Miracle-dz/UniFAR-FTUs huggingface dataset:
Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building

In the context of building heating systems control in office buildings, the current state-of-the-art applies either a deterministic Model Predictive Control (MPC) controller together with a nonlinear model, or a linearized model with a stochastic MPC controller. Deterministic MPC considers only one realization of the external disturbances, which can lead to a low performance solution if the forecasts of the disturbances are not accurate. Similarly, linear models are simplified representations of the building dynamics and might fail to capture some relevant behavior. In this paper, we improve upon the current literature by combining these two approaches, i.e. we adopt a nonlinear model together with a stochastic MPC controller. We consider a scenario-based MPC (SBMPC), where many realizations of the disturbances are considered, so as to include more possible future trajectories for the external disturbances. The adopted scenario generation method provides statistically significant scenarios, whereas so far in the current literature only approximate methods have been applied. Moreover, we use Modelica to obtain the model description, which allows to have a more accurate and nonlinear model. Lastly, we perform simulations comparing standard MPC vs SBMPC vs an optimal control approach with measurements of the external disturbances, and we show how our proposed scenario-based MPC controller can achieve a better performance compared to standard deterministic MPC.
An Approach for Behavioral Drift Detection in a Smart Home

With the global population growing older and more vulnerable, healthcare systems are considering new approaches to maintain people autonomous in their own homes. Recent advances in pervasive computing technologies have opened up new opportunities to unobtrusively monitor human behavior at home. Sensors data related to the activities of daily living (ADLs) performed by the inhabitant can be collected and labeled manually or by using activity recognition algorithms. The purpose of this work is to propose an approach for detecting changes (drift) in the inhabitant behavior in order to detect potential changes in the health-state of the inhabitant. Based on unsupervised clustering, our approach use activity starting time and duration as key features to detect changes between time periods. Variations from one behavior to another one can be identified for subsequent review or intervention by a healthcare professional. The relevance and the nature of the change are asserted using the clustering validation metric called Silhouette. Case study experiments on real life and simulated datasets suggest that some user’s behavior can go through smooth or abrupt changes and that these changes can be highlighted using our approach.
Netgen: A Fast and Scalable Tool for the Generation and Labeling of Networking Datasets

This article presents Netgen, an open-source tool for the generation of large networking datasets through the open-source tool Net2Plan. We show Netgen’s features and applicability by generating six large labeled datasets for data analysis and Machine Learning. The datasets comprise 100,000 different data points for different traffic matrices and topologies, labeled after solving IP routing, IP routing with link disjoint 1+1 protection and RWA for IP over WDM networks. Both datasets and Netgen’s code are publicly available.
0.18μm CMOS Ring Oscillator at Liquid Helium Temperature

This paper presents low power dissipation, low phase noise ring oscillators (ROs) based on Semiconductor Manufacturing International Corporation (SMIC) 0. 18μm CMOS technology at liquid helium temperature (LHT). First, the characterization and modelling of CMOS at LHT are presented. The temperature-dependent device parameters are revised and the model then shows good agreement with the measurement results. The ring oscillator is then designed with energy efficiency optimization by application of forward body biasing (FBB). FBB is proposed to compensate for the threshold voltage (VTH) shift to preserve the benefits of the enhancement of the carrier mobility at 4.2K The delay per stage (τp), the static current (ISTAT), the dynamic current (IDYN), the power dissipation (P) and the phase noise (L(foff)) are analyzed at both 298 K and 4.2 K, with and without FBB. The performance of the designed RO in terms of speed (τp=179ps), static current (23.55nA/stage), power dissipation (2.13μW) and phase noise (−177.57dBc/Hz@1MHz) can be achieved at 4.2K with the supply voltage (VDD) reduced to 0.9 V.
Two-Dimensional DOA Estimation with Partial Damaged Sensors in Rectangular Array Based on Tensor Reconstruction

In the field of direction of arrival (DOA) estimation, there exists no problem of angle merging and it has low sidelobe characteristics with the uniform rectangular array. But when sensors in the uniform rectangular array are partially damaged, the two-dimensional DOA estimation performance of the original algorithms will seriously decline, and even have a performance failure in some cases. To solve the problem, we study the inexact augmented Lagrange multiplier method (IALM). Through utilizing features of low-rank matrix, obtained by the tensor unfolding in different modes, in the tensor model of the received signal, we propose the IALM-ESPRIT algorithm. Compared with the direct ESPRIT algorithm, the performance of two-dimensional DOA estimation is significantly improved when partial sensors damaged.
Friend Recommendation Algorithm Based on Interest Classification with Time Decay

