Reviewerly/exHarmony huggingface dataset:
A convolutional neural network-based model for knowledge base completion and its application to search personalization

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB obtains better link prediction and triple classification results than previous state-of-the-art models on benchmark datasets WN18RR, FB15k-237, WN11 and FB13. We further apply our ConvKB to a search personalization problem which aims to tailor the search results to each specific user based on the user's personal interests and preferences. In particular, we model the potential relationship between the submitted query, the user and the search result (i.e., document) as a triple (query, user, document) on which the ConvKB is able to work. Experimental results on query logs from a commercial web search engine show that ConvKB achieves better performances than the standard ranker as well as strong search personalization baselines.
A novel robust compression-encryption of images based on SPIHT coding and fractional-order discrete-time chaotic system

In this article, a novel robust compression-encryption of images based on Set partitioning in Hierarchical Trees (SPIHT) coding-decoding algorithm and synchronization of chaotic maps with non-integer order is proposed. The essential innovation in the proposed communication system is that the source image is compressed and encrypted by several secret keys. The developed transmission scheme is designed to take profit of two advantages. The first is the use of standard SPIHT coding which is an efficient and computationally very fast technique for image compression. The second is to introduce farther complexity to the cryptosystem through the use of fractional-order chaotic maps which has many advantages such as the generation of supplementary secret keys with respect to the discrete-time chaotic systems. From the obtained simulation results, we can see that the original image has been well encoded in the cipher text and retrieved with success through the chaotic signals. Further, to verify the high confidentiality of the presented cryptosystem, cryptanalysis is studied in detail through the various tests.
On the KZ Reduction

The Korkine-Zolotareff (KZ) reduction is one of the often used reduction strategies for lattice decoding. In this paper, we first investigate some important properties of KZ reduced matrices. Specifically, we present a linear upper bound on the Hermit constant which is around 7/8 times of the existing sharpest linear upper bound, and an upper bound on the KZ constant which is polynomially smaller than the existing sharpest one. We also propose upper bounds on the lengths of the columns of KZ reduced matrices, and an upper bound on the orthogonality defect of KZ reduced matrices which are even polynomially and exponentially smaller than those of boosted KZ reduced matrices, respectively. Then, we derive upper bounds on the magnitudes of the entries of any solution of a shortest vector problem (SVP) when its basis matrix is LLL reduced. These upper bounds are useful for analyzing the complexity and understanding numerical stability of the basis expansion in a KZ reduction algorithm. Finally, we propose a new KZ reduction algorithm by modifying the commonly used Schnorr-Euchner search strategy for solving SVPs and the basis expansion method proposed by Zhang et al. Simulation results show that the new KZ reduction algorithm is much faster and more numerically reliable than the KZ reduction algorithm proposed by Zhang et al., especially when the basis matrix is ill conditioned.
Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification

Classification of the high-dimensional data is challenging due to the curse of dimensionality, heavy computational burden and decreasing precision of algorithms. In order to mitigate these effects, feature selection approaches that can determine an efficient subset of features are utilized in the processing. However, most of these techniques attain just one subset of non-redundant features including the best ones. Alternatively, clustering approaches can be used to find the most informative clusters of features instead of generating just a single subset. So called, Hybrid Feature Clustering (HFC) method is capable of maximizing the classification accuracy while keeping the amount of redundant features in each cluster low. The patterns of each cluster are classified by a neural tree that employs Radial Basis Function (RBF) for the nodes. Within each neural tree, a hierarchical approach is proposed to transfer the knowledge of synaptic weights from a parent RBF node to each child. A gating network is applied on the forest of these neural trees in order to aggregate the results. By assessing the classification accuracy and the computational complexity on high-dimensional datasets it can be shown that the proposed solution has outperformed the state of the art classifiers. Furthermore, the computational complexity and the convergence of this method are theoretically proven and the robustness analysis under noisy conditions is conducted.
Synchronization-based clustering on evolving data stream

Clustering streams of data is of increasing importance in many applications. In this paper, we propose a new synchronization-based clustering approach for evolving data streams, called SyncTree, which maintains all micro-clusters at different levels of granularity depending upon the data recency. Instead of using a sliding window or decay function to focus on recent data, SyncTree summarizes all continuously-arriving objects as synchronized micro-clusters sequentially in a batch fashion. Owing to the powerful concept of synchronization, the derived micro-clusters truly reflect the intrinsic cluster structure rather than summarize statistics of data, and old micro-clusters can be intuitively summarized at a higher level by iterative clustering to fit memory constraints. Building upon the hierarchical micro-clusters, SyncTree allows investigating the cluster structure of the data stream between any two time stamps in the past, and also provides a principled way to analyze the cluster evolution. Empirical results demonstrate that our method has good performance compared to state-of-the-art algorithms.
A Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT): Part II. The cloudy-sky model

