Miracle-dz/UniFAR-FTUs huggingface dataset:
The Smart Hanger System based on the RFID

In this paper, we proposed the smart hanger system based on the RFID. Proposed smart hanger system consists of smart hanger, server, and wireless communication module. The smart hanger consists of LCD, LED, ring sensor, RFID tag, and RFID reader. In order to verify the usefulness of the proposed smart hanger system, we experiment the clothing store and laundry monitoring system. The shopping center server connects with the SNS server through the internet. When the clothes are sold, the information is transmitted to the hanger. Shopping center server read the "Purchase" and “Good" information and transmit it to the smart hanger through the wireless communication module. Then the LCD shows this information. This smart hanger system can be used for shopping mall to increase sales. Next laundry monitoring system, when customers come to the store to wash their clothes, washable tag is attached on the clothes. All the customer information is saved in database on server. It is very convenient for the customer to check their cloth status through web application. The laundry store can search customer’s clothes by interface on computer, and collect it easily by indicated LCD on smart hanger.
Predicting Functional Recovery of Stroke Patients using Age Dependent Model

Predicting functional recovery of stroke patients is impor tant from both clinical and academic points of view. From the clinical point of view it is important to patients, f amilies and clinical workers. Most importantly, an accurate prediction enables us to provide more accurate pro gnoses, set goals, manage therapies and improve management of healthcare resources through optimal discha rge procedures. For example, being able to predict recovery of particular limbs we could potentially improve a dv nced planning of safe transfer in an optimally determined time frame. Functional recovery is usually eval uated using various functional indices that evaluate patients’ ability to perform daily living tasks. In this pap er we propose to predict functional recovery using two well established functional indices: functional indep endence measure and Barthels index. We model those indices as a age dependent polynomial functions with u nknown coefficients and estimate the unknown parameters. In order to demonstrate applicability of the pr opose technique we compare the performance of our non-linear polynomial model with the performance on lin ear MANOVA model.
How Low Can You Go? Privacy-preserving People Detection with an Omni-directional Camera

In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available. If these devices can be integrated in an embedded low-power sensor, it would form an ideal extension of automated room reservation systems in office environments. The main challenge we target here is ensuring the privacy of the people filmed. The approach we propose is going to extremely low image resolutions, such that it is impossible to recognise people or read potentially confidential documents. Therefore, we retrained a single-shot low-resolution person detection network with automatically generated ground truth. In this paper, we prove the functionality of this approach and explore how low we can go in resolution, to determine the optimal trade-off between recognition accuracy and privacy preservation. Because of the low resolution, the result is a lightweight network that can potentially be deployed on embedded hardware. Such embedded implementation enables the development of a decentralised smart camera which only outputs the required meta-data (i.e. the number of persons in the meeting room).
Social Touch in Human-agent Interactions in an Immersive Virtual Environment

Works on artificial social agents, and especially embodied conversational agents, have endowed them with social-emotional capabilities. They are being given the abilities to take into account more and more modalities to express their thoughts, such as speech, gestures, facial expressions, etc. However, the sense of touch, although particularly interesting for social and emotional communication, is still a modality widely missing from interactions between humans and agents. We believe that integrating touch into those modalities of interaction between humans and agents would help enhancing their channels of empathic communication. In order to verify this idea, we present in this paper a system allowing tactile communication through haptic feedback on the hand and the arm of a human user. We then present a preliminary evaluation of the credibility of social touch in human-agent interaction in an immersive environment. The first results are promising and bring new leads to improve the way humans can interact through touch with virtual social agents.
Credit card Fraud Detection based on Machine Learning Algorithms

Now a days online transactions have become an important and necessary part of our lives. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. In order to reduce fraudulent transactions, machine learning algorithms like Naïve Bayes, Logistic regression, J48 and AdaBoost etc. are discussed in this paper. The same set of algorithms are implemented and tested using an online dataset. Through comparative analysis it can be concluded that Logistic regression and AdaBoost algorithms perform better in fraud detection.
Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings

Our study employs sentiment analysis to evaluate the compatibility of this http URL reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative. On e-commerce websites such as this http URL, consumers can submit their reviews along with a specific polarity rating. In some instances, there is a mismatch between the review and the rating. To identify the reviews with mismatched ratings we performed sentiment analysis using deep learning on this http URL product review data. Product reviews were converted to vectors using paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit. Our model incorporated both semantic relationship of review text and product information. We also developed a web service application that predicts the rating score for a submitted review using the trained model and if there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the reviewer.
Research of Structural Risks of Object-Oriented Software Based on Ripple Degree of Software Network

