Miracle-dz/UniFAR-FTUs huggingface dataset:
Content validity index: A simple but significant approach in social science research

In this paper researchers tried to focus on a significant area for the development of research tool. Researchers endeavour to include content validity through the expert ratings in terms of quantitative way. During the assessment of any variables from social sciences or behavioural sciences researchers always try to develop a self-made questionnaire. If we want to standardised those tools, then we must have to measure objectivity, reliability, validity, etc of that particular research tool. Here the present researchers emphasised on the procedure of calculation of content validity in terms of Content Validity Index (CVI) of a self made questionnaire, which was used to measure pedagogical content knowledge (PCK) of Geography teachers. Content Validity Index (CVI) has been obtained for both item level content (I-CVI) as well as scale level content (S-CVI) also.
A Convolutional Neural Network Model for Predicting a Product's Function, Given Its Form

Quantifying the ability of a digital design concept to perform a function currently requires the use of costly and intensive solutions such as Computational Fluid Dynamics. To mitigate these challenges, the authors of this work propose a deep learning approach based on 3-Dimensional Convolutions that predicts Functional Quantities of digital design concepts. This work defines the term Functional Quantity to mean a quantitative measure of an artifact’s ability to perform a function. Several research questions are derived from this work: i) Are learned 3D Convolutions able to accurately calculate these quantities, as measured by rank, magnitude and accuracy? ii) What do the latent features (that is, internal values in the model) discovered by this network mean? iii) Does this work perform better than other deep learning approaches at calculating Functional Quantities? In the case study, a proposed network design is tested for its ability to predict several functions (Sitting, Storing Liquid, Emitting Sound, Displaying Images, and Providing Conveyance) based on test form classes distinct from training class. This study evaluates several approaches to this problem based on a common architecture, with the best approach achieving F Scores of > 0.9 in 3 of the 5 functions identified. Testing trained models on novel input also yields accuracy as high as 98% for estimating rank of these functional quantities. This method is also employed to differentiate between decorative and functional head-wear, which yields an 84.4% accuracy and 0.786 precision.
Cross-Platform Embedded-System Dynamic Information Acquisition Method

With the widespread application of embedded devices, embedded software vulnerabilities and oriented attacks to embedded system are also rising. As being wellknown, the hardware architecture of the embedded devices is heterogeneous, and the software stacks on them are customizable and flexible as well. Although dynamic information extraction and analysis technologies have been successfully used for general platform in vulnerability mining, malicious code detection and so on, its usage on embedded system lags behind significantly. The paper provides (1) a Cross- Platform - Dynamic Instrumentation Framework, which, by leveraging an extensible intermediate code module and a semantic constraint database, decouples the instrumentation plugins from underlying hardware and OS; (2) a set of instrumentation interfaces which may be implemented by plugins to extract runtime state of various code granularity; (3) ability of system semantic introspection to extract processes and modules information (system semantic views), by using system attribute description files and abstract state extraction functions. Compared with TEMU and DECAF, our method shows more advantages in performance of abstract state extraction, and reducing the time and memory overhead.
The South African public broadcaster’s financial sustainability: Internal stakeholders’ perceptions

Orientation: The existing funding framework of the South African Broadcasting Corporation (SABC) has proved to be unsustainable. Research purpose: Because sustainable funding has an impact on the organisational outcomes (customer benefits and performance) of the public broadcaster in South Africa, this study sought to identify factors that influence its financial sustainability. Motivation for the study: Past studies have mainly focused on defining public service broadcasters or ascertaining the future of public service broadcasters. These researchers did not attempt to develop a sustainable funding framework. Research design, approach and method: The study adopted a positivistic paradigm and a quantitative approach that used a self-administered, structured questionnaire for data collection. An EFA was conducted, Cronbach’s alpha coefficients were calculated and the relationships between variables were measured by calculating Pearson product-moment correlations and conducting multiple regression analyses. The respondents were selected through a stratification sampling technique that drew participants from each provincial SABC office by accessing the database of managers, unionised and non-unionised employees, as well as freelancers. A total of 167 respondents completed the survey. Main findings: The study determined that internal stakeholders, a competitive environment and the management of resources have a significant positive influence on the sustainable funding of the SABC. In turn, sustainable funding of the SABC has a significant positive influence on organisational outcomes. Practical/managerial implications: It is envisaged that this study could assist the SABC in understanding the factors influencing financial sustainability, thereby assisting the organisation during strategy development and decision-making. Furthermore, this study provides practical recommendations that could improve the financial sustainability of the SABC. Contribution/value-add: This study contributes to the body of knowledge relating to sustainable funding in the context of public broadcasting.
Design of A Grid Integrated PV System with MPPT Control and Voltage Oriented Controller using MATLAB/PLECES

