Reviewerly/exHarmony huggingface dataset:
Achievable simultaneous time and frequency domain energy concentration for finite length sequences

For numerous applications in the field of signal processing, it is desired to design a compact window that can simultaneously concentrate maximum energy in finite time interval and frequency band. Although zero-order discrete prolate spheroidal sequence (DPSS) meets this requirement, it is of infinite support. Limiting this sequence to finite support no longer guarantees the optimality property. This study aims at designing a discrete finite length window that can maximise the energy simultaneously in narrow time interval and frequency band. A multi-objective optimisation approach is adopted to obtain the upper bound of maximum achievable time and frequency domain energy concentrations for finite length sequences. The optimal sequence thus obtained is termed as the optimal window with finite support (OWFS), and its various associated properties are discussed. It is shown analytically that as the support of OWFS approaches infinity, it converges to zero-order DPSS. In order to illustrate its optimality, the compactness of the proposed OWFS is compared with those of various window functions. Extending the proposed OWFS, this study also discusses the formulation and associated properties of the zero-order periodic DPSS. Further, the closed form expression for the upper bound of achievable time and frequency domain energy concentrations is also derived.
COMET: A Contextualized Molecule-Based Matching Technique

Context-specific description of entities –expressed in RDF– poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities. We empirically evaluate the performance of COMET on a testbed from DBpedia. The experimental results suggest that COMET is able to accurately match equivalent RDF graphs in a context-dependent manner.
Mechanisms of swarm intelligence and evolutionary adaptation for solving PCB design tasks

The paper presents a swarm intelligence and evolutionary adaptation mechanism for solving PCB design tasks. One of the features of the swarm intelligence mechanism is exploring the solution vicinity based on genetic and evolutionary algorithms in terms of PCB design tasks. To solve the mentioned problems, the authors propose a greedy operator. The paper describes the swarm intelligence method and the hybrid algorithm, which uses the swarm and the evolutionary algorithms consistently for solving PCB design tasks. The developed method of bioinspired searching for the optimal solutions proposes the adaptive filter reducing the solutions with low values of fitness function. The experimental results show that the computational and time complexities are within the polynomial behavior. The results allow the authors to note that the genetic algorithm works mostly with the random factor and the evolutionary algorithm uses the directivity factor.
Exploration of Crime Scene Characteristics in Cyber-Related Homicides

Despite the alarming nature of homicides in which the offender meets the victim online, or cyber-initiated homicides, little empirical attention has been devoted to this phenomenon. The present study was designed to explore the behavioral patterns found prior to and during a cyber-initiated homicide event. Data on 61 homicide cases from various countries were collected through news media and legal sources. Smallest space analysis revealed that cyber homicides were characterized by four distinct themes: excessive violence, fatal escalation, crime-related incidents, and predatory behavior. Implications of the findings and avenues for future research are discussed.
Soft Ontologies as Fuzzy RDF Statements

Soft ontologies enable knowledge representation in a flexible way because they are more susceptible to changes in their interrelationships over time. The transformation of a nonstandardized model of information into a computer-interpretable representation is not an easy task. Our investigation expands the concept of soft ontologies to fuzzy data represented as fuzzy RDF triples. In this paper, we elaborate a process that transforms a soft ontology implemented in the form of a matrix of probabilities into a fuzzy RDF dataset. The study presents the way the matrix of probabilities are used in the representation and how the data elements are triplified exploring fuzzy characteristics. We apply the proposal in an experimental scenario, by constructing a soft ontology which expresses a repertoire of actions in an mBot robot. Then, the facts are triplified to fuzzy RDF statements. Our results present original aspects related to the transformation of soft ontologies to fuzzy RDF triples
ZhuQue: A Neural Network Programming Model Based on Labeled Data Layout

