Miracle-dz/UniFAR-FTUs huggingface dataset:
IT Security as a Special Awareness at the Analysis of the Digital/E-Learning Acceptance Strategies of the Early Z Generation

In the digital world IT security is every ones responsibility, ranging from ensuring that your devices are installed with the latest anti-virus and firewall software, through safe personal accounts, to being smart about how to handle yourself online. The digital natives are online from the very early stage of their life and when they are mature enough, their education to be IT security aware becomes crucial. This paper presents the relationship of IT security awareness with social factors, the features of digital learning and the use of smart phones within the framework of Technology Acceptance Model (TAM) using the Structural Equation Modeling (SEM) with the AMOS programme in the Hungarian context. These external factors as input variables are in correlation and this paper also strives to explore the nature of computer and internet literacy as well as IT security awareness of the member of the early Z generation.
Morphological Alterations and Increased Gelatinase Activity in the Superficial Digital Flexor Tendon of Chickens During Growth and Maturation

The superficial digital flexor tendon (SDFT) connects the superficial digital flexor muscle to the digits and its main function is to participate in digit flexion. The SDFT presents different regions along its length, which adapt to different biomechanical forces. During growth and maturation, the tendon may present changes in the regions subjected to compression and tension, with variations in the composition of the extracellular matrix (ECM), in the arrangement of collagen fibers and cellularity. With the purpose of analyzing the morphological and biochemical alterations of ECM of tendons during the growth and maturation, Gallus domesticus were euthanized at 1, 5, and 8 months of age and their SDFT were divided into regions of tension/compression (Sp) and tension (Sd). From 1 month of age, the Sp region already presented fibrocartilage characteristics with cells similar to chondrocytes. At 5 and 8 months, the Sd region displayed formation of a new structure similar to bone matrix, and intense metachromasia. The animals of 5 and 8 months presented an increase in MMP‐2 and ‐9 activities and a lower number of cells when compared with the animals of 1 month, in both regions. In conclusion, structural and biochemical alterations occur during the maturation process of the SDFT, involving a decrease in the number of cells and changes in the degradation and composition of the ECM. Anat Rec, 302:964–972, 2019. © 2018 Wiley Periodicals, Inc.
Dumbbell Shape Antenna for MIMO Applications

In this paper a wide band planar antenna is proposed. Two dumbbell shape antennas are designed using with and without slots. Each element resonates at 5.2GHz and radiates in broadside direction. The return loss of type-l antenna is 17dB with impedance bandwidth of 458MHz and return loss of type-2 antenna is 29dB with impedance bandwidth of 395MHz. This wide bandwidth is achieved by using staggered tuning, resulting from antenna element spacing and low impedance microstrip line for connecting the elements.
Polyhouse Automation Using PLC

Agriculture is the backbone of our country. In early days, polyhouse management mostly uses a traditional mode of management which is built on the experience to manually adjust the light, temperature, humidity and also irrigation and fertilization. This method leads to higher management costs, and also brings a set of problems, such as low production efficiency, waste of resources and environmental pollution. The available management is insufficient to that of requirement. Thus to remove these disadvantages of present agricultural polyhouse management, we proposed Feedback control system of Polyhouse pertaining farming to increase production and quality to achieve export level quality of crops. This paper presents the operation of Programmable Logic Controller (PLC) with PID controller in polyhouse. Result of PID controller are obtained by execution of PLC ladder diagram in WPLSoft. This paper shows describe the technique of Polyhouse Automation that prove helpful and quite user friendly.
Comparison of Pixel N-Grams with Histogram, Haralick's features and Bag-of-Visual-Words for Texture Image Classification

Texture image classification is very useful in many domains. It has been tried using statistical. , spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram. , Haralick's features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore. , Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas. , they were comparable with the BoVwapproach.
Work-in-Progress: WinoNN: Optimising FPGA-based Neural Network Accelerators using Fast Winograd Algorithm

