Miracle-dz/UniFAR-FTUs huggingface dataset:
Big data and semantic web, challenges and opportunities a survey

In recent years, vast and complex amounts of data are being created and making it difficult for traditional data processing applications to manage them. The coming of the Internet prompted monstrous spike in the volume of information being made and made accessible. World Wide Web consortium W3C and international standardization body of the web spread the Semantic Web. It is an extended form of current web which provide easier way to search, reuse, combine and share information. In the last few years, major businesses corporations have demonstrated interest in incorporating semantic web technology with big data for added value. Indeed this incorporation has some benefits as well; it increases end-users ability to self-manage data from various sources, it on focuses changing business environments and varying user needs and handles concepts and relationships, manages terminology while connecting different data from varied data sources. For Social Network Analysis (SNA) new methods are needed by combining Big Data and Semantic Web technologies as a way to utilize and add capacities to existing frameworks. Moreover, the fast changing business requirements and latest industry culture of Agile Development needs a robust yet flexible solution for Business Intelligence and by using distributed enterprise level ontologies Data Warehousing can be incorporated. This paper is an attempt to focus on effects of incorporating Big Data with Semantic web, how Semantic Web making Big Data smarter, revisit the Big Data and Semantic Web challenges and opportunities, relationship between them and finally we summarizes with future direction of this integration
Extended Kalman Filtering With Nonlinear Equality Constraints: A Geometric Approach

In this paper, we focus on extended Kalman filtering (EKF), in the difficult case where a function of the state has been perfectly observed, and is thus known with certainty, while the full state still has unobserved degrees of freedom. In the linear case, the Kalman filter seamlessly handles such constraints, which result in the state being in an affine subspace. Yet, in the nonlinear case, the EKF poorly handles such type of constraints. As a remedy, we propose a novel general methodology of EKF based on an (arbitrary) nonlinear error <inline-formula><tex-math notation="LaTeX">$e$</tex-math></inline-formula>. And we prove that under compatibility of the error <inline-formula><tex-math notation="LaTeX">$e$</tex-math></inline-formula> with the constraints, the EKF based on <inline-formula><tex-math notation="LaTeX">$e$</tex-math></inline-formula> seamlessly handles the constraints. Furthermore, when the state space is a Lie group, we prove the EKF based on invariant errors is exactly the invariant EKF (IEKF), and we prove further properties. The theory is applied to the problem of simultaneous localization and mapping, where the IEKF is shown to perfectly handle some partial deterministic information about the map. As a byproduct, the theory is also shown to readily allow devising EKFs on state spaces defined by a class of equality constraints.
Investigation of a Cruising Fixed Wing Mini Unmanned Aerial Vehicle Performance Optimization

The applications of unmanned aerial vehicles have been extended through the recent decades and they are utilized for both civil and military applications. The urge to utilize unmanned aerial vehicles for civil purposes has elevated researchers and industries interest towards the mini unmanned aerial vehicle (MUAV) category due to its suitable configurations and capabilities for multidisciplinary civil purposes. This study is an effort to further enhance the aerodynamic efficiency of MUAVs through a parametric study of the wing and proposing an innovative bioinspired wing design. The research is conducted utilizing numerical simulation and experimental validation. This research provides a better understanding of different wing parameter(s) effect on the aerodynamic performance of the wing and mini unmanned aerial vehicles. A new wing configuration is designed, implemented and evaluated. The wing is named as Alpine since it is inspired by biomimicry of alpine swift bird. Evaluation of the new wing geometry shows that the Alpine wing geometry performs 35.9% more efficient compared to an existing wing with similar wing area. Hence, the aerodynamic efficiency optimization is achieved for the Alpine wing which helps to enhance the performance of MUAVs.
Request Balancing Among Users in Multiple Autonomous Cloud Provider Environments

As an increasing number of providers are offering cloud service reservations, an increasingly competitive market has emerged. While most of these providers charge users for metered usage based on fixed prices, they cannot make a timely response to demand changes and may risk losing users. Hence, we highlight the importance of an autonomous provider who self-adjusts the price according to the latest demands of the cloud market. Extensive research has shown that this is not a problem for a single provider. However, when it comes to multiple providers, the main challenge is coordinating the interactions between users and users, providers and providers, as well as users and providers. In this paper, we focus on request balancing among users in the multiple-autonomous-provider environment. Since the request strategy and payment of each user are affected by decisions of other users, we take game theory to address this problem and formulate it into a noncooperative game among multiple users, without sharing specific strategies. Each user tries to maximize its utility, which combines the net profit with time efficiency. The variational inequality theory is employed and proved that there exists a Nash equilibrium solution set for the game. Then, an iterative proximal algorithm is proposed to find the Nash equilibrium solution, and its convergence is proven. Simulation results demonstrate that the proposed scheme makes requests balanced and increases users’ utilities, and also shifts demands from peak to off-peak periods.
Crowdsourcing For Top-K Query Processing Over Uncertain Data Base Paper

