Reviewerly/exHarmony huggingface dataset:
Precision medicine in digital pathology via image analysis and machine learning

In this chapter, we present the concept that digital pathology and artificial intelligence can add value and speed to a pathologist's diagnosis while striving toward precision medicine. We describe how image analysis and machine learning can segment images and compute object- and spatial-based data prior to analysis. This data analysis can identify patients who are at a high risk of succumbing to a disease, who may need more detailed clinical follow-up, or who will respond to specific therapy. We also describe how deep learning algorithms can learn complex morphological patterns from both human- and data-led input in order to perform diagnostic or prognostic tasks. Finally, we discuss the theory behind some commonly used machine learning algorithms and how they may attain regulatory approval.
Why Do Software Developers Use Static Analysis Tools? A User-Centered Study of Developer Needs and Motivations

As increasingly complex software is developed every day, a growing number of companies use static analysis tools to reason about program properties ranging from simple coding style rules to more advanced software bugs, to multi-tier security vulnerabilities. While increasingly complex analyses are created, developer support must also be updated to ensure that the tools are used to their best potential. Past research in the usability of static analysis tools has primarily focused on usability issues encountered by software developers, and the causes of those issues in analysis tools. In this article, we adopt a more user-centered approach, and aim at understanding why software developers use analysis tools, which decisions they make when using those tools, what they look for when making those decisions, and the motivation behind their strategies. This approach allows us to derive new tool requirements that closely support software developers (e.g., systems for recommending warnings to fix that take developer knowledge into account), and also open novel avenues for further static-analysis research such as collaborative user interfaces for analysis warnings.
Remote State Estimation With Stochastic Event-Triggered Sensor Schedule and Packet Drops

This article studies the remote state estimation problem of linear time-invariant systems with stochastic event-triggered sensor schedules in the presence of packet drops between the sensor and the estimator. Due to the existence of packet drops, the Gaussianity at the estimator side no longer holds. It is proved that the system state conditioned on the available information at the estimator side is Gaussian mixture distributed. The minimum-mean-square-error (MMSE) estimator can be obtained from the bank of Kalman filters. Since the optimal estimators require exponentially increasing computation and memory with time, suboptimal estimators to reduce computational complexities by limiting the length and numbers of hypotheses are further provided. In the end, simulations are conducted to illustrate the performance of the optimal and suboptimal estimators.
Home service robot task planning using semantic knowledge and probabilistic inference

In the face of unstructured home environment, home service robots are inevitably confronted with uncertainty and incompleteness of environment information. How to make the home service robot obtain enough environment information and plan a discrete sequence of actions through task planning is the key problem of robot intelligence. In this paper, a hierarchical task network based on semantic knowledge and probabilistic inference method is proposed. We use the object location ontology, the location relation between dynamic and static objects to build semantic knowledge of home environment, and build the probability model between dynamic and static objects, as well as between static objects and home scenes. The location of the object is determined by the semantic knowledge and the probability model. Hierarchical task network is selected as an engine of task planner, which can be provided with the location information to improve the autonomy and effectiveness of robot task planning. In order to prevent task execution failure and enhance the adaptability of robot to unstructured home environment, a mechanism of task execution diagnosis and replanning is designed. Experimental results in simulation and real home environment demonstrate that our method can effectively improve the performance of service robot task planning and generate better task execution sequence.
A Dynamic Task Scheduling Algorithm Improved by Load Balancing in Cloud Computing

Task scheduling is the main challenge for the service provider in cloud Computing. One of the most critical objective in the scheduling is to assign tasks to virtual machines so that some machines do not overload or under load. To do this, load balancing plays a crucial role in the scheduling problem. Using an appropriate load balancing method can reduce response time and increase resource utilization. In this paper, we present a dynamic method for scheduling a task to virtual machines to increase load balancing and reliability in cloud computing. The proposed method reduces the makespan, increases the degree of load balancing, and improves the system's reliability. The proposed method, in contrast to previous work, has been able to increase the reliability of the task scheduling based on previous experience of the virtual machines in addition to the fair distribution of workload among the virtual machines. We have compared the proposed algorithm with other task scheduling algorithms such as the honeybee load balancing and dynamic scheduling without load balancing. Simulation results show that the proposed method improves the reliability and degree of imbalance.
IoTShare: A Blockchain-Enabled IoT Resource Sharing On-Demand Protocol for Smart City Situation-Awareness Applications