Friend recommendation algorithm plays an important role in social networks. However, traditional recommendation algorithms do not take into account the drifting of user interests, and there are also some deficiencies when the recommendation's timeliness is considered. Aimed at this problem, the measure method of similarity was improved by combining with the characteristics of user interest's change with time. A time decay model was introduced to measure the predictive value. At the same time, the method refines the preference of the target users according to the homogeneity theory through the interest preference of friends. Through experiments on Sina microblog data set, and the results show that the algorithm to achieve better recommendation effect on precision, recall and F value.
Robust Transceiver Design for SWIPT Multicasting MIMO Relay System with Direct Link

A robust simultaneous wireless information and power transfer (SWIPT) scheme for amplify-and-forward relay systems with a direct source-receiver link is investigated, taking into consideration imperfect channel state information (CSI). According to the minimum-mean-squared-error criterion, we formulate a joint beamforming problem under an energy harvesting constraint at the receiver plus transmitting power constraints at the source and relay. The non-convex problem is solved by semi-definite programming (SDP) and iterative method. Numerical results for mean-squared error and harvested energy performance against transmission power are evaluated. Simulation results show the improved robustness of the proposed transceiver design against the CSI errors and adjustable power splitting factor for better signal quality or more power harvesting.
The Analysis of Indonesian Teenagers’ Motives for Using the Internet

As Internet use becomes widely used by Indonesian teenagers, knowing more detail about their motives for using the Internet is important for digital marketing interest. This study aims to determine the motives of the Internet in teenagers and to examine the differences in Internet motives based on gender, education level, and school affiliation. Data from a probability sample of 756 teenagers aged 10-19 years were represented by students of junior high schools and senior high schools in Jakarta. Descriptive analysis revealed that Indonesian teenagers’ Internet motives are status gaining, opinion expression, social interactions, information gaining, pastime, and entertainment. The three highest motives are pastime, entertainment, and information gaining. The t-test analysis revealed the level of education and school affiliation are significant differentiating factors, while gender is not a significant differentiating factor. Internet motives of senior high school teenagers differed significantly in all types of motives compared to junior high school student teenagers. In addition, in almost all types of motives, teenagers in public schools have a higher mean than teenagers in religion-affiliated schools. Teenagers need a complete social media application that provide tools that can represent their feelings, opinions, status, as well fun and entertaining. It has a lot of creative tools, pleasant and interesting interface, and interactive infographic content for news.
Evaluation of The Knowledge Management System In Bank CN Using Method of Technology Acceptance Model

Evaluation of the Knowledge Management System (KMS) is conducted to understand the State of KM Portal in Bank CN at the moment and give advice that can enhance the benefits and effectiveness of KM Portal for employees and the company. The purpose of this paper is to evaluate the application of KMS through factor Attitude Towards Using whether it influences Behavior intention to use. Analysis of the evaluation using Technology Acceptance Model 2 method, to analyze the factors which influence the awareness of any user in using KM Portal at Bank. Data collection used survey method by using questionnaire to employee selected department witch total sample 130 respondent. This sampling is a Proportionate Stratified Random Sampling, which is a way of taking samples from members of the population. The Subjective Norm, Facilitating Condition, Usefulness, Perceived Ease of Use, Perceived Attitude Toward Using, and Behavioral Intention to Use. In accordance with the processing of data, the State of the current Portal KM is far from satisfactory. These circumstances do not meet the standards of the company so desire to do further development to obtain a perfect score. The results of the analysis showed there are 9 factors influencing and 3 factors not influencing the consciousness of the user to use the KMS Portal.
Music Generation Using Bidirectional Recurrent Network

With the development of deep learning, neural networks are increasingly used in various art fields such as music, literature and pictures, and even comparable to humans. This paper proposes a music generation model based on bidirectional recurrent neural network, which can effectively explore the complex relationship between notes and obtain the conditional probability from time and pitch dimensions. The existing system usually ignored the information in the negative time direction, however which is non-trivial in the music prediction task, so we propose a bidirectional LSTM model to generate the note sequence. Experiments with classical piano datasets have demonstrated that we achieve high performance in music generation tasks compared to the existing unidirectional biaxial LSTM method.
A step towards Home Automation using IOT

