Reviewerly/exHarmony huggingface dataset:
Crucial File Selection Strategy (CFSS) for Enhanced Download Response Time in Cloud Replication Environments

Cloud Computing is a mass platform to serve high volume data from multi-devices and numerous technologies. Cloud tenants have a high demand to access their data faster without any disruptions. Therefore, cloud providers are struggling to ensure every individual data is secured and always accessible. Hence, an appropriate replication strategy capable of selecting essential data is required in cloud replication environments as the solution. This paper proposed a Crucial File Selection Strategy (CFSS) to address poor response time in a cloud replication environment. A cloud simulator called CloudSim is used to conduct the necessary experiments, and results are presented to evidence the enhancement on replication performance. The obtained analytical graphs are discussed thoroughly, and apparently, the proposed CFSS algorithm outperformed another existing algorithm with a 10.47% improvement in average response time for multiple jobs per round.
Possibility Theory Based Evaluation Scheme of UAS Detect Performance

In this paper, an evaluation scheme of UAS detect performance is conducted to provide assessment tool for UAS detect systems of different technical structure. The evaluation scheme is represent by an integrated model based on the possibility theory. Two categories of performance indicators, consist of detection performance indicator and threat tracking and alert performance indicator, act as the characteristic vector to provide the contribution of the possibility measure. The experimental results show that the proposed scheme is effective for the assessment of UAS detect systems.
Improving Data-to-Text Generation via Preserving High-Frequency Phrases and Fact-Checking

Transforming numerical data into natural language descriptions (data-to-text) requires presenting the data in the correct context, supplementing plausible details, and creating an overall coherent and non-conflicting narrative.In this work, we propose a generate-extract-correct pipeline for the task.We use transfer learning with an auxiliary task of keeping high-frequency word sequences from the training data for text generation.We then apply information extraction to the generated text to check its accuracy, followed by correction, and thus ensure the coherence of the generated narrative.We demonstrate the effectiveness of this approach with both objective and subjective evaluations.Using an empirical evaluation, we show that people rated our system's outputs similarly to human-written text regarding its coherence, conciseness, and grammar.
Higher prices for specialty carbon blacks plus surcharges for European products

In this paper, we have applied an efficient shifted second kind Chebyshev wavelet method (S2KCWM) to vibrating dynamical models arising in mechanical systems such as vibration of circular membrane, damped spring mass system and ship oscillatory motions. To the best of our knowledge, until now there is no rigorous wavelet based solution has been reported for the vibrating dynamical models. The power of the manageable method is confirmed. The wavelet solutions are compared with numerical simulations by MATLAB. Good agreement between the solutions is presented in this paper. Some numerical examples are given to demonstrate the validity and applicability of the proposed method. Moreover the use of Chebyshev wavelets is found to be simple, efficient, flexible, convenient, less computation costs and computationally attractive.
A Comparative Analysis of Parametric and Non-Parametric Video Object Segmentation Methods for IoT and Other Applications Areas

In this chapter, a comparative analysis of statistical methods for video object segmentation is presented. The methods are based on statistical modelling combined with the temporal domain. The viability of using parametric and non-parametric statistical hypothesis testing tools both in the univariate and multivariate domain for video object segmentation has been studied. The outcomes imply that the reviewed methods satisfactorily segment the foreground objects in a relatively challenging environment. Amongst the four methods, the non-parametric statistics based methods perform significantly better. Therefore, non-parametric methods are recommended for video object segmentation. We believe this comparative analysis will help further research in this direction by providing useful insights.
Gliding Trajectory Programming of Guided Projectile Based on Improved L1 Penalty Successive Convex Programming Algorithm

Taking the general control energy optimal trajectory programming model as the research object, the Radau pseudo-spectral method is used to discretize the continuous variables and then linearly convexify the nonlinear dynamic equation to establish the standard convex optimization model. In order to solve the problems of large amount of redundant computation and slow convergence speed of the traditional L1 penalty successive convex programming algorithm (LPSCP), an improved L1 penalty successive convex programming algorithm (ILPSCP) is proposed in this paper. The algorithm introduces the feasibility judgment process to avoid solving the more complex L1 subproblem P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> when the original problem <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> is feasible. Taking the gliding trajectory model of the guided projectile in the longitudinal plane as an example, the traditional LPSCP algorithm, the ILPSCP algorithm proposed in this paper and GPOPS2 are used to simulate and compare. The simulation results of ILPSCP algorithm are highly consistent with those of GPOPS2, which proves the effectiveness of the proposed ILPSCP algorithm for solving trajectory programming problems, and the convergence rate of ILPSCP algorithm is improved by 46.36% compared with traditional LPSCP algorithm.
Entanglement witnesses of four-qubit tripartite separable quantum states*

