Reviewerly/exHarmony huggingface dataset:
Security in the Cloud: understanding your responsibility

The popularity of public cloud services continues to grow with Gartner predicting the total worldwide revenue to almost double from $145 billion in 2017 to $278 billion in 2021<sup>1</sup> . Many cloud service types are components of this growth including Software-as-a-Service (SAAS), Platform-as-a-Service (PAAS) and Infrastructure-as-a-Service (IAAS). The use of cloud services brings many possible benefits such as scalability, high performance and availability, flexibility, cost effectiveness and security 2 . However; each of these benefits comes with some responsibilities and requires a detailed knowledge of the specific cloud services used. For example, in Amazon Web Services (AWS) shared responsibility model for security, AWS is responsible for securing the facilities, physical security of hardware, network infrastructure, and the virtualization infrastructure. The cloud service customer is responsible for securing and managing the applications that run in the cloud, the operating systems, data-at-rest, data-in-transit, policies and other responsibilities. This paper works through several different use cases and provides the details for properly securing the services with which Army Research Laboratory (ARL) researchers interact. The use cases include sample configurations and descriptions required to fulfill the customer security responsibilities in a public cloud environment. Cloud services used include AWS Elastic Computer Cloud (EC2) Windows and Linux instances, Relational Database Services (RDS), Simple Cloud Storage Service (S3), Glacier S3 Storage, and DynamoDB. Challenges and approaches associated with delegating temporary security credentials, Identity and Access Management (IAM) service, and securing data-at-rest and data-in-transit will also be discussed.
Rule-based program specialization to optimize gradually typed code

Both static and dynamic typing provide different benefits to the programmer. Statically typed languages support earlier type error detection and more opportunities for compiler optimizations. Dynamically typed languages facilitate the development of runtime adaptable applications and rapid prototyping. Since both approaches provide benefits, gradually typed languages support both typing approaches in the very same programming language. Gradual typing has been an active research field in the last years, turning out to be a strong influence on commercial languages. However, one important drawback of gradual typing is the runtime performance cost of the additional type checks performed at runtime. In this article, we propose a rule-based program specialization mechanism to provide significant performance optimizations of gradually typed code. Our system gathers dynamic type information of the application by simulating its execution. That type information is used to optimize the generated code, reducing the number of type checks performed at runtime. Moreover, program specialization allows the early detection of compile-time type errors, providing static type safety. To ensure the correctness of the proposed approach, we prove its soundness and efficiency properties. The specialization system has been implemented as part of a full-fledged programming language, measuring the runtime performance gain. The generated code performs significantly better than the state-of-the-art techniques to optimize dynamically typed code. Unlike the existing approaches, our system does not consume additional memory resources at runtime, because program specialization is performed statically. Program specialization involves an average compilation time increase from 2% to 11.75%.
Sub-bottom acoustic profiling as a remediation assessment tool for contaminated lakes

Frame Lake, a small (88.4 ha), shallow (< 6.5 m maximum depth), high-latitude lake found within the city limits of Yellowknife, Northwest Territories, Canada was selected due to the known legacy contamination of the lake’s sediments to test the feasibility of using seismic sub-bottom profiling to estimate total volumes of heavy metal contaminated sediments in lacustrine environments. To ground-truth the sub-bottom profiling results, physical and ICP-MS analyses were carried out on freeze cores collected from Frame Lake’s southern basin, and sedimentological marker beds and 14C dating was used to chronologically constrain the lake depositional history. ICP-MS results showed high levels of arsenic contamination (up to 1538 µg g−1) in late twentieth-century lake sediments, which contrasts sharply with measured Holocene values that averaged only 16 µg g−1 (n = 41, ± 5.4 SD). The high arsenic content in lakebed sediments, which tends to be concentrated within specific horizons, results in distinct seismic reflectors within the acquired Sonar data. Stratigraphic horizons where arsenic was concentrated do not necessarily correlate with actual depositional events as changes in lake hydrology and redox conditions have resulted in remobilization and migration of arsenic in lake sediments. Direct GIS software comparison of core data against the sub-bottom profiler transect results permitted an interpolated lateral and vertical reconstruction of the distribution of variously contaminated sediments throughout the entire lake basin. Based on our analysis, a minimum of ~ 230,000 m3 of contaminated sediments would need to be dredged from Frame Lake to achieve a minimum residual sediment arsenic concentration of < 150 µg g−1.
Forensic Analysis of LinkedIn’s Desktop Application on Windows 10 OS

