Miracle-dz/UniFAR-FTUs huggingface dataset:
On Optimal Input and Capacity of Non-Coherent Correlated MISO Channels under Per-Antenna Power Constraints

This paper investigates the optimal input and capacity of non-coherent correlated multiple-input singleoutput (MISO) channels in fast Rayleigh fading under per-antenna power constraints. Toward this end, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of the optimal input distribution and the uniqueness of the optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn-Tucker condition (KTC) on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine precisely the capacity gain of MISO over SISO via the phase solutions of a non-convex constrained quadratic optimization problem on a sphere. These phase solutions can be calculated effectively via a proposed penalized optimization algorithm.
Deep Convolutional Neural Network Learning for Activity Recognition using real-life sensor’s data in smart devices

Human activity recognition (HAR) recently has garnered a lot of attention in recent years especially in wearable smart devices due to its high demand in various application domains. Smart-devices are nowadays a perfect way of collecting personalized data from users. The data collecting from sensors in smart devices can be used for many purposes like health care, lifelogging or fitness. In this research, we propose an approach to have efficient and effective activity recognition with real-life data coming from smart devices. The proposed approach is based on Deep Convolutional Neural Network learning and is combined with an active learning approach which adds personalized recognition based on personalized data to the model. Experiments show the proposed model has the perfect efficiency with the state-of-the-art approach and with the personalized data and the personalized recognition for walking while holding a phone in different positions, and for all positions of holding phone and laying. Beside locomotion activities, more actions like sweeping, vacuuming have been analyzed with promising results.
Extended State Observer Based Speed Control Scheme for PMSM Drives

Researchers interest is increasing in control of Permanent Magnet Synchronous motor (PMSM) due to its advantages and increase number of applications. Many modern control techniques are used for control of PMSM. The speed control method for permanent magnet synchronous motor (PMSM) using ESO is studied in this paper. Extended state observer (ESO) is used to estimate the lumped disturbances. For speed control of PMSM state feedback controller is used in outer speed loop. Load torque is estimated as disturbance using ESO. In order to demonstrate the ability of proposed ESO based speed control of PMSM approach, several simulations were performed using MATLAB/SIMULINK.
The Efficacy of Lidocaine Spray in Pain Relief during Outpatient-Based Endometrial Sampling: A Randomized Placebo-Controlled Trial

Abnormal vaginal bleeding is one of the most frequent problems found in gynecology. Endometrial histopathology is needed for definite diagnosis. It was obtained either from endometrial tissue sampling or from standard uterine curettage. Office endometrial tissue sampling is an easy and low morbid procedure. It is usually associated with pain and discomfort. Topical anesthetic agent is needed for pain relieving. This study was conducted in outpatient gynecology clinic, Thammasat University Hospital, Thailand. It was a double blind randomized controlled trial. A total of 140 participants were enrolled in study and control group. Each group consisted of 70 cases. Study group received topical spray of 10% lidocaine (40 mg) before endometrial aspiration. Topical spray of 0.9% normal saline was performed in control group. Novak curettage was an application for endometrial tissue obtaining in this study. Visual analog scale (10cm-VAS) was used for pain evaluation. Demographic character of both groups showed no statistical difference. The percentage of participants who had severe pain (VAS≥7) during tenaculum application and Novak curettage insertion and during procedure were 28.5% (20/70) versus 12.9% (9/70), 55.7% (39/70) versus 38.5% (27/70), and 78.5% (55/70) versus 60% (42/70) in control and study group, respectively. Both groups had no significant differences of postoperative pain at 15 minutes and 2 hours. This study indicates that topical lidocaine spray can relieve pain during endometrial tissue sampling.
Developing User Perceived Value Based Pricing Models for Cloud Markets

With the rapid deployment of cloud computing infrastructures, understanding the economics of cloud computing has become a pressing issue for cloud service providers. However, existing pricing models rarely consider the dynamic interactions between user requests and the cloud service provider. Thus, the law of supply and demand in marketing is not fully explored in these pricing models. In this paper, we propose a dynamic pricing model based on the concept of user perceived value that accurately captures the real supply and demand relationship in the cloud service market. Subsequently, a profit maximization scheme is designed based on the dynamic pricing model that optimizes profit of the cloud service provider without violating service-level agreement. Finally, a dynamic closed loop control scheme is developed to adjust the cloud service price and multiserver configurations according to the dynamics of the cloud computing environment such as fluctuating electricity and rental fees. Extensive simulations using the data extracted from real-world applications validate the effectiveness of the proposed user perceived value-based pricing model and the dynamic profit maximization scheme. Our algorithm can achieve up to 31.32 percent profit improvement compared to a state-of-the-art approach.
Defect Detection in Porcelain Industry Based on Deep Learning Techniques