Object-oriented software systems emerge from mere keystrokes to form intricate functional networks connecting many collaborating objects. In this paper, building on complex networks, software collaboration networks contained within several open-source software systems have been examined. Because of the association between objects, the ripple effect exists in those software networks. The distributions of forward and reversal ripple degree in software networks are analyzed, and focusing on high ripple degree nodes, a metric formula that evaluates the significance is presented. According to our metric, the fragile nodes, rigid nodes and a “hidden danger” node in software system can be selected, which can be used to provide guidance for design and remodeling of software systems.
CAP Theorem: Revision of Its Related Consistency Models

The CAP theorem states that only two of these properties can be simultaneously guaranteed in a distributed service: (i) consistency, (ii) availability, and (iii) network partition tolerance. This theorem was stated and proved assuming that “consistency” refers to atomic consistency. However, multiple consistency models exist and atomic consistency is located at the strongest edge of that spectrum. Many distributed services deployed in cloud platforms should be highly available and scalable. Network partitions may arise in those deployments and should be tolerated. One way of dealing with CAP constraints consists in relaxing consistency. Therefore, it is interesting to explore the set of consistency models not supported in an available and partition-tolerant service (CAP-constrained models). Other weaker consistency models could be maintained when scalable services are deployed in partitionable systems (CAP-free models). Three contributions arise: (1) multiple other CAP-constrained models are identiﬁed, (2) a borderline between CAP-constrained and CAP-free models is set, and (3) a hierarchy of consistency models depending on their strength and convergence is built.
4MOST: Project overview and information for the First Call for Proposals

We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Visible and Infrared Survey Telescope for Astronomy (VISTA) at Paranal. Its key specifications are: a large field of view (FoV) of 4.2 square degrees and a high multiplex capability, with 1624 fibres feeding two low-resolution spectrographs ($R = \lambda/\Delta\lambda \sim 6500$), and 812 fibres transferring light to the high-resolution spectrograph ($R \sim 20\,000$). After a description of the instrument and its expected performance, a short overview is given of its operational scheme and planned 4MOST Consortium science; these aspects are covered in more detail in other articles in this edition of The Messenger. Finally, the processes, schedules, and policies concerning the selection of ESO Community Surveys are presented, commencing with a singular opportunity to submit Letters of Intent for Public Surveys during the first five years of 4MOST operations.
An improved similarity calculation method for collaborative filtering- based recommendation, considering neighbor’s liking and disliking of categorical attributes of items

Abstract Similarity measures play an important role in the accuracy of collaborative filtering based recommendation. Due to non-availability of adequate co-rated users, the accuracy of collaborative filtering decreases because it introduces the sparsity problem. In certain cases, if there is no similar user, recommendation is not possible. In order to abate the issue, we propose a novel approach that calculates the similarity between users not only based on the items rather the attributes of the items. To calculate the similarity more accurately, users’ liking and disliking of the similar attributes of a particular item are considered separately. This approach is particularly useful when there are no co-rated users in the similarity dataset. The performance of the proposed algorithm is tested on the MovieLens dataset using two accuracy metrics, MAE and RMSE.
Prediction of Learning Improvement in Mathematics through a Video Game using Neurocomputational Models

Learning math is important for the academic life of students: the development of mathematical skills is influenced by different characteristics of students such as geographical position, economic level, parents’ education, achievement level, teacher objectives, social level, use of information and communication technologies by teachers, learner motivation, gender, age, preferences for playing video games, and the school year of the students. In this work, these previously mentioned characteristics were considered as the attributes (inputs) of a multilayer neural network that uses a backpropagation algorithm to predict the percentage of improvement in mathematics through a 2D mathematical video game that was developed to allow the children to practice addition and subtraction operations. After applying the neural model, using the twelve attributes mentioned before and the backpropagation algorithm, there was a network of one layer with ten neurons and another network of two layers with 5 neurons in the first layer and 20 neurons in the second layer. Both architectures produced a mean squared error smaller than 0.0069 in the prediction of the student’s percentage of improvement in mathematics, being the best configurations found in this study for the neural model. These results lead to the conclusion that we are able to predict the percentage of improvement in math that the students could achieve after playing the game, and therefore, claiming if the video game is recommendable or not for certain students.
Researching on Multiple Machine Learning for Anomaly Detection

