Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Approach to Automatic Generation of Time-lapse Video Sequence

Time-lapse video sequences have recently become a highly utilised asset for marketing and advertising, particularly within the field of construction and landscape development. However, the manual generation of these videos, at a quality that can be used for marketing purposes, can be quite time-consuming. In this paper, a novel application for generating time-lapse videos is proposed, which will automatically select the optimal frames for time-lapse video generation, enhance these frames by applying a number of image preprocessing and machine learning techniques such as FAST super-resolution to improve the frames quality, and finally, provide an intuitive user interface to allow users to customise the time-lapse video with company branding. The auto-generated time-lapse videos will use techniques such as Laplacian filtering and temporal smoothing filtering to determine inactivity within the video sequence, classify day or night and, by use of optical character recognition, have the ability to remove unwanted artefacts such as the captured video date and time stamp. The obtained results from the proposed approach produce comparable video sequences to those produced manually, but with the advantage of being generated much faster and not requiring specialised video editing skills to complete.
The effect of concrete-pictorial-abstract learning strategy on spatial sense ability

This study aimed to determine whether the spatial sense ability of 8th grade students who got learning with Concrete-Pictorial-Abstract (CPA) learning strategy were better than those who got conventional learning. The population in this study was all the 8th grade students at SMP Negeri 4 Negara in second semester of the academic year 2017/2018 so that there were 240 students. The sample of this research was determined by cluster random sampling technique so that there were 29 students as experimental group members and 28 students as control group members. This research was categorized as quasi-experimental research with pretest – posttest control group design. Based on data analysis which has done were obtained tcount = 3,5365 and ttable = 1.6730. Therefore it can be concluded that the spatial sense ability of 8th grade students who got the learning with Concrete-Pictorial-Abstract strategy (CPA) were better than those who got conventional learning.
Behave Nicely! Automatic Generation of Code for Behaviour Driven Development Test Suites

Behaviour driven development (BDD) has gained widespread use in the software industry. System specifications can be expressed as test scenarios, describing the circumstances, actions and expected outcomes. These scenarios are written in a structured natural language (Gherkin), with each step in the scenario associated with a corresponding step implementation function in the underlying programming language. A challenge recognised by industry is ensuring that the natural language scenarios, step implementation functions and underlying system implementation remain consistent with one another, requiring on-going maintenance effort as changes are made to a system. To address this, we have developed behave_nicely, a tool, for automatically generating step implementation functions from structured natural language steps, with the intention of eliminating the need for maintaining step implementation functions. We evaluated our approach on a sample of 20 white box and 50 black box projects using behaviour driven development, drawn from GitHub. Our results show that behave_nicely can generate step implementation functions for 80% of the white box and 17% of black box projects. We conclude that (a) there is significant potential for automating the process of code generation for BDD tests and (b) that the development of guidelines for writing tests in Gherkin would significantly improve the results.
Modified ACS Centroid Memory for Data Clustering

Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm’s memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant’s movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.
Position and attitude tracking of uncertain quadrotor unmanned aerial vehicles based on non-singular terminal super-twisting algorithm

This paper proposes an improved non-singular terminal super-twisting control for the problem of position and attitude tracking of quadrotor systems suffering from uncertainties and disturbances. The super-twisting algorithm is a second-order sliding mode known to be a very effective control used to provide high precision and less chattering for uncertain nonlinear electromechanical systems. The proposed method is based on a non-singular terminal sliding surface with new exponent that solves the problem of singularity. The design procedure and the stability analysis of the closed-loop system using Lyapunov theory are detailed for the considered system. Finally, the proposed control scheme is tested in simulations and by experiments on the parrot-rolling spider quadrotor. Moreover, a comparison is made with the standard super-twisting algorithm in the simulation part. The results obtained show adequate performance in trajectory tracking and chattering reduction.
Performance Analysis of Software-Defined Multihop Wireless Sensor Networks