This paper presents an automated defect management system based on machine learning and computer vision that detects and quantifies different types of defects in porcelain products. The system is developed in collaboration with an industrial porcelain producer and integrates robots, artificial vision and machine learning. At present, in most of the companies involved in the porcelain industry, defect detection is performed manually by employees. An intelligent system for product monitoring and defect detection is very much needed. Our proposed system is implemented through a convolutional neural network which analyzes images of the products and predicts if the product is defective or not. Experimental evaluation on an image data set acquired at the industrial partner shows promising results. The proposed architecture will finally have a positive economic impact for the company by optimizing the production flow and reducing the production costs.
Importance of Colposcopy Impression in the Early Diagnosis of Posthysterectomy Vaginal Cancer

Objective The aim of the study was to investigate the value of cytology, high-risk human papillomavirus (hrHPV) status and colposcopy in the early diagnosis of vaginal cancer after hysterectomy. Materials and Methods A retrospective study was performed in the Obstetrics and Gynecology Hospital of Fudan University. Posthysterectomy patients who were diagnosed with vaginal high-grade intraepithelial lesion (HSIL) by colposcopy-directed biopsy with colposcopy impression of extensive HSIL or suspicion of cancer and underwent upper or total vaginectomy from January 2009 to December 2017 were included. Results Eighty-six posthysterectomy vaginal HSIL patients were included. Available abnormal cytology and positive hrHPV were observed in 90.7% (49/54) and 96.2% (51/53) of the patients, respectively. A total of 18.6% (16/86) of the patients were diagnosed with squamous cell cancer by vaginectomy, and the average interval between hysterectomy and vaginectomy was 3.5 years. Among them, 62.5% (10/16) cancers occurred after hysterectomy for cervical cancer, 31.2% (5/16) after hysterectomy for cervical precancer, and 6.3% (1/16) after hysterectomy for myoma. An indication for hysterectomy (cervical cancer vs HSIL, odds ratio = 7.2, 95% CI = 1.9–28.0, p = .004) and colposcopy impression of vaginal cancer (vaginal cancer vs HSIL, odds ratio = 5.9, 95% CI = 1.3–26.8, p = .021) were high-risk factors of cancer confirmed by vaginectomy in colposcopy-directed biopsy vaginal intraepithelial neoplasia 2/3 posthysterectomy in multiple logistic regression analysis. Conclusions Colposcopy is pivotal in the evaluation of abnormal cytology/hrHPV tests in follow-up of cervical cancer patients after hysterectomy and decision-making for vaginectomy in detecting early cancer.
Facial Expression Generation of an Android Robot based on probabilistic Model

Facial expressions are a nonverbal way to express our intent or feelings to others. Human-like android robots should also be able to make facial expressions for people to express natural behavior. We should generate the natural facial expressions of the android robot, but there is no rule for expressing it naturally. So, we have created facial expressions by controlling the motors one by one. This is a very repetitive and inefficient procedure, and the resulting expressions will vary depending on the developer's skill. In this paper, we propose a method to generate facial expressions based on facial action unit defined by Ekman. We used OpenFace to recognize the action unit of the face robot and obtained 13 recognizable action units. The maximum likelihood estimation was used to generate a probabilistic model of the motor parameter values representing the action unit and the least-square method was used to classify the motors involved in the action unit expression.
A New Hybrid Method Based on Nearest Neighbor Algorithm and 2-Opt Algorithm for Traveling Salesman Problem

Traveling salesman problem has been one of the mostly studied problems in combinatorial optimization. This paper proposes a new hybrid method combining the nearest neighbor algorithm and the 2-opt algorithm. The performance of this method has been tested to several benchmark problems from TSPLIB involving 11 instances of symmetric problems and three instances of asymmetric problems with various number of nodes from 17 to 783 nodes. The results show that the hybrid method can reduce the relative error significantly compared to the nearest neighbor algorithm only.
Favorite Video Classification Based on Multimodal Bidirectional LSTM

