Reviewerly/exHarmony huggingface dataset:
Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings

The aim of this work is to propose different statistical and machine learning methodologies for identifying anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings.Companies focused on energy sector for buildings are interested on statistical and machine learning tools to automate the control of energy consumption and ensure quality of Heat Ventilation and Air Conditioning (HVAC) installations.Consequently, a methodology based on the application of the Local Correlation Integral (LOCI) anomaly detection technique has been proposed.In addition, the most critical variables for anomaly detection are identified by using ReliefF method.Once vectors of critical variables are obtained, multivariate and univariate control charts can be applied to control the quality of HVAC installations (consumption, thermal comfort).In order to test the proposed methodology, the companies involved in this project have provided the case study of a store of a clothing brand located in a shopping center in Panama.It is important to note that this is a controlled case study for which all the anomalies have been previously identified by maintenance personnel.Moreover, as an alternatively solution, in addition to machine learning and multivariate techniques, new nonparametric control charts for functional data based on data depth have been proposed and applied to curves of daily energy consumption in HVAC.
Supervised classification using graph-based space partitioning

In the paper we consider the supervised classification problem using space partitioning into multidimensional rectangular boxes. We show that the problem at hand can be reduced to computational geometry problem involving heuristic minimum clique cover problem satisfying the k-nearest neighbor rule. We first apply heuristic algorithm for partitioning a graph into a minimal number of maximal cardinality cliques inscribed into the smallest (in volume) rectangular parallelepipeds called boxes. The main advantage of the new classifier called Box algorithm which optimally utilizes the geometrical structure of the training set is decomposition of the l-class problem (l > 2) into l binary classification problems. We discuss computational complexity of the proposed method and the resulting classification rules. The extensive experiments performed on the real and simulated data show that in almost all cases the Box algorithm performs significantly better than k-NN, SVM and decision trees.
Estimating Aggressiveness of Russian Texts by Means of Machine Learning

This paper considers emotional assessment of texts in Russian using machine learning on the example of aggression detection. It summarizes the related work, methods, models and datasets, describes actual problems, proposes a text processing pipeline and a software system for training neural networks on heterogeneous datasets. The experiments show that neural networks trained on the annotated corpora both in Russian and English, allow to determine whether a text item in Russian contains an aggressive message. Authors thoroughly compare different assessment methods, particularly corpus-based approaches, machine learning solutions and hybrid variants. Results, obtained here, can be used to estimate the aggressiveness probability, for example, to rank messages for subsequent manual verification. They also enable feasibility studies on the possibilities of detecting a particular type of emotion in a text using corpora in other languages. The paper highlights further research directions, where different Python toolkits (NLTK, Keras) could be used for better model performance.
2D-CWT IP core design and implementation for fringe pattern analysis

In order to meet the demand of high-speed or real-time analysis for digital interferograms, an advanced hardware accelerated method based on the field programmable gate array (FPGA) is proposed by using the two-dimensional continuous wavelet transform (2D-CWT) technique. In this paper, a 2D-CWT IP core is designed and implemented. It consists of a digital interferograms acquisition module, an interferograms data buffer module, a configuration module, a 2D-CWT operation module, and an output module. When the mother wavelet is selected, e.g., the Morlet wavelet, and the scale factors and rotation angles are setting, the spectrums of fringe patterns are obtained in the 2D-CWT operation module and transferred to the computer through the output module for further processing. Then, the phase distribution of the interferogram is extracted by using the wavelet ridge detection algorithm. Real experiments results show that the 2D-CWT IP core analysis method can reduce the time of phase extraction, and verify the validity and effectiveness of the proposed method.
Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

The smart jammer launches jamming attacks which degrade the transmission reliability.In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming Q learning algorithm (AQLA) is developed to obtain anti-jamming knowledge for the local region.To accelerate the learning process across multiple regions, a multi-regional intelligent anti-jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed.The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions.
Multi-party E-Commerce Protocol for B2C/B2B Applications

There are many multi-party fair exchange protocols with applications in e-commerce transactions for buying digital products, buying physical products, digital signature of contracts, certified e-mail and non-repudiation. However, none of the existing multi-party fair exchange protocols can be applied to provide fair exchange in complex transactions where a customer buys digital products from different merchants while maintaining atomicity. In this paper, we propose a multi-party fair exchange e-commerce protocol for complex transactions in that the customer wants to buy several digital products from different merchants. Our protocol has applications to B2C/B2B scenarios. In addition to strong fairness, our protocol provides effectiveness, timeliness, non-repudiation and confidentiality.
A New Fair Electronic Contract Signing Protocol