The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT) reported in Part I of this study is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 μm are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to provide the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering using the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution analysis is performed to assess the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2–3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.
Design and Implementation of a Tree-Based Blind Modulation Classification Algorithm for Multiple-Antenna Systems

This paper presents the design and testbed implementation of a tree-based algorithm, which classifies many of the linearly modulated signals, such as binary phase-shift keying (PSK), quadrature PSK (QPSK), offset QPSK, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> /4-QPSK, 8-PSK, minimum shift keying, and 16-quadrature amplitude modulation. The proposed modulation classification algorithm is applicable to spatially multiplexed multiple antenna systems over frequency-selective fading channels. It works in the presence of timing, phase, and frequency offsets, without having prior knowledge of the channel state information. Classification is performed by utilizing the combined properties of the correlation functions, cyclic cumulant (CC), and cumulant. The correlation function of the received baseband signal exhibits peaks at a particular set of time lag, which represents distinctive features for the modulation formats. The CC uses the position of nonzero cycle frequency of the received baseband signals to classify modulation formats, while the cumulant employs threshold values. To authenticate the proposed algorithm, implementation and measurement are performed in an indoor propagation environment by using a National Instrument testbed setup. The performance of the proposed algorithm is compared with existing methods via Monte Carlo simulations.
Impact of Virtual Reality in Healthcare

Virtual reality (VR) is the simulation of reality where the users would be immersed in an artificial/virtual environment that isn't there but creates an illusion as if it really exists. People using this technology get a feeling that they are performing everything in real time. This gives users a sense of satisfaction. Initially, VR technology was used for gaming purposes, but now it is used in many sectors, including healthcare. There are many situations wherein, when it is expensive or impossible to do something, in reality, a probable solution is virtual reality. An important area where it is explored is healthcare for training doctors, diagnosis, and treatment of various ailments. The main objective of this chapter is to shed light on the applications of VR in healthcare and to discuss some applications showing how the medical field has already started reaping the benefits of VR.
Challenges and recommended practices for software architecting in global software development

Global software development (GSD), although now a norm in the software industry, carries with it enormous challenges mostly regarding communication and coordination. Aforementioned challenges are highlighted when there is a need to transfer knowledge between sites, particularly when software artifacts assigned to different sites depend on each other. The design of the software architecture and associated task dependencies play a major role in reducing some of these challenges. The current literature does not provide a cohesive picture of how the distributed nature of software development is taken into account during the design phase: what to avoid, and what works in practice. The objective of this paper is to gain an understanding of software architecting in the context of GSD, in order to develop a framework of challenges and solutions that can be applied in both research and practice. We conducted a systematic literature review (SLR) that synthesises (i) challenges which GSD imposes on software architecture design, and (ii) recommended practices to alleviate these challenges. We produced a comprehensive set of guidelines for performing software architecture design in GSD based on 55 selected studies. Our framework comprises nine key challenges with 28 related concerns, and nine recommended practices, with 22 related concerns for software architecture design in GSD. These challenges and practices were mapped to a thematic conceptual model with the following concepts: Organization (Structure and Resources), Ways of Working (Architecture Knowledge Management, Change Management and Quality Management), Design Practices, Modularity and Task Allocation. The synthesis of findings resulted in a thematic conceptual model of the problem area, a mapping of the key challenges to practices, and a concern framework providing concrete questions to aid the design process in a distributed setting. This is a first step in creating more concrete architecture design practices and guidelines.
Multimodal Learning for Human Action Recognition Via Bimodal/Multimodal Hybrid Centroid Canonical Correlation Analysis

In this paper, we study the problem of human action recognition from multiple feature modalities. We propose bimodal hybrid centroid canonical correlation analysis (BHCCCA) and multimodal hybrid centroid canonical correlation analysis (MHCCCA) to learn the discriminative and informative shared space, by considering the correlation among different classes across two modalities (BHCCCA) and three or more modalities (MHCCCA). We then introduce a new human action recognition framework by using BHCCCA/MHCCCA for fusing different modalities (RGB, depth, skeleton, and accelerometer data). Performance evaluation on four publicly accessible data sets (MSR Action3D, UTD-MHAD, UTD-MHAD-Kinect V2, and Berkeley MHAD) demonstrated the effectiveness of the proposed framework.
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering

Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to finetune the OPF classifier in the context of anomaly detection in wireless sensor networks.
Co-designing wearable devices for sports: The case study of sport climbing

This paper presents the co-design process of a wearable device for rock climbing, an extreme sport that requires high physical, emotional, and cognitive involvement. From the preliminary investigation of climbers' needs, it emerged that their acceptance of wearables is mainly influenced by climbing sport-specific values like self-efficacy, trust, and adventure. Such values highlight the importance of the role technology should have beyond its functional purpose, i.e. how wearables should support climbers. Based on these insights, we designed and deployed a vibrotactile wearable device aimed at augmenting the communication between instructor and trainees and assessed its usefulness, usability, and pleasantness during an indoor climbing lesson. Finally, we conclude the paper discussing how the wearable we implemented meets the design criteria emerged from climbers' values and reflecting on the importance to gain new perspectives on the design of wearables for sports in general.
Artificial bee colony algorithm based on Parzen window method

