Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Prediction of Service-Level Events in Datacenter Infrastructure Using Deep Neural Networks

The complexity of cloud datacenter scenarios poses new challenges in infrastructure management processes such as the impracticality of collecting specific service level events from inside the datacenter infrastructure, and the scalability issues that can appear during event monitoring when thousands of virtual machines have to be polled at a granularity of seconds. Therefore, it would be desirable to provide mechanisms for obtaining these types of events without incurring in the previously described problems. To this end, we propose a generic and scalable method based on the application of deep neural network architectures for predicting service level events using only a reduced number of generic datacenter infrastructure statistics that can be monitored in a scalable way. We demonstrate in a controlled scenario of a real datacenter and using only three variables from a physical machine that it is possible to predict events in real-time and with decent accuracy, without needing to deploy any meter in the end-user equipment. Specifically, we demonstrate this over two service-level events: i) the so-called Noisy Neighbors effect, a harmful situation that appears in physical machines due to the interferences created by the interaction of virtual machines running on them; and ii) the jitter values of a multimedia call running in a virtual machine. We set up a testbed in a real datacenter deploying physical and virtual machines, running a large amount of different experiments for 1000 hours and collecting samples at a 10 seconds granularity in a dataset of 260,000 records. Two different scenarios, in which training and testing data sets contain significant statistical differences, are deployed to demonstrate a better generalization ability of deep models in changing scenarios when compared with traditional Machine Learning techniques. A set of different deep architectures are proposed for both use cases and approximately 4,000 deep models were trained and tested. In both use cases, the best deep models show a good performance when predicting service level events, even if the inputs do not exactly follow the statistical patterns of the data used during training.
An Opportunistic Routing in Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power

In energy-harvesting wireless sensor networks (EH-WSNs), node can harvest energy from the environment. However, the energy harvested is usually insufficient, time varying and unbalanced. In order to make full use of the energy harvested from the environment, each node can dynamically adjust its transmission power which may affect the associated link quality. Thus, the link quality between two same nodes is dynamic. For the forwarding node, the transmission cost of it and its one hop candidate relay node may change at any time. In this scenario, for the design of opportunistic routing, the size, members and priorities in the relay set need to be updated dynamically. In this paper, we propose an opportunistic routing in EH-WSNs with dynamic transmission power (ORDTP). ORDTP can help the sending node collect information of neighbor nodes in time, accurately estimate the transmission cost, and dynamically update the size, members and priorities in the relay set. The simulation results show that ORDTP can effectively reduce the delay, improve the transmission success rate, and reduce the packet redundancy rate.
Wireless Home Automation System Using IoT and PaaS

Home automation based on Internet of Things (IoT) is getting huge attention of mass people in recent years. Smart home eases and secures the management of the home appliances. This paper presents a low cost and reliable smart home system that assists the users to manage home appliances without the need of their physical presence. The proposed system includes smart door lock system using Radio Frequency Identification (RFID) card and password. It can store and display information of temperature & humidity of a home, and notifies the users of switching on/off time of light, fan and other home appliances using embedded micro-web server and an IoT platform. The user can also control the light intensity and fan speed based on LDR and temperature sensor values respectively. The system includes gas leakage & fire alarm. It has leakage gas removing & fire extinguishing facility and SMS notification system using IoT platform. The system uses real IP and RESTful API for controlling, monitoring and accessing the home appliances remotely from anywhere in the world using Android based smartphone app or web app. This system is user friendly and energy efficient.
Personality Detection from Text using Convolutional Neural Network

The distinguishing characteristics belong to a person are personality traits, which is predicted from person’s behavioral pattern. As most of the people provide lots of information knowingly or unknowingly into their writings, it is possible to extract personality traits from those texts. Individual personality traits detection from texts, yields enormous possibilities toward various applications named as forensic department, mental health diagnosis, etc. Meanwhile, deep learning algorithm performs fairly well in text based personality detection; however, its performance may vary with activation functions. Hence, this paper proposed an empirical approach to find the best personality detection performance by comparing several activation functions named as sigmoid, tanh, and leaky ReLU. Here, text documents were pre-processed and vectorized for input in convolutional neural network. The input size was multiple to length of word, sentence, documents, and feature vectors. Five personality traits named as EXT, NEU, AGR, CON, and OPN have been used for experimental analysis. The result showed that tanh and leaky ReLU performs over sigmoid in all datasets. The average F1-score of sigmoid, tanh and leaky ReLU showed 33.11%, 47.25%, and 49.07% respectively. However, Fl-score of leaky ReLU was high only for CON, tanh showed better result for others datasets. The overall performance showed by tanh is better than sigmoid and leaky ReLU for personality detection from text.
Optimum Design of Sensorless-Oriented IPMSM Considering Torque Characteristics

