Reviewerly/exHarmony huggingface dataset:
Tracing infectious agents with mobile location information: A simple and effective countermeasure against epidemic risks

The Sustainable Development Goals set by the United Nations request all countries to strengthen the capacity for early warning, risk reduction and management of national and global health risks. This study aims to reduce the risks caused by infectious agents by identifying individuals who accidentally come in close contact with affected patients, utilizing mobile location information. The proposed approach involves asking public health authorities to provide the trip data of patients to mobile carriers under a non-disclosure agreement. The carriers then determine the possible risks of contact of their mobile customers with patients and disclose only the risk information to their customers upon requests. This approach provides novel channels for public health authorities to announce the risk of contagious agents to residents, while maintaining the privacy of both the patients and residents. For a nationwide deployment of the proposed approach, several issues are discussed in this study, along with brief summaries of related studies.
Interdisciplinary Agile Teaching

This innovative practice paper describes a projectbased group work where conversion master students developed the skills required for agile software development methods. There are many examples of agile methods taught in, for instance, capstone courses in software engineering programmes. It has been shown to be a popular and beneficial method of learning. However, most agile teams in the software industry are made up of members with different educational backgrounds (i.e. not just software developers, as there will also business analysts, designers, testers etc.). Such a diversity of roles might be beneficial in teaching agile methods in an academic setting. This reflective educational paper describes the evolution of an industry sourced problem-based agile student project, which over three successive years has included students from three different faculties. Although the experience has broadly been positive each year, this paper describes and reflects on both the good and problematic observations that the teaching staff has made over the years. The changes of the projects has been based by reflecting on these insights. These reflections are described and several recommendations are drawn from the experiences. These include, that it is important that the cohorts collaborate and work together in parallel. Individual groups, of diverse cohorts, should work in isolation. It is important to have industrial partners who are participating actively in the process, and it is important to not underestimate the work that is required by the academics to coordinate the projects to make them successful.
Automatic Segmentation based Graphical Interface for Recognition of Tomography Image Analysis of Damage cell

The recent improvement in Medical picture development has facilitated Computed Tomography Image study by Automatic division of impaired cell. Automatic division of impaired cell will provide the required information, cross correlation and log value. The contrast between ordinary and impaired cell will provide a correct notion about the impaired cell. Employing the GUI technique, the operator can distinct the provided two image characteristics and accomplish the appropriate examination of the medical image. The benefits of this suggested technique are inexpensive, less time consuming and simple to operate. The operator can easily insert and speculate the values.
Distributed Soft Clustering Algorithm for IoT Based on Finite Time Average Consensus

Clustering is a common technique for statistical data analysis and it has been widely used in many fields. This article investigates data clustering over the Internet-of-Things (IoT) network. Facing the IoT network challenges, including data volume, communication latency, and information security, we here propose a distributed soft clustering algorithm for the IoT environments where each IoT node may have data from multiple clusters. Considering that the main task of soft clustering is to compute each cluster center in a weighted averaging fashion, our distributed clustering method resorts to an efficient finite-time average-consensus algorithm. Moreover, to make the distributed clustering algorithm more stable and be able to escape from some bad local optimum, we propose a distributed deterministic initialization method based on data variance partitioning. Experiments show that the proposed distributed soft clustering algorithm can offer the same performance as its centralized counterpart in terms of both convergence and clustering quality. Besides, unlike most clustering methods relying on probabilistic initialization, our algorithm could provide stable clustering quality which makes it more suitable for IoT networks. A real-world case study about the clustering analysis for distributed data sets collected by environmental monitoring stations is offered, which shows the potential of our algorithms in practical applications.
An Empirical Study of High-Impact Factors for Machine Learning-Based Vulnerability Detection

Ahstract-Vulnerability detection is an important topic of software engineering. To improve the effectiveness and efficiency of vulnerability detection, many traditional machine learning-based and deep learning-based vulnerability detection methods have been proposed. However, the impact of different factors on vulnerability detection is unknown. For example, classification models and vectorization methods can directly affect the detection results and code replacement can affect the features of vulnerability detection. We conduct a comparative study to evaluate the impact of different classification algorithms, vectorization methods and user-defined variables and functions name replacement. In this paper, we collected three different vulnerability code datasets. These datasets correspond to different types of vulnerabilities and have different proportions of source code. Besides, we extract and analyze the features of vulnerability code datasets to explain some experimental results. Our findings from the experimental results can be summarized as follows: (i) the performance of using deep learning is better than using traditional machine learning and BLSTM can achieve the best performance. (ii) CountVectorizer can improve the performance of traditional machine learning. (iii) Different vulnerability types and different code sources will generate different features. We use the Random Forest algorithm to generate the features of vulnerability code datasets. These generated features include system-related functions, syntax keywords, and user-defined names. (iv) Datasets without user-defined variables and functions name replacement will achieve better vulnerability detection results.
A Distributed Density-Grid Clustering Algorithm for Multi-Dimensional Data