This paper presents model of a grid-integrated photovoltaic array with Maximum Power Point Tracker (MPPT) and voltage oriented controller. The MPPT of the PV array is usually an essential part of PV system as MPPT helps the operating point of the solar array to align its maximum power point. In this model, the MPPT along with a DC-DC converter lets a PV generator to produce continuous power, despite of the measurement conditions. The neutral-point-clamped converter (NPC) with a boost converter raises the voltage from the panels to the DC-link. An LCL-filter smoothens the current ripple caused by the PWM modulation of the grid-side inverter. In addition to the MPPT, the system has two more two controllers, such as voltage controller and a current controller. The voltage control has a PI controller to regulate the PV voltage to optimal level by controlling the amount of current injected into the boost stage. Here, the grid-side converter transfers the power from the DC-link into the grid and maintains the DC-link voltage. Three-phase PV inverters are used for off-grid or designed to create utility frequency AC. The PV system can be connected in series or parallel to get the desired output power. To justify the working of this model, the grid-integrated PV system has been designed in MATLAB/PLECS. The simulation shows the P-V curve of implemented PV Array consisting 4 X 20 modules, reactive, real power, grid voltage and current.
Analysis of the SIRP epidemic model with anti-worm patching

ABSTRACT In this paper we analyse the effect of an anti-worm patching into a network inspired from human epidemiology. To achieve this we propose a new extended model of Susceptible–Infected–Recovered model. In which we introduced a new compartment, named as ‘Patched’. This is a compartment where nodes/computers are not prone to worm attacks owing to an anti-worm patch being installed in them. We call our model Susceptible–Infected–Recovered–Patched model. We perform an exhaustive equilibrium analysis both analytically and numerically for the ‘Worm’ and ‘Worm-free’ scenarios. The model assumes that the anti-worm patch spreads like the worm does in the network. The equilibrium analysis illustrates how the population dynamics responds to different parameters involved in the model. We have also shown the effect of user vigilance in the model and observed that a more vigilant population of computer users is helpful to remove worm quickly from a network.
Real-Time Data Verification

Recent improvements in the real-time acquisition of hydro-meteorological data have raised concerns regarding the quality of the data received. Water users having practically instant access to conditions in the field, are making water control decisions whose impact could be devastating if invalid data are used. This paper presents several techniques used in the verification process and reviews their performance. The discussion is limited to real-time data verification; thus, the techniques used are without the aid of human intervention via graphic analysis or complete time series statistical analysis. The real-time analyses described are already operational at over five locations throughout the United States.
Android Malicious Apps Detection and Notification to Prevent Malware Using New Framework

The attractiveness and openness of android makes markets targets for malware attacks and causes number of malware instances original hidden behind the large number of applications that seriously harmful to user privacy and security. Due to the popularity of android operating system and use of internet, android application developers are attracted towards cyber crime. For example, any person sends message to another person using internet to install particular application and that could be malicious. Malware is employed intentionally to cause harm to system by gaining confidential information from the device and modifying file contents. To prevent user privacy and provide security to user data by notifying them about malicious applications SVM with a linear classifier is used to differentiate between benign and malicious applications. For feature extraction and selection Fest tool will be used.
A New Malware Classification Approach based on Statistical Feature

Malware variants which use obfuscation and metamorphism techniques are seen as a critical security threat. Machine learning based malware classification techniques are able to discriminate different malware families and improve existing anti-malware tools. However, high dimensional feature space brings a higher time overhead and one-sided feature can decreases the accuracy. To solve this issue, we propose a statistical feature based malware classification approach and a new feature selection method which can select strong discriminative features. The results demonstrate that the proposed approach can classify modern malware variants effectively.
Evaluation of Biometric Systems: An SVM-Based Quality index

Biometric quality assessment is an active research ﬁeld from recent years. Quality information could be used during the enrolment step and it can also be incorporated in multimodal or soft biometrics approaches. We present in this paper an SVM-based method to compute the quality of a biometric sample using two information. The ﬁrst one is based on the image quality and the second is pattern-based metrics using the SIFT keypoints extracted from the image. Experimental results on three large and signiﬁcant face databases show the efﬁciency of the proposed quality method in predicting matching performance illustrated by the Equal Error Rate (EER).
Optimization of Radial Line Slot Array Antenna Feeder Design for Direct Broadcast Satellite Services