In this paper, we present WinoNN, which utilizes fast Winograd algorithm to optimize FPGA-based neural network accelerators. In particular, Winograd algorithm effectively reduces the resource occupation of FPGA, as well as boosts the system speedup, especially for embedded and mobile devices. The main contributions of this paper include implementation of single-channel convolution module, implementation of multi-channel convolution module, deployment and performance test on FPGA hardware platform. We use LeNet-5 as a case study, which demonstrates the performance of WinoNN is significantly better than the general ARM Cortex A series processors.
Parallel Higher Order Alternating Least Square for Tensor Recommender System

Many modern recommender systems rely on matrix factorization techniques to produce personalized recommendations on the basis of the feedback that users provided on different items in the past. The feedback may take different forms, such as the rating of a movie, or the number of times a user listened to the songs of a given music band. Nonetheless, in some situations, the user can perform several actions on each item, and the feedback is multidimensional (e.g., the user of an e-commerce website can either click on a product, add the product to her cart or buy it). In this case, one can no longer view the recommendation problem as a matrix completion, unless the problem is reduced to a series of multiple independent problems, thus loosing the correlation between the different actions. In this case, the most suitable approach is to use a tensor approach to learn all dimensions of the feedback simultaneously. In this paper, we propose a speciﬁc instance of tensor completion and we show how it can be heavily parallelized over both the dimensions (i.e., items, users, actions) and within each dimension (i.e., each item separately). We validate the proposed method both in terms of prediction accuracy and scalability to large datasets.
Using P4 to Enable Scalable Intents in Software Defined Networks

When designing Software Defined Networks (SDNs), there is a risk that the additional abstractions available can result in reduced scalability and performance. One such abstraction, intents, are a way in which network administrators can express policies rather than having to define specific forwarding rules. This provides a benefit to administrators in allowing automatic network reconfiguration and fault tolerance. In this paper, we highlight the performance overheads associated with the intents framework from a popular SDN controller, ONOS. We propose a novel prototype that leverages source-based routing and programmable data planes using P4 in order to reduce the overheads of intent-based forwarding
Voice Switching Control System (VSCS) for Air Traffic Control System for Small Airport

This paper will explain development of IP-based Voice Switching and Control System (VSCS) testbed. VSCS is a component of ATC that provides voice communication facilities as well as recording on the Controller Working Position (CWP) of an Air Traffic Control (ATC) System. Conventional and dominant VSCS technology is currently based on Circuit Switching (CS) and Time Division Multiplexing (TDM) technology. With the development of Packet Switching (PS) technology with TCP / IP protocol, complex and expensive VSCS systems with CS technology can be replaced with more modern and inexpensive systems based on Voice over IP (VOIP). The advantages of IP-based VSCS systems are also easier to integrate with other CNS (communication, navigation, surveillance) components through TCP / IP packet networks. The expensive and complex CS and TDM-based VSCS technologies also make it inappropriate for implementation in small and medium airports in Indonesia. Therefore, in this study, a prototype of IP-based VSCS testbed will be developed for small and developing Airport using VoIP technology.
Effects on the Self-disclosure of Elderly People by Using a Robot Which Intermediates Remote Communication

The social isolation of elderly people is becoming a big problem. To solve this problem, social robots have been regarded as suitable interfaces to support these elderly people to communicate with others (e.g. family members). In such robot-mediated communication, it can be expected to elicit self-disclosure from elderly people. In this study, we explored the requirements for intermediately robots to encourage elderly people's self-disclosure. The results suggested that an intermediately robot with human-like behavior can encourage elderly people's self-disclosure about experiences of loss (e.g. health and financial issues). Additionally, we found that warmth and competence judgments to the robot are correlated to elderly people's self-disclosure. From these results, it was suggested that the influence on the self-disclosure of the intermediately robot are strengthened by being regarded as a social other.
Enhancing Diagnostic Viewing of Medical Images with Histogram Equalization

This paper addresses the low contrast problem of medical images. The low contrast medical images are unable to process for effective diagnosis. This problem can be overcome with this proposed novel histogram based image enhancement technique. The enhancement process starts with computing histogram values of an image. These histogram values are further used to remap the low contrast image intensity values with enhanced pixel values. The resultant enhanced image is compared with standard (basic) enhancement techniques to check the high visual clarity of the proposed algorithm.
Designing a Koch-Type Wire Antenna by Regression Analysis