In the last five years, the research of neural network accelerators has made remarkable achievements and provided powerful hardware support for many deep learning algorithms. In order to improve the performance of the neural network accelerator, algorithm optimization and data layout in the neural network development kit (NDK) are indispensable. The rich data types in neural network algorithms determine the diversity of data layout information. How to add complex data layout information to the NDK, to guide the work of all aspects of the software, to avoid user perception and to provide a user-friendly API, has become a series of issues worth studying. This paper implements a neural network development kit based on labeled data layout to solve the above problems, and abstracts a neural network programming model. The programming model establishes a neural network computing graph at “creating time”, “compiling time” sets the data label and “runtime” uses the label to control the data transfer. Compared with the existing NDK, the software has an average performance improvement of 4.76×. In addition, this paper also defines dynamic tags and static tags of neural network data, and proposes a neural network data classification method.
An Ensemble Learning Method for Dialect Classification

Abstract Dialect Classification Task is the first step of the Multilingual Automatic Speech Recognition System. Because of the difference of accent between dialects in different regions, the problem of Dialect Classification is a very challenging one. Dialect classification is widely used in information processing, military information retrieval and other fields. Therefore, the study of dialect classification is of great significance. This paper proposes an ensemble learning method for dialect classification. Firstly, the low accuracy of dialect data sets is processed and amplified. Then, three models, GRU, CNN and DNN, are used to classify dialects respectively, and the final dialect types are determined by voting. The accuracy of dialect classification by this method is higher than that of the single model with the best performance, the validity of the model is verified.
A Cooperative Co-evolutionary Genetic Algorithm for Multi-Robot Path Planning Having Multiple Targets

A cooperative co-evolutionary genetic algorithm based method has been proposed in this work to plan multiple robots’ path. A domain knowledge-based operator named “Deletion Operator” and a method to find out the elite set of chromosomes have been developed for our proposed algorithm. A number of works have been carried out on multi-robot path planning, but most of them are suitable for robots having a single target only. Our proposed method can deal with the situation where each of the robots can have multiple targets without any predefined order. Hence, our objective is to get the shortest path from source to goal nodes considering that the goal nodes can appear in any order and the robots should not collide with any obstacles or other robots. The start nodes can also be the same as well as different for each of the robots. We have deployed our proposed method on a number of simulated environments to measure the efficacy of our proposed method. The experimental results reveal the effectiveness of the proposed method.
Gaussian approximations for maxima of random vectors under <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e21" altimg="si2.svg"><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mo linebreak="goodbreak" linebreakstyle="after">+</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math>-th moments

We derive a Gaussian approximation result for the maximum of a sum of random vectors under (2+ι)-th moments. Our main theorem is abstract and nonasymptotic, and can be applied to a variety of statistical learning problems. The proof uses the Lindeberg telescopic sum device along with some other newly developed technical results.
The role of ICT and effect of national culture on economic growth

The ubiquitous nature of ICT makes it an inevitable choice to address economic growth of a nation. The literature indicates a positive significance of ICT on economic growth, but intensity of the usage of ICT highly depends on the nature of the society. This study uses the perspective that sustained usage of ICT is highly dependent on various factors and facets of the society. Using national culture as a societal facet, the study demonstrates the necessity of congruence between ICT usage and national cultural values to attain desired level of economic growth. The results indicate that national culture dimensions and ICT have a significant influence on the economic growth of a nation. The study demonstrates variations in using ICT for economic growth depending on cultures in different regions of the world. The study has implications for policy makers at national and international levels regarding the usage of ICT for economic growth.
ATLAS: A Robust Algorithm for Temporal Sequence Alignment of Treatment Lines using Claim Databases

Comparison of cancer treatment protocols against patient history is important to assess the efficiency of chemotherapy protocols. However, manual identification of protocols is not possible when considering a large population of patients. This paper proposes a new method called ATLAS (Analysis of Treatment Lines using Alignment of Sequences) to tackle the problem related to protocol identification using claim databases as data input. The proposed algorithm is an extension of the Smith-Waterman algorithm and allows to compare any patient's medical history against a list of theoretical protocols taking into account temporal information in sequences as well as missing data. Numerical experiments show that the proposed method could identify the right protocol in over 95% of the cases for realistically noised sequences (15,000 generated patients aligned with 15 protocols). The method is meant to be used as a decision aid tool for practitioners.
An Activity Recognition Application Based on Markov Decision Process Through Fish Eye Camera