Electronic contract is an important part of electronic commerce, and the key issue is how to ensure the fairness of contract signing. In view of some electronic contract signing protocols in the network environment can not achieve fairness completely or need the third party involved. This paper proposed a new concurrent signature in virtue of ECDSA scheme and applied it to a fair contract signing protocol. This new protocol based on ECDSA ensure the fairness of contract signing in virtue of ambiguous signatures become valid concurrently, and ensure the confidentiality of the contents of the contract without the third party involved. This protocol is applied to most electronic contract signing in network environment.
Deeplearning Convolutional Neural Network based QoE Assessment Module for 4K UHD Video Streaming

With the rapid development of modern high resolution video streaming services, providing high Quality of Experience (QoE) has become a crucial service for any media streaming platforms. Most often it is necessary of provide the QoE with NR-IQA, which is a daunting task for any present network system for it’s huge computational overloads and often inaccurate results. So in this research paper a new type of this NR-IQA was proposed that resolves these issues. In this work we have described a deep-learning based Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. This model processes the RAW RGB pixel images as input, the CNN works in the spatial domain without using any hand-crafted or derived features that are employed by most previous methods. The proposed CNN is utilized to classify all images in a MOS category. This approach achieves state of the art performance on the KoniQ-10k dataset and shows excellent generalization ability in classifying proper images into proper category. Detailed processing on images with data augmentation revealed the high quality estimation and classifying ability of our CNN, which is a novel system by far in these field.
A System Architecture for Semantically Informed Rendering of Object-Based Audio

Object-based audio promises format-agnostic reproduction and extensive personalization of spatial audio content.However, in practical listening scenarios, such as in consumer audio, ideal reproduction is typically not possible.To maximize the quality of listening experience, a different approach is required, for example modifications of metadata to adjust for the reproduction layout or personalization choices.In this paper we propose a novel system architecture for semantically informed rendering (SIR), that combines object audio rendering with high-level processing of object metadata.In many cases, this processing uses novel, advanced metadata describing the objects to optimally adjust the audio scene to the reproduction system or listener preferences.The proposed system is evaluated with several adaptation strategies, including semantically motivated downmix to layouts with few loudspeakers, manipulation of perceptual attributes, perceptual reverberation compensation, and orchestration of mobile devices for immersive reproduction.These examples demonstrate how SIR can significantly improve the media experience and provide advanced personalization controls, for example by maintaining smooth object trajectories on systems with few loudspeakers, or providing personalized envelopment levels.An example implementation of the proposed system architecture is described and provided as an open, extensible software framework that combines object-based audio rendering and high-level processing of advanced object metadata.
Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network

The Generalized Adaptive Resonance Theory (GART) model is a supervised online learning neural network based on an integration of Adaptive Resonance Theory (ART) and the Generalized Regression Neural Network (GRNN). It is capable of online learning, and is suitable for undertaking both classification and regression problems. In this paper, we further enhance GART (EGART) with four improvements to formulate a new EGART model. Three operating strategies for the EGART model to undertake regression problems are suggested. The first operating strategy is a fully online learning EGART model. The second operating strategy involves Ordering Algorithm for determining the presentation sequence of training samples during the initial training of EGART model. This strategy is considered as offline learning because a set of data samples must be available for the Ordering Algorithm to compute the best presentation sequence (hereinafter denoted as Ordered-EGART). The third operating strategy aims to demonstrate online learning capability of EART model (the first operating strategy) can still be resumed after training on the Ordered-EGART. It is most suitable for applications with a set of ready data samples and their sequences are predetermined by Ordering Algorithm prior to training of EGART model in offline mode, and triggers online learning when more new data samples become available (hereinafter denoted as IO-EGART). A series of experiments with five benchmark data sets from various application domains is conducted to assess and compare the effectiveness of the EGART model and three operating strategies with those of other methods published in literature as well as two fire safety engineering problems, i.e., predicting the thermal interface height in a single compartment fire and evacuation times in the event of fire. The results and comparisons with other approaches positively demonstrate the efficacy and applicability of EGART model as a useful data regression model for tackling fire safety engineering problems.
A Modular Approach for Efficient Simple Question Answering Over Knowledge Base