This article presents the design process of a sensorless-oriented interior permanent-magnet synchronous motor (IPMSM) with concentrated winding, based on spatial saliency for the traction motor in electric vehicles. The purpose of the design method is to ultimately achieve both stable rotor position detection and acceptable torque characteristics such as cogging torque and torque ripple. First, the relationship between the sensorless controllability and the electromagnetic parameters is derived through a mathematical method for a design considering sensorless drive of the motor. After describing the evaluating procedure of the sensorless controllability, the effects of the stator chamfer and notch are analyzed to figure out their effectiveness on the sensorless drive. Focusing on the analysis results of the geometric parameters, the improved model is designed through optimization, considering both sensorless controllability and torque characteristics. Response surface methodology (RSM) is used for optimization. Then, the machine characteristics including the sensorless controllability as the simulation results of the prototype and improved model are compared. It was found that the accuracy of the rotor position estimation and torque characteristics are improved simultaneously by means of the optimum design. Finally, the validity of the simulation results is verified by comparison with the test results of the prototype.
Performance Improvement in Fingerprint Feature Extraction Using Minutiae Local Triangle Feature Set

Minutiae-based fingerprint recognition is a challenging problem, mostly due poor quality of the sensing image, large nonlinear distortion among dissimilar impressions of similar fingerprint and several common transactions. Those problems make poor feature extraction and matching. Most of the fingerprint matching system is constructed on minutiae-based feature vector. The minutiae position, size and direction of the fingerprint impression must be invariant to scaling and rotation. Such distortion problems are generated by the image capturing devices; in this study the scanned fingerprint image is subjected to feature extraction and minutiae evaluation. In the feature extracting stage image pre-processing, minutiae extraction is accomplished and in the minutiae evaluation stage the original minutiae was evaluated. The scope of this work is to attain invariance by extracting the characteristics based on the spatial connection between minutiae points. We suggest four minutiae based structural local feature vectors, at the same time it is expected to fulfill at least two necessary feature vector properties. These feature vector descriptions support in solving the spurious and missing minutiae problems and hence help us develop a robust person authentication system. The performance improvement of the proposed system is evaluated by Goodness of Index (GI) GI values. These techniques achieve effectiveness with the help of FVC databases.
Energy Trading for Shared Facility Control of a Smart Community Using Auction Process

This paper presents model where shared facility services (SFS) of a smart community are powered using electricity from the resident prosumers using an auction process. We consider a region composed of multiple prosumers sharing common SFC whose required energy is to be provided by them. The auctioneer collects bids from the prosumers to sell their energy to the shared facility services to gain extra revenue. In this paper, we present a market based on combinatorial auctions where all such trades are allowed to happen. The participants have a choice to bid in single bid format call atomic bids or with combinations of bids using logical bidding technique called packages. This paper also includes an algorithm to solve winner determination problem for combinatorial auctions based on evolutionary algorithm by using the genetic algorithm. Winner price determination (WPD) method based on average pricing model is also explained. Using MATLAB, performance evaluation of the proposed auction technique are performed and presented in this paper
Graph-Based Segmentation for Diabetic Macular Edema Selection in OCT Images

Diabetic macular edema results in severe complications leading to blindness and is characterized by specific areas in the optical coherent tomography images (OCT). We propose a technique for diabetic macular edema selection, which is based on the pre-processing of OCT images using the edge detection method and graph-based image segmentation. In the course of study, the value of $\sigma=3.5$ was demonstrated to be an optimal value of the $\sigma$ parameter of a filter kernel utilized at a preprocessing stage. The image binarization threshold in the Canny algorithm was chosen based on a criterion of reduction of spurious edges in the resulting image. The best result was attained at a threshold of 0.6. It has been experimentally demonstrated that when the percentage of minimum cluster size equals 2.5% it is possible to attain a retinal segmentation error of 2%.
Proteção da natureza e conflitos ambientais em assentamentos rurais * Nature protection and environmental conflicts in rural settlements

This paper aims at contributing to the debate on contemporary environmental issues in settlement areas in Brazil. Contrary to other works of evaluative and normative character, this work aims at capturing the dynamics of environmental conflicts and the limitations of nature protection actions in agrarian reform areas through the analytical framework of property rights issues and the efforts of the settled families
Mitigation of Cross-coupling effects in PV-D-MPPT systems with T-C-T configuration of String-Integrated-Converters using INC Algorithm