Video classification based on the user’s preference (information of what a user likes: WUL) is important for realizing human-centered video retrieval. A better understanding of the rationale of WUL would greatly contribute to the support for successful video retrieval. However, a few studies have shown the relationship between information of what a user watches and WUL. A new method that classifies videos on the basis of WUL using video features and electroencephalogram (EEG) signals collaboratively with a multimodal bidirectional Long Short-Term Memory (Bi-LSTM) network is presented in this paper. To the best of our knowledge, there has been no study on WUL-based video classification using video features and EEG signals collaboratively with LSTM. First, we newly apply transfer learning to the WUL-based video classification since the number of labels (liked or not liked) attached to videos by users is small, and it is difficult to classify videos based on WUL. Furthermore, we conduct a user study for showing that the representation of psychophysiological signals calculated from Bi-LSTM is effective for the WUL-based video classification. Experimental results showed that our deep neural network feature representations can distinguish WUL for each subject.
PolyNeuron: Automatic Neuron Discovery via Learned Polyharmonic Spline Activations

Automated deep neural network architecture design has received a significant amount of recent attention. However, this attention has not been equally shared by one of the fundamental building blocks of a deep neural network, the neurons. In this study, we propose PolyNeuron, a novel automatic neuron discovery approach based on learned polyharmonic spline activations. More specifically, PolyNeuron revolves around learning polyharmonic splines, characterized by a set of control points, that represent the activation functions of the neurons in a deep neural network. A relaxed variant of PolyNeuron, which we term PolyNeuron-R, loosens the constraints imposed by PolyNeuron to reduce the computational complexity for discovering the neuron activation functions in an automated manner. Experiments show both PolyNeuron and PolyNeuron-R lead to networks that have improved or comparable performance on multiple network architectures (LeNet-5 and ResNet-20) using different datasets (MNIST and CIFAR10). As such, automatic neuron discovery approaches such as PolyNeuron is a worthy direction to explore.
Benchmarking the ONOS Controller with OFCProbe

The increased popularity of Software Defined Networking (SDN) drives the development and research of all involved components. In particular, state-of-the-art SDN controllers are a central part of the resulting network architecture and have a large impact on the performance of the entire network. Hence, it is necessary to quantify various performance indicators of the SDN controller in order to assess its feasibility in the context of different network characteristics such as topology and network size w.r. t. the number of nodes and links.To this end, we extend the open-source controller benchmarking tool OFCProbe with several novel modules that are capable of recording relevant performance metrics and demonstrate their use in the context of benchmarking the ONOS controller. Furthermore, we investigate the impact of different types of topology characteristics on the performance of the controller.In this work, we focus on several performance indicators for SDN controllers. First, the time that the ONOS controller needs to discover the network topology and update its internal representation of the topology. Second, we investigate the duration of the installation of flow rules that are used for reactive path provisioning. Finally, we measure the response time of the controller when dealing with asynchronous messages.
Analysis and Classification of Danger Level in Android Applications Using Naive Bayes Algorithm

This paper considers danger level classification of Android applications based on permissions and vulnerabilities by using Naive Bayes (NB) algorithm in order to assist and inform users whether an application is safe to use or not. With the increasing development and use of Android, unfortunately, malicious software (malware) and malicious applications are also beginning to increase. Many methods have been proposed to protect Android, however, they are only able to detect or classify Android applications against malware based on permission. This kind of approach is still considered less effective, because there is no information in classifying the danger level of an Android application, be it malware or goodware. To overcome the problem, this research classifies the danger level into three categories namely, safe, suspicious, and dangerous. The accuracy obtained from this research is 97.2%. To our knowledge, this is the first and only work to use danger level classification of Android applications based on permissions and vulnerabilities.
Three-Dimensional Transport Simulations and Modeling of Densely Packed CNTFETs

