Miracle-dz/UniFAR-FTUs huggingface dataset:
Proving Reachability Properties by Coinduction (Extended Abstract)

The coinduction is dual to induction and both of them can be defined as fixed points. More precisely, a set is inductive if it is the least fixed-point (lfp) of a monotone endofunction on a complete lattice, and it is coinductive if it is the greatest fixed-point (gfp) of such a endofunction. The induction principle says that each set that is a pre-fixed point includes the lfp, and the coinduction principle says that any post-fixed point is included in the gfp. A convenient way to define (co) inductive sets is by means of rules. In contrast with the induction, which is a well-known proof principle that is taught in most undergraduate programs, the coinduction is not as widespread and its main applications includes bisimulation and behavioural equivalence. In this talk we show that the reachability properties of transition systems can be defined coinductively and we present coinductive proof systems for such properties, where the transition systems are specified by Logically constrained term rewriting systems. As an application, if the transition system describes the semantics of a programming language, then the reachability properties may be used to describe the partial correctness of programs. A main advantage of the presented proof system is that it can be automated. Logically constrained term rewriting systems are parametric in a builtin model, for which an automated theorem prover (e.g. a SMT solver) cand be used.
A Local Zernike Moment-based Fuzzy C-means Algorithm for Segmentation of Brain Magnetic Resonance Images

Segmentation of brain magnetic resonance (MR) images is essential for computer-aided diagnosis. However, accurate segmentation is mainly affected due to the presence of noise artifact in MR images. In this paper, to reduce the effect of noise, we compute the similarity weights between the pixel values in the moment domain using Zernike moments (ZMs) as opposed to the existing approaches which use spatial domain techniques for this purpose. The ZMs used in the proposed method uses low and middle order ZM coefficients to compute similarity weights for providing spatial information. The higher order ZM coefficients that are susceptible to image noise are discarded. The proposed method is observed to be very effective for brain MR image segmentation. Detailed experimental results are performed on simulated and clinical datasets.
Automatic Screening of Retinal Structrue using Adaptive Fuzzy Thresholding

We describe an adaptive thresholding method to identify the lesion present in the retina. Diabetic retinopathy [DR] happens when these small blood vessels leak blood and different liquids. [4] This makes the retinal tissue swell, bringing about overcast or obscured vision. A PC supported screening and reviewing framework depends on the programmed discovery of injuries is required for Screening of diabetic retinopathy. DR show dim lesions, for example, Micro aneurysms (MA) and haemorrhages (HE) and bright lesions, such as, exudates and cotton-fleece spots. [6] The existing systems utilized normal strategies, for example, thresholding. [10] However, after finding the detected vessels, these lesions are lost and not recovered in resulting handling and gives less characterization precision. With the end goal to stay away from this, a programmed recognition of both dim and bright lesions in image is presented and approved dependent on fuzzy thresholding technique that don’t require exact division of the areas to be ordered. Blood vessels are separated utilizing Line Tracking Method. [7] Optic Disc (OD) is removed using the Morphological activities. Lesions are identified utilizing Adaptive Fuzzy Thresholding strategies.
Metamaterial based compact quad band microstrip antenna for wireless applications

A compact quad-band microstrip antenna operating on frequency range from 2 to 6 GHz for wireless applications is proposed in this paper. A rectangular CSRR is placed at the patch whereas ground is loaded with one square and two circular CSRRs along with two rectangular slots around the microstrip feed.The physical dimension of the proposed antenna thus, optimized using HFSS software is found to be 37×37×1.6 mm3 which is compact in size. Proposed antenna is fabricated and measured. Four frequency bands having bandwidths of 475.1 MHz, 107.3 MHz, 207.6 MHz,and 159 MHz for respective resonant frequencies at 2.7 GHz, 3.8 GHz,5.4 GHz and 5.98 GHz are obtained. Simulated and measured results obtained using VNA for reflection coefficient and VSWR are in good agreement. Simulated peak gain is achieved to be 1.02 dB, 1.55 dB, 2.061 dB & 2.063 dB atcorresponding resonant frequencies. The simulated radiation patterns at resonant frequencies are drawn.
Setting of Candidate Solutions Considering Confidence Intervals in Differential Evolution

Differential evolution is easy to implement, is a good performance optimization algorithm, and is applied in various ways. Candidate solutions in differential evolution are often initialized randomly, but search performance depends greatly on the initial Candidate solutions. It is possible to solve by introducing random elements such as mutation and noise in the evolutionary algorithm, but if introduced beyond necessity, the search speed will be lowered. In the proposed method in this study, we assume that an ideal search point group exists in the confidence interval, and randomly change the next search point candidate within the confidence interval. We confirmed that this proposed method improves the performance of the differential evolution in numerical experiments.
A Novel Approach of Requirements Prioritization Using Logarithmic Fuzzy Trapezoidal AHP for Enhancing Academic Library Service

