language, the dialectical form of which is the dominant form on the Internet. This work utilizes 15,000 medical questions asked by the clients of Altibbi telemedicine company. The questions are classified into 15 medical specialities. As the number of medical questions received daily by the company has increased, a need has arisen for an automatic classification system that can save the medical personnel much time and effort. Therefore, this article presents an efficient medical speciality classification system based on swarm intelligence (SI) and an ensemble of support vector machines (SVMs). Particle swarm optimization (PSO) is an SI-based and stochastic metaheuristic algorithm that is adopted to search for the optimal number of features and tune the hyperparameters of the SVM classifiers, which are deployed as one-versus-rest for multi-class classification. In addition, PSO is integrated with various binarization techniques to boost its performance. The experimental results show that the proposed approach accomplished remarkable performance as it achieved an accuracy of 85% and a features reduction rate of 95.9%.
Autonomous Railway Traffic Object Detection Using Feature-Enhanced Single-Shot Detector

With the high growth rates of railway transportation, it is extremely important to detect railway obstacles ahead of the train to ensure safety.Manual and traditional feature-extraction methods have been utilized in this scenario.There are also deep learning-based railway object detection approaches.However, in the case of a complex railway scene, these object detection approaches are either inefficient or have insufficient accuracy, particularly for small objects.To address this issue, we propose a feature-enhanced single-shot detector (FE-SSD).The proposed method inherits a prior detection module of RON and a feature transfer block of FB-Net.It also employs a novel receptive field-enhancement module.Through the integration of these three modules, the feature discrimination and robustness are significantly enhanced.Experimental results for a railway traffic dataset built by our team indicated that the proposed approach is superior to other SSD-derived models, particularly for small-object detection, while achieving real-time performance close to that of the SSD.The proposed method achieved a mean average precision of 0.895 and a frame rate of 38 frames per second on a railway traffic dataset with an input size of 320 × 320 pixels.The experimental results indicate that the proposed method can be used for real-world railway object detection.
A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing

Recent developments in cloud computing have made it a powerful solution for executing large-scale scientific problems. The complexity of scientific workflows demands efficient utilization of cloud resources to satisfy user requirements. Scheduling of scientific workflows in a cloud environment is a challenge for researchers. The problem is considered as NP-hard. Some constraints such as a heterogeneous environment, dependencies between tasks, quality of service and user deadlines make it difficult for the scheduler to fully utilize available resources. The problem has been extensively studied in the literature. Different researchers have targeted different parameters. This article presents a multi-objective scheduling algorithm for scheduling scientific workflows in cloud computing. The solution is based on genetic algorithm that targets makespan, monetary cost, and load balance. The proposed algorithm first finds the best solution for each parameter. Based on these solutions, the algorithm finds the superbest solution for all parameters. The proposed algorithm is evaluated with benchmark datasets and comparative results with the standard genetic algorithm, particle swarm optimization, and specialized scheduler are presented. The results show that the proposed algorithm achieves an improvement in makespan and reduces the cost with a well load balanced system.
Deep learning based genome analysis and NGS-RNA LL identification with a novel hybrid model

The conventional image segmentation techniques have a lot of issues with highest computational cost and low level accuracy for medical image diagnosis and genome analysis. The deep learning based optimization models utilize to predict the liver cancer with RNA genome using CT images and the prediction of genome classification with NGS is a higher probable in recent medical disease classification. This paper proposes a hybrid deep learning technique constructs with SegNet, MultiResUNet, and Krill Herd optimization (KHO) algorithm to perform the extraction of the liver lesions and RNA sequencing that the optimization techniques used into the deep learning method. The proposed technique implements the SegNet for segregating the liver with genome from the CT scan; the MultiResUNet is constructed to perform the extractions of liver lesions. The KHO algorithm is combined with the deep learning approaches for tuning the hyper parameters to every Convolutional neural network model and enhances the segmentation process which may elaborately identifies the sequence that causes the liver classification disease. The proposed technique is compared with the related techniques on liver lesion classification (LL) for NGS in genome. The performance results show that the proposed technique is better to other algorithms on various performance metrics.
Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network

Abstract Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.
XOX Fabric: A hybrid approach to blockchain transaction execution

Performance and scalability are major concerns for blockchains: permissionless systems are typically limited by slow proof of X consensus algorithms and sequential postorder transaction execution on every node of the network. By introducing a small amount of trust in their participants, permissioned blockchain systems such as Hyperledger Fabric can benefit from more efficient consensus algorithms and make use of parallel pre-order execution on a subset of network nodes. Fabric, in particular, has been shown to handle tens of thousands of transactions per second. However, this performance is only achievable for contention-free transaction workloads. If many transactions compete for a small set of hot keys in the world state, the effective throughput drops drastically. We therefore propose XOX: a novel two-pronged transaction execution approach that both minimizes invalid transactions in the Fabric blockchain and maximizes concurrent execution. Our approach additionally prevents unintentional denial of service attacks by clients resubmitting conflicting transactions. Even under fully contentious workloads, XOX can handle more than 3000 transactions per second, all of which would be discarded by regular Fabric.
Deep Learning Model for Acoustics Signal Based Preventive Healthcare Monitoring and Activity of Daily Living

