Reviewerly/exHarmony huggingface dataset:
Cyberspace Surveying and Mapping: Hierarchical Model and Resource Formalization

Cyberspace surveying and mapping (CSM) is to measure, analyze, discover and visualize the cyberspace resources and their relationships. It is an efficient methodology to understand the status of the cyberspace. In this paper, we discuss the concept of CSM and cyberspace resources, and propose a hierarchical model for CSM from the point of view of its application. Then, we propose a formalized representation framework of cyberspace resources based on knowledge graph. Finally, we design the application scenarios for CSM in terms of data sharing, intelligent assets indexing and identification. This paper aims to motivate the researches on CSM in theory and applications.
Feature Selection Techniques to Predict the Religion of a Country from Its Flag

Feature selection is a process of preparing data to be more effective and efficient for machine learning problems. The purpose of feature selection is to select relevant features from huge number of features. To build a simple model which will be easy to understand data and take less time to train the model, thereby optimizing model performance. The paper proposes two feature selection techniques namely Lasso and Select From Model (meta-transformer) to select relevant features from flag dataset that intensifies the model performance. For the prediction of religion of a country, three tree-based classifiers are used—random forest, decision tree, and extra trees model. Among these, random forest classifier gives best prediction.
Unrestricted Generalized Column Distances: A Wider Definition

In this work we introduce the concept of Unrestricted Generalized Column Distance (UGCD) for convolutional codes. This is the concept equivalent to the generalized Hamming weights for block codes. We show that the hierarchy of UGCD is strictly increasing and show how to compute it of parity-check matrix in general form. We also provide a way to compute it out of a systematic parity-check matrix.
Proactive Knowledge-Goals Dialogue System Based on Pointer Network

Human-machine dialogue is the hot spot of current research. In this paper, we proposed an end-to-end dialogue system based on external knowledge, which realized active guidance and topic transfer in multiple rounds of dialogue. Our system is built on the pointer generator model so that the output token in the response can be generated or copied from the conversation history or background knowledge according to a trainable action probability distribution. At the same time, with the data processing and optimization of the model structure, the designed system is capable of generating high quality responses. In the 2019 NLP Language and Intelligence Challenge, our proposed dialogue system ranked third in the automatic evaluation, and ranked fifth in the manual evaluation.
Spacetime Programming

Search strategies are crucial to efficiently solve constraint satisfaction problems. However, programming search strategies in the existing constraint solvers is a daunting task and constraint-based languages usually have compositionality issues. We propose spacetime programming, a paradigm extending the synchronous language Esterel and timed concurrent constraint programming with backtracking, for creating and composing search strategies. In this formalism, the search strategies are composed in the same way as we compose concurrent processes. Our contributions include the design and behavioral semantics of spacetime programming, and the proofs that spacetime programs are deterministic, reactive and extensive functions. Moreover, spacetime programming provides a bridge between the theoretical foundations of constraint-based concurrency and the practical aspects of constraint solving. We developed a prototype of the compiler that produces search strategies with a small overhead compared to the hard-coded ones.
Leveraging Feature Similarity for Earlier Detection of Unwanted Feature Interactions in Evolving Software Product Lines

Software product lines enable reuse of shared software across a family of products. As new products are built in the product line, new features are added. The features are units of functionality that provide services to users. Unwanted feature interactions, wherein one feature interferes with another feature's operation, is a significant problem, especially as large software product lines evolve. Detecting feature interactions is a time-consuming and difficult task for developers. Moreover, feature interactions are often only discovered during testing, at which point costly re-work is needed. This paper proposes a similarity-based method to identify unwanted feature interactions much earlier in the development process. It uses knowledge of prior feature interactions stored with the software product line's feature model to help find unwanted interactions between a new feature and existing features. The paper describes the framework and algorithms used to detect the feature interactions using three path similarity measures and evaluates the approach on a real-world, evolving software product line. Results show that the approach performs well, with 83% accuracy and 60% to 100% coverage of feature interactions in experiments, and scales to a large number of features.
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET

The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results.
Feature Similarity: A Method to Detect Unwanted Feature Interactions Earlier in Software Product Lines

