Reviewerly/exHarmony huggingface dataset:
A New Distance Metric Exploiting Heterogeneous Interattribute Relationship for Ordinal-and-Nominal-Attribute Data Clustering

Ordinal attribute has all the common characteristics of a nominal one but it differs from the nominal one by having naturally ordered possible values (also called categories interchangeably). In clustering analysis tasks, categorical data composed of both ordinal and nominal attributes (also called mixed-categorical data interchangeably) are common. Under this circumstance, existing distance and similarity measures suffer from at least one of the following two drawbacks: 1) directly treat ordinal attributes as nominal ones, and thus ignore the order information from them and 2) suppose all the attributes are independent of each other, measure the distance between two categories from a target attribute without considering the valuable information provided by the other attributes that correlate with the target one. These two drawbacks may twist the natural distances of attributes and further lead to unsatisfactory clustering results. This article, therefore, presents an entropy-based distance metric that quantifies the distance between categories by exploiting the information provided by different attributes that correlate with the target one. It also preserves the order relationship among ordinal categories during the distance measurement. Since attributes are usually correlated in different degrees, we also define the interdependence between different types of attributes to weight their contributions in forming distances. The proposed metric overcomes the two above-mentioned drawbacks for mixed-categorical data clustering. More important, it conceptually unifies the distances of ordinal and nominal attributes to avoid information loss during clustering. Moreover, it is parameter free, and will not bring extra computational cost compared to the existing state-of-the-art counterparts. Extensive experiments show the superiority of the proposed distance metric.
Deep network compression based on partial least squares

Modern visual pattern recognition methods are based on convolutional networks since they are able to learn complex patterns directly from the data. However, convolutional networks are computationally expensive in terms of floating point operations (FLOPs), energy consumption and memory requirements, which hinder their deployment on low-power and resource-constrained systems. To address this problem, many works have proposed pruning strategies, which remove neurons (i.e., filters) in convolutional networks to reduce their computational cost. Despite achieving remarkable results, existing pruning approaches are ineffective since the accuracy of the network is degraded. This loss in accuracy is an effect of the criterion used to remove filters, as it may result in the removal of the filters with high influence to the classification ability of the network. Motivated by this, we propose an approach that eliminates filters based on the relationship of their outputs with the class label, on a low-dimensional space. This relationship is captured using Partial Least Squares (PLS), a discriminative feature projection method. Due to the nature of PLS, our method focuses on keeping discriminative filters. As a consequence, we are able to remove up to 60% of FLOPs while improving network accuracy. We show that our criterion is superior to existing pruning criteria, which include state-of-the-art feature selection techniques and handcrafted approaches. Compared to state-of-the-art pruning strategies, our method achieves the best tradeoff between drop/improvement in accuracy and FLOPs reduction.
Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization

In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.
Learning Landmark Guided Embeddings for Animal Re-identification

Re-identification of individual animals in images can be ambiguous due to subtle variations in body markings between different individuals and no constraints on the poses of animals in the wild. Person re-identification is a similar task and it has been approached with a deep convolutional neural network (CNN) that learns discriminative embeddings for images of people. However, learning discriminative features for an individual animal is more challenging than for a person's appearance due to the relatively small size of ecological datasets compared to labelled datasets of person's identities. We propose to improve embedding learning by exploiting body landmarks information explicitly. Body landmarks are provided to the input of a CNN as confidence heatmaps that can be obtained from a separate body landmark predictor. The model is encouraged to use heatmaps by learning an auxiliary task of reconstructing input heatmaps. Body landmarks guide a feature extraction network to learn the representation of a distinctive pattern and its position on the body. We evaluate the proposed method on a large synthetic dataset and a small real dataset. Our method outperforms the same model without body landmarks input by 26% and 18% on the synthetic and the real datasets respectively. The method is robust to noise in input coordinates and can tolerate an error in coordinates up to 10% of the image size.
Research on Moisture Content Detection of Wood Components Through Wi-Fi Channel State Information and Deep Extreme Learning Machine

