Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Hybrid Clustering Algorithm Based on Minimum Spanning Tree of Natural Core Points

Clustering analysis has been widely used in pattern recognition, image processing, machine learning, and so on. It is a great challenge for most existing clustering algorithms to discover clusters with complex manifolds or great density variation. Most of the existing clustering needs manually set neighborhood parameter <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> to search the neighbor of each object. In this paper, we use natural neighbor to adaptively get the value of <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> and natural density of each object. Then, we define two novel concepts, natural core point and the distance between clusters to solve the complex manifold problem. On the basis of above-proposed concept, we propose a novel hybrid clustering algorithm that only needs one parameter <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> (the number of final clusters) based on minimum spanning tree of natural core points, called NCP. The experimental results on the synthetic dataset and real dataset show that the proposed algorithm is competitive with the state-of-the-art methods when discovering with the complex manifold or great density variation.
Modeling Different Variables in Learning Basic Concepts of Programming in Flipped Classrooms

Learning the basic concepts of programming and its foundations is considered as a challenging task for students to figure out. It is a challenging process for lecturers to learn these concepts, as well. The current literature on programming training abounds with the examples of a wide range of methods employed. Within this context, one of the prominent approaches in programming training is flipped classroom (FC) model. This article has sought to illuminate the effect of cognitive flexibility, problem-solving skills (PSS), and flipped learning readiness (FLR) levels on students' programming achievements in programming training through FC model. A total of 149 freshmen computer science students studying in a state university in Turkey were recruited for this study. In this study, designed as a relational screening model, a personal form, an achievement test, and three different data collection instruments were employed to collect data. For the data analysis, structural equation modeling, a multivariate statistical analysis technique, was used to reveal a model explaining and predicting the relations between programming achievement and different variables. The findings clearly indicate that FLR is the most important predictor of the programming achievements of students in FC. Other important predictors were found as PSS and cognitive flexibility. The research model demonstrates that an increase or development in FLR, PSS, and cognitive flexibility levels in FC will enhance the achievements of students in programming.
Efficient GPU Implementation of Lucas-Kanade through OpenACC

Optical flow estimation stands as an essential component for motion detection and object tracking procedures. It is an image processing algorithm, which is typically composed of a series of convolution masks (approximation of the derivatives) followed by 2 × 2 linear systems for the optical flow vectors. Since we are dealing with a stencil computation for each stage of the algorithm, the overhead from memory accesses is expected to be significant and to yield a genuine scalability bottleneck, especially with the complexity of GPU memory configuration. In this paper, we investigate a GPU deployment of an optimized CPU implementation via OpenACC, a directive-based parallel programming model and framework that ease the process of porting codes to a wide-variety of heterogeneous HPC hardware platforms and architectures. We explore each of the major technical features and strive to get the best performance impact. Experimental results on a Quadro P5000 are provided together with the corresponding technical discussions, taking the performance of the multicore version on a INTEL Broadwell EP as the baseline.
Collaborative Merging of Radio SLAM Maps in View of Crowd-sourced Data Acquisition and Big Data

Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, this unlocks the potential of having such devices in commonplace around factories and homes, enabling an alternative method of navigation. Therefore, indoor anchor calibration becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we present a method to fuse radio SLAM (also known as Time-Of-Arrival self-calibration) maps together in a linear way. In doing so we are then able to collaboratively calibrate the anchor positions in 3D to native precision of the devices. Furthermore, we introduce an automatic scheme to determine which of the maps are best to use to further improve the anchor calibration and its robustness but also show which maps could be discarded. Additionally, when a map is fused in a linear way, it is a very computationally cheap process and produces a reasonable map which is required to push for crowd-sourced data acquisition. (Less)
Action Recognition Using Polyhedron Neighborhood Features

To utilize the geometry structure information and similarity information within the neighborhood surrounding a spatio-temporal interest point for human action recognition task, we employ the axes of a regular polyhedron as a reference locating system, and build a novel local feature named polyhedron neighborhood feature (PNF). Then, to reduce quantization error in the coding stage, locality-constrained linear coding method is used to encode the obtained PNFs. Next, multi-temporal-scale PNFs (MPNFs) are created for handling the problem of various action speeds. In classification, support vector machine (SVM) based on linear kernel is used as classifier taking time consumption into account. The experiments on the KTH and UCF sports datasets show that the recognition system based on PNFs achieves better performance than the competing local spatio-temporal feature-based human action recognition methods.
Design and simulation of a 2-DOF parallel linear precision platform utilizing piezoelectric impact drive mechanism