Abstract A quantum entangled state is easily disturbed by noise and degenerates into a separable state. Compared to the entanglement with bipartite quantum systems, less progress has been made for the entanglement with multipartite quantum systems. For tripartite separability of a four-qubit system, we propose two entanglement witnesses, each of which corresponds to a necessary condition of tripartite separability. For the four-qubit GHZ state mixed with a W state and white noise, we prove that the necessary conditions of tripartite separability are also sufficient at W states side.
Numerical Analysis of Evacuation Start in Pangandaran

Abstract Tsunami evacuations plan should consider the behavior of local people. In this study tsunami evacuation start was analyzed using tsunami evacuation simulation consist of evacuee generation and evacuee behavior model. Pangandaran west beach was selected as the target area due to its popularity as a tourist spot and fishery activities in West Java. Evacuation simulations were conducted using 500 evacuations agents with tsunami shelter and grand mosques as goal for the safer place. A total of six evacuation scenarios were made using a different set of evacuation triggers parameter value with the combination of influence weight and effective range. The simulation result indicates that the influence weight which made 57% of people start evacuation immediately after the earthquake happened showed an instantaneous evacuation. Simulation result that was using lower influence weight and lower exposure range showed a more gradual tsunami evacuation. The simulation gave promising results that Pangandaran people could be safely evacuating if they raise their awareness and preparedness. The low-cost simulations that were used in this study provide an insight into the behaviors of Indonesians during disaster evacuation and might use as support for tsunami evacuation planning. However, to produce more satisfactory result we need to conduct field survey and tsunami drill to gain more information of people behavior during tsunami evacuation.
Holographic and speckle encryption using deep learning: publisher’s note

This publisher's note contains a correction to Opt. Lett.46, 5794 (2021)10.1364/OL.443398.
Deep Convolution Neural Network Based CSI Signal Classifier to Recognize Human-Human Interactions

In this research, we have proposed a human-human interaction classifier which can classify twelve different interactions (approaching, departing, handshaking, high-five, hugging, kicking with the left leg, kicking with the right leg, pointing with the left hand, pointing with the right hand, punching with the left hand, punching with the right hand, and pushing) using CSI signal. This recognition model consists of three steps: input, feature extraction and recognition. The Feature extraction step utilized DCNNs to automatically extract features from captured CSI signals. It consists of three layers: convolutional layer, batch normalization layer, and rectified linear unit layer. In addition, the recognition phase classifies the extracted features into twelve different classes. It consists of four layers: the flatten layer, fully connected layer, softmax layer, and classification layer. In addition, we have used a Gaussian filter to denoising the CSI signal. To validate the above DCNNs model we have utilized the Chanel State Information (CSI) data obtained from commercial Wi-Fi Access Point and available online. The data set contain the CSI packets that were recorded for forty distinct pairs of subjects while performing twelve different human-human interactions inside an office. Each pair of subjects performed ten different trials of all the interactions. To train and test the proposed model, the train-test split procedure has been employed. Moreover Stochastic Gradient Decent (SGD) algorithm was employed to learn the weights and biases of the convolutional and fully connected layer by minimizing the categorical cross-entropy loss function. The proposed model achieves 88.66% accuracy for recognizing correct interactions.
Architecture for the integration of the Leap Motion device into application development

Leap Motion is one of the most prominent and widely used hand tracking devices, solutions that make use of this device range from fields like education, health, or videogames, although it is not reported a tool that make easier the integration of the device with the development of applications controlled by hand gesture interfaces. An architecture is proposed alongside a set of gestures ready to be used in the development process of gesture interface applications. To support gesture recognition, skeletal information provided by Leap Motion is used in combination with a machine learning model.
Detection of changes in the behaviour of the elderly person

In this paper, we propose a solution for detecting changes in the behaviour of the elderly person based on the monitoring of activities of daily living (ADL). The elderly person's daily routine is characterized by the following five indexes: 1) percentage of time lying down, 2) percentage of time sitting, 3) percentage of time standing, 4) percentage of time absent from home, and 5) number of falls during the day. In our framework, these indexes are computed using characteristics extracted from depth and thermal data. We hypothesize that elderly persons have a well-defined, regular life routine, organized around their environment, habits, and social relations. Then, given the indexes values, a day is defined as routine or non-routine day. Thus, looking for changes of day type allows to detect changes in a person's routine. The method has been tested on a database of depth and thermal images recorded in a nursing home over an 85 days period. These tests proved the reliability of the proposed method.
Research on Fresh Supply Chain Optimization Decision-Making Considering Blockchain and Freshness-keeping Effort

