Reviewerly/exHarmony huggingface dataset:
Fall Detection Method Based on Wirelessly-Powered Sensing Platform

Falls are one of the common reasons that affect the health of the elderly. Because of its high incidence and high occasionality, the assistance rate of the elderly is lower. Therefore, the fall detection method with an accurate and timely research and development can better help patients get effective assistance. We use a wirelessly-powered sensing platform to easily wear, which can convert RF signal as its own power without replacing any battery, as well as, it can work in non-line-of-sight environment and design a fall detection method based on wirelessly-powered sensing platform. Firstly, the wirelessly-powered sensing platform collects the acceleration data of the human waist and obtains the motion acceleration and its corresponding Euler angle information. Then, combining with Discrete Wavelet Transform and Hilbert-Huang Transform, a algorithm for decomposing acceleration signals is proposed to extract signal information. Finally, an abnormal detection algorithm for Euler angle is proposed, we use the Support Vector Machine algorithm with the abnormal detection algorithm for Euler angle to detect a behavior of the fall. At the same time, in order to alleviate the pressure of power consumption, a sampling factor is set to dynamically change the sampling frequency and reduce power consumption. Experiments show that this method has a higher accuracy, which is over 94.7% of accuracy of the lowest sampling frequency. In the meantime, it has important meaning for the assistance of patients with the fall.
Generalized Dictionary Matching Under Substring Consistent Equivalence Relations

Given a set of patterns called a dictionary and a text, the dictionary matching problem is a task to find all occurrence positions of all patterns in the text. The dictionary matching problem can be solved efficiently by using the Aho-Corasick algorithm. Recently, Matsuoka et al. [TCS, 2016] proposed a generalization of pattern matching problem under substring consistent equivalence relations and presented a generalization of the Knuth-Morris-Pratt algorithm to solve this problem. An equivalence relation $$\approx $$ is a substring consistent equivalence relation (SCER) if for two strings X, Y, $$X \approx Y$$ implies $$|X| = |Y|$$ and $$X[i:j] \approx Y[i:j]$$ for all $$1 \le i \le j \le |X|$$. In this paper, we propose a generalization of the dictionary matching problem and present a generalization of the Aho-Corasick algorithm for the dictionary matching under SCER. We present an algorithm that constructs SCER automata and an algorithm that performs dictionary matching under SCER by using the automata. Moreover, we show the time and space complexity of our algorithms with respect to the size of input strings.
Implementation of Machine Learning-Aided Imaging Analytics for Histopathological Image Diagnosis

In the last few decades, rapid advances in image-digitizing technologies have led to the development of scanners capable of producing digitalized slides for use in pathology. Digital slides are more interactive, simultaneously available to multiple remote users, can be easily annotated, and enables more standardized education in pathology. Increase in computer processing power, data transfer speeds, advances in software development, and cloud storage solutions have also enabled the use of machine learning-based approaches for a wide variety of applications in pathology. Machine learning-aided imaging analytics usually begins with the system computing image features that are assumed to be of importance in making the prediction or diagnosis of interest. Following which, it identifies the best combination of these image features for classifying the image or computing some metric for a given region of interest. There are now several learning algorithms that can be used, each with their own strengths and weaknesses in the computational settings. There are also open-source platforms that make it easier for the user to employ machine learning-based methods for image analysis. In summary, machine learning-based imaging analytics is now widely used for histopathological image diagnoses and this article will introduce its utilization in the clinical settings to illustrate in detail how such method could be applied in the routine practice of histopathology.
Blind Detection Based on Color SIFT

Blind detection algorithm based on keypoints has a small amount of computation and faster speed, however, its accuracy is lower, comparing with blind detection based on block. One factor is that keypoints are extracted in a grayscale image, which leads to loss of the color information of the image. In order to solve it, this paper proposes a novel blind detection algorithm based on key point image. Firstly, an image is changed from RGB space to HSV space, the color local binary patterns feature of each pixel is computed based on the HSV image. Then keypoints are extracted from the color local binary patterns image. which also carry color information. Then the nearest neighbors are used to find matching keypoints in all keypoints. Finally, wrong matching is filtered by the angle between matching points and morphology operations are used to come into being a forgery detection map to locate the tampered regions. Experimental results show that the proposed method based on color scale-invariant feature transform keypoint outperforms these methods based on traditional scale-invariant feature transform keypoint in the reliability of detection and the block-based method in efficiency.
Overpublication as a symptom of audit culture: A comment on Phaf (2020)