Forest resources are among the most important wealth on the earth. Therefore, rational utilization of woods and improving its service efficiency will introduce profound social significance and economic benefits. But general surveys find that incorrect treatment of moisture content in wood products causes increasingly severe quality problems. So an efficient and nondestructive detection of wood moisture content is of extremely necessary and great importance. Whereas the present research tools are either time-consuming, environmentally sensitive, or too expensive to afford. This work proposes a convenient measurement of wood component's moisture content through Channel State Information (CSI) of the Wi-Fi signal and its corresponding high precise hybrid feature extraction via Bimodal Deep Extreme Learning Machine (BDELM). We first set up a moisture content detection equipment with commodity Wi-Fi devices, then we acquire and process the CSI data which pass through the wood component. After that, the BDELM is adopted to calculate the integration of CSI' amplitude and phase data. Finally, the moisture content will be accurately distinguished by offline training and an online testing procedure. Our results show that the proposed method could achieve a classification accuracy of moisture content detection above 96% for six different wood components, which demonstrates its effectiveness in application scenarios like wooden structure health monitoring.
Rational actions or institutional actions: A study on the rationality in academic librarians' decision-making processes when purchasing e-book products

It is important for academic librarians to demonstrate that they are trusted stewards of public money when purchasing e-book products; however, prior studies seldom examine the rationality in librarians' decision-making processes. Interviews including 20 librarians from 19 different institutions were conducted to collect data about their decision-making processes when purchasing e-book products. Institutional theory was used as a framework to analyze interview data. Librarians' perceptions of the environment where their decision process occurred were described as full of uncertainty, with the e-book market viewed as non-competitive with limited alternative products. Seven strategies the interviewees adopted to justify their purchase decisions were then identified, including experimentation, incorporating users' input, reliance on written documents, learning from past experiences, learning from other libraries, group decision, and aggressive negotiation. Decision-making processes were a mixture of institutional and rational actions. Factors affecting rationality in librarians' e-book purchase decisions could be applied to other types of decision-making in library settings, allowing stakeholders to better understand and interpret the situations that either help or hinder the rationality in library's decision-making.
Emerging trends in Digital Forensic and Cyber security- An Overview

Digital forensics is the application of forensic and scientific knowledge to retrieve information legally from any digital device such as computers and smartphones. This legally fetched information is then presented as a piece of evidence in the courtroom further. Computer forensics also refers to digital forensics. It is the fusion of domains such as network forensics, server forensics, computer forensics, internet forensics, social media forensics, memory forensics, online gaming, data/disk forensics, and VR forensics. While investigating digital crimes, different steps are involved. These include the identification, recovery, investigation, validation, and presentation of evidence. Recent research has demonstrated the immense succession of cyber threats and attacks, requiring forensic experts and forensic scientists to simplify the digital world system. Since digital forensics is straightforwardly interconnected to data recovery and data carving, this area is dealing with different technical, legal, and resource challenges. In contrast to that, malware's constant rise allows forensic sector slacking. The objective of the present study was to explore new dimensions of digital forensics such as internet forensics, social media forensics, and IoT forensics in various emerging fields. Together, all our findings allow a better understanding of digital forensics, which can be helpful in forensic investigation.
A Systematic Literature Review in Multi-Agent Systems: Patterns and Trends

Multi-Agent Systems became a powerful solution to model and solve problems in complex and dynamic environments. While research in this area grew exponentially before 2009, there is a need to understand the status quo of the field from 2009 to June 2017 in order to comprehend the general evolution. The results of a SLR related to Multi-Agent Systems, its applications and research gaps, following Kitchenham and Wholin guidelines are presented in this paper. From the analysis of279 papers (out of3522 candidates), our findings suggest that: a) there is a general decreasing trend of publications (but it is increasing for specific domains), b) only 15% of the papers portrayed a real case study, c) the top 20 were formed by 67 authors, d) the papers were mostly published in journals and conferences, e) there is no unified methodology or framework, f) the top 3 application domains were transport/traffic, healthcare/biology, and logistics/manufacturing, g) MAS interact with different disciplines like machine learning. Finally, the MAS community should work together to close the gaps and unify the field, bridging with other disciplines and industry.
Phonon-mediated quantum discord in dark solitons