With rapid developments of micro/nano science and technology, precision platforms are widely required in the research and industry fields. This paper presents a 2-DOF parallel linear precision platform utilizing piezoelectric impact drive mechanism. With symmetrical flexible structure and specific piezoelectric driving manner, effective and decoupled actuation of the stator is achieved. FEA simulations are conducted to investigate the characteristics of the stator. With established dynamic model of the platform, motion responses of stator and slider in the two directions are simulated and analyzed. With simultaneous actuation of the 2-DOF motions, a motion interaction phenomenon is raised and discussed.
Different Technology Comparison For Home Automation System

Home Automation is becoming popular day by day due to numerous advantage. Home automation means the monitoring and control of household object intelligently for effective usage. Smart Home is the term commonly used to define a residence that has appliance, lighting, heating, Air conditioning, TVs, Computers, Entertainment audio and Video System, Security and Camera system that are capable of communicating with one and another. And can be controlled remotely and any location in the world by phone or internet. In this paper describe different type of technology use for home automation system like Bluetooth, ZigBee, Voice recognition, and IOT (Internet of Thing). Comparison of this technology to graphical view and which technology is best for home automation system.
Cross layer routing and scheduling for multi-channel Wimax mesh networks

Broadband wireless networks are becoming increasingly popular due to their fast and inexpensive deployment and their capabilities of providing flexible and ubiquitous Internet access. Due to the limitation of shared resources in wireless mesh network such as bandwidth, spatial reuse is introduced for concurrent transmissions. The simultaneous transmissions face many challenges regarding interference on the ongoing transmission. To maximize the network performance of mesh networks in terms of spatial reuse, it is essential to consider a cross-layer for resource allocation in different layers such as the routing network layer, the scheduling resource allocation Media Access Control (MAC) layer and physical layer. Therefore, this thesis focuses on improving the spatial reuse for resource allocation mechanism including routing tree construction by taking into consideration the reliable path, channel assignment and scheduling algorithms. Firstly, a Fuzzy based Constructed Routing Tree (FLCRT) is proposed to incorporate fuzzy logic with routing to enable cognitive capability in packet forwarding for uplink or downlink communication. Secondly, the link-aware routing path is proposed to satisfy the connection lifetime and better routing stability for successful requirements of transmission using multi sponsor node technique. Then, a better understanding of reliability analysis is pursued in the context of homogeneous wireless network. Ultimately, heuristic resource allocation including channel assignment and centralized scheduling algorithms are proposed based on the cellular learning automata to enhance the number of concurrent transmissions in the network by efficiently reusing the spectrum spatially. The attempt of heuristic resource allocation algorithms is to find the maximal number of nodes that could transmit data concurrently. The numerical and simulation results show that FLCRT, Learning Automata Heuristic Channel Assignment (LAHCA), and Learning Automata Heuristic Centralized Scheduling (LAHCS) perform better in terms of scheduling length, channel utilization ratio, and average transmission delay as compared with the existing approaches. The proposed FLCRT scheme with respect to the number of subscriber station (SS) nodes performs better in decreasing the scheduling length, average transmission delay, and channel utilization ratio by 38%, 19%, and 38% compared with Interference-Load-Aware routing. LAHCA algorithm improves the number of channels in comparison with random selection algorithm by 8%. LAHCS algorithm using multi channels proposed by LAHCA can reduce the scheduling time, average transmission delay as well as enhance channel utilization ratio versus number of SS nodes by 7%, 8%, and 6% respectively compared with Nearest algorithm in higher traffic demands.
Sense-Based Information Retrieval Using Fuzzy Logic and Swarm Intelligence