In this paper, we propose an artificial intelligence based solution to determine the number of customers in restaurants and automatize the production level arrangements. We employ computer vision techniques that are applied to the fish-eye cameras in fast-food restaurants in order to predict the number of customers in the restaurants, average waiting times and eventually determining the production level in the kitchen. We developed a people activity recognition framework based on Markov Decision Process (MDP) in a dynamic restaurant environment via fish-eye camera streams. The main purpose of the paper is to understand the activity knowledge of customers according to the movement sequence. Our framework infers the intention of each person from consequent movements of the person. The consequent actions of MDP are input parameters of the value function, which is solved by dynamic programming. After analyzing results of Faster-RCNN and YOLO on fish-eye images, we compared our Faster-RCNN-MDP framework with a rule based inference algorithm, which could not achieve a logical knowledge understanding due to detection noise of the fish-eye camera. However, our approach reached up to significant performance in accurately calculating the number of customers and average waiting time of customers in fast food restaurants.
I Got Your Point: An Investigation of Pointing Cues in a Spherical Fish Tank Virtual Reality Display

Pointing is a fundamental building block in human communication. While it is ubiquitous in our daily interactions within the real world, it is difficult to precisely interpret a virtual agent's pointing direction to the physical world, considering its complex and subtle gesture cues, such as the movements of the human hand and head. Fish Tank Virtual Reality (FTVR) display has the potential to provide accurate pointing cues as it creates a compelling 3D spatial effect by rendering perspective-corrected vision. In this paper, we conducted a study with pointing cues of three levels (Head-only, Hand-only, and Hand+Head) to evaluate how the head and hand gesture cues affect observers' performance in interpretation of where a virtual agent is pointing in a spherical FTVR display. The results showed that the hand gesture significantly helps people interpret the pointing both accurately and quickly for fine pointing (15°), with 19.4% higher accuracy and 1.42 seconds faster than the head cue. The combination of the head and hand yielded a small improvement on the accuracy (4.4%) with even slightly longer time (0.38 seconds) compared to the hand-only cue. However, for coarse pointing (30°), head cue appears to be sufficient with the accuracy of 90.2%. The result of this study provides guidelines on cues selection for designing pointing in the virtual environment.
Differed service broker scheduling for data centres in cloud environment

The cloud computing is based on pay as you use computing resources for controlling diverse services like a server, storage and applications. The applications and models are offered in terms of pay per usage using the data centre to the users. The data centres are positioned globally and moreover, these data centres could be overloaded with the increased number of client applications that are being serviced at the identical time and position which corrupts the comprehensive quality of service of the relayed services. Diverse user applications might need diverse customization and demands calibrating the performance of the user applications at differed resources are quite intricate. The service supplier is incapable of performing choices for the suitable set of resources. The design of differed service broker forwarding strategies is based on placing the submitted task in the datacenter with minimum path length and least loaded. The main objective is to accomplish minimal reply time based on the transmission medium, bandwidth, latencies and task size. The designed service broker strategy attempts in minimizing the overpopulated data centres by conveying the user demand to the subsequent data centres that obtain improved reply and processing time. The analysis reveals potential outcomes in terms of reply and processing time as estimated to the other existing broker strategies.
Automatic Speech Recognition for Kreol Morisien: A Case Study for the Health Domain

Automatic Speech Recognition (ASR) has revolutionized human-machine interactions as it allows the use of speech as an input modality. Speech is easy, natural and it is a skill that most people possess in their respective languages. Therefore, speech technology contributes to the usability and inclusivity of applications. ASR in languages such as English is extensively developed as there are large amounts of relevant resources available such as audio or transcribed data. For languages which are under-resourced, such as Kreol Morisien, ASR is a monumental task. In this paper, an attempt at developing an ASR system in Kreol Morisien is described. The ASR system was developed for the health domain to enable the automatic recognition of medical symptoms in spoken Kreol. The data collection process included the manual creation of a list of 848 symptoms along with 4000 audio files. Using the created corpus, the acoustic model for Kreol recognition was built and trained. This paper also describes a user evaluation which was conducted in different environments. Findings showed that the accuracy of the acoustic model was mainly affected by the level of noise. The gender of the speaker and the pronunciation style (depending on the region where the speaker originates from) did not cause any significant difference in the performance of the acoustic model.
6-DOF motion blur synthesis and performance evaluation of light field deblurring