We propose a blockchain-based, distributed protocol for enabling the deployment of Internet-of-Things (IoT) networks on-demand on top of IoT devices. Specifically, the proposed protocol leverages blockchain technology to: 1) enable distributed and secure authentication, registration, and management of participatory IoT devices; 2) provide fast discovery of IoT resources and scalable and secure instantiation of IoT networks-on-demand; and 3) manage payment operations and ensure reliable fund transfers among the network entities. The proposed protocol relies on a peer-to-peer network communication infrastructure to allow communications among the IoT devices in a distributed manner and uses a self-recovery/self-healing mechanism to ensure robustness against device failure and maliciousness. The protocol also introduces and uses a reputation system to monitor registered devices to keep track of their service delivery quality so that their service delivery reputations could be leveraged for future device selection and mapping. We implemented and evaluated the proposed protocol intensively using simulations and showed that it scales well with network parameters, is resilient to faulty devices, and is robust to 51% attack.
Blockchain-Based Applications in Higher Education

The utilisation of blockchain has moved beyond digital currency to other fields such as health, the Internet of Things, and education. In this paper, we present a systematic mapping study to collect and analyse relevant research on blockchain technology related to the higher education field. The paper concentrates on two main themes. First, it examines state of the art in blockchain-based applications that have been developed for educational purposes. Second, it summarises the challenges and research gaps that need to be addressed in future studies.
Supervised feature selection through Deep Neural Networks with pairwise connected structure

Feature selection is an important data preprocessing strategy, has been proven empirically that it contributes to reducing the dimensionality of feature and enhancing the performance of learning algorithms in practice. Typical sparse learning-based models select the features by removing ones that the feature scores are zero. However, linear models puzzle to build the non-linear relations between features and responses. The Deep Neural Network (DNN) has a strong capability to mode the non-linear relations and has been employed to select features. In this paper, we introduce a novel deep Neural network-based Feature Selection (NeuralFS) method to identify features. The new model comprises of a fully-connection network, a decision network, and connect them through a pairwise connected structure. In NeuralFS, the fully-connected network is the crucial structure in NeuralFS that transforms the features into their corresponding scores, and the decision network is the final structure that performs classification or regression. The pairwise connected can be regarded as a “bridge” to connect the two networks, and its weights are fixed as the normalized input as well as it is un-trainable during model training. After optimizing, the feature scores can be obtained by calculating the output of the fully-connected network. NeuralFS takes advantage of the deep network to model the non-linearity, and also make features scores sparse without the sparse regularization technology. We apply the proposed method to both synthetic datasets and benchmark datasets to prove its effectiveness.
Comparative Analysis of Inter-Centroid K-Means Performance using Euclidean Distance, Canberra Distance and Manhattan Distance

Abstract Clustering is a method needed to group data or objects based on the required level between data, K-means is one of the clustering methods used that can be used easily in its implementation, there are some additions to this method according to the data center and on the weighting of the distance between data, the weighting of the distance between data on K-Means traditionally can be done using Euclidean Distance, Canberra Distance and Manhattan Distance, making this an analysis of the accuracy generated from the method produced by a combination of the Z-score and Min-Max Normalization methods, and is carried out Cluster homogeneity test using the Silhouette Coefficient method. The results of this method show that the Canberra method is superior to Euclidean and Manhattan on Iris dataset and the Canberra combination method with Z-score and Min-Max can increase the value on the glass without using the Normalization Method 37. 44% to 67.46% use the Z-score and 56.52% use Min-Max and use an increase in the average value of the Silhouette Coefficient.
Sequence Compression Benchmark (SCB) database—A comprehensive evaluation of reference-free compressors for FASTA-formatted sequences

Abstract Background Nearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for a more efficient compression tool. Although numerous compressors exist, both specialized and general-purpose, choosing one of them was difficult because no comprehensive analysis of their comparative advantages for sequence compression was available. Findings We systematically benchmarked 430 settings of 48 compressors (including 29 specialized sequence compressors and 19 general-purpose compressors) on representative FASTA-formatted datasets of DNA, RNA, and protein sequences. Each compressor was evaluated on 17 performance measures, including compression strength, as well as time and memory required for compression and decompression. We used 27 test datasets including individual genomes of various sizes, DNA and RNA datasets, and standard protein datasets. We summarized the results as the Sequence Compression Benchmark database (SCB database, http://kirr.dyndns.org/sequence-compression-benchmark/), which allows custom visualizations to be built for selected subsets of benchmark results. Conclusion We found that modern compressors offer a large improvement in compactness and speed compared to gzip. Our benchmark allows compressors and their settings to be compared using a variety of performance measures, offering the opportunity to select the optimal compressor on the basis of the data type and usage scenario specific to a particular application.
mmWave Radar-based Hand Gesture Recognition using Range-Angle Image