The convenient and cheap access to mobile phones and laptops have significantly increased the use of interactive applications over the past couple of years. However, this has posed various threats to legitimate users in terms of sensitive data disclosure, if their device gets lost, compromised or stolen. This study focuses on the forensic analysis of Windows AppStore applications with special focus on LinkedIn’s Desktop application; since it is one of the most downloaded applications from Windows AppStore. The paper first provides a systematic literature review of the existing digital forensic analysis techniques and highlights their weaknesses. A comprehensive novel methodology for manual forensic analysis of Windows App Store application on Windows 10 Operating System (OS) has also been proposed. For experimentation purpose, LinkedIn’s desktop application has been targeted. The research considers all kinds of scenarios such as logged in users, logged out users and intentional data deletion etc. It is finally concluded that from the viewpoint of application forensic analysis, the live, storage and registry analysis, all hold equal importance.
A Taxonomy of Event Prediction Methods

Most of existing event prediction approaches consider event prediction problems within a specific application domain while event prediction is naturally a cross-disciplinary problem. This paper introduces a generic taxonomy of event prediction approaches. The proposed taxonomy, which oversteps the application domain, enables a better understanding of event prediction problems and allows conceiving and developing advanced and context-independent event prediction techniques.
Survey on Mitosis Detection for Aggressive Breast Cancer from Histological Images

The mitotic count is a relevant factor for grading invasive breast cancer. Since it is subject to human prone error, requires more time for completion and the nuclei look similar during all stages of mitosis, automatic detection of mitosis is a good solution to overcome these problems. In this paper, the top methodologies used for mitosis detection are analyzed. Some of them were a part of challenging competitions conducted worldwide. Analysis of the result shows that top approaches, either implemented Random Forest (RF) classifier exploiting intensity feature or used deep learning methods like Convolutional Neural Network (CNN) to give out the best results. It was also found that the ensemble classifiers gives better performance. A preliminary experiment conducted on cascaded RF and Artificial Neural Network (ANN) results in better accuracy than individual classifiers.
Performance Analysis of Compression Techniques Using LM Algorithm and SVD for Medical Images

This paper presents the techniques for compression and decompression of MRI, CT and X-ray images. These medical images are the pictorial representation of inner parts of human body which are used for the analysis of critical diseases. As the vast amount of data is required to store medical images for future reference of the patients and for the transmission, there is need to use image compression methods without reducing the quality of information. Two methods of compression i.e. back propagation neural network with LM training algorithm (BPNNLM) and Singular Value Decomposition (SVD) are used in this paper. The results of these two techniques are compared with respect to the performance metrics of Peak Signal to Noise Ratio (PSNR), Mean Squared Error and Structural Similarity Index Measurement (SSIM). From the results, it is observed that SVD image compression technique based on singular values provides more PSNR, less MSE and better SSIM values compared to BPNNLM Technique.
A multi-sensor algorithm for activity and workflow recognition in an industrial setting

In the recent revival of human labour in industry, and the subsequent push to optimally combine the strengths of man and machine in industrial processes, there is an increased need for methods allowing machines to understand and interpret the actions of their users. An important aspect of this is the understanding and evaluation of the progress of the workflows that are to be executed. Methods for this require both an appropriate choice of sensors, as well as algorithms capable of quickly and efficiently evaluating activity and workflow progress.
Exploiting LSTM for Joint Object and Semantic Part Detection

Object detection and semantic part detection are two tasks that can mutually benefit each other. Thus, in this paper we propose an approach to perform joint detection. Our approach is built upon a proposal-driven detection framework. In order to explore the mutual interaction between two tasks, we integrate an interaction module into the detection framework. The module contains a relationship modeling stage and a feature fusion stage. The former determines the relationship between each object proposal and semantic part proposal by formulating it as a binary classification problem. The second stage adopts Long Short Term Memory networks (LSTMs) to fuse the features of an object and its associated parts, as well as the features of a part and its associated object proposals. Experiments on publicly available datasets show that, with the proposed interaction module, our joint detection approach consistently outperforms the baselines using object or part appearance only. Our approach also shows superior performance when compared to other semantic part detection methods.
Confluence by Critical Pair Analysis Revisited