Requirements prioritization techniques are very useful to rank the requirements as per their priority of order. The existing approaches for solving the prioritization problem are complex in fuzzy environment. TFAHP (Trapezoidal Fuzzy analytical hierarchy process) based on FPA (Fuzzy Preference Approach) and Extent Analysis (EA) is a very appropriate methodology for requirement prioritization used to enhance ‘Academic Library Services’ [30]. Academic library services play very important role in upgrading a college's superiority. Fuzzy preference approach (FPA) and extent analysis (EA) based nonlinear techniques are efficient but create many issues like ambiguity, time complexity, scalability, provides negative degree of membership function, inconsistency and generates many non uniqueness optimal solution in fuzzy environment. In this research we proposed a novel technique based on Multiple Criteria Decision Making(MCDM) for prioritization of requirements called ‘Logarithmic Fuzzy Trapezoidal AHP (LFTAHP)’ to overcome the limitations of existing techniques for educational library benefit assessment and to decrease vulnerability in decision-making. A case study adopted from Quang Hung Do et al [17] using Logarithmic Fuzzy Trapezoidal AHP (analytic hierarchy process) to be ‘best academic library’ granting the highest satisfaction to the students and researchers in college/university. This research work may fill in as an mechanism for academic managers of advanced education organizations in enhancing the educational quality level
Robust Attitude Control based on Continuous Differentiator and Adaptive Super Twisting for 3 DOF Quadrotor

A robust scheme for the attitude stabilization and trajectory tracking in presence of parameter uncertainties and perturbations of the 3 DOF Hover is presented. The proposed control is based on an adaptive Super Twisting algorithm (ASTW) and a continuous differentiator. The continuous differentiator can estimate the unmeasurable states and fixe the problem of chattering in the output signals. The proposed control is validated by experimental tests under external disturbances and parameter uncertainties using the Quanser Hover platform. Experimental results show the smoothness of the continuous differentiator’s signals even with noised measurements induced by sensors. Morever, in spite of uncertainties and disturbances, the robustness, accuracy and finite-time convergence are shown by the experimental tests.
Load Balancing for Iterative Applications in Heterogeneous Architectures

The objective of this paper is to present a multilevel load balancing library for iterative applications, considering its execution in clusters of heterogeneous nodes, composed of multiple cores and accelerators. The main contribution of this research is the possibility of performing the load balancing between the heterogeneous nodes of the cluster and also in a second level, redistributing the load between the processor cores and accelerators. The library proved to be effective, allowing a higher architecture utilization rate and reducing the execution time of the applications.
Towards an interactive pronunciation dictionary for learners

Kim, Jong-mi. 2006. Towards an interactive pronunciation dictionary for learners. Studies in Phonetics, Phonology and Morphology 12.3. 513-531. Research towards developing an interactive pronunciation dictionary that features sensitivity to learners‟ native phonology is presented for the specific case of Korean learners of English as a Foreign Language (EFL). A speech corpus was collected to determine whether native-language (L1) dependent feedback on pronunciation improved speech quality as indicated by human perceptual tests. The feedback messages address segmental and prosodic problems in the speech of Korean EFL learners. The segmental phonetic variations that were deviated from native English phonological rules were analyzed to trigger the feedback messages on insertion, deletion and substitution problems of phones. The SUMMIT speech recognizer‟s capability to model phonetic variations through explicit , context -sensitive, phonological rules was leveraged to detect pronunciation problems common to Korean EFL learners. Prosodic features of the speech were quantified to determine suitability for detection of stress, rhythm, and intonation problems. Taken together, the segmental and prosodic error detection methods were substantiated for generating specific feedback messages to improve pronunciation quality. (Kangwon National University)
Motion Detection and Extraction of Human Body Using Hybrid Technique

In recent time digital image processing and its importance have increased significantly, subject to the development of automation systems. Digital image processing and related research areas have been paved for the invention of high end applications in pharmaceutical, robotics, satellite image processing, genetics etc. Many applications have been found in daily applications around human beings in image processing. Therefore, in real time images and video sequences are essential for locating human bodies and have been entered. In this paper, a bottom-up methodology is proposed for automatic detection and extraction of human bodies from single images. Work of this paper is Design and development of a hybrid algorithm for detection and extraction of human bodies from different images and currencies. Use of Artificial Intelligence and Automatic Learning with the combination of image processing techniques to detect and split different human currencies and movements on the basis of structure. The use of vision processing has also been proposed to allow the application of the system in real-time video received from various sources, such as CCTV, Motion Capture Camera etc. The purpose of the proposed system is to be much faster than the existing algorithm, and therefore the task of reducing the complexity of the location and time of the existing algorithms.
Fault Diagnosis of High-Speed Railway Turnout Based on Convolutional Neural Network