In recent years there have been many massive leaps in technology that have also resulted in large advancements in how we collect and use data. These advancements have caused a rise in the prominence of the field of Big Data. Organizations and businesses rely heavily on data analysis in almost every field of work. This need for data analysis combined with larger and more complex datasets has caused many challenges for these groups as they seek to keep up. Clustering is a field of data analysis, specifically unsupervised machine learning, that is heavily used in many different industries. Traditional clustering algorithms typically suffer in performance and accuracy as datasets increase in size and dimensionality. We previously proposed a new clustering algorithm called the Fast Density-Grid clustering algorithm that successfully alleviated some of the problems related to runtimes. In modern data analysis however, serial algorithms are still too slow to be of much use. The Fast Density-Grid algorithm was originally designed with parallelization in mind, and this paper discusses the steps taken to implement this. Our experimental results show that, when the number of records in the dataset exceed a certain amount, the parallel form of the algorithm overtakes the traditional in performance. Studying this critical point allows us to determine whether or not the algorithm is suitable for real world use.
Comparison of the efficiency of Machine Learning algorithms on Twitter Sentiment Analysis of Pathao

Nowadays, we are virtually living on social media, constantly sharing news and opinions regarding things that associate with us. As a result, social media sites are getting flooded with data in the form of opinions, especially on Twitter. However, all these tweets or opinions can be mined and used in various aspects of life and business for the betterment of human beings as a whole. Opinions on social media are unstructured and scattered and needs to be organized properly so that it contains valuable information regarding a specific subject. Sentiment analysis plays a big role here. In this paper, we have presented a way to automatically analyze the sentiment of tweets posted by Pathao (popular ride¬sharing service in South-East Asia) users. We have merged data mining with text mining and computational intelligence to label the tweets as negative or positive. We have used Twitter API to collect dataset from Twitter and followed the supervised learning approach for the development of training corpus. For text classification, we used three different machine learning algorithms-Naive Bayes, Support Vector Machine and Logistic Regression. We also made a comparative analysis of the efficiency of these three machine learning algorithms. Our proposed method is showing effective result and outcomes are satisfactory.
Breast Cancer Diagnosis Models Using PCA and Different Neural Network Architectures

One of the most wide-spreading diseases among women is Breast Cancer. For this reason, a proper diagnosis is necessary for designating necessary treatment. Using the previous information about patients, diagnosis is being performed by various machine learning algorithms. As the data are getting bigger, it is becoming more necessary to extract the useful information from the huge pile of information. In this paper, we have used the Wisconsin diagnostic breast cancer dataset (WDBC) and SEER 2017 Breast Cancer Dataset. Then we have used Principal component analysis in order to extract useful features. After that, we have classified the reduced datasets using multi-layer perceptron (MLP) and convolution neural network (CNN). Then we have provided a comparative comparison of our model for both the reduced datasets. Our MLP model has achieved an accuracy of 99.1% on reduced WDBC dataset and 89.3% on SEER 2017 Breast Cancer dataset whereas CNN Model has achieved 96.4% on reduced WDBC dataset and 88.3% on SEER 2017 Breast Cancer Dataset.
VeriBlock: A Novel Blockchain Framework based on Verifiable Computing and Trusted Execution Environment

