Reviewerly/exHarmony huggingface dataset:
Multispectral imaging: Monitoring vulnerable people

This paper describes the development of a new healthcare monitoring system for vulnerable people which uses a visible image sensor and passive infrared sensors, in an unconventional manner, to monitor daily living activities. It based on a novel method, using chromatic methodology, to process spatially and temporally the incoming multispectral data from the visible and infrared parts of the spectrum, to overcome the impact of noisy environments, illumination changes and a dynamic background. An efficient chromatic descriptor is suggested to improve activity recognition of vulnerable people. The new monitoring system is robust to distortions associated with healthcare systems and its descriptor has an improved quality of description. System performance was evaluated using a series of experimental data, the results showing the efficacy of using both spatial and temporal domains of multispectral data to deal with events that disturb monitoring systems. The chromatic descriptor achieved a better performance in comparison to traditional methods when describing daily living activities.
Particle filter with Markovian packet dropout and time delay

This technical note is concerned with particle filter for the discrete-time nonlinear networked control system. First, modified particle filter algorithm with Markovian packet dropout and time delay is proposed, and its error covariance is benchmarked by Markovian Cramér-Rao lower bound. Second, an upper bound of the Markovian Cramér-Rao lower bound is presented for some special nonlinear networked systems. Third, some necessary conditions for the boundness of error covariance are given by obtaining some sufficient conditions for the bounded Markovian Cramér-Rao lower bound. Finally, numerical examples are presented to illustrate the efficiency of proposed particle filter.
Editorial special issue on assumptions about the concept of childhood and the roles of children in design

Educators and researchers with varied backgrounds and expertise share a common vision of helping children expand their horizons and capabilities through learning design, digital fabrication, and making skills. We highlight the collaborative and transdisciplinary nature of these endeavors, underlining the perspective of value co-creation where value needs to be realized for all stakeholders. We encourage all to utilize this lens to continue to advance the field and benefit children as they learn and create.
Deep memory and prediction neural network for video prediction

Inspired by the concept of memory mechanism and predictive coding from the cognitive neuroscience, this paper presents a deep memory and prediction neural network (DMPNet) for video prediction. Correspondingly, memory and error propagation units are designed in DMPNet to capture the previous spatial-temporal information and compute current predictive error which is forwarded to the prediction unit for correcting the subsequent video prediction. Subsequently, prediction unit takes the information stored in memory unit and predictive error of previous frame as input to predict the next frame. We evaluate our method on two public real-world datasets and demonstrate that the proposed DMPNet outperforms some state-of-the-art methods quantitatively and qualitatively.
Learning to Skip Ineffectual Recurrent Computations in LSTMs

Long Short-Term Memory (LSTM) is a special class of recurrent neural network, which has shown remarkable successes in processing sequential data. The typical architecture of an LSTM involves a set of states and gates: the states retain information over arbitrary time intervals and the gates regulate the flow of information. Due to the recursive nature of LSTMs, they are computationally intensive to deploy on edge devices with limited hardware resources. To reduce the computational complexity of LSTMs, we first introduce a method that learns to retain only the important information in the states by pruning redundant information. We then show that our method can prune over 90% of information in the states without incurring any accuracy degradation over a set of temporal tasks. This observation suggests that a large fraction of the recurrent computations are ineffectual and can be avoided to speed up the process during the inference as they involve noncontributory multiplications/accumulations with zero-valued states. Finally, we introduce a custom hardware accelerator that can perform the recurrent computations using both sparse and dense states. Experimental measurements show that performing the computations using the sparse states speeds up the process and improves energy efficiency by up to 5.2× when compared to implementation results of the accelerator performing the computations using dense states.
Globally Valid Posterior Cramér–Rao Bound for Three-Dimensional Bearings-Only Filtering

This paper addresses the problem of three-dimensional target motion analysis from bearing measurements. The established description of uncertain directional data involves azimuth and elevation angles with additive Gaussian noise. Investigation of this widely accepted representation in terms of the corresponding Fisher information matrices reveals that the underlying probability densities are only valid for small absolute elevation angles and precise measurements. Employing spherical statistics, an alternative representation is proposed that is sensible for the whole state space and arbitrarily imprecise bearing sensors. Based on this, a globally valid posterior Cramér-Rao lower bound is derived.
High-dimensional quantum encoding via photon-subtracted squeezed states