Phaf suggests that, in order to address overpublication, academics should read more and publish less. Although many academics would like to take this advice, doing so is complicated by the audit culture that marketizes and metricizes everything they do. Working from the evolutionary metaphor introduced by Phaf, we argue that the evolution of science consists not simply in adapting theory to the demands of empirical investigation, but also in adapting scientific traditions and communities to the political and institutional forces that shape them. We point specifically to the generalized metrics (e.g., impact factors) that, in audit environments, arbitrate resources, in the process engineering professional precarity and overdetermining theory building. We argue that hyper-production can be understood as an adaptation to such an audit environment. We briefly discuss some suggestions for approaching the audit through relational accounting practices that disrupt and re-inscribe calculative audits, thus creating opportunities to read more and publish less.
Information technology for the deployment of the OpenStack cloud environment

Abstract This article discusses the information technology of implementing and adapting cloud services for a research laboratory using the OpenStack open environment as an example. The structure of the cloud computing environment is developed. An example of the implementation of the installation and configuration of client and server applications using three computers is given. The proposed technology will allow the user to deploy and configure the OpenStack environment with basic services for installing virtual machines with an internal network and a set of external addresses.
Deep Learning Algorithms and Their Applications in the Perception Problem

The objective of this paper is to summarize a comparative account of unsupervised and supervised deep learning models and their applications. The design of a model system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples and performance evaluation. Classification plays a vital role in deep learning algorithms and we found that, though the error backpropagation learning algorithm as provided by supervised learning model, is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the perception problem.
A Network Service for Preventing Data Leakage from IoT Cloud-assisted Equipment

The fact that most IoT solutions are provided by third parties, along with the pervasiveness of the collected data, raises privacy and security concerns. There is a need to verify which data is being sent to the third party, as well as preventing those channels from becoming an exploitation avenue. We propose to use existing API definition languages to create contracts which define the data that can be transmitted, their format and constraints. To verify the compliance with these contracts, we propose a Network Service architecture which validates REST-like API requests/responses against a Swagger schema. We deal with encrypted traffic using an Service Function Chaining (SFC)-enabled Man-in-the-Middle (MITM), allowing verifications in “real-time.” We devised a Proof of Concept and showed that we were able to detect (and stop) contract violations.
An Efficient Policy Gradient Method for Conditional Dialogue Generation

Encoder-decoder models have been commonly used in dialogue generation tasks. However, they tend to generate dull and generic response utterances. To tackle this problem, we consider dialogue generation as a conditional generation problem. For a given context history, our model can generate different response utterances with desirable dialog acts. Our model follows the SeqGAN framework, where the generator takes context history and dialog act as inputs and generates corresponding response utterances. The discriminator computes rewards by considering the quality of entire utterance and dialog act. Our model is trained by a policy gradient approach. To overcome the bottleneck of excessive time complexity incurred by the Monte Carlo search for training, we propose a local discriminator network to compute the individual reward in one forward propagation, thereby dramatically accelerating the training procedure. Experimental results demonstrate that our proposed method can achieve comparative performance with Monte Carlo search, while reducing the training time dramatically.
Fuzzy Logic Controller Operated Water Treatment Plant

In this paper, Fuzzy logic controller operated water treatment plant is implemented in Emerson's DeltaV distributed control system using MQ controller. This treated water can be used for domestic purposes for e.g. washing vehicles, utensils & clothes and gardening etc. The response of PID controller and FLC (fuzzy logic controller) are compared. Analysis of maintaining tank water level, temperature and water flow rate by both the controllers is done on actual setup.
Script Identification using Across- and Within-Image Distribution Estimation

In this paper, we apply several modifications to script identification, several of which inspired by techniques from the similar audio task of spoken language recognition. Specifically, we alter the architecture of a convolutional network with global average pooling to include variance pooling as well, we utilize score calibration of the output scores of the network, and we utilize prior distribution estimation to condition the calibrated scores. We show that these methods are effective in script identification, with the use of priors showing especially promising improvements. Furthermore, in the domain of script identification, several additional extensions of distribution estimation are available which consider the distribution within each image, and we demonstrate much larger improvements when employing these extensions. Finally, we also show that an embedding-plus-classifier approach performs similarly to the full network, and so its potential for increased flexibility may be beneficial for future consideration. With all modifications, overall accuracy on the ICDAR 2017 validation dataset increases from 89.7% to 93.6%.
Personalized College English Learning Based on Artificial Intelligence

With the powerful learning ability and computing ability, artificial intelligence can push personalized learning resources for learners after a comprehensive analysis of the collected big data, making the adaptive learning coefficient close to 1, for which learners can complete learning tasks in a more pleasant state and make more progress in study; AI-supported Multi-dimensional diagnostic evaluation and resource feedback of test proved to strengthen students' language cognition ability and promote the development of English learning.
Christiane Müller, Permeable Islands: A Contrastive Study of Swedish and English Adjunct Clause Extraction (Lundastudier i nordisk språkvetenskap A80). Lund: Lund University, 2019. Pp. 283.

