Miracle-dz/UniFAR-FTUs huggingface dataset:
A Probabilistic Approach to Measuring Driving Behavior Similarity With Driving Primitives

Evaluating the similarity levels of driving behavior plays a pivotal role in driving style classification and analysis, thus benefiting the design of human-centric driver assistance systems. This article presents a novel framework capable of quantitatively measuring the similarity of driving behaviors for human based on driving primitives, i.e., the building blocks of driving behavior. To this end, we develop a Bayesian nonparametric method by integrating hierarchical Dirichlet process (HDP) with a hidden Markov model (HMM) in order to automatically extract the driving primitives from sequential observations without using any prior knowledge. Then, we propose a grid-based relative entropy approach, which allows quantifying the probabilistic similarity levels among these extracted primitives. Finally, the naturalistic driving data from 10 drivers are collected to evaluate the proposed framework, with comparison to traditional work. Experimental results demonstrate that the proposed probabilistic framework based on driving primitives can provide a quantitative measurement of similar levels of driving behavior associated with the dynamic and stochastic characteristics.
Quantitative Performance Evaluation of PL PUF and RO PUF with ASIC Implementation

Physical unclonable functions (PUFs) have attracted attention as authentication of IoT devices. For the practical application of PUFs, evaluation in several manufacturing processes is very important; however, pseudo linear feedback shift register (PL) PUF and ring oscillator (RO) PUF, which are typical PUFs, have not been evaluated on application specific integrated circuit (ASIC) with 180nm process. This study quantitatively evaluates performance of PL PUF and RO PUF with ASIC implementation. Experimental results showed that target PUFs had a good performance.
Physical Layer Security in Multiuser VLC Systems with a Randomly Located Eavesdropper

This paper proposes a secrecy enhancement mechanism for multiuser visible light communication (VLC) systems. Thanks to the inherent advantages of visible light that it cannot penetrate opaque walls and its channel gain largely depends on the distance, VLC systems can serve multiple users at a time with high security and dense spatial reuse. Nevertheless, in the presence of multiple users, the interference caused by other users' signals should be carefully considered when analyzing the secrecy rate and data rate performance measures. By employing a continuous LED model, we formulate an optimization problem to find the optimal set of LEDs that should be used for communication such that the average secrecy rate for the secured user is maximized while satisfying data rate requirements for the other ordinary users. Numerical results are provided to verify that the relative locations of multiple users, the spatial distribution of a random eavesdropper, and the required data rates are significant factors that affect the secrecy performance in multiuser VLC systems.
Automated Feedback Generation System using Facial Emotion Recognition

Advertisements have come a long way from textual content to graphical content and finally video advertising, where an advertisement video is played before or while the user is viewing any content of his interest on the internet. Feedback for an advertisement or a service is essential for its improvement which is often taken from the user through forms. Instead of asking to fill up forms, here facial expression analysis can be used to generate the feedback without having the user intervention. For the purpose of emotion detection, a Support Vector Machine has been employed to detect six types of emotions namely - happy,sad,anger,surprise,disgust and neutral. First the face is detected and 68 facial landmarks co-ordinates are located on the face that correspond to eyes, mouth, nose and jaw. With this landmarks, a total of 15 real valued parameters are calculated taking into account the neutral face of the user. These real valued parameters are computed as ratios of different distant metrics corresponding to the actual face and neutral face. Experimental results show that an accuracy of 90.60 ± 1.65% has been achieved in recognizing the six emotions by using 20% of dataset for testing. An average accuracy of 88.06% is obtained in 10-fold cross validation testing. This information is used to generate a visual feedback to the advertiser. Apart from generating feedback for an advertisement, this can also be used to get opinions of people wherever communication or language becomes a hindrance either due to illiteracy or not knowing the language.
A New System for Lung Cancer Diagnosis based on the Integration of Global and Local CT Features