The aim of this paper is to develop home automation system based on IOT using Wi-Fi based microcontroller. As scope of technology is widening every day, we are making our tech advance in mobile, robotics, Machine Learning, then why an exception for our home. Today's houses are gradually transferring from ordinary/human's input-based appliances to smart/IOT enabled appliances to be controlled remotely. At Present, existing home automation systems use technology that is limited to only that device. So, in a nutshell, we are making our devices IOT enabled not our homes. As far as this paper is concerned, NodeMCU (ESP8266) microcontroller along with Relays is used to control electrical switches remotely from the server which is built on Node.js. User can control switches using a Web Application after authenticating.
Neural-Based Machine Translation System Outperforming Statistical Phrase-Based Machine Translation for Low-Resource Languages

Natural Language Processing(NLP) involves the development of computational models that aid in the development of automated tools for processing and generating natural language. Human developing these computational models require deep insight of linguistic knowledge and are a time consuming process. Hence, to automate this process and accelerate the computational science we use a data-driven approach i.e Statistical learning and Deep Learning. For devolving and sharing of information in natural language and making it accessible in other natural languages, Machine Translation(MT) is entailed. It is an application of NLP. Sanskrit being ‘father of informatics’ [1] was considered as “lingua franca” of world intellectuals [2]. It is also an important language in the Indo-European family and considered as truly “donor” language of India. It has vast knowledge reserves in different discipline of studies such as Ayurveda, astronomy, literature etc. MT makes this rich language available to others with help of the computer. We have proposed and presented the prominent Deep Neural-based MT system for translation of Sanskrit to Hindi. We also present a comparison of Neural MT outperforming Statistical baseline system for this language pair.
Three Dimensional Image Reconstruction Based on Normalized Convolution Algorithm of SIFT

With the rapid development of the information world, the diversification of information and diversity has become an inevitable trend, we perceive the mostly information from visual system, but three dimensional image is described by the whole vision system. The image data will be influenced by other factors such as noise or light in transmission process or conversion, it will inevitably result in image degradation and could not achieve our expected for 3D image, we make full use of the characteristics of the local stability characteristics for two-dimensional image through the reconstruction process of the 3D image, make normalized convolution of repairing algorithm, then re-establish of three-dimensional image.
Neural Networks for PAPR Reduction in Optical OFDM Signal Transmission

In recent years, with the improvement of computing performance, the application of neural networks in communication field has been widely studied. In this paper, we propose and apply neural networks (NNs) to improve the transmission performance of optical orthogonal frequency division multiplexed (O-OFDM) signals. A pair of NNs is added in transmitter and receiver respectively, to modify and restore the QAM constellation. The main target for NNs is to reduce the peak to average power ratio (PAPR) of the O-OFDM signals and the bit error rate (BER) of the received signals. The proposed scheme is verified by the simulations. And a transmission of 18.8 Gb/s, 16-QAM optical OFDM signals is successfully demonstrated and analyzed for the cases with and without NNs. The experimental results show that, the PAPR and BER can be improved significantly (~4 dB for BER) after using NNs, since PAPR is reduced and more importantly the channel distortion and noises are effectively compensated by NNs via learning procedure.
Trust-based Enhanced Secure Routing against Rank and Sybil Attacks in IoT

The Internet of Things (IoT) is an emerging technology that plays a vital role in interconnecting various objects into a network to provide desired services within its resource constrained characteristics. In IoT, the Routing Protocol for Low power and Lossy network (RPL) is the standardized proactive routing protocol that achieves satisfying resource consumption, but it does not consider the node's routing behavior for forwarding data packets. The malicious intruders exploit these loopholes for launching various forms of routing attacks. Different security mechanisms have been introduced for detecting these attacks singly. However, the launch of multiple attacks such as Rank attack and Sybil attacks simultaneously in the IoT network is one of the devastating and destructive situations. This problem can be solved by establishing secure routing with trustworthy nodes. The trustworthiness of the nodes is determined using trust evaluation methods, where the parameters considered are based on the factors that influence in detecting the attacks. In this work, Providing Routing Security using the Technique of Collective Trust (PROTECT) mechanism is introduced, and it aims to provide a secure RPL routing by simultaneously detecting both Rank and Sybil attacks in the network. The advantage of the proposed scheme is highlighted by comparing its performance with the performance of the Sec-Trust protocol in terms of detection accuracy, energy consumption, and throughput.
Risk-aware routing approach for software-defined networks