We present two methods for proving confluence of left-linear term rewrite systems. One is hot-decreasingness, combining the parallel/development closedness theorems with rule labelling based on a terminating subsystem. The other is critical-pair-closing system, allowing to boil down the confluence problem to confluence of a special subsystem whose duplicating rules are relatively terminating.
Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods

Abstract The stationary distribution is the key of any queueing system; its determination is sufficient to infer the corresponding characteristics. This paper deals with the <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>Er</m:mi> <m:mo>/</m:mo> <m:mi>M</m:mi> <m:mo>/</m:mo> <m:mn>1</m:mn> </m:mrow> </m:math> {\mathrm{Er}/M/1} queue. Bayesian inference is developed to estimate the system parameters, specially the root of the relative equation which allows the determination of the stationary distribution. A numerical study is performed with MCMC methods to support the results, and a comparison with another existing method in the literature (moments method) is provided.
Explosion gravitation field algorithm with dust sampling for unconstrained optimization

Gravitation Field Algorithm (GFA) is a novel optimization algorithm derived from the Solar Nebular Disk Model (SNDM) in astronomy and inspired by the formation process of planets. Although it has achieved good performance when solving many unconstrained optimization problems, which demonstrated its promising application potential in many real-world problems, GFA still has much room for improvement, especially when it comes to the accuracy and efficiency of the algorithm. In this research, an improved GFA algorithm called Explosion Gravitation Field Algorithm (EGFA) is proposed for unconstrained optimization problems, with the introduction of two strategies: Dust Sampling (DS) and Explosion Operation. The task of DS is to locate the space that contains the optimal solution(s) by initializing the dust population randomly in the search space; while the Explosion Operator is to improve the accuracy of solutions and decrease the probability of the algorithm falling into local optima by generating the new population around the center dust to replace the original population. A comparison of experimental results on six classical unconstrained benchmark problems with different dimensions demonstrates that the proposed EGFA outperforms the original GFA and several classical metaheuristic optimization algorithms, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in terms of accuracy and efficiency in lower dimensions. Additionally, the comparison of results on three real datasets indicate that EGFA performs better than the original GFA and k-means for solving clustering problems.
HSIC regularized manifold learning

At present, manifold learning is mainly applied to dimensionality reduction. However, from viewpoint of dimensionality reduction, manifold learning algorithms are only local feature preserving algorithms. For example, Local Linear Embedding is local linear preserving, Local Tangent Space Alignment is local homeomorphic preserving and Laplacian Eigenmap is local similarity preserving. The community of dimensionality reduction is now pursuing the algorithms which can preserve both local and global features of data during dimensionality reduction. In this paper, a new algorithm of dimensionality reduction, called Hilbert-Schmidt Independence Criterion Regularized Manifold Learning (HSIC-ML for short), is proposed, in which HSIC between the high dimensional data and the dimension-reduced data is added as a regularization term to the objective functions of manifold learning. The addition of HSIC regularization term makes HSIC-ML capable of preserving both local and global features during dimensionality reduction. HSIC is a criterion measuring the statistical dependence between two data sets and has been widely applied to machine learning in recent years. However, since HSIC was first proposed around 2005, there seems to have not been applied directly to dimensionality reduction, not applied as a regularization term either. The proposed HSIC-ML may be the first try in this respect. The experimental results presented in this paper show that the manifold learning with HSIC regularization performs better than that without HSIC regularization.
A Novel Kohonen Self-organizing Maps Using Exponential Decay Average Rate of Change for Color Clustering

Kohonen Self-Organizing Maps (KSOM) is the most preferred and commonly used algorithm for clustering high-dimensional or multi-dimensional data. Its success in various domains are undeniable but still, it suffers issues on its convergence which is directly affected by its learning rate decay function. This study adopted Average Rate of Change to modify the learning rate decay function of the algorithm and proposes a new Exponential Decay Average Rate of Change (EDARC) as the modified learning rate decay function of the Enhanced Kohonen Self-Organizing Maps (EKSOM) to address its issue on convergence. The enhanced algorithm and the conventional KSOM was applied to color clustering. The results show that EKSOM outperformed the clustering capability of the conventional KSOM algorithm.
Multi-label classification of Indonesian news topics using Pseudo Nearest Neighbor Rule