Carbon nanotube (CNT) field-effect transistor (FET) based applications require multiple CNTs with small tube pitches for competing with existing silicon technologies. The associated dense packing of CNTs leads to electrostatic interactions between the tubes also known as screening effects. In this paper, the impact of the latter on the CNT current and charge density has been investigated by means of three-dimensional (3D) device simulation (TCAD), in which the CNT is represented by its actual 3D structure rather than by a one-dimensional (1D) line, allowing to include the effects of an inhomogeneous charge and current density distribution along the CNT circumference. Different analytical models describing the impact of the oxide thickness, CNT diameter and CNT pitch on the oxide capacitance are compared. Using the most accurate model, the dependence of drain current, transconductance, subthreshold slope and transit frequency on the above mentioned parameters has been determined using TCAD simulations.
Improved Droop Control Method for Micro-Turbine Generator Set

In order to realize the stability of the output voltage index of microgrid isolated island operation with micro-turbine, a three-phase four-wire micro-turbine power generation system model with positive and negative buses was built, the traditional droop control was improved and a translational droop control strategy was proposed. The traditional droop control curve is shifted according to the power change of the system, to compensate the system voltage frequency and amplitude in real time. The strategy enables the unit's output to be quickly adjusted to ensure stable operation of the load. Simulation and experiment verify the feasibility and effectiveness of the strategy.
TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research

Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.
An Improved Algorithm for Tracking Mulitiple Extended Targets Based on Measurement Set Partitioning

In the background of clutter, the probability hypothesis density (PHD) filter is used to carry out the extended target tracking where the measurement set is difficult to partition and the computational efficiency is low. A method is proposed to divide the measurements for extended target by using the Clusters Optimization based on Density of Hierarchical Partition (CODHD) clustering algorithm. Firstly, the adaptive ellipsoid threshold method is used to pre-process the measurement set to filter ineffective clutter; then the optimal cluster result is obtained by evaluating cluster quality assessment for each partition; finally measurement partition is obtained through fuzzy C-means (FCM) operation. The simulation results have shown that the method can be used to divide the measurement set while the good performance of the extended target filter can be obtained, and the cost of the calculation is reduced.
Integrating Trickle Timing in Software Defined WSNs for Energy Efficiency

Several routing mechanisms for the Internet of Things (IoT) applications has been proposed by research community. RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks), an IP-based distributed routing paradigm is considered as one standard developed by IETF. More recently, SDN-WISE protocol with centralized routing solution based on Software Defined Networking (SDN), is introduced. SDN-WISE is a stateful Wireless Sensor Networks (WSNs) paradigm that minimize the complexity of WSNs by reducing the number of messages exchanged. In SDN-WISE, routing polices are defined by a controller which is a software that can be anywhere in the network. In this paper, we develop a new mechanism for SDN-WISE, called Trickle timer, in order to optimize the network performance. Trickle timer allows sensor nodes in a wireless network to exchange few packets per hour when network’s state remains stable. Results show that SDN-WISE protocol with Trickle timer consumes very few energy, compared to the original SDN-WISE protocol, and has better performance in term of radio duty cycle, compared to RPL.
Study on Most Important Factor and Most Vulnerable Location for a Forest Fire Case Using Various Machine Learning Techniques

Forest Fire is a nature disaster that can cause a series of consequences and impacts. This paper studies the Most Important Factor (MIF) and Most Vulnerable Location (MVL) for a forest fire case in Montesinho Natural Park using various machine learning techniques. We applied four algorithms to analyse the data and derive MIF and MVL. Hence, we evaluated experiment results to explore the relationships between the algorithms. We conclude that MIF can be achieved using both regression and classification approach, one using the corresponding weight value and one using the variable of the split cut. For MVL, it can be derived from the centroid of clusters and a heuristically counting of the frequent location point.
A Novel Multimedia Based Approach of Classification and Visualisation of Extremist Hotspots

Cyber extremism has become a major predicament in recent years, increasing the amount of research being conducted on it. In this work, we propose a three-staged data and social network-oriented approach to classify videos on YouTube and identify cyber extremism hotspots and visualise their emergence over the years. The first stage consists of building up a corpus by using tweets and audio clips of extremist groups and refining it further using tf-idf. Second stage involves searching extremist videos on Y ouTube with the help of bigrams developed from the corpus made in the previous stage. At the last stage, these videos are manually tagged and later, classified and clustered using Naive Bayes classifier and hierarchical clustering. Finally, locations from the thus extremist labelled videos are identified.