A family of symmetrical wire dipoles having the geometry of the Koch-type prefractal is considered. A regression analysis for the electrodynamic characteristics and geometric parameters of the antennas is performed. Regression models of the base frequency for dipoles having different scales are obtained. A subfamily of antennas with a resonance at frequencies of 2.4-2.5 GHz is identified via regression models. Further analysis of the reflection coefficient and the bandwidth of the antennas from the given family made it possible to select the most matching antennas for operation in the selected Wi-Fi band.
Edinburgh Research Explorer Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders

Background: Genetic influences on gene expression in the human fetal brain plausibly impact upon a variety of postnatal brain-related traits, including susceptibility to neuropsychiatric disorders. However, to date, there have been no studies that have mapped genome-wide expression quantitative trait loci (eQTL) specifically in the human prenatal brain. Results: We performed deep RNA sequencing and genome-wide genotyping on a unique collection of 120 human brains from the second trimester of gestation to provide the first eQTL dataset derived exclusively from the human fetal brain. We identify high confidence cis -acting eQTL at the individual transcript as well as whole gene level, including many mapping to a common inversion polymorphism on chromosome 17q21. Fetal brain eQTL are enriched among risk variants for postnatal conditions including attention deficit hyperactivity disorder, schizophrenia, and bipolar disorder. We further identify changes in gene expression within the prenatal brain that potentially mediate risk for neuropsychiatric traits, including increased expression of C4A in association with genetic risk for schizophrenia, increased expression of LRRC57 in association with genetic risk for bipolar disorder, and altered expression of multiple genes within the chromosome 17q21 inversion in association with variants influencing the personality trait of neuroticism. Conclusions: We have mapped eQTL operating in the human fetal brain, providing evidence that these confer risk to certain neuropsychiatric disorders, and identifying gene expression changes that potentially mediate susceptibility to these conditions.
Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.
Enterprise Architecture for the Sensing Enterprise a Research Framework

Internet of Things (IoT) will change many things, open up opportunities and enabling something that was not possible before. IoT provides sensing capability so that enterprise has better global context awareness. This is the Sensing Enterprise, an enterprise that obtains multidimensional information from physical or virtual objects in a connected environment. This sensing capability is expected to increase the capacity and capability of the enterprise in responding to sustainability challenges. This paper proposes a research framework as an alternative guide for researchers to produce various artifacts or theories to realize The Sensing Enterprise for sustainability achievement. The method used in developing the framework is modification/adaptation of transdisciplinary research model and information system research model. This framework has three main area consist of scientific base, enterprise architecture research process and sustainability achievement.
Ontology Modelling Approach for Personality Measurement Based on Social Media Activity

The advancement of technology has affected human behavior. Social media has become a prominent platform for our lives to share opinion, thoughts, and information. Those activities are stored as a digital trace. Meanwhile in linguistic approach, the unique way of people writing is able to reveal the real personality. The open access data on social media give us an opportunity to assess the characteristic of a person based on a digital trace. The human personality has been classified according to Five Factor Model (FFM). We propose the ontology modeling approach to measure human personality using social media data, particularly in Bahasa Indonesia. The present personality measurement commonly use a test or questionnaire survey. This research enriches current methodology to measure human personality by observing writing and linguistic usage on Twitter. The result is beneficial for marketing study to forecast consumer behavior. It also valuable fo human resources study to decide employee's recruitment and promotion.
Investor Overconfidence, Firm Valuation, and Corporate Decisions

Behavioral theory predicts that investor overconfidence leads to overpricing because overconfident investors overestimate the quality of their information and underestimate risk. We test this prediction by using a measure of investor overconfidence derived from the characteristics and holdings of U.S. equity mutual fund managers. We find that firms with more overconfident investors are relatively overvalued based on the market-to-book ratio and a misvaluation measure. The result is stronger among stocks with greater mutual fund ownership, particularly by active mutual funds. Firms with more overconfident investors also exhibit lower subsequent stock returns, issue more equity, and invest more. Overall, our findings suggest that investor overconfidence is significantly related to firm valuation and corporate decisions. The Internet appendix is available at https://doi.org/10.1287/mnsc.2017. 2806. This paper was accepted by Neng Wang, finance.
Tracking and Position of Drogue for Autonomous Aerial Refueling