A two-echelon supply chain consisting of a fresh food supplier and an e-commerce was studied in this paper. By constructing a centralized and decentralized decision model, the influence of transparency elasticity of demand, freshness elasticity of demand was analyzed, whose effectiveness on the optimal decision by considering the influence of transparency and freshness on consumer demand were studied. A “cost-sharing-profit-sharing” contract was designed for supply chain optimization and coordination, and the result of profit function optimization based on cost and benefit was obtained by employing the multi-objective optimization. The result showed that the increase of product demand elasticity is beneficial to both suppliers and e-merchants, and the impact of transparency demand elasticity on profit level is more sensitive. Both parties optimize toward the direction of validity improvement, and the adjustment of coordination parameters was applied to achieve a reasonable distribution of profits for both parties within the Pareto frontier solution.
A big data problem involving stick-slip oscillations in an electromechanical system

This work analyzes the stochastic response of an electromechanical system with stick­ slip oscillations. The system is composed of two subsystems that interact, a mechanical and an electromagnetic (a DC motor). The mechanical subsystem is subject to a dry frictional force mod­ eled as a Coulomb friction. An imposed source voltage in the DC motor stochastically excites the system. This excitation induces in the mechanical subsystem stochastic stick-slip oscillations. The resulting motion of the mechanical subsystem can be characterized by two qualitatively different and alternate modes, the stick- and slip-modes, with a non-smooth transition between them. Me­ chanical and electromagnetic parameters influence this stochastic sequence. The objective of this paper is to analyze the joint influence of these parameters in the statistical model of the system response. With Monte Carlo simulations, scatter plots and joint histograms are constructed for different combinations of parameter values.
Perancangan Aplikasi Estimasi Harga Pokok Penjualan dan Produksi Berbasis Web

The speed of data transformation is very much needed in order to increase work and information needs. this is very much needed in all fields. especially in the industrial sector. we can use this growing technology for many things, to minimize errors and also make work easier. it is a pity if we can take advantage of existing technology but calculations and work are still done manually. therefore, in research this is designed a web-based application using php and mysql. with this application design, it is expected to be able to make work more effective and efficient. and in this sophisticated digital age, it would be better if we choose to reduce the use of paper. this can also save operational costs and a step to reduce global warming. and during a pandemic like this, it is very necessary to reduce face-to-face interactions. to reduce that interaction and make it easier to store data and search for data in making reports and calculating cost estimates,make it application design of web- based cost of goods sold and production estimation application.
Teknologi Pengolahan Citra Digital Untuk Ekstraksi Ciri pada Citra Daun untuk Identifikasi Tumbuhan Obat

The leaf image identification process depends on the feature extraction results. Each medicinal plant has different shapes and patterns of leaf venation. But for one type of medicinal plants have the same pattern of venation shape and pattern even though the size is different. One of the methods for extraction of leaf image form characteristics is by fractal-based feature extraction. Through fractal can be calculated the value of leaf dimensions and searched parts of leaves that have similarities between one part with other parts. As for the method of extracting the characteristics of venation pattern using B-Spline method. Benefits of research conducted is to help people identifying the types of medicinal plants found, knowing the benefits and ways of brewing. While the research contribution is prototype software application based on information technology that can be used by the people through mobile phones for the identification of medicinal plants. To identify or match the results of feature extraction on the leaf found whether included in the medicinal plant, conducted by Euclidean Distance method. In the experiments we used 1100 data consist of 55 variety of medicinal plants for each 20 samples.The experimental result show that the accuracy of identification using of fractal and b-spline is 85.30%.
Yarım Bant Nyquist Filtre ile Sinyallerin Ayrıştırılması ve Gürültünün Giderilmesi Üzerine Bir Araştırma

