Reviewerly/exHarmony huggingface dataset:
Texture Analysis Via Hierarchical Spatial-Spectral Correlation (HSSC)

A hierarchical spatial-spectral correlation (HSSC) method is proposed for texture analysis in this work. The HSSC method first applies a multi-stage spatial-spectral transform to input texture patches, which is known as the Saak transform. Then, it conducts a correlation analysis on Saak transform coefficients to obtain texture features of high discriminant power. To demonstrate the effectiveness of the HSSC method, we conduct extensive experiments on texture classification and show that it offers very competitive results comparing with state-of-the-art methods.
CUED@WMT19:EWC&amp;LMs

Two techniques provide the fabric of the Cambridge University Engineering Department's (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs).We report substantial gains by finetuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC.A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains.As in previous years, we also extract n-gram probabilities from SMT lattices which can be seen as a source-conditioned ngram LM.
Development of augmented reality to learn history

Augmented Reality (AR) is a technology that enables a new information delivery environment. AR promotes both engagement and motivation for people to obtain and acquire certain knowledge or information including those concerning history. People, especially the young generation, often view history as an uninteresting and boring subject matter. This may be due to the lack of interactivity and visual images that accompanying the information on history. This could affect our level of understanding about the history of our country such as the fall of Melaka Empire and weaken our spirit of patriotism. Thus, this research aims to study the effect of combining the AR technology together with the traditional information to create excitement in learning history. The development of the AR application in this project is to enhance the traditional book by allowing users to see the digital visual of historical events. The development of the application involves five phases that are analysis, design, develop, implement, and evaluate. The mobile application of AR book on the fall of Melaka Empire history has been developed successfully and the findings show that most users agree that the application contributes to higher users’ satisfaction.
Differentially Private Covariance Estimation

The covariance matrix of a dataset is a fundamental statistic that can be used for calculating optimum regression weights as well as in many other learning and data analysis settings. For datasets containing private user information, we often want to estimate the covariance matrix in a way that preserves differential privacy. While there are known methods for privately computing the covariance matrix, they all have one of two major shortcomings. Some, like the Gaussian mechanism, only guarantee (epsilon, delta)-differential privacy, leaving a non-trivial probability of privacy failure. Others give strong epsilon-differential privacy guarantees, but are impractical, requiring complicated sampling schemes, and tend to perform poorly on real data. In this work we propose a new epsilon-differentially private algorithm for computing the covariance matrix of a dataset that addresses both of these limitations. We show that it has lower error than existing state-of-the-art approaches, both analytically and empirically. In addition, the algorithm is significantly less complicated than other methods and can be efficiently implemented with rejection sampling.
A chaotic image encryption scheme based on cat map and MMT permutation

This paper proposes an image encryption scheme based on cat mapping and matrix magic transformation (MMT) replacement method. First, the secure hash algorithm SHA-512 is used to generate 12 sets of keys, which are used for the initial values of the system and the required values of the algorithm for cat mapping and 2D-Logistic-adjusted-Sine map (LASM). Secondly, the plaintext image is scrambled by the cat mapping. Then, the matrix after the cat map replacement is again scrambled by the MMT replacement method to generate the permutation matrix. Finally, the permutation matrix is used for the bitxor operation with the chaotic sequence matrix generated by the 2D LASM system to realize the diffusion process of the scrambled image. Experimental results and security analysis show that the encryption algorithm has good performance and can resist various typical attacks.
Accurate and efficient extraction of fringe orientation from the poor-quality ESPI fringe pattern with a convolutional neural network

The fringe orientation is an important feature of the electronic speckle interferometry (ESPI) fringe pattern. Accurate and efficient calculation of the fringe orientation is very important for subsequent electronic speckle processing such as skeleton extraction and image filtering. To accurately and efficiently estimate fringe orientation, we propose an effective method based on a convolutional neural network. In the proposed method, the network needs clean-noisy image pairs to train and noisy images with theoretical value to test. The aligned noise-free ESPI fringe pattern orientation fields are fairly good estimations for the corresponding noise ones. After the model training is done, the other multiple ESPI fringe patterns are fed to the trained network simultaneously; the corresponding orientation results can be obtained accurately and efficiently. The advantage of using this method to extract the orientation is that the fringe orientation information can be extracted accurately and efficiently without complicated parameter adjustment. We evaluate the performance of our method via applying our method to the computer-simulated and experimentally acquired ESPI fringe patterns and comparing the results with those of three extensively used methods.
MCE 2018: The 1st Multi-Target Speaker Detection and Identification Challenge Evaluation