In order to achieve autonomous aerial refueling, in this paper, we propose a method to solve the position and orientation of drogue based on stereo vision. The method can be mainly decomposed into three stages: initialization stage, tracking stage, and calculation stage. The pre-training model and the binocular constraint are used to initialize the position of drogue. Through the information fusion method, the position of drogue in each pair of images acquired by the binocular camera is located. After positioning the drogue, the center point of the ring region of the drogue is calculated by matching key points in binocular vision. The characteristics of this system is that it can operate simply and be easy to transplant which makes it apply widely. This system doesn’t need to add cooperation logo on the drogue, which is convenient and feasible in actual projects. The method was carried out on a pair of ABB robots, and the experimental results demonstrate the effectiveness of proposed method.
Modeling Recommendation System for Real Time Analysis of Social Media Dynamics

With the increasing popularity of twitter, Sentiment analysis of data from twitter has become a research trend. With the help of Twitter API, large number of tweets can be retrieved in real time related to our interest for the analysis. Millions of tweets are posted daily which contain opinions of users around the world. The aim of this project is to develop a desktop application which present users with tweets they may have an interest in. This model analyzes the most used keyword and most mentioned username from the user timeline and henceforth recommend recent tweets with the same keyword from that user. The proposed model assists user in finding relevant tweets related to a particular keyword or hashtag which are categorized into positive and negative using sentimental analysis. The model also deals with different visualization techniques to illustrate the analysis of the most used keyword and mentioned username with the help of various R packages.
CPAR: Cloud-Assisted Privacy-preserving Image Annotation with Randomized KD-Forest

With the explosive growth in the number of pictures taken by smart phones, organizing and searching pictures has become important tasks. To efficiently fulfill these tasks, the key enabler is annotating images with proper keywords, with which keywords-based searching and organizing become available for images. Currently, smart phones usually synchronize photo albums with cloud storage platforms, and have their images annotated with the help of cloud computing. However, the "offloading-to-cloud" solution may cause privacy breach, since photos from smart photos contain various sensitive information. For privacy protection, our preliminary research made effort to support cloud-based image annotation on encrypted images by utilizing cryptographic primitives. Nevertheless, for each annotation, it requires the cloud to perform linear checking on the large-scale encrypted dataset with high computational cost. This paper addresses the challenge and proposes a cloud-assisted privacy-preserving image annotation with randomized kd-forest, namely CPAR. A novel privacy-preserving randomized kd-forest structure is proposed in CPAR as a secure and efficient index for the dataset, with which CPAR significantly improves the annotation performance. Thorough analysis is carried out to demonstrate the security of CPAR. Experimental evaluation on the well-known IAPR TC-12 dataset validates the efficiency and effectiveness of CPAR.
Toward Standard Near-Field Measurement Solutions for Stochastic Electromagnetic Fields

This paper investigates directions towards standardization of Near-Field measurement solutions for stochastic electromagnetic fields. A proposal for standardization of Near-Field measurement of Stochastic Electromagnetic Fields led by the European COST Action 1407 is being initiated. An overview of Single-Probe, Dual-probe and Multi-Probe Array based measurements for deterministic fields is given and their capabilities and performances including test-time for industrial deployment are discussed. Importance of Field-Field correlation based analysis is underlined both from an experimental and modeling stand points and finally for combination of measurements and fullwave EM modeling.
The Influence of the Mesh Element Size on the Natural Frequencies of Beam

Mechanical engineering software products such as ANSYS, Catia, SolidWorks and others offer both modules for designing different components or technological processes as well as modules for simulating their dynamic behavior. Therefore, a calculation of the vibration modes and frequencies of a structure through an analytical approach is very complicated, but the numerical approach is not so complex. However, in Finite Element Analysis the mesh size of the mesh elements is closely related to the calculation speed and the required precision. The paper aims to track the effect of the size of the finite element on the natural frequencies of a beam.