Reviewerly/exHarmony huggingface dataset:
Keyword Spotting for Industrial Control using Deep Learning on Edge Devices

Spoken commands promise unique advantages for the control of industrial machinery. Operators are enabled to keep their eyes on safety critical aspects of the process at all times and are free to use their hands in other parts of the process, instead of remote control. Current keyword spotting systems are prone to misunderstanding spoken utterances, especially in noisy environments, and are commonly deployed as non-realtime cloud services. Consequently, these systems can not be trusted with safety critical industrial control. We adapt a DS-CNN and a CNN for keyword spotting and use augmented training data, including real industrial noise, to increase their robustness. Furthermore, we apply post-training quantization and analyze the performance of both networks using multiple embedded systems, including a Google Edge TPU. We carry out a systematic analysis of accuracies, memory footprint and inference times using different combinations of data augmentations, hardware platforms, and quantizations. We show that augmented training data increases the inference accuracy in noisy environments by up to 20 %. Among others, this is demonstrated using an integer quantized network with a memory footprint of 0.57 MByte, reaching inference speeds of less than 5 ms on an embedded CPU and less than 1 ms on the Edge TPU. The results show that keyword spotting for industrial control is feasible on embedded systems and that the training data augmentation has a significant impact on the robustness in challenging environments.
Entropy Targets for Adaptive Distillation

The focus of this paper is the problem of targets in knowledge distillation. Compared with hard targets, soft targets can provide extra information which compensates for the lack of supervision signals in classification problems, but there are still many defects such as high entropy's chaos. The problem is addressed by controlling the information entropy, which makes the student network adapt to the targets. After introducing the concepts of the system and interference labels, we propose the entropy transformation which can reduce information entropy of the system using interference labels and maintain supervision signal. Through entropy analysis and entropy transformation, entropy targets are generated from soft targets and are added to the loss function. Due to the decrease in entropy, the student network can better adapt to learn the inter-class similarity from the adaptive knowledge and can potentially lower the risk of over-fitting. Our experiments on MNIST and DISTRACT dataset demonstrate the benefits of entropy targets over soft targets.
Band depth based initialization of K-means for functional data clustering

Abstract The k -Means algorithm is one of the most popular choices for clustering data but is well-known to be sensitive to the initialization process. There is a substantial number of methods that aim at finding optimal initial seeds for k -Means, though none of them is universally valid. This paper presents an extension to longitudinal data of one of such methods, the BRIk algorithm, that relies on clustering a set of centroids derived from bootstrap replicates of the data and on the use of the versatile Modified Band Depth. In our approach we improve the BRIk method by adding a step where we fit appropriate B-splines to our observations and a resampling process that allows computational feasibility and handling issues such as noise or missing data. We have derived two techniques for providing suitable initial seeds, each of them stressing respectively the multivariate or the functional nature of the data. Our results with simulated and real data sets indicate that our F unctional Data A pproach to the BRIK method (FABRIk) and our F unctional D ata E xtension of the BRIK method (FDEBRIk) are more effective than previous proposals at providing seeds to initialize k -Means in terms of clustering recovery.
A Hybrid Approach for Movie Recommendation based on User Behaviour

The propose framework carrying out a hybrid approach for the movie suggestion dependent on user behavior that upgrades the properties of the previous framework with a more up-to-date and more productive methodology that lessens the framework run time and decides thing relations with more prominent precision. To develop a hybrid model fit for making a decent proposal dependent on metadata about the movie and the behavior of the user. To assess the proposed framework on the boundary of RMSE.
SR-EM: Episodic Memory Aware of Semantic Relations Based on Hierarchical Clustering Resonance Network

An intelligent robot requires episodic memory that can retrieve a sequence of events for a service task learned from past experiences to provide a proper service to a user. Various episodic memories, which can learn new tasks incrementally without forgetting the tasks learned previously, have been designed based on adaptive resonance theory (ART) networks. The conventional ART-based episodic memories, however, do not have the adaptability to the changing environments. They cannot utilize the retrieved task episode adaptively in the working environment. Moreover, if a user wants to receive multiple services of the same kind in a given situation, the user should repeatedly command multiple times. To tackle these limitations, in this article, a novel hierarchical clustering resonance network (HCRN) is proposed, which has a high clustering performance on multimodal data and can compute the semantic relations between learned clusters. Using HCRN, a semantic relation-aware episodic memory (SR-EM) is designed, which can adapt the retrieved task episode to the current working environment to carry out the task intelligently. Experimental simulations demonstrate that HCRN outperforms the conventional ART in terms of clustering performance on multimodal data. Besides, the effectiveness of the proposed SR-EM is verified through robot simulations for two scenarios.
Weighted risk models for dynamic healthcare fraud detection