To cope with the increasing healthcare costs and nursing shortages in the Aging Society the care system is transferred, as much as possible, to the home environment, making use of ambient assisted living (AAL) monitoring and communication possibilities and to actively involve informal cares to fill in large part of the care that is needed. The proposed system is the AAL based, acoustics sensing system ready to dissect, recognize, and distinguish specific acoustic events occurring in day-by-day life situations, which empowers not only the individual subjects but also the healthcare professionals to remotely follow the status of each individual continuously. This system only processes the background acoustics related to the activity of daily living (ADL) for preventive healthcare. The novel contribution of the research is based on prototype development, audio signal processing algorithms and deep learning algorithms to satisfy the research gap.
Distributed Ledger Technology (DLT): The Beginning of a Technological Revolution for Blockchain

In recent years there are lot of thrills spinning around BLOCKCHAIN technology. Blockchain initially released in 2008 by Satoshi Nakamoto as a fundamental technology for the first ever global decentralized cryptography based digital currency known as BITCOIN. Todays in tech world every person and industry discussing about blockchain, and they feel blockchain technology changed the world like open source software change. Every industry wants to use and explore blockchain technology in different application like trading, supply chain, healthcare etc. Blockchain is a revolutionary technology but the question is from where this technology invented. In this paper author will discuss about distributed ledger technology which is known as parental technology of blockchain. This paper will break the myth that distributed ledger and blockchain both are the same technology, and will tell that every DLT is not blockchain but blockchain is a DLT. This paper presents the comparison of different DLT's and blockchain. In this paper author discuss different type of potential application area where DLT is very helpful. In the end of paper author present future research direction for blockchain and DLT with some challenges.
Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism

Partial Multi-Label learning (PML) learns from the ambiguous data where each instance is associated with a candidate label set, where only a part is correct. The key to solve such problem is to disambiguate the candidate label sets and identify the correct assignments between instances and their ground-truth labels. In this paper, we interpret such assignments as instance-to-label matchings, and formulate the task of PML as a matching selection problem. To model such problem, we propose a novel grapH mAtching based partial muLti-label lEarning (HALE) framework, where Graph Matching scheme is incorporated owing to its good performance of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one graph matching algorithm does not satisfy the constraint of PML problem that multiple instances may correspond to multiple labels, we extend the traditional probabilistic graph matching algorithm from one-to-one constraint to many-to-many constraint, and make the proposed framework to accommodate to the PML problem. Moreover, to improve the performance of predictive model, both the minimum error reconstruction and k-nearest-neighbor weight voting scheme are employed to assign more accurate labels for unseen instances. Extensive experiments on various data sets demonstrate the superiority of our proposed method.
Estimating Example Difficulty using Variance of Gradients

In machine learning, a question of great interest is understanding what examples are challenging for a model to classify. Identifying atypical examples ensures the safe de-ployment of models, isolates samples that require further human inspection and provides interpretability into model behavior. In this work, we propose Variance of Gradients (VoG) as a valuable and efficient metric to rank data by difficulty and to surface a tractable subset of the most chal-lenging examples for human-in-the-loop auditing. We show that data points with high VoG scores are far more difficult for the model to learn and over-index on corrupted or mem-orized examples. Further, restricting the evaluation to the test set instances with the lowest VoG improves the model's generalization performance. Finally, we show that VoG is a valuable and efficient ranking for out-of-distribution detection.
The “TRUAA” Seismic Network: Upgrading the Israel Seismic Network—Toward National Earthquake Early Warning System

Abstract Following the recommendations of an international committee (Allen et al., 2012), since October 2017, the Israeli Seismic Network has been undergoing significant upgrades, with 120 stations being added or upgraded throughout the country and the addition of two new datacenters. These enhancements are the backbone of the TRUAA project, assigned to the Geological Survey of Israel (GSI) by the Israeli Government, to provide earthquake early warning (EEW) capabilities for the state of Israel. The GSI contracted Nanometrics (NMX), supported by Motorola Solutions Israel, to deliver these upgrades through a turnkey project, including detailed design, equipment supply, and deployment of the network and two datacenters. The TRUAA network was designed and tailored by the GSI, in collaboration with the NMX project team, specifically to achieve efficient and robust EEW. Several significant features comprise the pillars of this network:Coverage: Station distribution has high density (5–10 km spacing) along the two main fault systems—the Dead Sea Fault and the Carmel Fault System;Instrumentation: High-quality strong-motion accelerometers and broadband seismometers with modern three-channel and six-channel dataloggers sampling at 200 samples per second;Low latency acquisition: Data are encapsulated in small packets (&amp;lt;1 s), with primary routing via high-speed, high-capacity telemetry links (&amp;lt;1 s latency);Robustness: High level of redundancy throughout the system design:Dual active-active redundant acquisition routes from each station, each utilizing multicast streaming over an IP security Virtual Private Network tunnel, via independent high-bandwidth telemetry systemsTwo active-active independent geographically separate datacentersDual active-active redundant independent automatic seismic processing tool chains within each datacenter, implemented in a high availability protected virtual environment. At this time, both datacenters and over 100 stations are operational. The system is currently being commissioned, with initial early warning operation targeted for early 2021.