Improvement of the quality of information retrieval (IR) using the word sense disambiguation (WSD) was subject of controversy among a lot of authors. However, the recent researches pay tribute to the positive role for using the WSD to improve the IR systems. Query expansion is one of the means to improve the IR using the WSD. Many authors reported that the insertion of synonyms into query after exam the best senses improves the IR quality. However, there are two problems still facing IR systems based on sense, the first is the extent of WSD accuracy, and the second is the delay when addressing a large-scale document collections. This work aims to develop an innovative model to address these two problems. It uses the fuzzy logic to improve the WSD accuracy through tuning the synonyms weights while uses the swarm intelligence, specifically the artificial bee colony (ABC) approach, to address the latency problem. Experimental results show that the proposed model is superior to the traditional model in terms of the precision, recall and latency.
Augmented Reality Indoor Navigation Based on Wi-Fi Trilateration

— Location based systems have become a vital part of our daily life. While outdoor localization has been around for years, indoor localization is still under heavy research due to the limitations of GPS satellite. In this paper, we present an indoor localization method based on existing Wi-Fi access points and the trilateration approach. In addition, we present an augmented reality navigation solution that enables the users to augment their real word with virtual characters to guide them to the desired destinations instead of traditional routes and arrows. The system has been implemented and tested and showed promising results.
Modeling and Computing Probabilistic Skyline on Incomplete Data

The skyline query is important in the database community. In recent years, the researches on incomplete data have been increasingly considered, especially for the skyline query. However, the existing skyline definition on incomplete data cannot provide users with valuable references. In this paper, we propose a novel skyline definition utilizing probabilistic model on incomplete data where each point has a probability to be in the skyline. In particular, it returns K points with the highest skyline probabilities. In addition, we propose incomplete models and estimate probability density functions of missing values on independent, correlated, and anti-correlated distributions, respectively. Meanwhile, it is a big challenge to compute probabilistic skyline on incomplete data. We propose three efficient algorithms SPISkyline, SPCSkyline, and SPASkyline for probabilistic skyline computation on incomplete data complying with independent, correlated, and anti-correlated distributions, respectively. They employ pruning strategy, optimization of the process of probability computation, and sorting technique to improve the efficiency of probabilistic skyline computation on incomplete data. Our experimental results demonstrate that our proposed concept of probabilistic skyline is an effective method to tackle skyline query on incomplete data and our algorithms are tens of times faster than the naive algorithm on both synthetic and real datasets.
Deep Neural Networks for Android Malware Detection

In this paper we present a study of the application of deep neural networks to the problem of pattern matching in Android malware detection. Over the last few years malware have been proliferating and malware authors keep developing new techniques to bypass existing detection methods. Machine learning techniques in general and deep neural networks in particular have been very successful in recent years in a variety of classification tasks. We study various deep neural networks as potential solutions for pattern matching in malware detection systems. The effectiveness of the different architectures is compared and judged as a potential replacement for traditional approaches to malware detection in Android systems.
Privacy-Preserving and Residential Context-Aware Online Learning for IoT-Enabled Energy Saving With Big Data Support in Smart Home Environment

Energy-saving (ES) systems developed on the basis of the Internet-of-Things (IoT) by heavily relying on automated understanding of human behaviors and activities recognition is of paramount importance in smart home. However, classic approaches are incapable to understand the relations among users’ contexts and ES of appliances very well, and they cannot handle massive metering and time-varying user context datasets. Moreover, privacy concern is thoroughly aroused from both the residential and utility provider sides as to its essentiality. To tackle these problems, we propose a privacy-preserving and residential context-aware online ES (PRCOES) system in an IoT-enabled smart home environment. We model the repeated interaction of ES of appliances and the activity recognition of user context as a contextual multiarmed bandits (CMAB) problem, where the context-aware online learning algorithm can predict appropriate energy offers (EOs) that could meet the users’ satisfaction, task completion rate, and ES purposes for appliances. We utilize a tree-based structure expanding from top to bottom to recommend EOs, which supports ever-increasing big metering datasets with user context-awareness. Theoretical analysis shows that our proposal achieves sublinear regret and differential privacy for both residents and utility provider. Experiments results validate that PRCOES could enhance users’ experience and prolong users’ engagement in everyday ES while guarantee the privacy for both residents and utility provider.
A Method of JTIDS Feature Extraction Based on Nonlinear Transformation of High Order Cumulant