Abstract Despite efforts to prevent it, fraud in the United States healthcare system remains a serious and pressing issue. Since healthcare fraud is a complex and multi‐faceted problem, fraud‐fighting solutions must be flexible enough to address the ever‐evolving nature of the crime. Here, we present a method to identify healthcare fraud in such a manner that incorporates both potential fraud as well as risky provider behavior. The proposed weighted risk model provides a framework for creating a dynamic fraud detection database that can be easily scaled up to incorporate emerging fraud schemes.
Interactive Theorem Prover Based on Calculational Logic to Assist Finite Difference and Summation Learning

Universities show difficulties and resistance to carry out virtual mathematics instruction, especially due to the high probability that cheating will occur among students, at the time of assessments. In this work an interactive theorem prover based on calculational logic is presented, adjusted to the topic of finite differences and summation, which aims to reduce the probability of cheating in virtual education, forcing the student to understand each step of a given exercise during the interaction with the aforementioned theorem prover. In this work, data and positive educational experiences of the use of the tool are shared, including virtual assessments experiences, where it was shown that there was no cheating among students.
Method Towards Service Composition Optimization on Cost-effective using Mixed Flower Pollination Algorithm

Web services have been widely used in various industries in recent years. Faced with a large number of services with the same functions and different quality, Web service composition optimization is an important technical approach for the widespread application of Web services. Most of the existing researches regard the service composition as a single-objective optimization problem, but this does not meet the diverse needs of users. In response to the complex needs of users, quality of service and service cost are chosen as two optimization goals to achieve a cost-effective oriented service composition. For this purpose, a mixed flower pollination algorithm (MFPA) is proposed. In this paper, the original flower pollination algorithm is combined with the fast non-dominated sorting and population selection strategy to solve the Pareto optimal set. In addition, differential evolution strategy and random disturbance strategy are added during the execution of the algorithm to improve the optimization ability. The experimental results show that MFPA has more advantages in the field of service composition with multi-objective optimization than other algorithms for multi-objective optimizations.
An Image Cryptosystem Using Chaotic Baker Map in DFT

The encrypted data play a valuable role in image processing applications especially during transmission. For fast and secure image encryption, chaotic maps are applied to improve image encryption efficiency as it is highly sensitive to system parameters initial values and has a large key space. This paper introduced a secure algorithm for images encryption and decryption by using chaotic Baker Map in the discrete Fourier transform (DFT). The proposed method employs baker map in the frequency domain to act on the transformed image coefficients which achieves high encryption efficiency. Simulation for statistical analysis using MATLAB are designed and tested. The achieved results demonstrated the superiority of the proposed approach with respect to encryption quality, statistical, information entropy, maximum deviation, and irregular deviation, differential, and noise immunity tests.
Implementing Sequence to Sequence Neural Networks Using C#.Net

The concept of using two neural networks to translate one Sequence to another sequence presented by google in 2014 has led to a revolutionary result of translation between the input sequence as source language and the output sequence as the target language. It overcomes the weakness of previous translation methods. On the other hand, C#.Net programming language becomes a widely-used programming language, as it is similar to the English language. Besides, it has a strong memory backup. However, it doesn’t support other scientific functions like the sigmoid and tahn functions. This paper proposes an implementation of Sequence to Sequence algorithm using C#, and resolving the inability of C# in calculating the activation function by polynomial simplifying the sigmoid and tahn functions. Moreover, creating a small training and testing dataset using United Nation Arabic and English Letters from the official UN website as an approved translation source.
Cost-Efficient Scheduling of Workflow Applications with Deadline Constraint on IaaS Clouds

Nowadays, the vast majority of workflow applications are deploying on clouds for fast execution. Meanwhile, the market-oriented and price-driven characteristics of cloud computing make cost become a factor that cannot be ignored and challenge traditional workflow scheduling algorithms which focus only on the optimization of finish time (a.k.a makespan). A general way to consider cost and makespan at the same time is to model the problem as a constrained optimization problem. In this paper, we study the deadline-constrained and cost-minimization workflow scheduling problem, and propose the cost-efficient scheduling with deadline constraint (CESDC) algorithm. CESDC is a typical list scheduling algorithm, and contains three scheduling phases: deadline distribution, task prioritization and service selection. CESDC firstly distributes deadline to tasks by their levels and workloads, then prioritizes tasks according to a modified upward rank, and finally assigns services to tasks which meets the sub-deadline and minimizes the cost. Experiment results demonstrate that CESDC performs better in terms of success ratio and cost than those of several state-of-the-art approaches.
DNA Encrypting rules with Chaotic Maps for Medical Image Encryption