Software product lines enable the reuse of shared software across a family of products. As new products are built in the product line, new features are added. A feature is a unit of functionality. Unwanted feature interactions, wherein one feature hinders another feature’s operation, are a significant problem, especially as large software product lines evolve. Detecting feature interactions is a time-consuming and difficult task for developers. Moreover, feature interactions are often only discovered during testing, at which point costly re-work is needed. The work described here investigates how to discover feature interactions much earlier in the development process. Toward this goal, we propose a similarity-based approach that mines prior feature interactions stored in the software product line’s artifacts to predict unwanted interactions between a new feature and existing features. Initial results show that the planned methodology performs well in terms of accuracy and coverage both in experiments on three small software product lines in the literature and in experiments on one large, real-world software product line.
Debate Dialog for News Question Answering System ‘NetTv’-Debate Based on Claim and Reason Estimation-

NetTv is a questionMarumoto, Rikito answeringTanaka, Katsuyuki systemTakiguchi, Tetsuya forAriki, Yasuo net news videos with a voice interface. When watching any news, a question answering system can answer a wide range of questions from users for deeper understanding of news. In this study, in order to help users to understand news deeply, when using NetTv, we propose a debate system which can debate with users about the watching news. The debate system estimates the user’s claim (approval/disapproval/neither) and reason (presence/absence) using CNN, and uses the information to select an opinion stored in debate database and debates by presenting it to the user. In the experiment, eight subjects actually use the system to evaluate naturalness (whether the system response to the user’s utterance was natural), deepness (whether the discussion was established), and user satisfaction (whether new knowledge was obtained) in 5 stages. From the result of subjective evaluation, we show that the debate system gives new knowledge and clarify the present problem of the debate system.
Mutual Information for the Stochastic Block Model by the Adaptive Interpolation Method

We rigorously derive a single-letter variational expression for the mutual information of the asymmetric two-groups stochastic block model in the dense graph regime. Existing proofs in the literature are indirect, as they involve mapping the model to a rank-one matrix estimation problem whose mutual information is then determined by a combination of methods (e.g., interpolation, cavity, algorithmic, spatial coupling). In this contribution we provide a self-contained direct method using only the recently introduced adaptive interpolation method.
A New Fine-Tuning Architecture Based on Bert for Word Relation Extraction

We introduce a new attention-based neural architecture to fine-tune Bidirectional Encoder Representations from Transformers (BERT) for semantic and grammatical relationship classificaiton at word level. BERT has been widely accepted as a base to create the state-of-the-art models for sentence-level and token-level natural language processing tasks via a fine tuning process, which typically takes the final hidden states as input for a classification layer. Inspired by the Residual Net, we propose in this paper a new architecture that augments the final hidden states with multi-head attention weights from all Transformer layers for fine-tuning. We explain the rationality of this proposal in theory and compare it with recent models for word-level relation tasks such as dependency tree parsing. The resulting model shows evident improvement comparing to the standard BERT fine-tuning model on the dependency parsing task with the English TreeBank data and the semantic relation extraction task of SemEval-2010Task-8.
Point Estimator Log Tracker for Cloud Monitoring

A cloud management system depends mainly on monitoring systems to perform the right management actions, especially in a high change configuration parameters environment. Monitoring system must provide needed information to cloud manager to describe cloud dynamic state by reading cloud-generated logs and sending them to cloud manager. Log updates should be accurate, instantaneous and sent with minimum time delay. Data sources vary from low to high level of cloud infrastructure resources, or it can be generated from workload demands. Logs are used to discover cloud system status, which is input for future actions in cloud management resources orchestration. A good monitoring system must reduce number of communication transactions with cloud manager and keep a fresh and consistent log update. This work introduces a new method of logs tracking and sampling that can achieve lower logging transactions and cloud system reconfiguration actions, under several types of workload and log data sources. The proposed method, Point Estimator (PE) log tracker, can dynamically adapt to the type of workload providing accurate fresh logs values to cloud manager with minimum number of data transactions.
A Blind Print-Recapture Robust Watermark Scheme by Calculating Self-Convolution

A blind print-recapture robust watermark scheme is proposed. Watermark patterns are embedded into the space domain of a color image and can be detected from a print-recaptured version of the image without knowledge of the original image. The process of embedding invisible watermarks to convert RGB color images to CIE Lab color spaces and embed periodic watermarks in both color channels at the same time. Watermark extraction is achieved by calculating self-convolution and inverting the geometric transformation such as rotation and scale. Normalized correlation coefficients between the extracted and the embedded watermark pattern is calculated to determine whether there is watermark. The decision about the presence/absence of the watermark pattern is then determined by a threshold which is set 0.13, and the detection rate of 241 pictures is about 0.79.
Cyber security and the brand