The Multi-target Challenge aims to assess how well current speech technology is able to determine whether or not a recorded utterance was spoken by one of a large number of blacklisted speakers. It is a form of multi-target speaker detection based on real-world telephone conversations. Data recordings are generated from call center customer-agent conversations. The task is to measure how accurately one can detect 1) whether a test recording is spoken by a blacklisted speaker, and 2) which specific blacklisted speaker was talking. This paper outlines the challenge and provides its baselines, results, and discussions.
The differentiated duty of care: a response to the Online Harms White Paper

A consensus has formed that the negative social externalities of online harms combined with huge market power of internet intermediaries justify regulation of online service providers.1 Fake news, ...
Groningen explosion database

In the Netherlands a seismic network is in place to monitor both induced and natural seismicity. Most natural seismicity occurs in the south, over an extensional tectonic regime that can be seen as an extension of the Rhine Graben. Most induced seismic activity occurs in the north of the country and is primarily related to gas extraction and reactivation of existing faults at reservoir level (Spetzler and Dost, 2017; Willacy et al., 2019). In Groningen, in the north east of the Netherlands, an especially dense network is in place. The network is operated by the Royal Netherlands Meteorological Institute (KNMI). Both event data and continuous recordings are publicly available (KNMI, 1993). In the Nineties, a seismic network has been installed to monitor seismicity from the Groningen field and a string of surrounding gas fields (Dost et al., 2017). Since 2014 this network has been expanded with a dedicated network to monitor seismicity from the Groningen field (the G-network, Figure 1), and two gas storage plants (the N- and GK- networks, Figure 1). The area has soft soil and high seismic noise conditions. As a remedy, most of the seismic sensors have been installed in boreholes - in a set-up shown on Figure 1(c). This set-up yields a seismic power reduction up till about 30 dB in the relevant bandwidth (Ruigrok and Dost, 2019).
An Agile Approach to Validate a Formal Representation of the GDPR

Modelling in a knowledge base of logic formulæ the articles of the GDPR enables a semi-automatic reasoning of the Regulation. To be legally substantiated, it requires that the formulæ express validly the legal meaning of the Regulation’s articles. But legal experts are usually not familiar with logic, and this calls for an interdisciplinary validation methodology that bridges the communication gap between formal modelers and legal evaluators. We devise such a validation methodology and exemplify it over a knowledge base of articles of the GDPR translated into Reified I/O (RIO) logic and encoded in LegalRuleML. A pivotal element of the methodology is a human-readable intermediate representation of the logic formulæ that preserves the formulæ’s meaning, while rendering it in a readable way to non-experts. After being applied over a use case, we prove that it is possible to retrieve feedback from legal experts about the formal representation of Art. 5.1a and Art. 7.1. What emerges is an agile process to build logic knowledge bases of legal texts, and to support their public trust, which we intend to use for a logic model of the GDPR, called DAPRECO knowledge base.
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed graph regularized deep neural network with adversarial augmentation, named DiagNet. Firstly, we use adversarial learning to generate positive and negative mass-contained mammograms for each mass class. After that, a signed similarity graph is built upon the expanded data to further highlight the discrimination. Finally, a deep convolutional neural network is trained by jointly optimizing the signed graph regularization and classification loss. Experiments show that the DiagNet framework outperforms the state-of-the-art in breast mass diagnosis in mammography.
The City Metaphor in Software Visualization

A city metaphor has become a popular method of visualizing properties of program code.This paper provides an overview of research projects that employ this metaphor for a wide range of software engineering tasks.Until now, projects employing the city metaphor have primarily focused on visualizing static and semi-static properties of software repositories, such as understanding how a program's source code structure is changing over time, and who is changing what.This paper compares these existing code cities and suggests likely avenues of future research.
Software Product Quality in DevOps Contexts: A Systematic Literature Review

DevOps is a change in the organizational culture that aims to reduce the gap between development and operation teams, accelerating the software release process. However, little is known about the impact of this approach on software product quality. This study aims to analyze the influence of the application of DevOps on software product quality; therefore, a systematic literature review was conducted. Thirty-one articles related to DevOps and its influence on product quality were identified. The studies indicate a strong influence of some product quality characteristics, specifically: Reliability and Maintainability. Additionally, practices associated with DevOps, such as the minimum viable product, deployment automation, test automation, cloud computing and team cooperation, show a relationship with the improvement in software product quality, however, its adoption also brings new challenges to preserve security.
Query-Based Entity Comparison in Knowledge Graphs Revisited