Fault diagnosis is critical to ensure the safety and reliable operation of high-speed railway. The traditional fault diagnosis methods for high-speed railway turnout rely on manual features extraction using turnout raw data, but the process is an exhausted work and greatly impacts the final result. Convolutional neural network (CNN), as a typical deep learning model, can automatically learn the representative features from the raw data. This paper investigates an intelligent fault diagnosis method for high-speed railway turnout based on CNN. The turnout current signals in time domain are converted to the 2-D grayscale images, and then the grayscale images are fed into the CNN for turnout fault classification. The proposed method is an automatic fault diagnosis system which eliminates the complex process of handcrafted features. The experimental results show a significant improvement over the state-of-the-art on the real turnout dataset for current curve and prove the effectiveness of the proposed method without manual feature extraction.
Scene-Change Detection using Locality Preserving Projections

Scene-Change Detection / Video Shot Boundary Detection is an important pre-processing step in applications like automatic content analysis or auto-captioning etc. The goal of scene-change detection is to divide the video stream into a set of meaningful and manageable segments (shots) that can be used in subsequent processing. Camera action and special cinematic effects pose a great challenge in scene-change detection. This article proposes a robust scene-change detection method based on Dimensionality Reduction Techniques. The main contribution of this article is a scene-change detection method that handles gradual changes efficiently. The proposed method exploits the similarity preserving property of Locality Preserving Projection (LPP) to detect the dissimilar frames in the video stream. The performance of the LPP based proposed method is compared with the Principal Component Analysis (PCA) based method. The experiments done on movie scenes, television shows and animated videos show that the proposed method outperforms PCA based methods when video has challenging scene-changes like gradual dissolving or fade-in, fade-out.
A Model-Free Control Method for Synchronous Vibration of Active Magnetic Bearing Rotor System

The unbalanced force on the active magnetic bearing (AMB) rotor system is the main reason of system vibrations, which always have periodic repetitive characteristics. However, the closed-loop feedback control system (CFCS) is difficult to achieve effective control of these repetitive problems. In this paper, we firstly analyzed the synchronous vibration by taking AMB rotor mass imbalance as an example. Then, we proposed a model-free control (MFC) method based on the Newton-type ILC algorithm, and the key of this MFC is to use a partial derivative (P-D) of the output with respect to the input. The simplicity of this algorithm lies in that and the P-D is calculated by only using the input and output (I/O) data of the system and then used to adjust the ILC gain. Subsequently, we proposed a parallel plug-in control scheme based on existing AMB control system to suppress synchronous vibration. Finally, we carried out the simulation and experiment to research the control method mentioned in this paper. The results show that the MFC has a good control effect on AMB synchronous vibration. Notably, this MFC for synchronous vibration has the advantages of being without system model, simple design and good portability, and can provide theoretical and experimental basis for the application of AMB in high precision and high speed fields.
Analogous Examination of Various Machine Learning Algorithm Applied to Big Data

All domains from criminal justice to real estate to health care have adopted Big Data analytics forreaping multifold benefits. Prime need of business is actionable data. Big Data analysis business results and suggests future plans. It’s application ranges from tracking everything from crime to weather to shopping to brands. Distinguishing factor here is Big Data’s capacity for dealing with vast quantities of real-time unstructured data. Big Data Analytics along with machine learning helps in making big impact on service quality and customer satisfaction. In this work we explore the machine learning models for estimating the absolute quality of a model. On the basis of these properties, a candid evaluation of these models brings out the relative merits of all the models.
A Study on the Rabbit Farm Environmental Monitoring System based on Internet of Things

On account of the low degree of automation in a rabbit farm, this paper proposes a rabbit farm environmental monitoring system based on internet of things. First, this paper designs a rabbit farm environmental monitoring system according to the three-tier architecture of the internet of things. Then it chooses a wireless transmission module as required by the system in addition to the proposal of a new gateway initialization flow for the internet of things. After that, it analyzes the flow to query the farm monitoring data and the flow of reverse control on the environmental parameters to verify finally the feasibility of the system through the experiment.
Power Consumption of Parallel Programming Interfaces in Multicore Architectures: A Case Study

This paper evaluates the power consumption of different parallel programming interfaces (PPI) in a multicore architecture. These PPIs are: PThreads, OpenMP, MPI-1 and MPI-2 (spawn). We measure the total energy and execution time of 11 applications in a single architecture, varying the number of threads/processes. The goal is to show that these applications can be used as a parallel benchmark to evaluate the power consumption of different PPIs. The results show that PThreads has the lowest power consumption among the interfaces, consuming less than the sequential version for memory-bound applications.
A Survey on Selfish Node Detection in MANET