A brand is an organisation's most valuable asset. It gives out strong messages, both explicit and implicit, to anyone who encounters it – customers, employees, advocates, investors and even competitors. It will leave a clear understanding of what the product or organisation is all about and if it is something which they may want to get more involved with. Many top brands such as Apple and Coca-Cola are valued in the tens of billions of pounds. 1, 2 Brand protection has become more of a priority in recent years and a key element of this for many organisations is keeping their customers safe. Incorporating cyber security best practices into brand-protection strategies as well as working with the right registrar are good places to start, explains Akino Chikada of MarkMonitor.
QoE Performance of Adaptive Video Streaming in Information Centric Networks

In Adaptive Bit Rate (ABR) utilized in MPEG-DASH (MPEG Dynamic Adaptive Streaming over HTTP), there have been proposed three representative algorithms, rate-based, buffer-based and hybrid-based ones. In the information centric networks, which have been widely studied as a network architecture for efficient content distribution including video streaming, routers are generally equipped with cache storage. When some segments of a required content is occasionally downloaded from cache storage in a router, content download from a router might be faster than segment retrieval from the original content server. This inaccurate throughput estimation might lead to inappropriate bitrate selection of ABR algorithm running on the user’s application and significant degradation of QoE (Quality of Experience) performance. In this paper, from the viewpoint of QoE, we evaluate the influence of in-network caches on three types of ABR algorithm in video streaming.
Predicate Privacy and List Privacy for a ρ-Recoverable Function

For a given function of user data, a querier must recover with at least a prescribed probability, the value of the function based on a user-provided query response. Subject to this requirement, the user forms its query response so as to maximize probability of error-based predicate privacy or list privacy of the data from the querier. Achievability schemes with explicit randomization mechanisms for query responses are given and their privacies compared with converse upper bounds.
A New Methodology for a Functional and Logic Programming Course

We have been giving a course on functional and logic programming models for third- and fourth-year undergraduate students of a university in Japan. For students mostly trained in imperative programming (e.g., the C language) and object-oriented programming (e.g., the Java language), transitioning to the logic paradigm after making a significant effort to first get used to the functional paradigm incurs a heavy cognitive load, with the students often giving up for this final step (i.e., on to logic programming and relational thinking). For the 2018 academic year, we have experimented a novel pedagogical method to smoothen the transition between these two paradigms, relying on the intermediate language Curry. In this paper, we describe the proposed method by giving some details of the course and then quantitatively evaluate our approach by conducting several comparative experiments. The obtained results are promising in that they show a significant increase in the examination scores and reveal several positive aspects at the course evaluation by the students.
Advanced in Mach-Zehnder Interferometer Using Windowed Fourier Transform to Analyse Coupled Heat and Mass Transfer

Series of thermodiffusion experiments using optical digital interferometry (ODI) have been conducted onboard the International Space Station. Conventionally, the two-dimensional (2D) fast Fourier transform (FFT) fringe analysis technique has been applied as a fast and reliable technique to extract data. In this study, for the first time, the windowed Fourier transform (WFT) method is used to analyse the same experiments. In this method, a Fourier transformation is applied on the fringes at two different stages: initially, during the filtration of the non-zero peaks and then on the wrapped phase image. We provide a detailed comparison between FFT and WFT results of binary and ternary mixtures for ODI thermodiffusion experiments. The substantial enhancements of this method are presented and discussed for different experiments conducted for both binary and ternary mixtures. We show that while disturbances in the phase fringe pattern can cause significant error in FFT techniques, if the windowed Fourier filtration (WFF) parameters are properly chosen this type of noise can be eliminated during WFF analysis. The importance of replacing the FFT method becomes more pronounced for the ternary system, as this method fails to reconcile reliable concentration profiles. The results of this chapter can show that the application of the windowed Fourier transform in optical digital interferometry investigations show improved results over the same experiments analysed using FFT methods, especially for experiments involving very small heat and mass fluxes such as the Soret effect in multicomponent mixtures.
PlayMapper: Illuminating Design Spaces of Platform Games