Ka-Band transponders today in communication satellites requires high bandwidth to meet the demands for high data rates, as a result; modification of the feeder architecture for the Radial Line Slots Array Antennas (RLSA) to meet this dire need at 28GHz becomes pertinent. This paper presents Artificial Bee Colony (ABC) as a tool for optimization of beamsquint radial equations antenna feeder parameters such as exact precision on air gaps and dimensions suitable for the customization of the conventional SMA connector which guarantees enhanced bandwidth realization for the Ka Band (26 – 30) GHz required for Direct Broadcast Satellite Services (DBS) applications. Numerical solutions realized from the ABC algorithm using MATLAB computations were included in a visual BASIC program deposited into the macros of CST 2017 for pattern generation and antenna radiation characteristics computations. A radiation efficiency value of 97% was achieved, Bandwidth of 60.50% and Gain of 22dBi were achieved. Results were compared with earlier studies on Ka Band (26 – 30) GHz and shows remarkable improvement in bandwidth utilization using this optimal feeder optimization technique implored.
Japanese Optical and Geometrical Art

We may not be able to make you love reading, but japanese optical and geometrical art will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.
Telemedicine Medical Image Compression based on ROI (A Case Study of Spine Medical Images)

In recent years, telemedical need arose, as a result of the increasing numbers of patients significantly, where using image compression is one of the most important techniques in the field processing research field, as well as in multiple applications such as medicine, reconnaissance, aerial surveillance, and space. Image compression necessary to reduce transport costs due to rising transport prices and distances In this method, we divide the original image into two separate sub-images called ROI and non-ROI. Region of interest (ROI) is the decisive area in a medical image, which is very important. This area may indicate a disease and must result in a right diagnosis. We use the spread spectrum-embedding algorithm to embed a binary mark into DCT transform of non-ROI part of an image. In this search review the compression of the medical images and encoded and then decoder with no distort medical images was taken for sections of the spine for sick people. Used in the field of telemedicine, Medical images encoded works with LSK through use WT (wavelet transform), During the user interaction (encryption) obtained of the ROI regions that lead us to get high compression ratio.The method used in this technology decompress on the block and like-minded countries on the basis of the value stored, note that the time of implementation of this method is low when compared with other methods. Keywords : Telemedicine, Medical Image Compressing, ROI, fractal image compression, LSK.
Felzenszwalb-Baum-Welch: Event Detection through Changing Appearance

We propose a method which can detect events in videos by modeling the change in appearance of the event participants over time. This method makes it possible to detect events which are characterized not by motion, but by the changing state of the people or objects involved. This is accomplished by using object detectors as output models for the states of a hidden Markov model (HMM). The method allows an HMM to model the sequence of poses of the event participants over time, and is effective for poses of humans and inanimate objects. The ability to use existing object-detection methods as part of an event model makes it possible to leverage ongoing work in the object-detection community. A novel training method uses an EM loop to simultaneously learn the temporal structure and object models automatically, without the need to specify either the individual poses to be modeled or the frames in which they occur. The E-step estimates the latent assignment of video frames to HMM states, while the M-step estimates both the HMM transition probabilities and state output models, including the object detectors, which are trained on the weighted subset of frames assigned to their state. A new dataset was gathered because little work has been done on events characterized by changing object pose, and suitable datasets are not available. Our method produced results superior to that of comparison systems on this dataset.
Overcoming Challenges of Social Policy Reform in Indonesia

Social security program is a common social policy to overcome the poverty in many countries. The policy making and implementation of this policy are different in every state. In many European countries, government has a strong support to fund the social protection program while in United States of America has a less government financial support. This paper will describe the social policy implementation in United States of America, South Korea, and Indonesia. Those experiences might be a lesson learned to identify some challenges and recommendation in implementing social policy in Indonesia.
Uma Proposta para Editoração, Indexação e Busca de Documentos Científicos em um Processo de Avaliação Aberta

Resumo. Este artigo descreve uma proposta para editoração, indexação e busca de documentos científicos digitais em um processo de avaliação aberta. O foco principal deste artigo é a editoração de documentos digitais com revisão interativa pelos pares e de discussão pública com o tratamento das diversas versões dos documentos publicados ao longo da sua evolução. Além disso, a proposta oferece um serviço de editoração e avaliação da qualidade de artigos em Computação de forma pública, permitindo a livre avaliação pelos pares.
Generating Robotic Manipulation Trajectories with Neural Networks