Large-scale knowledge graphs are increasingly being used in applications, and there is a growing need for tools that can effectively support users in analysis and exploration tasks. One such important task is entity comparison—to describe in an informative way the similarities between two given entities as described in a knowledge graph. In our previous work the result of entity comparison is modelled as a similarity query—that is, a SPARQL query having the input entities as part of the answer over the input graph; for instance, one can describe the similarity between two companies such as Telenor and Vodafone in the YAGO graph as a query asking for all telecom companies based in Europe. In this paper, we extend the results of our prior work in different ways. First, we expand the language of similarity queries to consider a richer fragment of SPARQL allowing for numeric filter expressions; this enables us to express that Telenor and Vodafone are also similar in that they both have at least 30,000 employees. We then propose algorithms for computing similarity queries satisfying certain additional desirable properties, such as being as specific as possible. Such algorithms are, however, impractical; hence, we also propose and implement a scalable algorithm that is guaranteed to compute a similarity query, but not necessarily a most specific one.
Assessment of physical rehabilitation movements through dimensionality reduction and statistical modeling

The article proposes a method for evaluation of the consistency of human movements within the context of physical therapy and rehabilitation. Captured movement data in the form of joint angular displacements in a skeletal human model is considered in this work. The proposed approach employs an autoencoder neural network to project the high-dimensional motion trajectories into a low-dimensional manifold. Afterwards, a Gaussian mixture model is used to derive a parametric probabilistic model of the density of the movements. The resulting probabilistic model is employed for evaluation of the consistency of unseen motion sequences based on the likelihood of the data being drawn from the model. The approach is validated on two physical rehabilitation movements.
Solar radiation forecasting in nigeria based on hybrid PSO-ANFIS and WT-ANFIS approach

&lt;p&gt;For an effective and reliable solar energy production, there is need for precise solar radiation knowledge. In this study, two hybrid approaches are investigated for horizontal solar radiation prediction in Nigeria. These approaches combine an Adaptive Neuro-fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) and Wavelet Transform (WT) algorithms. Meteorological data comprising of monthly mean sunshine hours (SH), relative humidity (RH), minimum temperature (Tmin) and maximum temperature (Tmax) ranging from 2002-2012 were utilized for the forecasting. Based on the statistical evaluators used for performance evaluation which are the root mean square error and the coefficient of determination (RMSE and R²), the two models were found to be very worthy models for solar radiation forecasting. The statistical indicators show that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². The results show that hybridizing the ANFIS by PSO and WT algorithms is efficient for solar radiation forecasting even though the hybrid WT-ANFIS gives more accurate results.&lt;/p&gt;
A Novel Approach to Optimize the Performance of Hadoop Frameworks for Sentiment Analysis

Twitter is one among most popular micro blogging services with millions of active users. It is a hub of massive collection of data arriving from various sources. In Twitter, users most often express their views, opinions, thoughts, emotions or feelings about a particular topic, product or service, of their interest, choice or concern. This makes twitter a hub of gargantuan amount of data, and at the same time a useful platform in getting to know and understand the underlying sentiment behind a particular product or for that matter anything expressed in twitter as tweets. It is important to note here that aforesaid massive collection of data is not just any redundant data, but one which contains useful information as noted earlier. In view of aforesaid context, Sentiment analysis in relation to twitter data gains enormous importance. Sentiment analysis offers itself as a good approach in classifying the opinions formulated by individuals (tweeters) into different sentiments such as, positive, negative, or neutral. Implementing Sentiment analysis algorithms using conventional tools leads to high computation time, and thus are less effective. Hence, there is a need for state-of-the-art tools and techniques to be developed for sentiment analysis making it the need of the hour to facilitate faster computation. An Apache Hadoop framework is one such option that supports distributed data computing and has been commonly adopted for a variety of use-cases. In this article, the author identifies factors affecting the performance of sentiment analysis algorithms based on Hadoop framework and proposes an approach for optimizing the performance of sentiment analysis. The experimental results depict the potential of the proposed approach.
Decentralizing democracy: approaches to consensus within blockchain communities

Creating fair, transparent and genuinely democratic modes of decentralized decision-making has been a key concern for many developers and users of blockchains. This article evaluates several popular methods of maintaining consensus and achieving decentralized decision-making on blockchain networks in order to assess the extent to which blockchains challenge the norms of the liberal-democratic order. In particular, it compares and contrasts Proof-of-Work, Proof-of-Stake and Practical Byzantine Fault Tolerance consensus mechanisms, assessing not just how they operate in a technical sense but also (and most important) the political, economic and social dimensions of these different blockchain governance strategies. This comparison highlights efforts by blockchain communities to redefine or push the bounds of democracy, as well as the challenges they have faced in their efforts to create digital democracies that do not reproduce the same economic and social inequalities present in traditional democratic systems.
LignoIndo: image database of Indonesian commercial timber