Lung cancer leads deaths caused by cancer for both men and women worldwide, that is why creating systems for early diagnosis with machine learning algorithms and nominal user intervention is of huge importance. In this manuscript, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. This system integrates global and local features to give an implication of the nodule prior growth rate, which is the main point for diagnosis of pulmonary nodules. 3D adjustable local binary pattern and some basic geometric features are used to extract the nodule global features, and the local features are extracted using 3D convolutional neural networks (3D-CNN) because of its ability to exploit the spatial correlation of input data in an efficient way. Finally all these features are integrated using autoencoder to give a final diagnosis for the lung nodule whether benign or malignant. The system was evaluated using 727 nodules extracted from the Lung Image Database Consortium (LIDC) dataset. The proposed system diagnosis accuracy, sensitivity, and specificity were 92.20%,93.55%, and 91.20% respectively. The proposed framework demonstrated its promise as a valuable tool for lung cancer detection evidenced by its higher accuracy.
Should methods of correction for multiple comparisons be applied in pharmacovigilance?

Purpose. In pharmacovigilance, spontaneous reporting databases are devoted to the early detection of adverse event ‘signals’ of marketed drugs. A common limitation of these systems is the wide number of concurrently investigated associations, implying a high probability of generating positive signals simply by chance. However it is not clear if the application of methods aimed to adjust for the multiple testing problems are needed when at least some of the drug-outcome relationship under study are known. To this aim we applied a robust estimation method for the FDR (rFDR) particularly suitable in the pharmacovigilance context. Methods. We exploited the data available for the SAFEGUARD project to apply the rFDR estimation methods to detect potential false positive signals of adverse reactions attributable to the use of non-insulin blood glucose lowering drugs. Specifically, the number of signals generated from the conventional disproportionality measures and after the application of the rFDR adjustment method was compared. Results. Among the 311 evaluable pairs (i.e., drug-event pairs with at least one adverse event report), 106 (34%) signals were considered as significant from the conventional analysis. Among them 1 resulted in false positive signals according to rFDR method. Conclusions. The results of this study seem to suggest that when a restricted number of drug-outcome pairs is considered and warnings about some of them are known, multiple comparisons methods for recognizing false positive signals are not so useful as suggested by theoretical considerations.
A Distributed Control System for Software Defined Wireless Sensor Networks Through Containerisation

Software-Defined Networking and Network Function Virtualisation are gaining much prominence in the technological space. Major technologies are built on their principles. As their impact permeates, many architectural designs in networking, computing and storage have to be rethought because this affects the conventional way of running and deploying applications. The urge to save costs in this rapidly changing technological era, have operators continually searching for better methods. This paper extends the work previously conducted on Fragmentation-based distributed control system for Software-Defined Wireless Sensor Networks (SDWSN). The system entails a two-level architecture comprising local controllers and a global controller to overcome the geographical space between the infrastructure elements and the controller. This was in response for a better control system for SDWSNs, given their distinct nature. The paper explores different methods of infrastructure deployment for this model, contrasting virtual machines (VMs) and containers. The design of the Fragmentation model is such that the local controllers are lean and inexpensive. The evaluation shows that software containers are faster and more efficient as compared to the VMs and the infrastructure deployment method do contribute to the efficiency of the model.
Design and Comparison of Rectangular and Circular Horn Antenna Feeders for 2.4 GHz Band

In wireless communication field antennas play a very important role. Wireless communication works on the principle of transmission and reception of radio waves on specific frequencies. Horn antennas are used as feeding antennas to parabolic reflector antenna. Horn antennas are used because of their overall advantages. The feeder antenna must have good matched impedance with the transmission line and the designed horn antennas provide the same. The design of rectangular and circular monopole horn antennas is done using CST software. 2.4 GHz frequency band is used because of its wide coverage area and penetration through walls, providing better connectivity.
Comparative Analysis of Security Issues in the Layered Architecture of IoT

This Unquestionably, Internet of Things (IoT) stands as a novel and more prominent internet based communicating architecture for future applications includes smart cities, smart transportations, smart grid etc. However the most concentrated and research aspect of IoT is security provision to the devices connected and the data transmitting through it. Since the devices communicate through internet in IoT, there exist number of adversaries/ attacks which tries to compromise the network. Herein this leads into the loss of confidentiality, integrity, availability and trust of the devices and network resources. This paper presents a brief survey on the possible attacks happening in different layers of IoT and also presents the possible mitigation schemes to reduce the effect of attacks.
Two-Tier Ensemble Model for Demand Side Prediction in Smart Grid Environment