We investigate the quantum correlation dynamics in dark-soliton qubits with a special attention to quantum discord. Recently, dark-soliton qubit exhibiting appreciably long lifetime is proved to be an excellent candidate for information processing. Depending on the precise distance between the dark-soliton qubits, the decay rate of Dicke symmetric and antisymmetric state is suppressed or enhanced. With the Renyi-2 entropy, we derive a simple analytical expression for the quantum discord and explore the generation and decay of correlation for different initial states. We believe that the present work could pave the stage for a new generation of quantum discord based purely on matter-wave phononics.
Noncoherent Energy-Modulated Massive SIMO in Multipath Channels: A Machine Learning Approach

This article considers the design of the transmitter and receiver in a noncoherent massive single-input and multiple-output (SIMO) system over a multipath channel, representing a typical Internet-of-Things (IoT) scenario that consists of multiple single-antenna transmitters and one receiver with a large number of antennas. In particular, the autoencoders, which consist of multiple independent neural networks (NNs), are adopted at the transmitters and the receiver and are trained jointly, while working separately. To avoid the delicate design for mitigating the intersymbol interference (ISI) caused by multipath channels, the modulation schemes at the transmitters and the demodulation rule at the receiver are learned by the NNs over a limited number of channel samples. Moreover, the relationship between the number of channel samples and the performance of the trained transceiver is analyzed. The simulation results show that the proposed method achieves a lower error probability in comparison with the conventional optimization-based methods under typical channel conditions.
Diffusion Maps Kalman Filter for a Class of Systems With Gradient Flows

In this paper, we propose a non-parametric method for state estimation of high-dimensional nonlinear stochastic dynamical systems, which evolve according to gradient flows with isotropic diffusion. We combine diffusion maps, a manifold learning technique, with a linear Kalman filter and with concepts from Koopman operator theory. More concretely, using diffusion maps, we construct data-driven virtual state coordinates, which linearize the system model. Based on these coordinates, we devise a data-driven framework for state estimation using the Kalman filter. We demonstrate the strengths of our method with respect to both parametric and non-parametric algorithms in three tracking problems. In particular, applying the approach to actual recordings of hippocampal neural activity in rodents directly yields a representation of the position of the animals. We show that the proposed method outperforms competing non-parametric algorithms in the examined stochastic problem formulations. Additionally, we obtain results comparable to classical parametric algorithms, which, in contrast to our method, are equipped with model knowledge.
On certain linearized polynomials with high degree and kernel of small dimension

Let f be the Fq-linear map over Fq2n defined by x↦x+axqs+bxqn+s with gcd⁡(n,s)=1. It is known that the kernel of f has dimension at most 2, as proved by Csajbók et al. in [9]. For n big enough, e.g. n≥5 when s=1, we classify the values of b/a such that the kernel of f has dimension at most 1. To this aim, we translate the problem into the study of some algebraic curves of small degree with respect to the degree of f; this allows to use intersection theory and function field theory together with the Hasse-Weil bound. Our result implies a non-scatteredness result for certain high degree scattered binomials, and the asymptotic classification of a family of rank metric codes.
Centralized swarming UAV using ROS for collaborative missions

Recently, UAV technology is growing rapidly. One of the technologies in the UAV is swarming. Many applications of UAV can utilize the swarming technology, such as collaborative searching, monitoring, and mapping. In this research, basic structure of swarming system was created. The objective is to develop a system with the ability to adapt to various operational conditions, such as UAV connection loss and system failure in some UAVs. In such conditions, the system should be able to generate new mission plan for each UAV based on the number of remaining UAVs. Since swarming technology can be applied to many purposes, the swarming system has to be created to accommodate various missions. For this research, ROS framework was chosen because it has been equipped with complete tools. The swarming system created was then tested using SITL simulation because it can connect to the swarming system with ease. Several simulations were performed with various number of UAV. Results show that the created swarming system was able to finish the given missions regardless of the failure in some UAVs. The more number of UAV utilized, the faster the mission can be finished.
Thickness profiling of transparent plate using wavelength-tuned phase-shifting analysis