The radar sensing on fine human-motion/hand-gesture provides further human-computer interaction (HCI) experience. Most of the studies about gesture recognition with mmWave frequency modulated continuous wave (FMCW) radar adopts the range and the velocity estimated from the raw data, such as the time-frequency spectrogram, micro-doppler spectrogram, or range-Doppler image (RDI). Besides, the angle estimated using multiple receive antennas also contains rich information of gesture, especially in the discrimination among the horizontal movement of the gesture. Thus, we propose to use the range-angle image (RAI) as the input and train a model consisting of the convolutional neural network and long short term memory that is capable of recognizing hand gestures. We validate the proposed scheme based on the collection of hand gestures by several subjects in different classrooms using 77 - 81GHz mmWave radar of Texas Instrument. Based on the configuration of one transmit antenna and four receive antennas, we show that the hand gesture recognition using RAI outperforms that using RDI. Also, we adopt the fusion strategy to consider both RDI and RAI to further improve accuracy.
A Graph Model Based Blockchain Implementation for Increasing Performance and Security in Decentralized Ledger Systems

Blockchains are being recently used as a supporting technology framework for decentralized applications requiring functionalities such as exchange of value through tokens, cryptocurrency and smart contracts. In this paper, we have developed a decentralized application model in Python, where blockchain data are stored in a Neo4j graph database. Following the basic principles of Ethereum blockchain network, we implemented a Casper-like consensus mechanism and tested its effectiveness in achieving finality. For block proposing, we employed both Proof of Work and Proof of Stake protocols and examined how participants' incentives and consensus criteria differ according to each one. A major part of this work is to incorporate the graph model in the functionality of the blockchain and its components, while also exploiting its benefits in data analysis by finding relationships between data and extracting their true value. Through this approach, we were able to monitor and visualize changes in blockchain data in various use case scenarios. Lastly, we ran a series of simulated experiments to test the efficiency of the implemented technologies and mechanisms in preventing the most common blockchain attacks such as the 51% Attack, Catastrophic Crashes and Attack from dynamic validator sets. We show how the modelling of the blockchain data as a distributed graph can assist protocols operations, enhance their security, and facilitate the application of analytical methods to the stored information through path-dependent queries.
Improving Text Preprocessing For Student Complaint Document Classification Using Sastrawi

Abstract In the text mining there are stages that must be passed namely the text preprocessing stage. Text preprocessing is the stage to do the data selection process in each document, including case folding, tokenizing, filtering, and stemming. The results of the preprocessing process can affect the accuracy of document classification. In documents Bahasa Indonesia, there are still often over-stemming and under-stemming, so improvements are needed in the stemming process. In this study, it is proposed to use sastrawi libraries to improve the results of previous studies that are still not optimal in the results of preprocessing, especially in the filtering and stemming process. From the results of the study, the sastrawi library is able to reduce over stemming and under stemming and a faster processing time compared to using a Tala stemmer.
An empirical comparison of commercial and open‐source web vulnerability scanners

Summary Web vulnerability scanners (WVSs) are tools that can detect security vulnerabilities in web services. Although both commercial and open‐source WVSs exist, their vulnerability detection capability and performance vary. In this article, we report on a comparative study to determine the vulnerability detection capabilities of eight WVSs (both open and commercial) using two vulnerable web applications: WebGoat and Damn vulnerable web application. The eight WVSs studied were: Acunetix; HP WebInspect; IBM AppScan; OWASP ZAP; Skipfish; Arachni; Vega; and Iron WASP. The performance was evaluated using multiple evaluation metrics: precision; recall; Youden index; OWASP web benchmark evaluation; and the web application security scanner evaluation criteria. The experimental results show that, while the commercial scanners are effective in detecting security vulnerabilities, some open‐source scanners (such as ZAP and Skipfish) can also be effective. In summary, this study recommends improving the vulnerability detection capabilities of both the open‐source and commercial scanners to enhance code coverage and the detection rate, and to reduce the number of false‐positives.
Sentiment Analysis via Deep Multichannel Neural Networks With Variational Information Bottleneck