In this paper we introduce a novel risk-aware routing approach for software defined networks vulnerable to cyber attacks. We focus on continuous routing supervision with an objective of minimizing operational risk faced by the users due to utilization of imperfectly secure network elements, such as links and switches, for transmission of data with different protection requirements. Initial tests confirm that the proposed routing mechanism, adapting to changes in the level of threat to which network elements are exposed, can help to increase the customers’ satisfaction, expressed by the volume of successfully completed service requests, while ensuring an efficient use of network resources and maintaining the acceptable level of risk associated with data transfer.
The Evaluation of Business Process Simulation Software from User Experience Perspective using the User Experience Questionnaire

Industrial Revolution 4.0 requires the education sector to find new ways to learn. The education sector must be able to create industry-ready graduates who require graduates with good analytical thinking, capable of solving complex problem, creative and adaptable. BeeCEO is one of the business process simulation software developed to meet these needs. Users can practice the business process theory in this business process simulation. However, to ensure that BeeCEO has been used properly by users, it is important to know in advance if BeeCEO has fulfilled all the dimensions of the user experience (UX). User Experience Questionnaire is a technique that can be used to assess whether BeeCEO has met users’ expectations from the user experience dimensions. The results of this study show which BeeCEO UX dimensions need to be improved before they can be fully utilized by users.
3D Points Localization Using Defocused Images

3D points reconstruction has attracted increasing attentions both in computer vision and robotic intelligence areas. However, the real depth measurement still much relies on depth measurement instruments. Although many measurement methods for depth exist, they usually need additional instruments which always increase the cost of the measurement system. To better localize the position of 3D points without use of other instruments, a direct method is proposed which acquires depth from defocus of current images in this paper. The method utilizes the property of camera lens system and mechanism of SFM to remove the ambiguity of structure scale and the relative error between these 3D points. In addition, a multiple images setting for improving the robustness of depth estimation is proposed which can further eliminate depth error from some kinds of nature noises. Experiments on the real scene are implemented, which shows that the proposed method outperforms the ordinary 3D points localization method.
A Magnetic Integration Half-Turn Planar Transformer and its Analysis for LLC Resonant DC-DC Converters

A high frequency planar transformer with integrated leakage inductance and half-turn secondary windings is proposed for LLC isolated DC-DC converters. The primary windings are wound on both the center leg and the sides leg of an EI planar ferrite core to integrate the leakage inductance. With the proposed method, the magnetizing inductance is constant while adjusting the leakage inductance, thus the design of the magnetizing inductance and the leakage inductance can be decoupled. To adapt the high voltage transform ratio from 380V to 12V, the number of turns of the secondary windings is designed to be 0.5. The output voltage ripple of the LLC converter equipped with the proposed transformer is analyzed and its output filter is designed. The characteristics of the proposed transformer are simulated by ANSYS Maxwell and Simplorer. Finally, a 360V~400V input, regulated 12V output, 750W LLC resonant converter with silicon carbide (SiC) devices was built with the proposed transformer. The evaluated converter realized a power density of 23.1W/cm3 and a peak efficiency of 97% with all the auxiliary power and control circuit included.
IoT Generic Frameworks: What Needs to Improve

Internet of things (IoT) is one of the trending technologies which is available in the current technology world. The term IoT could be described as devices which could be connected via the internet. The number of devices in the world is increasing rapidly every minute. The world is moving towards IoT enabled smart cities. To control these devices, a generic framework is needed. This paper contains a review on technologies, techniques, and domain found out by ongoing research which introduces a generic framework to manage IoT devices.
MaxNet: Neural Network Architecture for Continuous Detection of Malicious Activity

This paper addresses the detection of malware activity in a running application on the Android system. The detection is based on dynamic analysis and is formulated as a weakly supervised problem. We design an RNN sequential architecture able to continuously detect malicious activity using the proposed max-loss objective. The experiments were performed on a large industrial dataset consisting of 361,265 samples. The results demonstrate the performance of 96.2% true positive rate at 1.6% false positive rate which is superior to the state-of-the-art results. As part of this work, we release the dataset to the public.