Demand side load prediction is one of the most challenging tasks in smart grid environment due to uncertainties between demand and supply. Hence, in order to overcome this issue, this paper presents a scheme based on machine learning and deep learning for energy load forecasting by considering the weather condition of the area. We propose a two-tier Ensemble model, which ensembles the results of machine learning model (Support Vector Machine) and deep learning models (Convolu- tional Neural Network One Dimensional and Long Term Short memory) with a simple neural network to predict the load and demand gap. Then, we train and test the model with the UMass Smart* Dataset - 2017 release by taking the readings of appliances and weather conditions. The experimental results demonstrate that the proposed scheme has a significant improvement over the existing load forecasting methods having short-term and long- term load prediction models with an overall accuracy of 95.6%
A Discrete Particle Swarm Optimization Algorithm With Adaptive Inertia Weight for Solving Multiobjective Flexible Job-shop Scheduling Problem

A discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW) is proposed to solve the multiobjective Flexible Job-shop Scheduling Problem. The algorithm uses a two-layer coding structure to encode the chromosomes, namely operation sequence (OS) and machine assignment (MA). The initial population combined random selection of OS and the global selection based on operation (GSO) of MA. In order to obtain the Pareto optimal solution, non-dominated fronts are obtained by rapid non-dominated sorting. In the evolution process, the discrete particle swarm optimization algorithm is used to directly solve the values of the next generation chromosomes in the discrete domain, and the population diversity is enhanced by adaptively adjusting the variation of the inertia weight $\omega $ , and the Pareto optimal solution obtained in the process is stored in the Pareto optimal solution set (POS). Finally, numerical simulation based on two sets of international standard instances and comparisons with some existing algorithms are carried out. The comparative results demonstrate the effectiveness and practicability of the proposed DPSO-AIW in solving the multiobjective Flexible Job-shop Scheduling Problem.
Enabling Computation Offloading for Autonomous and Assisted Driving in 5G Networks

Connected and automated vehicles currently leverage on-board resources to implement autonomous and assisted driving operations. Such functionalities, which are characterized by tight latency demands, require significant processing resources and can generate a considerable amount of data. Cloud computing is considered the one-stop solution for executing computationally intensive workloads. However, accommodating autonomous and assisted driving requirements using a centralized cloud computing platform is not always feasible due to the latency and reliability constraints they impose. In this paper, we introduce a multi-access edge computing platform suitable for offloading certain autonomous and assisted driving tasks to the edges of the network. We also illustrate how both paradigms (centralized and edge cloud computing) can coexist complementing each other in the challenging task of supporting autonomous and assisted driving, thus opening up new horizons for connected vehicles, for which service instantiation and migration needs to be seamless due to its impact on road safety.
When Backscatter Communication Meets Vehicular Networks: Boosting Crosswalk Awareness

The research of safety applications in vehicular networks has been a popular research topic in an effort to reduce the number of road victims. Advances on vehicular communications are facilitating information sharing through real time communications, critical for the development of driving assistance systems. However, the communication by itself is not enough to reach the most desired target as we need to know which safety-related information should be disseminated. In this work, we bring passive sensors and backscatter communication to the vehicular network world. The idea is to increase the driver (or vehicle) awareness regarding the presence of pedestrians in a crosswalk. Passive sensors and backscatter communication technologies are used for the pedestrians’ detection phase, while the vehicular network is used during the dissemination of the detection information to surrounding vehicles. The proposed solution was validated through end-to-end experimentation, with real hardware and in a real crosswalk with real pedestrians and vehicles, demonstrating its applicability.
A Novel Approach for Performance Analysis of IoT Enabled Uplink Network with Matérn Cluster Process