News is a form of text data that must be categorized to facilitate retrieval of information for the reader. One problem that arises when categorizing news is the many topics that news can discuss, which is known as a multi-label condition. To solve this problem, a system that can perform multi-label classification using a Pseudo Nearest Neighbor Rule (PNNR) algorithm—a variant of the k-Nearest Neighbor (k-NNR) algorithm—was developed in this study. This system yielded a cross-validation error of 0,1495, measured using the hamming loss method via Cosine proximity. From the experiment, it can be concluded that the performance of the PNNR algorithm is influenced by the type of proximity used and the number of nearest neighbors.
Representation Learning on Graphs by Integrating Content and Structure Information

The problem of representation learning on graph can be difficult due to limited knowledge of training data and large presence of missing edges. Real-world social networks do not provide complete information about the network due to hidden information and privacy constraints. In such scenarios, typical representation learning methods are not able to capture network information effectively. In order to make them more useful, any available feature information can be used in addition to the network structure. In this paper, we aim to learn better representations by exploiting both content (or feature) information of nodes and structural information of the network. Our approach leverages generative adversarial networks to learn embedding for generator and discriminator in a minimax game. While the generator estimates the neighborhood of a node, the discriminator distinguishes between the presence or absence of a link for a pair of nodes. We demonstrate the effectiveness of our approach on five real-world publicly available datasets on the problems of link prediction and node classification. On both tasks, we achieve significant gains, outperforming current state-of-the-art methods by considerable margins. Our code is available on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
TweetSenti: Target-dependent Tweet Sentiment Analysis

TweetSenti is a system for analyzing the sentiment of an entity in tweets. A sentence or tweet may contain multiple entities, and they do not always have the same sentiment polarity. Therefore, it is necessary to detect the sentiment for a specific target entity. This type of target-dependent (entity level) sentiment analysis has become attractive and has been used in many applications, but it is still a challenging task. TweetSenti employs a new approach for detecting the entity level sentiment. Our model splits a sentence into a left context and a right context according to the target entity, and it also exploits two different types of word embeddings to represent a word, the general word embedding and the sentiment specific word embedding. A hybrid neural network is used to capture both the sequence and structure information of the two sides of the target entity. The sequence information is learned by attention-based bi-directional LSTM models. The structure information is captured by multi-context CNN models. Based on this algorithm, we built a web-based application that users can interact with and analyze an entity's sentiment in Twitter at real-time.
Roadside Traffic Sign Detection Based on Faster R-CNN

This paper presents an improved traffic sign detection method based on Faster R-CNN with dataset augmentation and subcategory detection scheme. Firstly, we extract natural scene frames from given videos and determine 20 categories of traffic signs. Secondly, we extend the image dataset and extract regions of interest, then manually annotate all categories. Thirdly, we train the Faster R-CNN model based on TensorFlow, then test the model and obtain the following evaluation indexes: the mean average precision is 99.07%, the recall rate is 99.66%, and the precision rate is 97.54%. Finally, we add the subcategory detection scheme to determine traffic light states, and we get the following evaluation indexes: the mean average precision is 99.50\%, the recall rate is 100%, and the precision rate is 94.40\%. Our experiments prove the robustness and accuracy for both traffic sign detection and subcategory detection of traffic light.
Can You Give Me a Reason?: Argument-inducing Online Forum by Argument Mining

This demonstration paper presents an argument-inducing online forum that stimulates participants with lack of premises for their claim in online discussions. The proposed forum provides its participants the following two subsystems: (1) Argument estimator for online discussions automatically generates a visualization of the argument structures in posts based on argument mining. The forum indicates structures such as claim-premise relations in real time by exploiting a state-of-the-art deep learning model. (2) Argument-inducing agent for online discussion (AIAD) automatically generates a reply post based on the argument estimator requesting further reasons to improve the argumentation of participants.
TypeCNN: CNN Development Framework With Flexible Data Types

The rapid progress in artificial intelligence technologies based on deep and convolutional neural networks (CNN) has led to an enormous interest in efficient implementations of neural networks in embedded devices and hardware. We present a new software framework for the development of (approximate) convolutional neural networks in which the user can define and use various data types for forward (inference) procedure, backward (training) procedure and weights. Moreover, non-standard arithmetic operations such as approximate multipliers can easily be integrated into the CNN under design. This flexibility enables to analyze the impact of chosen data types and non-standard arithmetic operations on CNN training and inference efficiency. The framework was implemented in C++ and evaluated using several case studies.