This paper proposes the Total-Cross- Tied (T-C-T) configuration of String-Integrated-Converters (SICs) to photovoltaic-distributed -maximum power point tracking (PV-D-MPPT) architecture. The proposed configuration mitigates the cross-coupling effects existed in the series configuration. The non-isolated boost converter is considered as the SIC. The incremental conductance (INC) MPPT algorithm is used to extract the maximum power. The implementation of the proposed configuration is evaluated and correlated with the conventional configuration under different partial shading conditions (PSCs).
A Performance Improved Kretschmann Configuration Based Surface Plasmon Resonance (SPR) Sensor

A surface plasmon resonance (SPR) sensor has been designed having improved sensor performance by taking the advantage of higher sensitivity of silver (Ag). We observe, when a titanium (Ti) layer is integrated in the sensor configuration the metal adheres well on the prism glass. Inclusion of an air gap also increases the sensor performance. Addition of a thin dielectric layer of indium phosphide (InP) enables surface plasmons (SPs) to spread along it and supports a guided mode. The proposed SPR sensor’s performance has been measured with a number of parameters, namely, sensitivity factor, full width at half maximum (FWHM) value, detection accuracy and quality factor. The reflectance curve of the proposed SPR sensor and its metallic film optimization were studied numerically using finite difference method. In comparison to standard Kretschmann configuration, the proposed sensor provides better sensitivity of 70.650/RIU and higher quality factor of 365.9 RIU-1 which is 2.4 times higher.
Optimization of Wide-Spectral Mode-Locking Fiber Laser Carriers in Free Space Optical Communication

We experimentally demonstrate an optimization of wide-spectral mode-locking fiber laser carriers by the introduction of two-pulse pump scheme with fiber dispersion management. We also verify the improved transmission performance of optimized wider-spectral carriers in 1km simulated atmospheric turbulent channel. For preferable compatibility and superiority, it can be an adaptive scheme for free space optical communication in the foreseeable future.
An improved algorithm for the Low Band Spectral Tilt estimation for pathological voice detection

In previous studies it was verified that just one parameter, the Low Band Spectral Tilt, and one threshold, allows the discrimination of pathological voices. The Low Band Spectral Tilt is estimated between the first two harmonics and the vowel /a/ first formant. This paper presents an optimization in the algorithm for estimation of these bands that avoids the pitch estimation. The pitch estimation is inaccurate or even an impossible task for pathological voices in advanced stages of the disease. The new algorithm can also operate with reduced FFT dimensions as it needs less frequency resolution. In consequence, the new algorithm is more computationally efficient and have an accuracy improvement of 4.9% in the MEEI database.
Ultrasensitive Microfiber Refractive Index Sensor Based on Mach-Zehnder Interference of Core Offset Structure

An ultrasensitive microfiber refractive index (RI) sensor was proposed and demonstrated based on the core offset Mach-Zehnder interference (MZI) fiber structure. It consisted of a core offset single-mode fiber (SMF) fusion spliced between two SMFs, where the center SMF was tapered to diameter of few micrometers. The experimental results showed that the sensor exhibits an ultrahigh RI sensitivity of 16485 nm /RIU (RI unit) within the RI range between 1.410 and 1.413 where the core offset and tapered fiber diameter were 7.5 μm and 9 μm, respectively. This proposed fiber sensor has the great potential as a base for biosensor in applications of air pollution monitoring, chemical elements detection and medical diagnostics.
Toll-like receptor 4 and the possible mechanism underlying TLR4 mediated neuropathic pain

Toll-like receptor 4(TLR4) is a type of transmembrane signal transduction receptors,which mediate innate immune respones.Recently,studies have found that TLR4 is not only an important protein involved in the body immune response,but also able to induce the release of neuroactive substances and pro-inflammatory cytokines by activating glial cells following noxious stimuli,which eventually contributes to inflammation and neuropathic pain.This article briefly describes the TLR4 structure and its biological function,as well as TLR4 function-related molecules.Furthermore,the role of TLR4 in neuropathic pain and the mechanism underlying TLR4 mediation of neuropathic pain are discussed.
Domain Adaptation for Car Accident Detection in Videos

In this paper, we implement a deep learning model for car accident detection using synthetic videos while adapting the model, using domain adaptation (DA), to real videos from CCTV traffic cameras. The synthetic data are rendered using a video game. The reason to use such data is the lack of real videos of car crashes from CCTV. Though a video game may allow us to generate car crashes in a variety of scenarios, the distinction in synthetic and real videos can negatively affect the model’s performance. Accordingly, our aim is three-fold: render numerous synthetic videos having significant variations, train a 3D CNN based deep model on the collected videos, and use DA to adapt the model from synthetic to real videos. Our experimental results, obtained under a variety of experimental setups, demonstrate the feasibility of using our approach for car accident detection in real videos.
Design and Development of a Portable Recording System for Simultaneous Acquisition of SCG and ECG Signals