Uncertain data are inherent in some important application. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. Crowdsourcing has emerged as an effective way to perform tasks that are easy for humans but remain difficult for computers. When data uncertainty cannot be reduced algorithmically, crowdsourcing proves a feasible method, which consists in posting tasks to humans and attaching their decision for improving the confidence about data values or relations. This project tackles the problem of processing Top-K queries over uncertain data with the help of crowdsourcing for quickly converging to the real ordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and distinguished on both artificial and real data sets, with the purpose of minimizing the crowd relations necessary to find the real ordering of the result set. Keywords—User/Machine Systems, Query processing.
Nepali Speech Recognition using RNN-CTC Model

This paper presents a Neural Network based Nepali Speech Recognition model. RNN (Recurrent Neural Networks) is used for processing sequential audio data. CTC (Connectionist Temporal Classification) [1] technique is applied allowing RNN to train over audio data. CTC is a probabilistic approach of maximizing the occurrence probability of the desired labels from RNN output. After processing through RNN and CTC layers, Nepali text is obtained as output. This paper also defines a character set of 67 Nepali characters required for transcription of Nepali speech to text.
Collaborative Content Placement Among Wireless Edge Caching Stations With Time-to-Live Cache

Content caching at the Internet edge using a network of wireless edge caching stations (ECSs) is recently considered as a key solution to alleviating the backhaul traffic burden and improving the quality of experience in 5G networks. This paper studies wireless edge caching systems with the following features: first, content files can be partitioned into many coded packets, which then can be cached in multiple ECSs for collaborative content delivery; second, the service provider (SP) deploys time-to-live cache at ECSs and each cached content file has an occupancy time that needs to be guaranteed; third, the content-to-be-cached arrives at the caching system following a stochastic process as users request new content over time. Unlike existing works that determine which content to cache, this paper focuses on how to distribute the coded packets of content-to-be-cached among the network of ECSs in order to reduce the content downloading time. A novel content placement strategy, called stochastic collaborative content placement is proposed based on Lyapunov techniques. The proposed algorithm makes content placement decisions using only currently available information without foreseeing future content arrivals, takes advantage of the spatial content popularity variation with coded caching, and achieves the provable close-to-optimal long-term caching performance. Simulations are carried out on a real-world YouTube video request trace and the results demonstrate a tremendous caching performance improvement against a variety of benchmark schemes.
MEMORY BASED DECISION MAKING: A SPIKING NEURAL CIRCUIT MODEL

Conscious decision making is one of the important functions of human behavior. Episodic memory is the source of knowledge for conscious decision making. The mechanism of how episodic memory affects conscious decision-making is unclear. To investigate the brain mechanism of conscious decision making, we investigated a biologically-based network model of spiking neurons for competition between automatic response and conscious decision making. The proposed model integrates episodic memory modular and brain decision-making modular, and uses episodic memory output as the top-down input of decision making. In the decision making, the network realizes the competition between decision patterns through mutual inhibition, finally reaches the conscious decision making. The simulations show that the proposed model can well implement multimodal coherent decision making under sequential memory control. The proposed model can effectively explain the transmission mechanism of conscious decision information.
COLORINGS AND LIST COLORINGS OF GRAPHS AND HYPERGRAPHS BY MOHIT KUMBHAT DISSERTATION

In this thesis we study some extremal problems related to colorings and list colorings of graphs and hypergraphs. One of the main problems that we study is: What is the minimum number of edges in an r-uniform hypergraph that is not t-colorable ? This number is denoted by m(r, t). We study it for general r-uniform hypergraphs and the corresponding parameter for simple hypergraphs. We also study a version of this problem for conflict-free coloring of hypergraphs. Finally, we also look into list coloring of complete graphs with some restrictions on the lists. Let t be a positive integer and n = ⌊log2 t⌋. Generalizing earlier known bounds, we prove that there is a positive ǫ(t) such that for sufficiently large r, every r-uniform hypergraph with maximum edge degree at most ǫ(t) t ( r ln r ) n n+1 is t-colorable. The above expression is also a lower bound for m(r, t). A hypergraph is b-simple if no two distinct edges share more than b vertices. Let m(r, t, g) denote the minimum number of edges in an r-uniform non-t-colorable hypergraph of girth at least g. Erdős and Lovász [10] proved that m(r, t, 3) ≥ t 2(r−2) 16r(r − 1)2 and m(r, t, g) ≤ 4 · 20rt. A result of Z. Szabó [30] improves the lower bound by a factor of r2−ǫ for sufficiently large r. We improve the lower bound by another factor of r and extend the result to b-simple hypergraphs. We also get a new lower bound for hypergraphs with a given girth. Our results imply that for fixed b, t and ǫ and sufficiently large r, every r-uniform b-simple hypergraph H with maximum edge-degree at most trr1−ǫ is t-colorable. Some results hold for list coloring, as well. We also study the same problem for conflict-free coloring. A coloring of the vertices of a hypergraph H is called conflict-free if each edge e of H contains a vertex whose color does not get repeated in e. The smallest number of colors required for such a coloring is called the conflict-free
AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING (v2.1.0)