Artificial Bee Colony (ABC) algorithm, based on the metaphor in foraging behavior of honey fee swarm, has been repeatedly criticized for its poor convergence, due to its known exploration bias. In order to enhance the performance of ABC, the paper develops a novel approach (named ABCPW). First, three popular search strategies with different characteristics are employed to construct a strategy candidate pool for obtaining high quality candidate individuals. Next, to cut down on computational cost, the Parzen window method is applied to estimate these candidate individuals and then select one as the offspring. In addition, two different neighborhood mechanisms are adopted to balance the convergence and the population diversity. Finally, the performance of ABCPW is tested on a series of benchmark functions. The experimental results not only demonstrate the stability and convergence of ABCPW, but also show ABCPW outperforms several popular algorithms.
Information Geometric Approach to Multisensor Estimation Fusion

Distributed estimation fusion is concerned with the combination of local estimates from multiple distributed sensors to produce a fused result. In this paper, we characterize local estimates as posterior probability densities, and assume that they all belong to a parametric family. Our starting point is to consider this family as a Riemannian manifold by introducing the Fisher information metric. From the perspective of information geometry, the fused density is formulated as an informative barycenter in the space of probability densities and sought by minimizing the sum of its squared geodesic distances from the local posterior densities. Under Gaussian assumptions, a geodesic projection (GP) method and a Siegel distance (SD) method in the information-geometric framework are proposed to tackle the problem. The GP method gives a fusion result in accord with the covariance intersection estimate but under an information-geometric criterion, while the SD method appears to achieve a better approximation of the informative barycenter. Numerical examples are provided to demonstrate the performance of the proposed estimation fusion algorithms.
Projector distortion residual compensation in fringe projection system

In fringe projection profilometry system, the lens distortion of the projector is usually represented with the distortion coefficients, but it will result in large residual. To compensate this residual, a projector distortion residual compensation method is proposed in this paper. After system calibration, the original distortion map is generated with the distortion coefficients and the original ideal pattern can be pre-distorted with the original distortion map. Then, with pinhole model and fringe projection technique, a residual distortion map is built based on per-pixel measurement. Finally, the original distortion map can be refined with the residual distortion map, and the lens distortion residual error can be compensated. Experimental results show considerable accuracy improvement in both the full-field error of projector and the final point cloud of system. In addition, the proposed compensation method can be implemented conveniently without any sophisticated ancillary equipment or complicated procedure.
Constructing quotient inductive-inductive types

Quotient inductive-inductive types (QIITs) generalise inductive types in two ways: a QIIT can have more than one sort and the later sorts can be indexed over the previous ones. In addition, equality constructors are also allowed. We work in a setting with uniqueness of identity proofs, hence we use the term QIIT instead of higher inductive-inductive type. An example of a QIIT is the well-typed (intrinsic) syntax of type theory quotiented by conversion. In this paper first we specify finitary QIITs using a domain-specific type theory which we call the theory of signatures. The syntax of the theory of signatures is given by a QIIT as well. Then, using this syntax we show that all specified QIITs exist and they have a dependent elimination principle. We also show that algebras of a signature form a category with families (CwF) and use the internal language of this CwF to show that dependent elimination is equivalent to initiality.
A hybrid approach to building face shape classifier for hairstyle recommender system

Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This framework enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Support Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these individual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.3% of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor.
Deep convolutional extreme learning machines: Filters combination and error model validation

In recent years, deep convolutional neural network models have been increasingly used in various computer vision tasks, like plate number recognition, object recognition, automatic digit recognition, and medical applications supporting diagnosis by signals or images. A disadvantage of these networks is the long training time. It can take days to adjust weights with iterative methods based on gradient descent. This can be an obstacle in applications that need frequent training or in real time. Fast convolutional networks avoid gradient-based methods by efficiently defining filters in feature extraction and weights in classification. The issue is how to set the convolutional filter banks, since they are not learned by the backpropagation of gradients? In this work we propose a deep fast convolutional neural network based on extreme learning machine and a fixed bank of filters. We demonstrate that our model is feasible to be used in cost-effective non-specialized computer hardware, performing the training task faster than models running on GPUs. Results were generated on EMNIST dataset representing the widely studied problem of digit recognition. We provide a deep convolutional extreme learning machine (CELM) with two feature extraction stages and combinations of selected filters. For the proposed network, we find that the empirical generalization error is explained by the error model based on a theorem by Rahimi and Retch. In comparison to the state-of-the-art, the proposed network resulted in superior accuracy as well as competitive training time, even in relation to approaches that employ processing in GPUs.