We introduce a high-dimensional quantum encoding based on coherent mode-dependent single-photon subtraction from multimode squeezed states. This encoding can be seen as a generalization to the case of nonzero squeezing of the standard single-photon multirail encoding. The advantage is that the presence of squeezing enables the use of common tools in continuous-variable quantum processing, which in turn allows us to show that arbitrary $d$-level quantum states can be generated and detected by simply tuning the classical fields that gate the photon-subtraction scheme. Therefore, the scheme is suitable for mapping arbitrary classical data in quantum mechanical form. Regardless of the dimension of the data-set alphabet, the mapping is conditioned on the subtraction of a single photon only, making it nearly unconditional. We prove that this encoding can be used to calculate vector distances, a pivotal primitive in various quantum machine-learning algorithms.
Maximum accurate medical image demosaicing using WRGB based Newton Gregory interpolation method

Color image demosaicing is an important image processing technique for obtaining high quality image. This operation is complicated for digital camera, since it converts a Bayer mosaic image to a full color image, which estimates the output image quality of the camera. Nowadays, many techniques are developed to reduce the size and weight of electronic gadgets. In this way, to reduce the size of camera Color Filter Array is used which has only one sensor for three primary colors (Red, Green and Blue). So many demosaicing techniques are developed which reconstruct the full color image from the incomplete color samples in CFA. But till today there is no demosaicing algorithm for medical image re-construction is available in existing literature. In this paper a hybrid medical image demosaicing technique is developed for obtaining better image quality, higher sensitivity as well as higher resolution for high quality medical diagnosis. To improve the medical image quality, Newton Gregory Interpolation is used. This technique mainly focused on Green channel and produce interpolated Red, Green and Blue channels and for enhancing the sensitivity and resolution, updating White channel texture component with RGB channels. In post processing Cross Multilateral Filter is used which decomposes the smoothness component from the texture component. The proposed newton Gregory interpolation based medical image Demosaicing achieves 42.31 db for brain database of NIH and 39.52 db for Iris database of UPOL.
Awareness and use of self-archiving options among academic librarians in Nigerian universities

Purpose The purpose of this study was to investigate the extent to which academic librarians in Nigerian universities use self-archiving options to make their research papers visible globally. Design/methodology/approach An online survey was designed using SurveyMonkey software to collect data from 394 academic librarians in Nigerian Universities. Findings The study revealed that the academic librarians in Nigerian universities know and actually use self-archiving options such as ResearchGate, institutional repository and Academia.edu to self-archive their publications. While other promotional tools such as kudos, Mendeley.com and personal websites/servers are not popularly used by the academic librarians. Increased exposure of previously published work, broadens the dissemination of academic research generally, and increases institutions’ visibility were among the factors the academic librarians indicated as very important that motivate them to contribute their scholarly output to self-archiving options. Research limitations/implications One major challenge to the study is that many academic librarians in Nigeria do not check their e-mails regularly to enable them to respond to a request to participate in an online survey; some of them do not have stable internet facilities, whereas others are reluctant to respond to an online questionnaire. These reasons led to a low response rate which makes it difficult to generalize findings. Practical implications Findings from the study will create awareness for academic librarians in developing countries to see the need to self-archive their pre-print and accepted version of their papers in different self-archiving platforms. Originality/value Self-archiving of papers by authors will lead to an increased visibility of the author and possible citation of the work and chances of collaboration with international colleagues for research projects.
Generation of ultra violet signal from visible light using photonic crystal fiber: A realization of PCF based UV torch

Though photonic crystal fiber has been exhibiting an exhilarating application since a couple decades, the current communication divulges an another noteworthy upshot vis-à-vis Ultra Violet (UV) signal from Visible light. The photonic crystal fiber is taken as square type silicon based photonic crystal, where nature, position and configuration are mainstay of this research work. The principle of investigation of present study is based on the distribution of electric field in the said suggested fiber in such way that emerging signal from such fiber lies with the range of UV signal. Finally the proposed device acts as fiber based torch light, which could be used for UV applications.
A Cartography of Web Tracking using DNS Records

Web tracking plays a crucial role in the Web ecosystem. It relies on third-party tracking actors collecting user information that are used for various applications such as advertisement and analytics, etc. With the massive growth of the Internet, understanding the geography of tracking is of strategic importance. The goal of this paper is to propose a thorough investigation of web tracking inside China taking advantage of a large dataset (1011 records) containing two days of full DNS access from a major ISP providing both mobile and landline ADSL. Our results show that a strong Pareto principle applies on the traffic toward trackers, with only 26 trackers, representing 90% of tracking activity. We then show that although most first-party sites accessed from China are owned by Chinese corporations, large proportion of trackers belong to US ones. This raises concerns about the advertisement industry in China, and more generally shed new lights on the international data flows, the interdependency of the main actors, and the complexity of the threats for both people and states.
Combining and matching keypoints and lines on multispectral images