Firstly, we introduce intrusion detection system and anomaly detection. And then we do some research on machine learning techniques for anomaly detection by network dataset NSL-KDD. The machine learning algorithms such as J48, Random forest, SVM, Vote, Stacking are selected. Random Forest, Vote and stacking are ensemble learning methods. We try to test and verify performance of multiple machine learning methods on a 20 per cent NSL-KDD dataset by experiment. The experiment data has two parts. First, the 20 per cent NSL-KDD dataset is classified into normal and anomaly. Second, the feature of attack type is added to the 20 per cent NSL-KDD dataset, and then a new dataset is generated. It is classified into normal and other four classes of attack. The experiment is accomplished by WEKA. The result is compared on the basis of typical indexes and confusion matrix. At last,we can draw a conclusion that an appropriate ensemble classifier can achieve better classification performance than a single classifier for anomaly detection.
Game Theoretic Equilibrium Analysis of Energy Auction in Microgrid

The future energy grid is expected to be a decentralized network where household units acting as agents can trade energy with others within local neighborhoods by means of an action mechanism. When agents can establish their own price of energy, it is essential to analyze the auction from a market equilibrium standpoint. This paper provides a proof that such a mechanism, although previously formulated as a gradient ascent algorithm to maximize the welfare (i.e. the sum of the utilities of all the agents), converges to the generalized Nash equilibrium (GNE) under physical grid operating constraints, where no agent is incentivized to deviate from its bid. The theoretical analysis is accompanied by simulations of a modified IEEE 37 node system showing convergence towards the equilibrium.
Analisis Transfer Rate Penambahan Node Pada Infrastruktur Mobile Adhoc Network (MANET) untuk File Server

Teknologi Wireless Network sudah lama ditemukan dan seiring waktu juga mengalami perkembangan, Namun sifat teknologi ini menggantungkan diri pada infrastruktur jaringan yang ada. Hal ini bisa menjadi kelemahan tersendiri saat kondisi infrastruktur jaringan sedang mengalami gangguan, karena setiap komunikasi yang melewati infrastruktur jaringan tersebut tidak akan sampai pada tujuan. Teknologi jaringan Mobile Ad-hoc Network (MANET) diciptakan sebagai antisipasi jika infrastruktur jaringan sedang mengalami gangguan. Dengan jaringan MANET sistem komunikasi yang dilakukan tidak membutuhkan infrastruktur jaringan karena tiap node pada jaringan tersebut bersifat mobile . Untuk menguji kemampuan MANET , pada penelitian ini akan menerapkan File Transfer Protocol (FTP) sebagai media untuk melakukan komunikasi data file transfer yang diimplementasi pada jaringan MANET . Dari pengujian yang telah dilakukan diperoleh hasil bahwa File Transfer dapat berfungsi dengan baik saat diterapkan pada jaringan MANET .
Impoved Hardware Design of IoT Prosthetic Device

Our previous IoT based prosthetic arm prototype used servo motors to control finger movement through an Arduino Mega, which is connected to the muscle, pulse, and temperature sensors. The Arduino Mega was also connected to a Raspberry Pi 3 model B to transfer data from/to an online web application. One major limitation encountered during testing this prosthetic device was the space occupied by these components, which makes the device bulky. In addition, these servo motors cannot control the movement of the prosthetic device precisely. In this paper, we propose to improve on the existing prosthetic limb prototype by transitioning the electromechanical system to linear actuators and replacing the larger Arduino Mega and Raspberry Pi 3 with the smaller Arduino Nano and Raspberry Pi Zero W, respectively. These changes will result in a more costeffective, more stable, and more accurate prototype, resulting in better performance.
PARADIGMENWECHSEL IN DER PHOTOGRAMMETRIE DURCH DIGITALE LUFTBILDAUFNAHME

We describe the UltraCam-D large format digital aerial camera system as a contribution to end the role of aerial film. We discuss the persuasive advantages of digital sensors, which result from replacing film by pixel arrays. But we also address the most interesting aspect of the new technology, representing a „paradigm shift“ away from the 100 years of minimizing the number of aerial images for a given accuracy goal. This will be replaced by the use of redundant images for the optimization of the robustness of procedures, the succes of automation and the increase of geometric accuracy. This invitation to acquire redundant data results from the lack of any costs for film, photo processing and scanning. However, to take full advantage of this redundancy the photogrammetric work flow must become totally digital and software must get enhanced to deal with this type of redundant data. Because of the economic disadvantage and the inability to provide this type of redundancy, the use of film in aerial photogrammetry may no longer be of interest. 1. EINLEITUNG
Secure Access of Encrypted Cloud Data Based on Top-K Multikeywords with User Side Ranking