In the view of the target dense region, the feartures of different target’s time difference are not obvious, which leads to the problem of JTIDS multi-user sorting useless based on time difference. A nonlinear transformation sorting algorithm of high-order cumulant based on doppler frequency shift is proposed: First of all,make the nonlinear transformation based on the second order cumulant,the sixth order cumulant, the normalized skewness and the normalized kurtosis for single station signals respectively, achiece a sharp rise occurs in the degree of the frequency shift; Then,build the eigenvector according to the transformation;Finally,use spectral clustering algorithm to cluster the datasets. Simulation experiments were carried out,based on four targets which could not be sorted by the time difference. The result showed that compared with the traditional method,the success rate of our method was significantly improved, and the effectiveness of the algorithm was verified.
A Performance Model for GPU Architectures that Considers On-Chip Resources: Application to Medical Image Registration

Graphics processing units (GPUs) have become extremely important devices for accelerating computing performance in many applications. However, there have been few accurate models to estimate the performance of such applications running on modern GPUs. In this paper, we propose a performance model to estimate the execution times for massively parallel programs running on NVIDIA GPUs, one that takes on-chip resources and cost of data transfer between CPU and GPU into consideration. Four different GPUs with different architectures were used to evaluate our model. We demonstrated the effectiveness of the proposed model by applying it to various tasks in medical image registration. Experiments have demonstrated that by capturing on-chip GPU resources and data transfer time with our model, we were able to obtain a more accurate prediction of the actual running time, compared to the traditional model. Moreover, by using the optimal value of the block size parameter, estimated by our model, to accelerate the landmark tracking task on GPU devices, speedups of approximately 80×, 100×, 200× and 800×, on the C2050, K20c, M5000 and P100 can be achieved, making it possible to track massive numbers of landmarks and thereby improving the registration accuracy.
Combining compound eyes and human eye: a hybrid bionic imaging method for FOV extension and foveated vision

Based on artificial compound eyes and human vision mechanisms, we propose a hybrid bionic imaging method to achieve field of view (FOV) extension and foveated imaging simultaneously. The imaging model of the proposed method is built, and the key parameters are deduced. Then, simulations are carried out to estimate the properties of the model, including FOV extension ratio (FER), foveal ratio, fovea moving range and so on. Finally, a prototype is developed, and imaging experiments are carried out. The experimental results accord with the simulations well, proving the potential of the proposed method for intelligent surveillance, automatic object detection and recognition with low cost.
3D Keypoints Detection for Objects Recognition

In this paper, we propose a new 3D object recognition method that employs a set of 3D local features extracted from point cloud representation of 3D views. The method makes use of the 2D organization of range data produced by 3D sensor. A detector of 3D interest points requires the expression of the local surface variation around points. In our case, we opted for a curvature-based approach. We test six methods which combine principles curvatures values under the form of: 1) a measure of the Shape Index (SI), 2) a measure of a Quality Factor (FQ), 3) a map of Shape Index (SI) and curvedness(C), 4) a map of Gaussian (H) and Mean (K) curvatures, 5) a combination of 3 and 4 (SC_HK) and 6) a combination of 5 and 4(SC_HK_FQ). For each extracted point, a local description using the point and its neighbors is done by combining the shape index histogram and the normalized histogram of angles between normals. This local surface patch representation is used to find the correspondences between a model-test view pair. Performance evaluation of the detectors in terms of stability and repeatability shows the robustness of the proposed detectors to viewpoint variations. Experimental results on the Minolta data set are presented to demonstrate the efficiency of the proposed approach in view based object recognition.
A Comparative Assessment of Ontology Weighting Methods in Semantic Similarity Search

Semantic search is the new frontier for the search engines of the last generation. Advanced semantic search methods are exploring the use of weighted ontologies, i.e., domain ontologies where concepts are associated with weights, inversely related to their selective power. In this paper, we present and assess four different ontology weighting methods, organized according to two groups: intensional methods, based on the sole ontology structure, and extensional methods, where also the content of the search space is considered. The comparative assessment is carried out by embedding the different methods within the semantic search engine SemSim, based on weighted ontologies, and then by running four retrieval tests over a search space we have previously proposed in the literature. In order to reach a broad audience of readers, the key concepts of this paper have been presented by using a simple taxonomy, and the already experimented dataset.