In this article, we propose a model-based characterization of energy consumption in a software-defined wireless sensor network (SD-WSN) architecture in an effort to examine the implications for network performance when making the WSN reprogrammable. The proposed model consists of breaking down all key functions involved in the correct functioning of an SD-WSN, namely; neighbor discovery, neighbor advertisement, network configuration, and data collection. The model is analyzed from a multihop network perspective. We consider two static SD-WSN scenarios to examine scalability, and one scenario to assess the performance implications in a pseudo-dynamic SD-WSN. Extensive simulation results are presented regarding the control overhead introduced, the percentage of alive nodes and remaining energy, and the impacts on network lifetime. We show that the accumulated control overhead is inversely proportional to the interaction period with the controller, whereas the remaining energy and the network lifetime are directly proportional to this parameter. Results show that the control overhead, for static SD-WSNs, can take up to 10%–29% of the total data flowing to the controller for the large SD-WSN and 6–19% for the small SD-WSN. For a pseudo-dynamic network, the control overhead can take up to two-thirds of the total data sent to the controller, and the network lifetime was reduced by up to 80% compared with the static scenarios.
Privacy Protection for Recommendation System: A Survey

Recommendation system have become one of the most well-liked and accepted way to solve overload of information or merchandise. By collecting user’s personal data for processing, suitable lists of information or merchandise are provided to the potential consumers. For online business, recommendation systems have become an extremely effective revenue driver and developed rapidly. Although recommendation systems are great beneficial, directly exposing privacy data to the recommender may lead to leakage of privacy and cause risks. Therefore, quality of recommendation and privacy protection are both important metrics in recommendation. In this paper we present a review investigating development in recommendation systems with privacy protection, including the definition of privacy, classification of privacy leakage, taxonomy of privacy, measuring of privacy risk, policies for privacy protection, approaches of privacy protection and models of privacy protection. We also speculate on the future direction.
Optimization and Parallelization of Fuzzy Clustering Algorithm Based on the Improved Kmeans++ Clustering

In the field of big data, fuzzy clustering algorithm is one of the most widely used clustering algorithm. Although fuzzy c-means (FCM) algorithm performs well, it still is difficult to ascertain the number of clusters and is sensitive to initial clustering center. To solve these problems, an improved fuzzy clustering algorithm based on Spark is proposed in this article. The proposed algorithm integrates the L2 norm and uses the kmeans++ algorithm improved by the Canopy algorithm to initialize the cluster center. Experimental results show that the proposed algorithm performs well in clustering accuracy and computational performance.
3-D Path Following of Helical Microswimmers With an Adaptive Orientation Compensation Model

Controlling magnetic microswimmers toward 3-D manipulation tasks has received considerable attention. Although related studies on manipulating helical microswimmers have been developed, stable closed-loop controls and accuracy swimming models should be still investigated. This article addresses the problem of 3-D path following for magnetically driven helical microswimmers with an adaptive-compensation scheme. The orientation-compensation model in the global coordinate frame is learned by radial basis function (RBF) networks trained with backpropagation algorithms, which is used to express the motion of the helical microswimmer in the presence of the weight of the swimmer and lateral disturbances from the boundary effects. A proxy-based sliding-mode control (PSMC) approach is developed to design stable controllers based on the kinematic error model. The effects of variable parameters and boundary effects are also considered. Experimental results including different paths in 3-D space validated the path following with submillimeter accuracy using the helical microswimmer. Note to Practitioners—This article is motivated by the issue of the following predefined paths for magnetically driven helical microswimmers in 3-D space. The proposed closed-loop controller employs the error model in 3-D space to formulate the control law according to an orientation-compensation model learned by neural networks. It is demonstrated that the helical microswimmer is able to follow different paths in 3-D space with submillimeter accuracy using the proposed control scheme.
LONG-TERM THERMAL COMPENSATION OF 5-AXIS MACHINE TOOLS DUE TO THERMAL ADAPTIVE LEARNING CONTROL

This paper presents a prediction and compensation approach for thermal errors of 5-axis machine tools, based on supervised online machine learning. Process-intermittent probing is used to identify and update a thermal autoregressive with exogenous input (ARX) model. The approach is capable of predicting and compensating thermal displacements of the tool center point based on changes in the environmental temperature, load-dependent changes and boundary condition changes and states, like dry or wet machining. The self-optimized machine tool shows very stable long-term behavior under drastically varying machining and boundary conditions. The implementation is validated on a set of thermal test pieces. The test pieces show that the major share of thermal workpiece errors are reduced by the thermal adaptive learning control.
Blind Recognition of LDPC Codes Over Candidate Set