In this paper, we focus on analyzing the performance measure of cellular network for massive connectivity of IoT devices considering that the base stations (BSs) are distributed according to Mat\'{e}rn Cluster Process (MCP) while devices are distributed according to Poisson Point Process (PPP). In particular, we develop a generalized approach to calculate connection failure probability of IoT devices in the random access channel (RACH) phase of uplink (UL) transmission. The proposed approach uses a calculation of the devices' association probability rather than using an approximation of the Voronoi tessellation's cells area distribution that is used in PPP. To adopt this approach, we derive the void probability for MCP which is defined as the probability of having no children point of MCP in a given distance. The merit of the proposed approach besides its exact analysis, is that it can be applied for general scenarios of devices and BSs' distributions. Presented numerical results demonstrate the effectiveness and accuracy of the proposed approach. We validate our analysis comparing with the numerical simulations via MATLAB.
Relationship between Weekly Physical Activity Frequency and Metabolic Syndrome

그동안의 연구에 의하면 규칙적 신체 활동은 사망과 이환의 위험 을 낮추고, 혈압, 혈당, 혈중 지질, 체질량지수 등 심혈관 질환의 위험 요인들과 관련이 있는 것으로 알려져 있다. 특히, 규칙적 신체 활동 은 대사증후군의 위험을 감소시키는 것으로 보고되어 있다. 반대 로, 비활동성은 사망의 주요 위험 요인이며, 심혈관 질환의 위험 요 인들과 부정적인 관련이 있다. 국제 보건 기구(WHO 2010)는 일주일에 150분 이상 중간 강도의 유산소 신체 활동을 성인에게 권고하고 있다. 그러나, 2013년 국민건 강영양조사 결과 만 19세 이상 우리나라 사람들의 중간 강도 신체 활 동 실천률은 걷기를 포함하여 47.2%로 나와 인구의 반만이 신체 활동 을 적절히 하는 것으로 조사되었다. 즉, 우리나라 사람들 중 절반은 신 체 활동이 부족한 상태이다. 그리고, 비활동성과 관련이 있는 비만, 대 신체 활동 빈도와 대사증후군의 관련성
Development and enhancement of CFR estimate in WLAN and testing in Vector Signal Transceiver

Use of Wireless Local Area Networks (WLAN) has dominantly supported the field the wireless communication in achieving high data rates and security. Instead of sending a single copy of data from transmitter to receiver, it is preferable to send copies of the same data to maintain the integrity of the information. This includes preamble or pilot symbols prior to data. Modern OFDM receivers complying to 802.11n / ac / ax WLAN standards use legacy equalizer training sequence (L-LTF) to equalize SIG symbols, which carry details pertaining to the Data being transmitted. However, Data itself is equalized using HT / VHT / HE- LTFs. The aim of this project is to leverage the legacy CFR, in conjunction with the actual one, to compute a better response to equalize Data. This helps in characterizing the transmitter more accurately to provide the best possible Error Vector Magnitude (EVM) result to the customer. A novel Channel Frequency Response (CFR) combining technique for equalizer training of data is presented in this paper. The results include a comparison of EVM values for both with and without equalizer training in LabView.It also includes the test results of the same in NI PXIe Vector Signal Transceiver (VST) hardware.
Forensic radiology: An emerging tool in identification

In any mass disaster condition, identification of the person is most important. For this purpose, the forensic investigators use different methods for identifying the dead. They consider skeletal remains of the dead as the initial step in identification. Radiographs carry great evidence to act as antemortem records and also assist in identifying the person, age, gender, race, etc. Forensic dentistry is also emerging as a new branch in forensics. So, the forensic dentist must be aware of different techniques, developments, and resources to incorporate the technology in order to achieve success in human identification. So, our aim of the present review is to focus on different radiological techniques and new developments available for successful identification of the dead.
Respiratory effects of extradural nerve block in the postoperative period.