Digital Images are widely used in medical diagnosis because of medical images contain sensitive information of patients. The most critical issues like attacks arise during storage and transmission of medical images through public network. To care for the privacy of patient's information, confidential sharing of medical images and computational time are challenges. To reduce these robust DNA rule-based sequences with chaotic map technique is presented. This method consists of three steps 1) The chaotic map generates the transformations to shuffles the Image Gray values to get high randomness in pixel positions 2) The DNA complement rules are generated for confusing hackers 3) Image gray values are substituted by unique DNA sequences for random distribution of pixels in the Image. Because of good confusion, random distribution properties and randomness of chaotic technique, image pixels sequence estimation is highly difficult to intruders. So that, the proposed technique runs sufficient security along with reduced computational time.
Blockchain and Its Applications in Healthcare

Growing organizations, institutions, and SMEs demand for transformation in all the aspects of their businesses along with the progression in time and technology. When it comes to healthcare, the growth should be heightened to higher levels with necessity. The need of providing quality of service (QoS) in healthcare is taking significant place, allowing health institutions and medical compliances to develop an ecosystem with cutting-edge technology with the same reliability but better productivity and performance. Moreover, the healthcare systems are aiming for a more patient-centric strategy. Healthcare systems work on complicated and traditional methods, oftentimes administered via teams of professionals who manage data and supportive mechanisms of the system. Blockchain could streamline and automate those methods, conserving weeks of effort in the company's production line to increase the overall revenue and discover new opportunities. This chapter aims to illustrate blockchain technology along with its state-of-the-art applications in healthcare.
Collaborative learning in bounding box regression for object detection

Object detection has attracted growing attention due to its extensive application prospect, in which bounding box regression is an essential component. Dedicated to collaborative learning in bounding box regression, we explore the unified framework of smooth ℓ1 and intersection over union, named SLIoU. On the basis of that, we propose a SLIoU loss as localization loss, which focuses on the geometric relationships of pairs of rectangular bounding boxes in overlapping degree, central position and structural shape. Furthermore, we propose a SLIoU-NMS for suppressing redundant detection boxes, which adaptively maps the evaluation value of detection boxes to meet the evaluation metric using nonlinear representation. By incorporating SLIoU loss and SLIoU-NMS into the state-of-the-art one-stage detectors, the detection performance is considerably improved.
A practical and efficient model for intensity calibration of multi-light image collections

We present a novel practical and efficient mathematical formulation for light intensity calibration of multi-light image collections (MLICs). Inspired by existing and orthogonal calibration methods, we design a hybrid solution that leverages their strengths while overcoming most of their weaknesses. We combine the rationale of approaches based on fixed analytical models with the interpolation scheme of image domain methods. This allows us to minimize the final residual error in light intensity estimation, without imposing an overly constraining illuminant type. Unlike previous approaches, the proposed calibration strategy proved to be simpler, more efficient and versatile, and extremely adaptable in different setup scenarios. We conduct an extensive analysis and validation of our new light model compared to several state-of-the-art techniques, and we show how the proposed solution provides a more reliable outcomes in terms of accuracy and precision, and a more stable calibration across different light positions/orientations, and with a more general light form factor.
Augmented Robust Cubature Kalman Filter Applied in Re-Entry Vehicle Tracking

In this work, an improved cubature Kalman filter (CKF), called augmented robust CKF (ARCKF), for the reentry vehicle trajectory tracking is presented, in which the model strong nonlinearity, heavy-tailed measurement noise, and the non-independence of measurement noise in iterations are considered. Firstly, the derivative-free robust approach is employed instead of the conventional Huber's technique, thereby eliminating linearization errors and exhibiting robustness to measurement outliers. Secondly, the framework of iterated unscented Kalman filter is adopted to avoid linearization in the iterative measurement update, in which new cubature sample points are regenerated in each iteration, and then the probability density function is propagated through the measurement model. Furthermore, in the measurement update stage, the state vector is augmented with the measurement noise vector to address their correlation after the first iteration. To demonstrate the validity of the proposed algorithm, CKF, robust CKF (RCKF), and ARCKF are compared via Monte Carlo simulations. The results show that ARCKF performs with sufficient suitability in non-Gaussian noise environments and is superior in terms of target tracking accuracy.
Architecture Model Optimization of Cyclical Order Algorithm with Binary Sigmoid and Linear Function for Prediction