In order to interact with environments and appliances made for humans, robots should be able to manipulate a large variety of objects and appliances in human environments. When having experience with manipulating a certain object or appliance, a robot should be able to generalize this behaviour to novel, but similar objects and appliances. When a human has to do a simple task like pushing a previously unseen button or rotating a knob, it has an idea of how to do this. Based on prior experience with similar object parts, a concept for this kind of manipulation tasks is formed. In this work we use a similar idea to learn robots to infer how an object or appliance should be manipulated. We make use of a neural network approach to generate manipulation trajectories for a robot. An instruction in natural language and a pointcloud of the 'manipulatable' object part are encoded into a compact feature representation. We use a recurrent neural network to generate a manipulation trajectory, conditioned on this learned feature representation. We report on experimental results with our model, first letting our recurrent neural network hallucinate manipulation trajectories. This shows that it has learned reasonable patterns. Then we compare the generated trajectories, conditioned on the learned feature representation, with the current state of the art. We show that for some simple tasks our model generates better trajectories, but in general does not have enough training data to generate reasonable trajectories for more challenging and complex tasks.
Decision Support for Re-Designed Medicinal Products - Assessing Consequences of a Customizable Product Design on the Value Chain from a Sustainability Perspective

Despite advances in pharmacological research providing means for individually customized patient attribute treatments, the 'one-size-fits-all' paradigm remains. Customization is associated with cost increases and the value assessment of customized medicinal products shows upon a narrow economic focus. Inspired by value models, emerging in manufacturing industry research, this study suggests a novel methodology encompassing a full sustainability perspective, including the social, economic and ecological dimension, for design decision support for medicinal products. A concept screening matrix is adapted, using sustainability criteria as value indicators. The focus is to create value for the whole pharmaceutical value chain whilst keeping the core purpose of medicinal products, i.e. to bring societal benefits. An illustrative case study presents an application of the methodology on a commercial product for curing hypertension. The traditional product design for hypertension treatment is compared to a customized product design. Results indicate that a customized product design is preferable if value is to be created from a social or/and an ecological sustainability perspective.
Understanding the evolution of science: analyzing evolving term co-occurrence graphs with spectral techniques

Given the high number of scientific papers that are published every year, it is a challenge to observe the evolution of scientific fields. While, for example, computer science and biology were considered rather unrelated 40 years ago, today, bioinformatics is a well-established field. One way to analyze these questions is to observe the evolution of the term co-occurrence graphs of the abstracts of scientific publications. In a term co-occurrence graph, two terms are connected if they appear together in an abstract of a publication. We weight the edges of this graph with the number of common occurrences. We analyze the evolution of this co-occurrence graph, that we constructed for each year, on the basis of a large collection of scientific articles, with the help of spectral techniques. We present our preliminary observations and discuss our ongoing work.
A laser-based multi-robot collision avoidance approach in unknown environments

As a fundamental problem, collision avoidance for multiple robots still need further investigation, especially for many robots aggregating in a tight space. In this article, a laser-based collision avoidance approach is proposed in complex multi-robot environments. The laser data is analyzed and processed in terms of distance thresholding, safe passageway analysis, obstacles repulsion, goal position attraction, and inter-robot safety. Specifically, the safe passageway analysis is used to remove unsafe directions effectively, and inter-robot safety processing reduces the possibility of being selected for the directions affected by the neighboring robots. On this basis, an optimized decision is made to guarantee that the robots achieve collision-free motions even in crowded environments. The validity of the proposed approach is verified by simulations.
Advanced sliding mode control for solar PV array with fast voltage tracking for MPP algorithm

ABSTRACT This paper presents a photovoltaic (PV) system with a maximum power point tracking (MPPT) method. The main aim of this work is to collect the maximum power generated from a solar photovoltaic generator (PVG). This goal is accomplished by integrating a sliding mode controller (SMC) that drives a voltage source inverter (VSI) connected between the PVG and the load. The system is modelled and tested in MATLAB/SIMULINK. In simulation, the SMC gives a fast and accurate convergence to the MPP that is tracked by incremental conductance (INC) type MPPT algorithm. The consequence of the system is capable of tracking MPPs accurately and rapidly without steady-state oscillation and also its performance is satisfactory. The INC type MPPT algorithm is used to track MPPs because it exhibits a precise control under rapidly changing atmospheric conditions. As the solar energy gives the variable light energy at different times and is not constant, the SMC performance is calculated at variable irradiation points by using one slider gain for the variation of the insolation levels.