In this work, we proposed a method of combining lines and keypoints and matching them on multispectral images. Existing keypoint techniques usually assume images contain abundant texture information and employ hand-crafted gradient information to match keypoints. Unfortunately, mutltispectral images may lack sufficient texture information and gradient information is unable to represent well the common feature, which makes the matching of keypoints very unreliable. Lines on the contrary can be well extracted and serve as a complementary and dual image featues, but robustly describing neighboring region around lines is difficult due to inaccurate ending points and line fragmentation. In observance of the strength of lines and keypoints,this work proposes combining the keypoints and lines and matching them together. Firstly,keypoints and lines are extracted and grouped according to spatial affinity. Then we sample points in the local region for each combination and the sampled points are then used to build descriptors for each group which will be used to match the combinations of keypoints and lines. The main contribution is that the proposed method can achieve reliable feature matchings without requiring accurate end points oweing to the use of combination of keypoints and lines. The proposed method is tested on a large number of multispectral images and experimental results show that it can effectively match lines and keypoints simultaneously.
Target tracking based on the cognitive associative network

To extend the application of the cognitive associative network and improve the performance of the target tracking method, a novel tracking method based on the new network is proposed, i.e. the cognitive associative network is transformed and used to perform the target tracking. The local hue histograms are used to model the target, and the circle matching criterion is used to locate the target, which can restrain the disturbance from the adjacent regions in the background. It can also adjust adaptively the size of the tracking target according to the local matching results. Therefore, the tracking method based on the cognitive associative network has good invariability to the scale, the rotation, and the partial occlusion. Simulation results show that the tracking method can perform the target tracking in the disturbance environment or in the scene of the complex motion. The tracking method can locate the target more accurately than the common tracking methods such as the Camshift method or the compressive tracking method.
Islamic inheritance calculation system based on Arabic ontology (AraFamOnto)

Recently, a large number of automated applications are developed to improve the retrieval of different types of knowledge. However, there are few automated applications of semantic web technologies (ontology) for the retrieval of Islamic knowledge and in particular for Arabic language, despite the strong demand and need for this knowledge by Muslims and also by non-Muslims. In this paper, we present AraFamOnto, an Arabic ontology-based inheritance calculation system. The use of ontology is becoming increasingly important to store knowledge about the person's family relationships in order to facilitate research, the processing of information about the person and family members, and the calculation of the inheritance of the deceased person's heirs. We present a practical method to limit the time needed to process family data and reduce human effort in the search for family relationships to calculate the Islamic Inheritance correctly.
Direct causal structure extraction from pairwise interaction patterns in NAT modeling Bayesian networks

Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A CPT may be directly expressed as a NAT model or compressed into a NAT model. Once CPTs are NAT-modeled, efficiency of BN inference (both space and time) can be significantly improved. One of the critical operations in NAT modeling CPTs is extracting NAT structures from interaction patterns between causes. The existing method does so through NAT databases coupled with search trees. Although the databases and search trees are compiled offline, the computation is costly and the dependency of NAT extraction on them adds a resource requirement for online computation. We present a novel method for direct NAT structure extraction from full and valid causal interaction patterns, based on bipartitions of causes. We then extend the method to NAT extraction from partial and invalid interaction patterns. The resultant algorithm suite enables direct NAT extraction from all conceivable practical scenarios, with significantly reduced computational complexity, while eliminating dependency on NAT databases and search trees.
Design methods for semi-bent functions

Semi-bent functions play an important role in the construction of orthogonal variable spreading factor codes used in code-division multiple-access (CDMA) systems as well as in certain cryptographic applications. In this article we provide several infinite classes of semi-bent functions, where each class is characterized by either a different decomposition of such a function with respect to the Walsh spectra of its subfunctions, or by the method used for its derivation. In particular, we also give the exact number of possibilities of decomposing bent functions, in a subclass of the Maiorana–McFarland class.
Architectures and algorithms for on-device user customization of CNNs