Adaptive modulation and coding (AMC) techniques have attracted more attention recently because of flexible strategies and dynamic channel quality. Blind recognition of encoder is an important technology for AMC systems because it can improve communication efficiency very well. In this letter, we present a new algorithm to tackle blind low-density parity-check (LDPC) encoder identification over a given candidate set. Our proposed scheme establishes a cosine conformity (CC) of syndrome, which is a posterior probability that can measure the probability that all parity check relations of the codes are satisfied, and classifies the LDPC encoder leading to maximum average CC over the candidate set. Simulations show that the new method is effective for LDPC encoder identification; the theoretical correct ratio of recognition is consistent with the simulation results, and its performance is improved by nearly 1.5 dB when compared with the existing algorithm.
TRAJECTOGRAPHY ESTIMATION FOR A SMART POWERED WHEELCHAIR ORB-SLAM2 VS RTAB-MAP A PREPRINT

This work is part of the ADAPT project relating to the implementation of a trajectography functionality that aims to measure the path travelled by a patient during the clinical trials. This system (hardware and software) must be reliable, quickly integrable, low-cost and real-time. Therefore, our choices have been naturally made towards visual SLAM-based solutions coupled with an Intel real-sense consumer sensors. This paper is a comparison of two well-known visual SLAM algorithms in the scientific community, ORB-SLAM2 and RTAB-Map, evaluated in different path configurations. The added value of our work lies in the accurate estimation of the trajectories achieved through the use of a VICON motion capture system.
A Facial Feature Extraction and Classification Model for Loan-Default-Detection

: The purpose of this paper is to make a new trial to explore the influence factors of loan default in Internet finance loan business. A facial feature extraction and classification model is proposed. The optimal facial feature extraction algorithm is obtained by comparing four commonly used facial feature extraction algorithms and certain facial features based on physiognomy are selected and classified in 117, 507face images. An experimental study with the help of the proposed model is conducted to explore the correlations between loan defaults and Internet finance loan users’ classified facial features based on physiognomy. The findings are as follows: among male Internet finance loan users, short eyebrows are related to default and eye angle, nose height-to-width ratio (nHWR), lip thickness and facial width-to-height ratio (fWHR) are positively related to default behavior and the mouth length is negatively related to default; among female Internet finance loan users, eyebrows angle, eyes angle, lip thickness and facial width-to-height ratio are positively correlated to default and mouth length is negatively correlated with default. Additionally, the conclusion of that male fWHR is positively related to default of the proposed study is echoed with the research results of [1] and [2].
Pets without PETs: on pet owners’ under-estimation of privacy concerns in pet wearables

Abstract We report on a mixed-method, comparative study investigating whether there is a difference between privacy concerns expressed about pet wearables as opposed to human wearables – and more importantly, why. We extracted the privacy concerns found in product reviews (N=8,038) of pet wearables (activity, location, and dual-function trackers), contrasting the (lack of) concerns and misuse to a curated set of reviews for similar human-oriented wearables (N=20,431). Our findings indicate that, while overall very few privacy concerns are expressed in product reviews, for pet wearables they are expressed even less, even though consumers use these devices in a manner which impacts both personal and bystander privacy. An additional survey of pet owners (N=201) eliciting what factors would cause them to not purchase (or stop using) pet wearables indicated comparably few privacy concerns, strengthening the representativeness of our findings. A thematic analysis reveals that the lack of privacy concerns may be explained by, among other factors, emotional drivers to purchase the device, and prioritization of (desired) functionality to support those emotional drivers over privacy requirements. Moreover, we found that pet wearables are used in different ways than originally intended, which raise novel privacy implications to be dealt with. We propose that in order to move towards more privacy-conscious use of pet wearables, a combination of understanding consumer rationale and behavior as well as ensuring data protection legislation is adequate to real-world use is needed.
Um Método não Supervisionado Baseado em Tópicos para Identificar Dimensões de Reputação em Microblogs

Atualmente, redes sociais se tornaram grandes fontes de estudos, pois, com elas, e possivel encontrar uma gama de informacoes relacionadas a gostos, interesses, desejos e opinioes de seus usuarios. A identificacao de dimensoes de reputacao e uma tarefa do gerenciamento da reputacao digital, que consiste em segmentar uma base de opinioes sobre uma entidade em dimensoes de reputacao, estas dimensoes refletem percepcoes afetivas e cognitivas da entidade por diferentes grupos de pessoas. Tecnicas supervisionadas aplicadas a essa tarefa tem sido propostas, no entanto, elas se tornam inviaveis em aplicacoes reais, pois dependem de um conjunto de exemplos de treino que normalmente sao manualmente rotulados. Este trabalho apresenta um novo metodo nao supervisionado para identificar dimensoes de reputacao em microblogs baseado em modelagem de topicos. Visto que, a maioria dos algoritmos de modelagem de topicos nao possuem um bom desempenho em identificar essas dimensoes em textos curtos, a abordagem proposta visa melhorar esse desempenho. Este trabalho foi avaliado sobre o conjunto de dados do desafio RepLab 2014 e teve performance superior ao vencedor desse desafio, que e um metodo supervisionado. Porem, o metodo proposto neste artigo nao necessita de, manualmente, rotular exemplos de treino.
A Microservice Store for Efficient Edge Offloading