In this work, we propose an approach for efficient question answering (QA) of simple queries over a knowledge base (KB), whereby a single triple consisting of (subject, predicate, object) is retrieved from a KB for a given natural language query. In fact, most recent state-of-the-art methods exploit complex end-to-end neural network approaches to achieve higher precision while making it difficult to perform detailed analysis of the performance and suffering from long execution time when training the networks. To this problem, we decompose the simple QA task in a three step-pipeline: entity detection, entity linking and relation prediction. More precisely, our proposed approach is quite simple but performs reasonably well compared to previous complex approaches. We introduce a novel index that relies on the relation type to filter out subject entities from the candidate list so that the object entity with the highest score becomes the answer to the question. Furthermore, due to its simplicity, our approach can significantly reduce the training time compared to other comparative approaches. The experiment on the SimpleQuestions data set finds that basic LSTMs, GRUs, and non-neural network techniques achieve reasonable performance while providing an opportunity to understand the problem structure.
Head Nod Detection in Dyadic Conversations

In face-to-face interactions, head gestures play an important role as one of the back-channel signals. As one of them, head nods can be used to display the approval or interest of listeners as a feedback in dyadic conversations. Hence detection of head nods is expected to improve understanding of the given feedback and to improve human-computer interaction. This study targets to detect head nods in the purpose of making human-computer interaction more human like. In the process, 3D head model is obtained by the Microsoft Kinect and the Openface application. Binary classification is performed on spectral features, which are extracted from 3D head motion, with the Support Vector Machine (SVM) classifier. Consequently, upon the classification, ‘head nod’ or ‘not head nod’ outputs are obtained. In the experimental studies, head nod detection accuracy is obtained as 92% for Microsoft Kinect and 91% for Openface over the Joker dataset.
Training Non-designers in Co-design Methods Through an Active Assisted Living Interactive Workshop

In the era of participation, design and development teams are called to utilize co-design methods in their work and thus required to master the use and appropriate application of those types of methods. However, not all teams, and certainly not all team members, are learned or trained in co-design. This raises challenges not only to the use of co-design methods, but also to its skilful application. This paper reports on an interactive co-design workshop with seventeen EU-funded project coordinators, to investigate their perception on the extent to which the workshop activities impacted their level of empathy towards others, ease of communication, and openness to employ co-design methods. Considering the hands-on and playful nature of the methods, we also investigate participants’ perceptions on the methods’ ease of application in a real-world context as well as their effectiveness in increasing participants’ knowledge of co-design methods. Results indicate that the proposed activities positively contribute to all the dimensions investigated, with the highest effect being achieved in increasing participants’ openness to employ co-design methods and the playful nature of the activities being perceived as contributing more to the learning effectiveness than the hands-on approach of the activities.
Improvement of Image Denoising Algorithms by Preserving the Edges

Image restoration is one of the well-studied problems in low-level image processing tasks. Recently, deep learning based image restoration techniques have shown promising results and outperform most of the state of the art image denoising algorithms. Most of the deep learning based methods use mean square error as a loss function to obtain the denoised output. This work focuses on further improving the existing deep learning based image denoising techniques by preserving edges using Canny edge based loss function, and hence improving peak signal to noise ratio (PSNR) and structural similarity (SSIM) of the images while restoring the visual quality.
Internet immunity and the freedom to code

A call to preserve the capability of developing the next generation of Internet services.
Building Chinese Legal Hybrid Knowledge Network

Knowledge graphs play an important role in many applications, such as data integration, natural language understanding and semantic search. Recently, there has been some work on constructing legal knowledge graphs from legal judgments. However, they suffer from some problems. First, existing work follows the Western legal system, thus cannot be applied to other legal systems, such as Asian legal systems; Second, existing work intends to build a precise legal knowledge graph, which is often not effective, especially when constructing the precise relationship between legal terms. To solve these problems, in this paper, we propose a framework for constructing a legal hybrid knowledge network from Chinese encyclopedia and legal judgments. First, we construct a network of legal terms through encyclopedia data. Then, we build a legal knowledge graph through Chinese legal judgments which captures the strict logical connections in the legal judgments. Finally, we build a Chinese legal hybrid knowledge network by combining the network of legal terms and the legal knowledge graph. We also evaluate the algorithms which are used to build the legal hybrid knowledge network on a real-world dataset. Experimental results demonstrate the effectiveness of these algorithms.
Study on Improved Singular Value Decomposition De-noising Method Applied to UAV Flight Parameter Data