With the rapid development of e-commerce, online consumption has become a mainstream form of consumption in recent years. Text sentiment analysis for a large number of customer reviews on the e-commerce platform can dramatically improve the customers’ consumption experience. Although the sentiment analysis approaches based on deep neural network can achieve higher accuracy without human-design features compared with traditional sentiment analysis methods, the accuracy still cannot meet the demand and the training suffers from the issues of over-fitting, vanishing gradient, etc. In this paper, a novel sentiment analysis model named MBGCV is designed to alleviate these problems and improve the accuracy, MBGCV employs a multichannel paradigm and integrates Bidirectional Gated Recurrent Unit (BiGRU), Convolutional Neural Network (CNN) and Variational Information Bottleneck (VIB). The multichannel is exploited to extract multi-grained sentiment features. In each channel, BiGRU is utilized to extract context information, and then CNN is adopted to extract local features. Furthermore, the model combines the advantages of VIB and Maxout activation function to overcome shortcomings such as over-fitting, vanishing gradient in existing sentiment analysis models. By using real review datasets, we carry out extensive experiments to demonstrate that our proposed model can achieve superior accuracy and improve the performance of text sentiment analysis.
Regulating Blockchain, DLT and Smart Contracts: a technology regulator’s perspective

Abstract Blockchain, Smart Contracts and other forms of Distributed Ledger Technology provide means to ensure that processes are verifiable, transparent, and tamper-proof. Yet the very same enabling features that bring decentralisation also pose challenges to providing protection for the various users and stakeholders. Most jurisdictions which have implemented regulatory frameworks in this area have focused on regulating the financial aspects of cryptocurrency-based operations. However, they have not addressed technology assurance requirements. In this paper we present a world-first technology regulatory framework.
Blockchain Architecture for Auditing Automation and Trust Building in Public Markets

Business transactions by public firms must be reported, verified, and audited periodically, which is labor intensive and time consuming. To streamline this procedure, we have designed Future Auditing Blockchain to automate the reporting and auditing process, allowing auditors to focus on discretionary accounts to better detect and prevent fraud.
A two-stage real-time YOLOv2-based road marking detector with lightweight spatial transformation-invariant classification

In recent years, Autonomous Driving Systems (ADS) become more and more popular and reliable. Road markings are important for drivers and advanced driver assistance systems by better understanding the road environment. While the detection of road markings may suffer a lot from various illuminations, weather conditions and angles of view, most traditional road marking detection methods use fixed threshold to detect road markings, which is not robust enough to handle various situations in the real world. To deal with this problem, some deep learning-based real-time detection frameworks such as Single Shot Detector (SSD) and You Only Look Once (YOLO) are suitable for this task. However, these deep learning-based methods are data-driven even while there is no public road marking dataset. Besides, these detection frameworks usually struggle with distorted road markings and balancing between the precision and recall. We propose a two-stage YOLOv2-based network to tackle distorted road marking detection as well as to balance precision and recall. The proposed spatial transformer layer is able to handle the distorted road markings in the second stage, so as to achieve the improvement of precision. Our network is able to run at 58 FPS in a single GTX 1070 under diverse circumstances. Furthermore, we present a dataset for the public use of road marking detection tasks, which consists of 11,800 high-resolution images captured under different weather conditions. Specifically, the images are manually annotated into 13 classes with bounding boxes. We empirically demonstrate both mean average precision (mAP) and detection speed of our system over several baseline models.
What is BitChute?

In this paper, we characterize the content and discourse on BitChute, a social video-hosting platform. Launched in 2017 as an alternative to YouTube, BitChute joins an ecosystem of alternative, low content moderation platforms, including Gab, Voat, Minds, and 4chan. Uniquely, BitChute is the first of these alternative platforms to focus on video content and is growing in popularity. Our analysis reveals several key characteristics of the platform. We find that only a handful of channels receive any engagement, and almost all of those channels contain conspiracies or hate speech. This high rate of hate speech on the platform as a whole, much of which is anti-Semitic, is particularly concerning. Our results suggest that BitChute has a higher rate of hate speech than Gab but less than 4chan. Lastly, we find that while some BitChute content producers have been banned from other platforms, many maintain profiles on mainstream social media platforms, particularly YouTube. This paper contributes a first look at the content and discourse on BitChute and provides a building block for future research on low content moderation platforms.
Research on Power Load Forecasting Method Based on LSTM Model

Power load forecasting is an important part of power system planning and the foundation of power system economic operation. It is very important for power system planning and operation. The LSTM forecast model is used to Get more accurate power load prediction results. According to the time series rule of power load, the LSTM prediction model for load prediction is established in this paper. A verification experiment has been done to reflect the effect of this method. Experimental results show that accuracy of power load prediction is increased by using LSTM model.