A convolutional neural network (CNN) architecture supporting on-device user customization is proposed. The network architecture consists of a large CNN trained on a general data and a smaller augmenting network that can be re-trained on-device using a small user-specific data provided by the user. The proposed approach is applied to handwritten character recognition of the Latin and the Korean alphabet, Hangul. Experiments show a 3.5-fold reduction of the prediction error after user customization for both the Latin and the Korean character set compared to the CNN trained with general data. To minimize the energy required when retraining on-device, the use of a coarse-grained reconfigurable array processor (CGRA) in a low-power, efficient manner is presented. The CGRA achieves a speedup of 36× and a 54-fold reduced energy consumption compared to an ARMv8 processor. Compared to a 3-way VLIW processor, a speedup of 42× and a 12-fold energy reduction is observed, demonstrating the potential of general-purpose CGRAs as light-weight DNN accelerators.
Entanglement in four qubit states: Polynomial invariant of degree 2, genuine multipartite concurrence and one-tangle

Characterization of the multipartite mixed state entanglement is still a challenging problem. This is due to the fact that the entanglement for the mixed states, in general, is defined by a convex-roof extension. That is the entanglement measure of a mixed state ρ of a quantum system can be defined as the minimum average entanglement of an ensemble of pure states. In this paper, we show that polynomial entanglement measures of degree 2 of even-N qubits X states is in the full agreement with the genuine multipartite (GM) concurrence. Then, we plot the hierarchy of entanglement classification for four qubit pure states and then using new invariants, we classify the four qubit pure states. We focus on the convex combination of the classes whose at most the one of the invariants is non-zero and find the relationship between entanglement measures consist of non-zero-invariant, GM concurrence and one-tangle. We show that in many entanglement classes of four qubit states, GM concurrence is equal to the square root of one-tangle.
Unidirectional Representation-Based Efficient Dictionary Learning

Dictionary learning (DL) has been widely studied for pattern classification. Most existing methods introduce multiple discriminative terms into objective functions for accuracy improvement, leading to complex learning frameworks and high computational burdens. This paper proposes a simple yet effective DL algorithm for classification, namely unidirectional representation dictionary learning (URDL). Unidirectional constraint is proposed to guide coefficient directions in the representation to be discriminative. Besides, direction-thresholding is proposed to exploit the direction property in the classification scheme. It suppresses the disturbance from undesired non-zero coefficients, and improves the representation discriminability. We adopt squared ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm-based regularization for efficient coding, and systematically analyze the mechanism of the proposed method. Extensive experiments on five data sets are conducted, including object categorization, scene classification, face recognition, and fine-grained flower classification. The experimental results demonstrate that the proposed approach not only outperforms the state-of-the-art DL algorithms in terms of recognition accuracy significantly, but also exhibits a much higher computational efficiency.
Delineation of deep prospecting targets by combining factor and fractal analysis in the Kekeshala skarn Cu deposit, NW China

Three-dimensional visualization, factor analysis, and fractal/multifractal modeling are combined to study the geochemical data of borehole primary halo in the Kekeshala mining area in Northwestern China to screen exploration targets. First, based on the borehole spatial database comprising collar, survey, geology, and sample tables, 3D borehole primary halo samples are extracted. Their 3D block model is then constructed by spatial interpolation. Second, sample factor analysis of the borehole primary halo mainly includes four factors: F1 consists of Ag–Cu–Sn combination, F2 consists of Pb–Zn combination, F3 consists of As–Sb combination, and F4 consists of Mo–W combination. Moreover, 3D geochemical zoning is conducted for the samples. Third, based on the 3D block model of the primary halo, the F1 factor score is further modeled by Concentration–Volume (C–V) fractal model. Furthermore, based on the fractal fitting line's four segments obtained from the inflection points, geochemical barren host rocks, weakly deep prospect, moderately deep prospect, and highly deep prospect are extracted. Results indicate the following: (a) the F1 factor embodies the main metallogenic process or stage, which can be used as a comprehensive indicator of the spatial aggregation of major metallogenic elements in the study area; (b) the C–V fractal model can effectively determine the F1 factor deep prospect threshold of the skarn-Cu deposit in Kekeshala; and (c) the moderately and highly deep prospects of the F1 factor can well reflect the favorable geological conditions of metallogenesis and be used as the basis for delineating the prospecting target. Therefore, the fractal deep prospect of the geochemical F1 factor of the borehole primary halo has a good application effect in deep prospecting and prediction, which can be effectively used to explore the depth and edge of known deposits.