A network connecting mobile nodes wirelessly is known as mobile ad-hoc network (MANET). In MANET the communication takes place with the help of mobile nodes. Every node in a MANET forwards data so that it can reach to its destination. It is assumed that all the nodes in the network behave properly while forwarding the data packets. Selfish nodes are those nodes who do not participate in data forwarding. The presence of such selfish nodes degrade the performance of MANET. In this paper, we survey the various mechanisms to detect selfish node in MANET.
Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis

The acquirement of video data for crowd anomaly detection and behavior analysis is a challenging practical issue due to the short of and deficiencies within real-life video footages. The construction of simulated crowd scenes using real actors is costly and often carrying potential safety hazards. In order to address this issue, a simulation or synthesis-based crowd video generation techniques is proposed and explored in this research through the investigation and integration of various behavioral models from physics, social science and human psychology fields. The investigation has been focusing on the separation and convergence behaviors among agents from different groups within a large crowd. This research also introduced an innovative crowd grouping model through adopting the concept of Velocity Perception based Social Force Model (VPSFM) and the Boids behavioral model. In the experiments the devised model successfully empowered a game-engine-driven crowd scene simulator that is capable of configuring and generating random crowd scenes of desired aesthesis, visual and behavioral realism. Furthermore, based on the proposed model, a defined grouping attraction force is proven effective when utilized to segment the randomly distributed and mixed crowds.
Polystore Database Systems for Managing Large Scientific Dataset Archives

Palomar Transient Factory (PTF) has a tremendous amount of astronomical data. The data type varies from images of the astronomical objects, unstructured texts, relations and, key-values. To manage such varieties of data in a single database may have performance issues. So, in this study, we present an approach based on Polystores to manage multiple databases with a federation of information and a uniform query language. We also present a web-based query system built around the Polystore database architecture and try to provide a solution for the growing size of data in the astronomical domain.
Performance Analysis of Tri-channel Active Electrode EEG Device Designed for Classification of Motor Imagery Brainwaves for Brain ComputerInterface

This paper presents a modest, low cost method todesign a modular electroencephalography (EEG) signal acquisitionsystem for Brain Computer Interface (BCI) application.The system is based on active electrode EEG sensors along withArduino-Uno hardware, to acquire 3 channels for recording EEGsignals. In addition to that, a graphical user interface (GUI)was developed using Matrix Laboratory (MATLAB) for the purpose of signal processing and visualization. The performanceof designed EEG acquisition system was evaluated on the basisof classification of Motor Imagery (MI) brainwaves for imaginedleft-hand (LH) and right-hand (RH) movement. The designedsystem was used to record the MI brainwaves of 10 differentsubjects and the recorded data was split into a training set andtest set. Independent Component Analysis (ICA) was used for theremoval of EEG artifacts. The feature extraction was done usingWavelet Decomposition (WD) using Morlet as mother wavelet.The wavelet decomposed signal features like Maximum Power(MP) and Approximate Frequency (AF) was used as classificationfeatures for the classification of MI brainwaves. The classificationof MI brainwave signals was done using Linear DiscriminantAnalysis (LDA) which showed the accuracy of 81.6 percentagesfor testingdata set. Thus, the designed three channel active electrode EEGdevice could be used in various BCI applications.
Samsara Architecture: Exploring Situation Awareness in Cloud Computing Management

Issues related to energy consumption and its efficiency in large-scale computing environments have emerged as critical points in the development of modern computer systems. This article presents the Samsara architecture concept, which aims to manage energy efficient computational clouds. The architecture was developed exploring situation awareness strategies, operating autonomously and with minimal human intervention, essential aspects in massive data processing centers. Samsara has been implemented as one of the modules of the OpenStack cloud platform and considers the maximum allocation capacity of each physical machine, to consolidate the workloads. In the evaluations carried out with synthetic loads, reductions of up to 12.3% in the energy consumption in the managed computational infrastructure were achieved, demonstrating the potential of Samsara for the operation of computational clouds.
Spectrum-Based Hand Gesture Recognition Using Millimeter-Wave Radar Parameter Measurements

Radar sensors offer several advantages over optical sensors in the gesture recognition for remote control of electronic devices. In this paper, we investigate the feasibility of human gesture recognition using the spectra of radar measurement parameters. With the combination of radar theory and classification methods, we found that the frequencies of different gestures’ parameters could be utilized as features for gesture recognition. Six kinds of periodic dynamic gestures are designed to avoid the complexity of defining and extracting the start and end of the dynamic gesture. In addition to the frequency ratio, we also extracted some features related to motion range and detection coherence to eliminate the interferences brought by the unintended gestures. The decision tree classifier designed on the basis of experimental phenomena can guarantee effective classification between different gestures, and in general, the correct recognition rate of each gesture is higher than 90%. Finally, we collected the position and the Doppler velocity information of hand for classification by a W-band millimeter wave radar in the experiment and verified the usability of the proposed method.