Cloud computing has become popular due to the huge storage and flexible access from around the world. The data owners are motivated to outsource their data from local servers to the commercial public cloud for great flexibility and cost savings. But the data owner cannot trust the cloud server provider due to the confidential data. So to protect data privacy, sensitive data have to be encrypted before outsourcing to the public cloud, due to which traditional data based on plaintext keyword search is not possible. So there is a requirement for cloud data search on encrypted data. As the number of users are large and huge number of documents in the cloud, it is important to allow multiple keywords in the search request and return the resultant documents in the order of their relevance to these keywords. Existing works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multikeyword ranked search over encrypted data in cloud computing (MRSE). We proposed a set of strict privacy requirements for such a secure cloud data accessing system. There are many multikeyword semantics, among those we choose the efficient similarity measure of “coordinate matching”. We divide the application into three parts. First the data owner uses an encryption tool to encrypt the file to be uploaded and to generate the index file. Second, the encrypted files will be stored in the cloud, Third, The user uses a decrypted tools to decrypt the downloaded file. So this architecture assures us for better security as the used encryption schemes will not exposed to the network and the cloud. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication.
Solar Powered LED Lighting with High GainBoost Converter

High gain converters are extensively used in the solar powered Light-Emitting Diode (LED) lighting systems due to its several advantages, such as high voltage conversion, reduced voltage stress of power semiconductor devices as compared to conventional DC-DC converters. Solar energy is one of the sustainable energy resources and is able to collect the energy by using PhotoVoltaic (PV). In order to achieve high efficiency the PV should operate at maximum power point (MPP). This paper deals with the design of high gain DC-DC converter for solar powered LED lighting applications. The proposed configuration developed by voltage lift technique and has advantages of high voltage conversion ratio, economical, simple control and suitable for low and medium power applications. Different modes of the proposed topology are explained in detail with corresponding voltage and current equations.Asimple Perturb and Observe (P&O) MPPT controller is applied for the proposed topology to control the output voltage from its non-linear characteristics of PV module. Simulation of high gain DC-DC converter and conventional DC-DC converter with 120 watts of LED load has been developed using MATLAB/Simulink software. Load has modeled by 120 LEDs in seriesparallel combination by supplying 3.5 V & 20 mA for each LED. The results Journal of Green Engineering, Vol. 8 3, 411–430. River Publishers doi: 10.13052/jge1904-4720.8310 This is an Open Access publication. c © 2018 the Author(s). All rights reserved. 412 P. Padmavathi and N. Sudhakar are evident that the proposed converter offers low voltage stress with high gain as compared with conventional DC-DC converters and hence increase the life span of LED lighting system.
A Secure Framework with Remote Configuration of Intellectual Property

In this work, an intellectual property (IP) licensing framework is proposed that is secure against IP theft (cloning and redistribution). This security is provided by utilizing built-in features of modern field programmable gate arrays (FPGAs), e.g. secure boot, state-of-the-art cryptography and trusted execution environments (TEE). The scheme is also the least restrictive in comparison to other publications in this area. Using this scheme, multiple IP core vendors (CVs) can configure their IPs remotely by connecting directly to an FPGA. Devices are booted securely using an authenticated and encrypted boot loader that initiates an authenticated and encrypted hypervisor, which in turn provides a TEE by partitioning the system resources into secure and non-secure sections. At this stage, a secure operating system (OS) is loaded that handles all the security critical functions such as communication with CVs, storage and analysis of bitstreams, enforcement of license constraints and configuration of IPs. Then, a second, non-secure OS is loaded, which provides an isolated execution environment with unrestricted access to non-secure resources. Hence, they are not limited to predefined APIs. Both OSes can interact via the hypervisor. The implementation of this framework is a work-in-progress and results presented within this paper are subject to change.
Multiple Evaluation Models for Education Based on Artificial Neural Networks

Teachers and students' performances are of great importance in education. However, how to evaluate teachers' works and students' academic levels are extremely difficult and complex because it contains various effects of weights that should be used to assess the achievements. Also, education workers often find it difficult to manipulate the large-scale data while evaluating the education works and students' performances. To address this problem, we used machine learning techniques to develop two groups of models for evaluating teachers and students' performances respectively. Using artificial neural networks (ANNs) can ensure the accuracy and fairness of the evaluation works. Our results successfully proved that general regression neural network (GRNN) model can effectively generate the robust responses to analyze different independent variables and give out correct results to distinguish different achievements done by teachers and students.