In the interferometric phase-shifting analysis, the phase-shift nonlinearity causes both uniform and non-uniform phase errors in the phase distribution. In this research, a novel 13-frame phase-extraction algorithm that can suppress the uniform phase error was derived. The characteristics of the new 13-frame algorithm are discussed using a Fourier description. Additionally, the error suppression ability of the new 13-frame algorithm was performed by numerical error analysis, with comparisons to de Groot 13, Hibino 19, Hanayama 11, and 17-frame algorithms. Numerical analysis indicates that the new 13-frame algorithm shows the best performance (under 2 nm when 30% linear miscalibration and under 0.5 nm when the nonlinearity 10%). Finally, the variation in thickness of a transparent plate was profiled by the new 13-frame algorithm and a Fizeau interferometer. The repeatability standard deviation of experiments using the 13-fram algorithm was 3.344 nm, which shows the better performance that those of other conventional algorithms.
Deep Learning based Object Detection and Recognition Framework for the Visually-Impaired

Vision impairment or blindness is one of the top ten disabilities in humans, and unfortunately, India is home to the world's largest visually impaired population. In this study, we present a novel framework to assist the visually impaired in object detection and recognition, so that they can independently navigate, and be aware of their surroundings. The paper employs transfer learning on Single-Shot Detection (SSD) mechanism for object detection and classification, followed by recognition of human faces and currency notes, if detected, using Inception v3 model. SSD detector is trained on modified PASCAL VOC 2007 dataset, in which a new class is added, to enable the detection of currency as well. Furthermore, separate Inception v3 models are trained to recognize human faces and currency notes, thus making the framework scalable and adaptable according to the user preferences. Ultimately, the output from the framework can then be presented to the visually impaired person in audio format. Mean Accuracy and Precision (mAP) scores of standalone SSD detector of the added currency class was 67.8 percent, and testing accuracy of person and currency recognition of Inception v3 model were 92.5 and 90.2 percent respectively.
Online Extreme Learning Machine Design for the Application of Federated Learning

In this paper, we propose a federated extreme learning machine system (Fed-ELMS) to meet the demand for federated learning scenarios. In the scenario of federated learning, data is kept on edge devices to preserve the privacy of data, while metadata, such as model parameters, are exchanged between a centralized cloud server and edge devices. Despite non-independent and identically distributed (non-IID) and imbalanced data across edge devices, we show that Fed-ELMS can still achieve comparable performance with only 3.3% accuracy loss compared to a centralized ELM trained with IID and balanced data. (a) Moreover, by quantizing input weights and biases, parameters of a model and transmission power consumption between cloud and edge are dramatically reduced. Compared With conventional neural networks (NNs) with the same transmission cost, the proposed Fed-ELMS outperforms FederatedAveraging NN (Fed- NN) by 2.3% accuracy and the fine-tuning process is 7%-33% less time-consuming. Therefore, the proposed Fed-ELMS is a promising system for edge devices to support the future trend of federated learning.
Video Based Vehicle Counting Using Deep Learning Algorithms

The following topics are dealt with: learning (artificial intelligence); Internet of Things; feature extraction; image classification; medical image processing; support vector machines; cloud computing; microcontrollers; diseases; neural nets.
Digital Resource Rights Confirmation and Infringement Tracking Based on Smart Contracts

The protection of digital resource copyrights has drawn extensive public concern in the past decade. Traditional methods, however, fail to satisfy the requirements due to their poor timeliness, frequent infringement, and cumbersome in rights confirmation, etc. In this paper, we investigate and design a novel approach for cross-platform digital resource rights confirmation and infringement tracking based on smart contracts. We utilize smart contracts to realize rights transactions and protection. As another contribution, we propose to establish fine-grained digital resource rights, which are divided into ownership and usufruct. For tracking infringement acts, we invoke smart contracts to extract the rights transfer chain for resources. Furthermore, we reveal the source of resource leakage by embedding digital watermarks when distributing the resources. By combining resource rights transfer chain and digital watermarking, we can effectively achieve digital resource rights confirmation and infringement tracking. Extensive experiments show the effectiveness of our proposed method.
Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this article, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1 Gbps testbed, and 10 Gbps large-scale simulation network.