Many enterprise (permissioned) blockchain applications demand low latency to commit their transactions on the ledger. One way to reduce latency is by reducing the number of peers required to execute/endorse a transaction in a secure and consistent way. However, by reducing the number of endorsements, blockchain network can be vulnerable to attacks such as collusion. In this paper, we introduce a novel scheme VeriBlock to overcome this problem. Our idea is to reduce the redundant execution of smart contracts without compromising on the security of the blockchain system by leveraging Verifiable Computing (VC) (which provides mathematically verifiable proof of execution) and Trusted Execution Environment (TEE) (which provides an attestation of the code executed) approaches. We also implemented these approaches to derive insights. In the proposed scheme, few nodes execute the smart contact logic and all the other nodes verify it. We propose two different models in the proposed scheme, 1. Endorser-Verify model, where the verification is done as part of transaction endorsement, and 2. Committer- Verify model, where the verification is done at the time of transaction commit. We have built Endorser-Verify model using Hyperledger Fabric blockchain platform, Pinocchio and Intel SGX as VC and TEE respectively and performance of the proposed scheme is analyzed by running bidding use case. Based on the results, we observe that the running time for VC based technique is in the order of magnitude two when compared with the naive implementation. On the other hand the performance of SGX is better than VC based approach.
DSTARS: A multi-target deep structure for tracking asynchronous regressor stacking

Several applications of supervised learning involve the prediction of multiple continuous target variables from a dataset. When the target variables exhibit statistical dependencies among them, a multi-target regression (MTR) modelling permits to improve the predictive performance in comparison to induce a separate model for each target. Apart from describing the dependencies among the targets, the MTR methods could offer better performance and less overfitting than traditional single-target (ST) methods. A group of MTR methods have addressed this demand, but there are still many possibilities for further improvements. This paper presents a novel MTR method called Deep Structure for Tracking Asynchronous Regressor Stacking (DSTARS), which overcomes some existing gaps in the current solutions. DSTARS extends the Stacked Single-Target (SST) approach by combining multiple stacked regressors into a deep structure. In this sense, it is able to boost the predictive performance by successively improving the predictions for the targets. Besides, DSTARS exploits the dependency of each target individually by tracking an asynchronous number of stacked regressors. Additionally, our proposal explores the inter-targets dependencies by exposing and measuring them through a nonlinear metric of variable importance. We compared DSTARS to SST, Ensemble of Regressor Chains (ERC) and Multi-objective Random Forest (MORF). Also, the ST strategy with different algorithms was used to compute independent regressions for each target. We used Random Forest (RF) and Support Vector Machine (SVM) as base-learners to investigate the prediction capability of algorithms belonging to different machine learning paradigms. The experiments carried out on eighteen diverse datasets showed that the proposed method was significantly better than the other compared approaches.
Scalable Anomaly Detection Method for Blockchain Transactions using GPU

Blockchain is a distributed ledger system composed of P2P network proposed as an electronic cash system which can transfer money without a trusted third party. Blockchain has high tamper resistance by the structure which cannot modify a transaction by everyone including the creator of it. However, it also becomes a problem that Blockchain system cannot modify fraudulent transaction already approved. This problem means once an illegal transaction occurs, the damage expands. It is necessary to detect the transaction by the anomaly detection and modify it before approval in order to suppress the damage. However, existing anomaly detection methods of Blockchain need the processing for all the past transactions in Blockchain. The execution time exceeds the approval interval of the major Blockchain system (Ethereum). In this paper, we propose an anomaly detection method using a fixed size user-centric subgraph which is extracted from whole graph made from all the transactions, to prevent the increase of the execution time. Furthermore, to accelerate the anomaly detections, we propose the subgraph structure which is suitable for GPU processing so that all of the subgraph making, the feature extraction, and the anomaly detection are performed in GPU. When the number of transactions is 300 million, our proposed method archives 195 times faster than the existing GPU-based method and the execution time is shorter than the approval interval of the Ethereum. In terms of accuracy, the true positive rate is significantly higher than the existing method in the case of small scale transactions because the local anomaly can be detected by the subgraph with locality. And the rate in the case of large scale and the false positive rate are close to the existing method.
IoT Service Recommendation Scheme Based on Matter Diffusion

With the development of Internet of Things(IoT), more and more smart devices and services appeared in people's life.These services make life more convenient, while it is difficult for people, especially the elderly, to find them in time, because they are widely distributed in great numbers with small sizes.In order to solve this problem, the service recommendation scheme, which is becoming increasingly important, needs to be used in IoT.However, the traditional web service recommendation schemes are not suitable for the IoT, because they rely more on the historical information rather than the energy state or the user's habit attributes, which are important for IoT.We research the service recommendation scheme, finding that the service recommendation process in IoT is a process of matter flows, which follows the conservation of matter.Therefore, we propose the service recommendation scheme based on tripartite graph with matter diffusion and use the habit feature as a dynamic tag.Based on the balance of matter, we use the positive and negative matter diffusion results on the tripartite as the recommendation results.The results of our evaluation show that the performance of the service recommendation scheme is improved in the precision and recall.
Hierarchical Attention-Based Fusion for Image Caption With Multi-Grained Rewards