In this paper, we present PlayMapper, a novel variant of the MAP-Elites algorithm that has been adapted to map the level design space of the Super Mario Bros game. Our approach uses player and level based features to create a map of playable levels. We conduct an experiment to compare the effect of different sets of input features on the range of levels generated using this technique. In this work, we show that existing search-based techniques for PCG can be improved to allow for more control and creative freedom for designers. Current limitations of the system and directions for future work are also discussed.
Augmented Reality Applications for Teaching Chemistry across the K-20 Curriculum

This chapter presents the use of augmented reality (AR) applications in science classes. Additionally, a study is presented in which the augmented reality application, Elements 4D by Daqri, was used for teaching chemistry in an elementary science methods class. Findings suggest that augmented reality is an exciting way to immerse students in the subject matter by using technology.
Abrupt Scene Change Detection Using Block Based Local Directional Pattern

In the present communicationKar, T. an ingenious and robust method of abrupt scene change detection in video sequences inKanungo, P. presence of illumination variation based on local directional pattern is proposed. A similarity measure is developed by evaluating the difference between the new texture based feature descriptor which is compared to an automatically generated global threshold for evaluation of the scene change detection. The proposed framework is tested on few publicly available videos and TRECVid dataset. The encouraging results are in favor of the credibility of the proposed framework.
Social Regularized Collaborative Metric Learning

In recommender systems, Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users' preferences but also the user-user and item-item similarity has achieved superior performance in comparison with matrix factorization techniques. However, how to integrate social information into metric learning based recommendation models to cope with the challenges of data sparsity remains as an open issue. For this, we present a Social Regularized Collaborative Metric Learning model (SRCML) for item recommendation. SRCML first defines a similarity function which integrates social preferences and rating preferences to measure user similarity, and then constructs a regularization component to reconstruct the loss function of collaborative metric learning, so that users with similar hobbies are closer to each other in the metric space. At the same time, the metric function that measures the distance between the users and the items is optimized. Finally, a model learning method based on gradient descent is given. Extensive experiments show that SRCML can improve the accuracy and recall rate of recommendations.
Comparative Performance of Filtering Methods for Reducing Noise in Ischemic Posterior Fossa CT Images

Posterior Fossa (PF) of head Computed Tomography (CT) image appears to be one of the intricate regions as it is located in the deepest part of the brain. This appears to cause significant degradation in CT image quality due to the effect of beam hardening and bone thickness. Whilst these issues may not fundamentally limit the scanning procedure, it does appear to be the contributory factors in reducing the performance of the ischemic diagnosis procedure. Thus, it is seen that image filtering is playing an important role in improving the CT image quality and effectively eliminates the influence of Poisson noise in the PF region. Therefore, this paper attempts to assess the feasibility of four different filtering methods; Anisotropic diffusion, Bilateral, Median and Wiener to eliminate noise in the CT image of PF containing ischemic. To the best of our knowledge, this is the first study to report the performance of filtering in ischemic PF. The efficacy of these four filters is evaluated in details using qualitative and quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index (SSIM) and processing time. The experimental works demonstrate that Bilateral filtering offers promising results in which this method can eliminate Poisson noise in CT images for ischemic PF with average PSNR, RMSE, SSIM values of 32.95, 5.7416 and 0.9749 respectively. This filter has provided the flexibility of being applicable even in cases where ischemic is presented in PF.
Communication-Aware and Energy Saving Virtual Machine Allocation Algorithm in Data Center

Data centers can provide users with reliable and on-demand cloud services. Efficient virtual machine allocation strategies are important for improving data center performance and increasing allocation efficiency. However, the existing allocation algorithms have limited consideration for the communication delay between virtual machines caused by the data center topology. Therefore, this study formalizes the network communication problem of virtual machines in data centers by studying these centers network topology. Accordingly, this paper proposes an energy saving allocation algorithm based on communication-aware, called ESBCA, which optimizes the data center traffic from the two stages of virtual machine migration and placement. In the migration phase, virtual machines with high traffic volumes are preferred to be migrated. In the placement stage, different targets are set and the destination physical machine is systematically determined. Experimental results show that the proposed algorithm evidently reduces the data traffic, improves the efficiency of the data center migration and decreases the energy consumption of data centers.