Motion deblurring is essential for reconstructing sharp images from given a blurry input caused by the camera motion. The complexity of this problem increases in a light field due to its depth-dependent blur constraint. A method of generating synthetic 3 degree-of-freedom (3-DOF) translation blur on a light field image without camera rotation has been introduced. In this study, we generate a camera translation and rotation (6-DOF) motion blur model that preserves the consistency of the light field image. Our experiment results show that the proposed blur model can maintain the parallax information (depth-dependent blur) in a light field image. Furthermore, we produce a synthetic blurry light field dataset based on the 6-DOF model. Finally, to validate the usability of the synthetic dataset, we conduct extensive benchmarking using state-of-the-art motion deblurring algorithms.
Toward Case-based Reasoning Facilitation for Online Discussion in Deliberation

This paper presents a novel case-based reasoning (CBR) application to crowd-scale deliberation. We propose a CBR approach to facilitating online discussion for crowd-scale deliberation. The objective is to smooth the discussion by avoiding flaming and to efficiently achieve a consensus. Accordingly, several challenging issues are addressed, including case definition and its structure, case creation, and implementation by incorporating with COLLAGREE, a crowd-scale deliberation platform supporting online discussion with the help of facilitators. After introducing an overview of the crowd-scale deliberation and the COLLAGREE, the paper presents the details of the proposed CBR approach for facilitation of online discussion along with some preliminary results. The feasibility of CBR-based facilitation support for crowd-scale deliberations is demonstrated.
A multi‐dimensional index for privacy‐preserving queries in cloud computing

Summary Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography‐based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation‐based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high‐efficiency multi‐dimensional index is built for answering range counting queries under differential privacy, which embeds privacy‐preserving R‐tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad‐opt , our method provides about 20% better data utility under the same differential privacy principles.
A systematical approach to classification problems with feature space heterogeneity

Purpose Feature space heterogeneity exists widely in various application fields of classification techniques, such as customs inspection decision, credit scoring and medical diagnosis. This paper aims to study the relationship between feature space heterogeneity and classification performance. Design/methodology/approach A measurement is first developed for measuring and identifying any significant heterogeneity that exists in the feature space of a data set. The main idea of this measurement is derived from a meta-analysis. For the data set with significant feature space heterogeneity, a classification algorithm based on factor analysis and clustering is proposed to learn the data patterns, which, in turn, are used for data classification. Findings The proposed approach has two main advantages over the previous methods. The first advantage lies in feature transform using orthogonal factor analysis, which results in new features without redundancy and irrelevance. The second advantage rests on samples partitioning to capture the feature space heterogeneity reflected by differences of factor scores. The validity and effectiveness of the proposed approach is verified on a number of benchmarking data sets. Research limitations/implications Measurement should be used to guide the heterogeneity elimination process, which is an interesting topic in future research. In addition, to develop a classification algorithm that enables scalable and incremental learning for large data sets with significant feature space heterogeneity is also an important issue. Practical implications Measuring and eliminating the feature space heterogeneity possibly existing in the data are important for accurate classification. This study provides a systematical approach to feature space heterogeneity measurement and elimination for better classification performance, which is favorable for applications of classification techniques in real-word problems. Originality/value A measurement based on meta-analysis for measuring and identifying any significant feature space heterogeneity in a classification problem is developed, and an ensemble classification framework is proposed to deal with the feature space heterogeneity and improve the classification accuracy.
MetricHaven: More than 23,000 Metrics for Measuring Quality Attributes of Software Product Lines

Variability-aware metrics are designed to measure qualitative aspects of software product lines. As we identified in a prior SLR [6], there exist already many metrics that address code or variability separately, while the combination of both has been less researched. MetricHaven fills this gap, as it extensively supports combining information from code files and variability models. Further, we also enable the combination of well established single system metrics with novel variability-aware metrics, going beyond existing variability-aware metrics. Our tool supports most prominent single system and variability-aware code metrics. We provide configuration support for already implemented metrics, resulting in 23,342 metric variations. Further, we present an abstract syntax tree developed for MetricHaven, that allows the realization of additional code metrics.Tool: https://github.com/KernelHaven/MetricHavenVideo: https://youtu.be/vPEmD5Sr6gM