In-vivo observing of patient's heart mechanics by means of seismocardiography (SCG) was recently rediscovered and presented (Demonstrated). Most of the time, seismocardiography is recorded utilizing 2D or 3D accelerometers set on patient's sternum. This research represents the ability and performance development of a PVDF piezoelectric film transducer by placing them in multiple positions on the subject to record real time mechanical movement of the heart (seismocardiogram and heart sounds) and furthermore of the breath related thorax movement. Patient's ECG signal was also recorded simultaneously to give a time reference of the cardiovascular movement and better assessment of SCG signal acquisition. The piezoelectric sensor has been able to record respiratory signal, seismocardiography and heart sounds all along. These signals later have been processed on MATLAB by constructing simple digital filter. The seismocardiogram waveform gives off an impression of being reliant on the different periods of the respiratory cycle. The recorded signals of piezoelectric sensor show important data such as pulse rate, respiratory cycle (Inhale and exhale), timing of mechanical occasion inside the heart cycle, and so forth.
Design Fabrication and Characterization of Transceiver for Wide-band System

The use of wide-band technology influences great promise for remote monitoring of vital signals which has numerous application in medicals as well as in other field. Wide-band system has widely acknowledged as a forthcoming technology for high data rate, short range application with precise time resolution and high energy efficiency. This paper deals with one such transceiver based system design for short range application. The system consists of transmitter as well as receiver. The transmitter side consists of RF-pulse generator and transmitting antenna while the receiving side have receiving antenna, low noise amplifier and spectrum analyzer. The received pulse on the receiver side can be used for various application by extracting various features from the pulse. Nevertheless the wide-band performance of systems imposes numerous design challenges that have not realized before in the traditional narrow band system.
A UML Profile for Prediction of Significant Software Requirements

The preliminary phase of the software development life cycle is Requirements engineering that is nearest to the user’s world. This phase contains tasks that are knowledge concentrated. Therefore, the practice of Bayesian Belief Network (BBN) for modelling this knowledge would be worthful assistance. Accordingly, predicting significant requirements is essential. When poorly identified, numerous problems happen, such as budget overrun, software failures and schedule overrun. However, this phase is usually not performed and skipped by assuming as an inconsequential phase. Significant requirements identification become more vital and challenging when the complexity of the software increases. Therefore, managers and developers recurrently lose confidence in software artifacts. Wavering in the developer’s confidence may in return distress the decisions of which requirement is to implement first. In this paper Bayesian belief network (BBN) approach for predicting significant requirements is proposed to solve the problem by UML Profile mechanism. This probabilistic model supports in accomplishing the uncertainties and inaccuracy generally exists in the requirements engineering process. It makes the prediction more precise, intuitionistic and reasonable. The Profile is imported into a UML tool, which helps in prompt validation of meta-model concepts in practice. The approach is practicable in a realistic context and addresses uncertainties.
Lightweight Attribute-Based Keyword Search with Policy Protection for Cloud-Assisted IoT

With the deep integration of the Internet of Things (IoT) and cloud computing, cloud-oriented IoT is embraced as an important paradigm for efficiency and productivity. On the other hand, it is also becoming an increasingly attractive target for cybercriminals, who attempt to breach data security and privacy. As a potential and promising solution to secure data, ciphertext-policy attribute-based keyword search (CP-ABKS) can provide both fine-grained keyword search and access control over the encrypted data. However, prior CP-ABKS schemes either fail to support lightweight computation or lack of policy protection. In this paper, with offline computation and inner product encryption, we propose a lightweight CP-ABKS scheme with policy protection, such that the encrypted data can be efficiently retrieved and accessed by data users in a fine-grained manner without leaking any sensitive information. We prove the correctness of the proposed scheme and its security in the standard model under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. We also implement our proposed scheme to demonstrate its efficiency.
Full-Duplex Decode and Forward Relay-Aided Device-to-Device Communication

Device-to-Device (D2D) communication in cellular networks allows the direct communication between devices without traversing the Base Station (BS) and extend coverage via relay strategy. In this paper, a full-duplex relaying system assists D2D communication to exchange information in interference environment. Not only a power optimization problem is proposed to maximize the sum rate and signal-to-interference-plus-noise ratio (SINR) of the cellular link while satisfying the power constraints of the relay nodes and D2D users, but imperfect channel state information (CSI) is also assumed. Furthermore, a comparison between perfect channel and imperfect channel is drawn, to show that an imperfect channel can degrade the system performance and coverage. Finally, results show that the power optimization algorithm proposed for the transmitting power of the relay nodes and D2D users, help to improve the coverage probability, increase the system sum rate, and enhance the cellular communication quality.