To solve the noise problem of unmanned aerial vehicle (UAV) flight parameter data, the paper proposed an effective singular value decomposition method optimized by chaos ratio bat algorithm (CRBA). The basic bat algorithm is improved by using sigmoidal map, energy of singular values is taken as the objective function in proposed algorithm to optimize the structure of Hankel matrix. The number of effective singular value determined by using the singular value's singularity detection ability, and the de-noised data obtained from singular value and its corresponding vector. According to the experiment results of linear signal and flight parameter data, the proposed method is applicable to nonlinear signals without obvious characteristic frequency and linear signal, which has good denoising results.
Detecting and Reducing Bias in a High Stakes Domain

Ruiqi Zhong, Yanda Chen, Desmond Patton, Charlotte Selous, Kathy McKeown. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Ledger-Based Sorting Algorithm

Research on sorting algorithms is a very core research field in computer science. Today, any advancement in sorting algorithms would lead to less time and space required to perform the sorting task. This paper discusses a newly proposed algorithm—‘ledger-based sorting algorithm’. It is basically an advancement to ‘counting sort’, but not exactly counting sort. The purpose of this paper is to reduce the time and space required for sorting when the numbers given to you belong to a particular range (0–n) and all of them are distinct. This paper also compares the newly proposed algorithm with insertion sort, bubble sort, selection sort, quick sort, merge sort and mainly counting sort through graphical time analysis of these algorithms. The paper especially compares counting sort algorithm with the proposed algorithm in terms of both time and space complexity.
Machine Learning For DAS Microseismic Event Detection

Summary Emerging acquisition systems based on fiber optic technologies such as Distributed Acoustic Sensing are enabling dense spatial and temporal sampling of strain fields. This has resulted in a large increase in the volume in the volume of data and the rate at which data is generated. These increases can be interpreted as satisfying two of the 3 ‘V&apos;s of ‘Big Data’ i.e. Volume and Velocity (the third V refers to data Variety). In this presentation we show how we have used big data technologies such as Apache Hadoop and Apache Spark to tackle these data issues for the specific problem of microseismic event detection. Furthermore, traditional approaches were thought to be unlikely to efficiently scale to these new data so we turned to machine learning approaches based on computer vision. Rather than trying state of the art technologies such as Convolutional Neural Nets we decided to try a mature technology used for face detection known as a Haar Cascade. We have tested this approach on field data and found that this approach can work well and are motivated to try newer machine learning techniques with the expectation of moving beyond microseismic event detection.
CHICOM: Code for comparing weighted or unweighted histograms in Fortran-77, C++, R and Python

Improved a program that calculates test statistics to compare weighted and unweighted histograms. The program is presented in Fortran-77, C++, R and Python. The code calculates test statistics for histograms with either normalized or unnormalized weights of events. Program Title: CHICOM Program Files doi: http://dx.doi.org/10.17632/424sd4fhj8.1 Licensing provisions: GPLv3 Programming language: Fortran-77, C++, Python, R Journal reference of previous version: CHICOM: A code of tests for comparing unweighted and weighted histograms and two weighted histograms, N. D. Gagunashvili, Comput. Phys. Commun. 183 (2012) 193-196 Does the new version supersede the previous version?: Yes Reasons for the new version: To use an improved version of the chi-square test with better statistical properties, instead of the median statistic [3] as in the previous version. Summary of revisions: An improved version of the test statistic [2] was used that uses an improved chi-square test [4]. The algorithm has been implemented in four commonly used programming languages (Fortran-77, C++, Python and R). Nature of problem: The program calculates test statistics for comparing weighted or unweighted histograms. Solution method: An improved test statistic for comparing weighted histograms is calculated using the formulas presented in Ref. [2]. In order to find the test statistic, we must find the probability that minimizes the sum of the goodness of fit test statistic of each histogram. To do so, the Polak–Ribière conjugate gradient method is used to converge to the minimum from an initial guess suggested in the article. References [1] N. D. Gagunashvili, Comput. Phys. Commun. 183(2012)193. [2] N. D. Gagunashvili, Eur. Phys. J. Plus (2017) 132: 196. [3] N. D. Gagunashvili, Nucl. Instrum. Meth. A 596(2008)439. [4] N. D. Gagunashvili, J. Instrum. 10(2015)P05004.