Abstract This research is a development of previous research to predict the life expectancy of people in Aceh province. The purpose of this study is to determine a model that is better and more optimal than previous studies that have been conducted with the 8-9-1 model, with a prediction accuracy rate of 91% using the ANN Cyclical Order Algorithm. Meanwhile, the current research will use the same algorithm, the same data, but the analysis process will use the activation process and different models. The results of this study are in the form of a better model that can be used to predict the life expectancy of people in Aceh province with an accuracy rate of 100%. The architectural model is 8-3-1. Also, this model produces a lower MSE level, namely 0.0008332545 compared to the previous 8-9-1 model of 0.0010800577. As is well known, the smaller the MSE value, the better.
Attended-over Distributed Specificity for Information Extraction in Cybersecurity

Cybersecurity has become a critical necessity in aerospace, especially given the recent prevalent utilization of cyberspace as well as computing infrastructure, tools, and platforms in the domain. Cybersecurity relies on the timely discovery of potential and active vulnerabilities to mitigate threats, cyberattacks and theft of intellectual property. These vulnerabilities are commonly communicated via textual channels which can be mined and detected by information extraction techniques, such as Automatic Terminology Extraction (ATE), in order to mitigate imminent and future attacks. Distributed Specificity, a modern and highly effective ATE paradigm, was first introduced by [4], [3] and further improved by [21]. These methods, however, regard the context words linearly and indiscriminately, which 1) disregards the non-linear relations among the context words and 2) assigns an equal weight to each word edge in the sequence despite the fact that the connections of the nodes are not of equal strength. As our first contribution, the present article addresses this shortcoming by integrating the self-attention mechanism into the distributed specificity paradigm to enhance candidate terms' representation resulting in a more accurate extraction, which in turn lends itself to the higher coverage and a more timely discovery of cyberattacks. Since no ATE dataset exists in the cybersecurity domain, to the best of our knowledge, the present endeavor offers a dataset, as its second contribution, that will be the benchmark for our experiments and the ones to follow by both ATE and cybersecurity research communities. This complements the SemEval's SecureNLP [35] information extraction repository. We extend the MalwareTextDB (comprised of 147 cybersecurity reports) to include labeled terminologies as ground truth. The labeled dataset is annotated by cybersecurity practitioners using Term Evaluator [25]. Although for most industries higher coverage and a more timely discovery of cyberthreats will translate into protecting vital data, customer privacy, and sizable funds to name but a few advantages, in aerospace the risk and therefore the reward may be much higher directly resulting in saving human lives in both small and large scales.
Hierarchical Ontology Based on Word Sense Disambiguation of English to Hindi Language

There are numbers of issues when research comes to question paper translation which should be effectively handled by applying suitable approaches and word sense disambiguation (WSD) algorithms. In order to have a machine translation (MT) system, this could be used for practical purposes. The further study and the analytical work carried in the present research are to develop an efficient MT system. MT would greatly reduce the dependency on human experts in translating questions into different Indian languages for various exams that require bilingual papers. This paper proposes an algorithm based on a hierarchical ontology which uses a hierarchical tree structure. Use of hierarchical structure reduces the time of translation while ambiguity was also reduced. The experiment was done on a real dataset of questions of English language of NCERT and other source.
Automated Contactless Temperature and Facemask Detection Using Deep Learning

In Our proposed project we are using raspberry pi to detect face mask and temperature of a person. COVID-19 is an infectious disease caused by the corona virus. Corona virus is nothing but a family of viruses which cause the illness in humans. The common symptoms of this virus are fever, dry cough, breathing problem, etc. It is necessary to maintain a social distance and wear a face mask to avoid the chances of getting the virus, as it is affecting the whole world. And for this we need a system which will keep an eye on everyone to ensure the safety of ourselves as well as of others. To ensure the safety of the public, we tried to build a system for contactless Face Mask and Temperature Detection. This project will be helpful in crowded places or workstations to provide support for the prevention and control of Covid-19.
The construction of local convex hull on the task of pattern recognition

In this research paper analyzes and explores the problems of pattern recognition methods and biological object identification algorithms. We encountered two different problems in the process. The first is the modernization of the pattern recognition algorithm; the second is associated with the identification of a biological object. The algorithm problem was solved by constructing local convex hull during modernization. In the second, the Orthoptera insect group was selected as a biological object on the recommendation of experts. Orthoptera is a taxonomic insect division that includes locusts and other winged insects. Approaches to determining the characteristics of orthoptera types and preparing their samples, as well as the formation of identification models, are recommended. The algorithm for calculating estimates (ACE) was chosen when identifying the choice of orthoptera. The ACE based on the principle of partial precedence. Approaches to the formation of a training-test selection for the choice of orthopters are proposed. As mentioned above, in this work is proposed to develop an information recognition system designed to solve the problems associated with the identification of Orthoptera Insecta, as well as for the experimental study of the specie of the locust family.