Image caption based on reinforcement learning (RL) methods has achieved significant success recently. Most of these methods take CIDEr score as the reward of reinforcement learning algorithm to compute gradients, thus refining the image caption baseline model. However, CIDEr score is not the sole criterion to judge the quality of a generated caption. In this paper, a Hierarchical Attention Fusion (HAF) model is presented as a baseline for image caption based on RL, where multi-level feature maps of Resnet are integrated with hierarchical attention. Revaluation network (REN) is exploited for revaluating CIDEr score by assigning different weights for each word according to the importance of each word in a generating caption. The weighted reward can be regarded as word-level reward. Moreover, Scoring Network (SN) is implemented to score the generating sentence with its corresponding ground truth from a batch of captions. This reward can obtain benefits from additional unmatched ground truth, which acts as sentence-level reward. Experimental results on the COCO dataset show that the proposed methods have achieved competitive performance compared with the related image caption methods.
Optimum Navigation of Four-Wheeled Ground Robot in Stationary and Non-stationary Environments Using Wind-Driven Optimization Algorithm

In this article, the atmospheric motion-based inspired wind-driven optimization (WDO) algorithm is implemented to minimize the traveling path length of a four-wheeled ground robot (FWGR) in different stationary and non-stationary environmental conditions. This optimization algorithm works on the principle of atmospheric motion of very small air particles, which revolves over the multi-dimensional search area. In the present study, WDO algorithm is employed to search a minimal or near-minimal steering angle for the (FWGR); this steering angle minimizes the path length during motion, orientation, and collision avoidance. The objective function for the WDO algorithm has been created for two reasons: for obstacle avoidance and traveling path optimization in the environments from the source point to the endpoint. Simulation results demonstrate that the FWGR covers a shorter path length using WDO algorithm as compared to the path length obtained by the FWGR using particle swarm optimization (PSO) algorithm and genetic algorithm (GA).
Automated Assessment of Building Damage from Seismic Events Using Smartphones

This paper addresses automated reconnaissance of building damage from seismic events using smartphones. Damage assessment is enabled by computation of interstory drift ratios (IDR) and comparison of those ratios with known damage limits. Through this process, it is possible to electronically tag buildings in terms of their level of damage right after a seismic event. To illustrate this idea, this paper presents a process for estimating IDR at each floor. The premise is that each floor has a number of smartphones providing records of the acceleration response. The proposed method addresses noise reduction, sliding detection, data fusion, and double integration errors. Shake table testing is conducted to demonstrate and validate the proposed process. The experimental variables include number of smartphones and smartphone type, and hence noise associated with the measured signal, and friction coefficient of the shells used to protect the phones. It is shown that multiple smartphone records need to be aggregated to reduce stochastic errors but the presence of amplitude dependent scale factor errors in current smartphone technology limit the value of using too many devices. These limitations are expected to be alleviated as the quality of smartphone accelerometers improves in the future.
A Prosodic Mandarin Text-to-Speech System Based on Tacotron

The Tacotron performs well in English speech synthesis and successfully aligns two arbitrary sequences from different domain in an automatic way. However, to introduce Tacotron into Mandarin Chinese Text-to-Speech (TTS), a prosody system is needed for generating more natural speech. This paper proposes a practical method to involve the prosodic annotation into Tacotron training for Mandarin Chinese synthesis system. A prosody model predicting the prosodic boundaries from the given text serves as the front-end system in our approach, followed by a Tacotron synthesis system trained with well-labeled TTS database containing the prosodic annotations. Under subjective evaluation in terms of the prosody, results show that the synthesis system performs better by adding the prosodic system as the front-end system for Tacotron.
A Microservices-Based Virtualized Blockchain Framework for Emerging 5G Data Networks

The paper addresses the possibility of leveraging a microservices-based paradigm to leverage blockchain technology in the emergent programmable distributed infrastructure for 5G and future networks. Different use-cases such as user data privacy management, healthcare emergency services, or edge information delivery, and for emerging 5G use-cases leveraging such distributed blockchain infrastructure are considered. In particular, for data privacy related management, a separation of the control plane and the user/data plane is considered to record blockchain transaction data in the control plane, while recording a hash associated with the transaction in the user/data plane. The paper also discusses possible future extensions in emerging future networks.