Christiane Müller, Permeable Islands: A Contrastive Study of Swedish and English Adjunct Clause Extraction (Lundastudier i nordisk språkvetenskap A80). Lund: Lund University, 2019. Pp. 283. - Volume 43 Issue 1
THE EFFECT OF APPLICATION OF READING ALOUD ON SENIOR HIGH SCHOOL STUDENTS’ SPEAKING ABILITY

This research aimed to find out the effect of the application of reading aloud to improve students’ speaking ability at grade X of SMA Bina Mulya Bandar Lampung 2019. Reading aloud was used to help the students to improve their speaking ability and make them enjoyed in learning English. Data were collected with a pre-test, treatment and post-test. Then, they were analysed with t-Test for two group design. Treatment using reading aloud was administered to experimental group, while control group received technique which was usually used by the teacher. Data analysis was attempted by using t-Test for two group design. Based on the computation, t-value: 7.61 was bigger than p-value in 1%: 2.68 and 5%: 2.01. Therefore, the alternative hypothesis (Ha) was accepted, while the null hypothesis (Ho) was rejected. I concluded that there was an effect of the application of reading aloud to improve students’ speaking ability.
State-of-Art, Modular, Cloud Enabled Video Collaboration Solution for Next Generation Manufacturing Industries

Video collaboration aims to connect and communicate one or more remote locations, transmits two-way audio and video simultaneously. Video collaboration in Industry 4.0 environment provides live visual connectivity to the site. Client server-based video solutions is not suitable for real time video collaboration due to processing and setup delay. Peer-to-Peer (P2P) video solutions have been employed in a wide variety of distributed applications such as video conferencing, streaming and Voice-Over Internet Protocol (VOIP). The resilience and scalability provided by P2P systems make them ideal for many real time industrial applications. WebRTC is next generation P2P solution best suited for real time video collaboration and remote diagnosis and it does not require any plugin or custom module. This paper proposes modular and scalable design approach for real time video collaboration, remote diagnosis application for industrial video collaboration using webRTC. Proposed state-of-art approach enables seamless portability and usage of the video collaboration solution.
A Novel Approach With Dynamic Fuzzy Inference for Big Data Classification Problems

Currently, big data and its applications have become emergent topics. To deal with the uncertainty in data sets, fuzzy system-based models were explored and stand out for many applications. However, when a given observation has no overlap with antecedent values, no rule can be invoked, or even the invoked rules with missing values in classical fuzzy inference can also appear in big data environment, and therefore, no consequence can be derived. Fortunately, fuzzy rule interpolation techniques can support inference in such cases. Combining traditional fuzzy reasoning technique and fuzzy interpolation method may promote the accuracy of inference conclusion. Therefore, in this chapter, an initial investigation into the framework of MapReduce with dynamic fuzzy inference/interpolation for big data applications (BigData-DFRI) is reported. The results of an experimental investigation of this method are represented, demonstrating the potential and efficacy of the proposed approach.
Map Based Human Motion Prediction for People Tracking

Mobile service robots deployed in populated environments like train stations, airports or offices are not only required to move safely, but also in socially acceptable ways. In order to achieve this, robots need to be able to track people within their vicinity. This work presents an approach to tracking people from a mobile robot platform, incorporating a novel approach to human motion prediction. Unfavorable viewing angles, motion blur and ever-changing light conditions are constant issues for sensors on mobile vehicles. Therefore, a system is needed which increases the tracking quality of humans in order to cope with a low detection rate. The scientific contribution of this paper lies in a precise human model for tracking which utilizes historical spatial data of pedestrians from previous detection and from simulation. The model is embedded into a particle-filter based tracking approach designed for the use on a moving platform and is able to incorporate a variety of person detectors. Experiments conducted prove that the proposed method increases tracking quality, especially at a low detection rate.
Designing determination of procrastination level in students utilize genetic algorithms method in data mining classification