: The effects of continuous extradural block in the postoperative period on arterial hypoxaemia are discussed. Comparing two matched groups of patients following upper abdominal surgery, one receiving continuous extradural analgesia and one narcotic analgesia, there were small but statistically insignificant improvements in PaO2 in the former group. It was concluded that abdominal muscle spasm is not an important cause of postoperative arterial hypoxaemia.
Adjuvant chemoradiotherapy in patients with gastric cancer: Single-center experience

Background/aims: The purpose of this study was to review survival analysis, side effects and prognostic factors in patients with gastric cancer who were treated with adjuvant chemoradiotherapy. Materials and Methods: In this study, we retrospectively evaluated the data of 90 patients with gastric cancer followed at Cumhuriyet University Faculty of Medicine, Oncology Center. Re- sults: This study analyzed the data of 90 patients (68 (76%) males, 22 (24%) females). The median follow-up was 19 months (range: 4-105). The two-year overall and disease-free survival rates were 62% and 58%, respectively. In uni- variate analysis, the factors that determined survival were grade (p: 0.039), T (p: 0.001) and N (p: 0.020) stages, weight loss (p: 0.001), Eastern Cooperati- ve Oncology Group performance status (p: 0.002), anemia (p: 0.019), and hypoalbuminemia (p: 0.001). After multivariate analysis, weight loss (p: 0.008), T stage (p: 0.024) and Eastern Cooperative Oncology Group perfor- mance status (p: 0.033) were determined as indepenent prognostic factors that affect survival. The most frequent side effects were nausea and vomiting in 66 patients (73%), dyspepsia in 52 patients (58%), anemia in 45 patients (50%), neutropenia in 36 patients (40%), diarrhea in 34 patients (38%), and weight loss in 29 patients (32%). Conclusions: It was shown that the overall survival and disease-free survival in non-metastatic gastric cancer patients who received adjuvant chemoradiotherapy complied with the literature. Girifl ve Amac: Bu cal›flmada, adjuvan kemoradyoterapi ile tedavi edilen mi- de kanserli hastalar›n sakal›m analizi, tedavi yan etkileri ve sakal›m› etkile- yen prognostik faktorlerin incelemesi amacland›. Gerec ve Yontem: Cal›flma- da Cumhuriyet Universitesi T›p Fakultesi Araflt›rma ve Uygulama Hastanesi Onkoloji Merkezinde takip edilen 90 mide kanserli hasta verileri retrospektif olarak incelendi. Bulgular: Cal›flmada 68 (%76) erkek, 22 (%24) kad›n top- lam 90 hastan›n verileri analiz edildi. Ortanca takip suresi 19 ay (4-105 ay) idi. ‹ki y›ll›k genel sakal›m ve hastal›ks›z sakal›m s›ras›yla %62 ve %58 idi. Tek deiflkenli analizde hastalar›n sakal›m›n› grade (p:0,039), T evre (p:0,001), N evre (p:0,020), kilo kayb› (p: 0,001), ECOG performans duru- mu (p:0,002), anemi (p:0,019), hipoalbunemi (p:0,001) etkilerken, cok de- ¤iflkenli analizde ba¤›ms›z prognostik faktorler kilo kayb› (p:0,008), T evresi (p:0,024) ve ECOG performans durumu (p:0,033) idi. Hastalarda en s›k go- rulen yan etkiler; bulant›-kusma 66 (%73), dispepsi 52 (%58), anemi 45 (%50), notropeni 36 (%40), ishal 34 (%38) ve kilo kayb› 29 (%32) hastada goruldu. Sonuc: Bu cal›flmada, adjuvan kemoradyoterapi uygulanan mide kanserli hastalar›n genel ve hastal›ks›z sakal›m› literatur ile benzer sonuclar gostermifltir. Bu hasta grubunun en onemli prognostik faktorleri ise kilo kay- b›, T evresi ve ECOG performans durumu olarak belirlendi. Anahtar kelimeler: Mide kanseri, kemoradyoterapi, sakal›m, prognoz
A Survey on Tracing for Default of Cloud Service

After the default of cloud service, tracing can provide evidence for the final determination of responsibility attribution dispute. This paper surveys the research status of tracing towards cloud service accountability mechanism. This paper first reviews the research status of tracing; Secondly, summarizes the classification of cloud service default and its tracing; Thirdly, analyzes two essential differences between attack tracing and responsibility tracing, and then clarify that the existing results of tracing are difficult to directly apply to the tracing research of cloud services default; Finally, cloud service accountability is oriented to suggests on the research approach of default tracing are given.