Current edge computing frameworks require tight coupling between mobile clients and surrogates, i.e., the offloaded code has been preconfigured with its required execution environment. In many cases, this includes prior transfers of code blocks or execution environments from mobile devices to the offloading infrastructure. This approach incurs additional latency and is detrimental for the energy consumption of the mobile devices. In this paper, we propose the concept of a microservice store. Using the microservice abstraction common in software development and following the serverless paradigm, we envision a repository through which said services are made accessible to developers and can be re-used across applications. We implement a proof-of-concept edge computing system based on a microservice repository and demonstrate its benefits with real-world applications on mobile devices. Our results show that we were able to reduce latencies by up to 14x and save up to 94% of battery life.
Evolution of random access process: From Legacy networks to 5G and beyond

In this paper, we discuss the random access procedures in legacy networks, long‐term evolution, LTE‐Advanced, and 5G networks. Random access is the first and incumbent step for connection establishment between base station/eNodeB/gNodeB and user equipment. Keeping in view its importance, there has been a regular thrive to improve the procedures of random access in communication networks. With the advent of new concepts such as machine‐type communication and Internet of Things, random access becomes extremely critical because huge number of devices try to connect to the network simultaneously. We present the evolution of random access process from legacy networks such as global system for mobile or wideband code division multiple access to 5G networks and beyond and their analysis. Simulations are performed in MATLAB to show various performance metrics such as access delay, total service time, and collision probability to demonstrate the pros and cons of one technique over another.
Sales Forecast of CNC Machine Tool Suppliers Based on Ensemble Learning

CNC machine tools, whose product sales forecast is a complicated dynamic process, are the basic equipments for manufacturing industry. It is difficult for a single prediction model to achieve the expected effects because product sales can be affected by multiple factors with features like time-varying, non-linear and random. This paper, which takes the sales data of a CNC machine tool supplier as an example, designs an ensemble learning model based on XGboost and random forest, obtaining lower prediction error through data cleaning, data conversion and parameter optimization. The experimental results show that the generalization ability of this model is excellent, so it can provide decision supports for CNC machine tool suppliers in purchase and sales plans.
HIGH SPEED SYNCHRONOUS RELUCTANCE DRIVES FOR MOTOR SPINDLES

This article presents a new design for setting up high speed synchronous reluctance motors (SynRM) in motor spindle applications for up to 30,000 rpm. The transversal laminated SynRM rotor package is supported by thin steel sheets and a shaft nut to exert a constant axial clamping force. Both, numerical and practical investigations are carried out using FEM-analysis and a test bench for centrifugal force load tests. The objective is to find out at which speed the elastic deformation changes into the plastic deformation. The beginning of plastic deformation is a failure criterion in electric motor construction.
IDENTIFYING TWITTER BOTS BASED ON TRENDING HASHTAGS USING MACHINE LEARNING ALGORITHMS

: A bot is a piece of software that completes automated task over the internet . On social media the prevalence of bot is ubiquitous. Nearly 48 million twitter accounts are automated using bots. Detecting bots is necessary in order to identify bad actors in “Twitter verse” and protect genuine users from misinformation and malicious intents. According to a study released by Pew Research Center, these bots contribute to approximately 66% of total tweets in twitter.Bots are becoming smarter, they mimic humans to avoid being detected and suspended, and increase throughput by creating many accounts.The idea is to develop a bot detection algorithm to identify twitter bot accounts by using attributes like followers _ count, no of tweets or likes, status _ count etc. Thus evaluate these features using k-nearest neighbour algorithm and Random Forest. Finally the result of two models are combined using suitable ensemble method to produce more accurate solution. The Test dataset is prepared specifically for any hashtag. The dataset consists details of the accounts which uses that hashtag. This dataset is cleaned and used as a test dataset.