Someone who has difficulties in doing something in deadline, often did postpone, overprepare in something, even fail in completing the task in deadline, called as someone who did procrastination. This study utilize Genetic Algorithm method to determine the consideration in designing information system. The objective of this study is to apply the database management in determinig procrastinative characteristic in students at school. In Genetic Algorithm method, this study utilizemost appropriate determined indicators with Chromosome Representative in similarity between data characteristics in the constituent criteria classification that can influence the procrastinative characteristic in students. Based on the test and result, the conclusions are, Information System in Procrastination Determination Program would be computerized system, thus the Information System in Determining Procrastination in students would be useful in the future.
Sine based Bacterial Foraging Algorithm for a Dynamic Modelling of a Twin Rotor System

This paper introduces a sine-based position update strategy into Bacterial Foraging Algorithm (BFA). In solving many optimization problems, BFA produces an acceptable accurate solution but it presents a slow convergence speed towards the global solution. On the other hand, a sine-based position update strategy from the Sine Cosine Algorithm (SCA) utilizes an elitism in its position update equation. The elitism strategy contains a promising solution that guiding search agents toward global optima with relatively a faster speed. In this proposed technique, the sine-based position update strategy is incorporated prior to the reproduction phase of the BFA. With the consideration of the advantages of the elitism, it helps the BFA to converge faster and hence acquires better accuracy of the final solution. The proposed algorithm is tested on several CEC2014 benchmark functions that have various fitness landscapes and features. For solving a real world problem, it is applied to optimize a dynamic model of a Twin Rotor System. Result of the test shows that the proposed algorithm significantly outperforms the original BFA for both convergence speed and accuracy performances. On the other hand, result of the modelling shows that the proposed algorithm acquires the dynamic model for the Twin Rotor System with a significant smaller error.
Development and Implementation of Projection-Based Installation in Gallery Space and Visitors Experience

In this paper, we present a projection-based installation designed for gallery-based exhibition. The installation system based on the Spatial Augmented Reality or known as "SAR," in which digital images were projected onto real objects or materials using projectors such as projection mapping techniques. This project studied the use of digital technology for gallery exhibition installation works to enrich and create an engaging audience's art experience through an immersive space created through SAR application. The study involved 79 youth who responded about their experience after viewing the projection-based installation work through a questionnaire survey. The findings show that the use of digital technology to the installation creates dreamlike scenes, imitating nature ambiance complemented with visuals and sound and well provides immersive experience among the visitors through the mixing of virtual images with real objects. The significance of this research on SAR application in exhibition artworks is essential to understand how it affects the audience's experience. The results are necessary as contributions for the development of innovative art mediums intended for gallery-based exhibitions and visitors' engagement, mainly targeted among the youth as the modern audience.
Efficiency of distance measures in the automatic grouping of electronic radio devices by k-means algorithm

Abstract This study investigates the problem of separation mixed lot of semiconductor devices for space applications into homogeneous production batches. The impact of distancemeasures in clustering algorithmk-means on the accuracy of the separating a mixed production batch is studied. This paper proposes the K-means algorithm withsquared Mahalanobis distance as an alternative the squared Euclidean distance. With the various combined mixed lots of semiconductor devices it was shown, that the proposed squared Mahalanobis distance highly depends on the method for determining initial cluster centers.
Text-to-Image Synthesis Based on Machine Generated Captions

Text-to-Image Synthesis refers to the process of automatic generation of a photo-realistic image starting from a given text and is revolutionizing many real-world applications. In order to perform such process it is necessary to exploit datasets containing captioned images, meaning that each image is associated with one (or more) captions describing it. Despite the abundance of uncaptioned images datasets, the number of captioned datasets is limited. To address this issue, in this paper we propose an approach capable of generating images starting from a given text using conditional generative adversarial network (GAN) trained on uncaptioned images dataset. In particular, uncaptioned images are fed to an Image Captioning Module to generate the descriptions. Then, the GAN Module is trained on both the input image and the “machine-generated” caption. To evaluate the results, the performance of our solution is compared with the results obtained by the unconditional GAN. For the experiments, we chose to use the uncaptioned dataset LSUN-bedroom. The results obtained in our study are preliminary but still promising.
The foreign language, Big and Small culture, the truth of life are the key imperatives of successful vitality of contemporary establishment of higher learning in the MVD system

Quite recently we have celebrated the 90th anniversary of the establishment of the Nizhny Novgorod Academy of the Interior of the Russian Federation. The time has been very fruitful: it has trained a big number of professionals specializing in the fight against economic crimes. It has improved its material-and-educational status and it is still expanding.&#x0D; The author reviews one of the latest trends of its development — international cooperation with colleagues from other countries. He emphasizes that knowledge of foreign languages is a key to success in this area.