Abstract Macroscopic image database of Indonesian commercial timber “LignoIndo” has been developed to support the development of a computer vision-based wood identification system. A total of 809 wood species were taken their macroscopic images by using digital loupe at two magnifications: 50 times and 200 times. All of these timber species are Indonesian commercial timber listed in LIU (License Information Unit) in November 2016. Cross section images of 186 wood species listed in the Ministry of Forestry Degree No 163/2003 were also collected by using mobile phone camera that was attached with 60x magnify lens, as the basis data to the mobile application of automatic wood identification tool (AIKO). The number of wood species in this database will continue to grow as the number of commercial wood species increases. Besides, the number of replicates of specimens for each species of wood can also continue to be added to accommodate wood variations in the same species, the same species from different trees, and different geographic locations. This paper presents data collection methods and summaries of wood data that have been collected including trade name, botanical name, family, durability classes, strength classes, commercial class classification, conservation status, and recommended utilization.
Teleoperation Robot Arm using Depth Sensor

Recently, Robotics is increasingly being used for industry and daily activities. Th development is getting faster, starting from how the robot is controlled to other features. For jobs that have a danger in their work area, a teleoperation robot is needed. But most teleoperation robots have complicated control devices with many restrictions on users. In 2010 Microsoft released a camera to play Xbox with a depth sensor feature named Kinect. This depth sensor is used by researchers to develop robotic teleoperation control device. By using depth sensor, users are not limited when moving to control robots. Users only need to make movements such as when they work and the depth sensor will recognize it. In this study Arm Robot 4 DOF control system was made using the Kinect camera. For the accuracy of robot control with human hand movements was tested.
Analysis of building energy efficiency dataset using naive bayes classification classifier

Abstract The phenomenon of global warming has become a very important and serious issue in recent years. One effort to reduce emissions by applying the green architecture concept to buildings. The purpose of this paper is to classify energy efficiency data sets. The method used by the Naïve Bayesian Classifier algorithm. Results obtained by Kappa statistics Y1 (0.3357) and Y2 (0.4203); Mean absolute error Y1 (0,0368), Y2 (0,0335); Root mean squared error Y1(0,1491), Y2(0,1412). The results of the analysis showed overall height, relative compactness, wall area, and glazing area affected the heating and cooling loads, while the surface area affected the heating load and the roof area affected the cooling load.
Surf-Based Registration for Hyperspectral Images

The alignment of images, also known as registration, is a relevant task in the processing of hyperspectral images. Among the feature-based registration methods, Speeded Up Robust Features (SURF) has been proposed as a computationally efficient approach. In this paper HSI-SURF is proposed. This is a method to register hyperspectral remote sensing images based on SURF that takes advantage of the full spectral information of the images. In this sense, the proposed method selects specific bands of the images and adapts the keypoint descriptor and the matching stages to benefit from the spectral information, thus increasing the effectiveness of the registration.
Band Segmentation and Detection of DNA by Using Fast Fuzzy C-mean and Neuro Adaptive Fuzzy Inference System

Currently, band segmentation is used in medical science, because it helps the scientist to detect and calculate the band and line respectively. Segmentation is very important in medical images, because it provides the clinical assistance to physician. Such as, the DNA images are used to detect and segment the bands and line and calculate the local minima and maxima. For segmentation, we use Fast Fuzzy C-Mean Clustering (FFCM). FFCM is a clustering method that allows a piece of data to be in two or more clusters. Clustering involves the task of dividing the data points into homogeneous classes or the clusters, so that items in the same class are as equitable as possible and the items in the different classes are different. Our results indicate that our proposed method effectively detects the band and counts the lines.
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions

The TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration (MIER-19) tackles explanation generation for answers to elementary science questions.It builds on the AI2 Reasoning Challenge 2018 (ARC-18) which was organized as an advanced question answering task on a dataset of elementary science questions.The ARC-18 questions were shown to be hard to answer with systems focusing on surface-level cues alone, instead requiring far more powerful knowledge and reasoning.To address MIER-19, we adopt a hybrid pipelined architecture comprising a featurerich learning-to-rank (LTR) machine learning model, followed by a rule-based system for reranking the LTR model predictions.Our system was ranked fourth in the official evaluation, scoring close to the second and third ranked teams, achieving 39.4% MAP.