Medical signal processing is often used for analyzing and detecting diseases from bio-signals. Segmentation, filtering, and noise removal are application examples in this area. The segmentation or decomposition of signals is investigated in this study. Each branch of two band quadrature-mirror orthogonal filter bank structure (analysis and synthesis parts are cascaded) is a half band Nyquist filter when down-up samplers are removed. To perform partitioning, one branch of the filter bank was adapted to a component of input signal to suppress the other element and filter out the target part of the signal. This was done by obtaining the filter-tap weights, which minimize - norm of error at the output. This goal was achieved by employing the exhaustive search method. The filter performance was tested for three scenarios: filtering with and without down-up samplers and wavelet de-noising using one-level decomposition. The comparison was done with Daubechies and Symlet filters. The approach was run and tested for separating synthetically generated evoked potential from noise, and the results show that the designed filter achieves lower total absolute error than the classical wavelet filters.
A LSTM-Based Fall Prediction Method Using IMU

Injuries caused by falls greatly affect quality of the elder’s life. Predicting and preventing a fall accident plays an important role in health care. Existing fall prediction methods can hardly be generalized into real-world applications since falling is a highly nonlinear and time-dependent process. The long-short term memory (LSTM) is known for its ability to learn from nonlinear sequential data. In this paper, a fall prediction method is proposed based on LSTM algorithm using a 9-axis Bluetooth inertial measurement unit (IMU). Tri-axial accelerations, angular velocities at the hip are collected by the IMU and used as inputs to train the fall prediction algorithm. By analyzing the sensor data of each motion sequences, fall risk curves are defined and serve as the output of the algorithm. By setting a proper threshold on the fall risk, a fall can be predicted. To validate the proposed algorithm, five activities of daily life (ADLs) and two common falls are performed by 10 participants for 5 times. 4/5 of the collected data are used for training the model while others are used for validation. Result shows that the accuracy, sensitivity and specificity are 97.1%, 100.0%, 96.0%, respectively. Meanwhile, it is found that a fall prediction can be made about 360 ms ahead of the collision, which verifies the applicability of the proposed algorithm.
Study of Flood Disaster Mitigation Analysis for Transportation Routes in Panakukkang District, Makassar City

Abstract This study conducted an analysis study of flood disaster mitigation for transportation routes in the Panakukkang district of Makassar City. By using ArcGis software, the results of the simulation of safe and vulnerable zone levels based on color indicators are known. There are 5 villages in Panakukkang District which are flood safe zones, with the number of evacuation sites, namely 21 buildings. Then there are 4 villages which are flood alert zones with 2 evacuation sites, 2 buildings. On the first evacuation route there are 8 reference points namely Reference Point C with the distance to the nearest evacuation site 3.22 km and a travel time of 64.3 minutes. Then the reference point A with a distance to the nearest evacuation site is 2.85 km and a travel time of 57 minutes. While the reference point F is the closest point to the nearest evacuation distance 0.71 km and the travel time is 14.2 minutes. All these reference points require travel speeds of 3 km / h on foot. On the second evacuation route there are 6 Reference Points namely reference point A with distance to the nearest evacuation point 1.94 km and travel time 38.8 minutes, reference point E with distance to nearest evacuation location 1.23 km and travel time 24.6 minutes. Then at the reference point C is the closest point to the nearest evacuation distance 0.72 km and the travel time is 14.4 minutes.
Reflection of Corporation Social Responsibility in Supporting of Saudi Health Care Sector through Covid 19 Pandemic

Current study aimed at highlighting reflections of CSR and its role in supporting healthcare system in KSA during COVID 19 pandemic. Quantitative approach was employed through utilizing an online questionnaire distributed on total of (97) individuals within KSA hospitals which included managers of public relations, external relations or marketing in Saudi governmental or private hospitals within Saudi hospitals. Results of study indicated that the main hypothesis was accepted, and it was seen that CSR has the ability to positively influence and support the healthcare system in Saudi Arabia during COVID 19. Among the chosen dimensions of CSR (Reputation, Ethics, Social Orientation and Profitability), it appeared that ethics was the most influential aspect that has a main role in supporting and backing up the healthcare system in KSA during the pandemic, and this concept helped in supporting the Saudi healthcare system during the breakout of the pandemic from collapsing. Study recommended increasing the awareness of social responsibility among organizations in order to increase the relationship between stakeholders (organizations) and the community.
Unmanned Aircraft Trajectory Prediction Based on Isolated Forest and LSTM Algorithm

Nowadays, the technology of unmanned aircraft trajectory prediction has been more mature. But one of the reasons that affect the accuracy of the prediction curve is the inevitable systematic error of the radar itself. In addition, outliers and random oscillations generated in the process of data acquisition and transmission will also interfere with the prediction results. In order to solve this problem, we use Isolated Forest algorithm for data cleaning, and design a parallel LSTM system to deal with the inherent system error of radar. Experiments show that the performance of this method is improved compared with the mainstream methods.