Abstract. Analog methods (AMs) use synoptic-scale predictors to search in the past for similar days to a target day in order to infer the predictand of interest, such as daily precipitation. They can rely on outputs of numerical weather prediction (NWP) models in the context of operational forecasting or outputs of climate models in the context of climate impact studies. AMs require low computing capacity and have demonstrated useful potential for application in several contexts. AtmoSwing is open-source software written in C++ that implements AMs in a flexible way so that different variants can be handled dynamically. It comprises four tools: a Forecaster for use in operational forecasting, a Viewer to display the results, a Downscaler for climate studies, and an Optimizer to establish the relationship between predictands and predictors. The Forecaster handles every required processing internally, such as NWP output downloading (when possible) and reading as well as grid interpolation, without external scripts or file conversion. The processing of a forecast requires low computing efforts and can even run on a Raspberry Pi computer. It provides valuable results, as revealed by a 3-year-long operational forecast in the Swiss Alps. The Viewer displays the forecasts in an interactive GIS environment with several levels of synthesis and detail. This allows for the provision of a quick overview of the potential critical situations in the upcoming days, as well as the possibility for the user to delve into the details of the forecasted predictand and criteria distributions. The Downscaler allows for the use of AMs in a climatic context, either for climate reconstruction or for climate change impact studies. When used for future climate studies, it is necessary to pay close attention to the selected predictors so that they contain the climate change signal. The Optimizer implements different optimization techniques, such as a semiautomatic sequential approach, Monte Carlo simulations, and a global optimization technique, using genetic algorithms. Establishing a statistical relationship between predictors and predictands is computationally intensive because it requires numerous assessments over decades. To this end, the code was highly optimized for computing efficiency, is parallelized (using multiple threads), and scales well on a Linux cluster. This procedure is only required to establish the statistical relationship, which can then be used for forecasting or downscaling at a low computing cost.
Cloud and IoT based Smart Car Parking System by using Mamdani Fuzzy Inference System (MFIS)

Internet of Things (IoT) and cloud computing technologies have connected the infrastructure of the city to make the context-aware and more intelligent city for utility its major resources. These technologies have much potential to solve thechallenges of urban areas around the globe to facilitate the citizens. A framework model that enables the integration of sensor’s data and analysis of the data in the context of smart parking is proposed. These technologies use sensors anddevices deployed around the city parking areas sending real time data through the edge computers to the main cloud servers. Mobil-Apps are developed that used real time data, set from servers of the parking facilities in the city. Fuzzification is shown to be a capable mathematical approach for modeling city parking issues. To solve the city parking problems in cities a detailed analysis of fuzzy logic proposed systems is developed. This paper presents the resultsachieved using Mamdani Fuzzy Inference System to model complex smart parking system. These results are verified using MATLAB simulation.
Database for Gene Variants and Metabolic Networks Implicated in Familial Gastroschisis

Gastroschisis is one of the most prevalent human birth defects concerning the ventral body wall development. Recent research has given a better understanding of gastroschisis pathogenesis through the identification of multiple novel pathogenetic pathways implicated in ventral body wall closure. Deciphering the underlying genetic factors segregating among familial gastroschisis allows better detection of novel susceptibility variants than the screening of pooled unrelated cases and controls, whereas bioinformatic-aided analysis can help to address new insights into human biology and molecular mechanisms involved in gastroschisis. Technological advances in DNA sequencing (Next Generation Sequencing), computing power, and machine learning techniques provide opportunities to the scientific communities to assess significant gaps in research and clinical practice. Thus, in an effort to study the role of gene variation in gastroschisis, we employed whole exome sequencing in a Mexican family with recurrence for gastroschisis. Stringent bioinformatic analyses were implemented to identify and predict pathogenetic networks comprised of potential gastroschisis predispositions. This is the first database for gene variants and metabolic networks implicated in familial gastroschisis. The dataset provides information on gastroschisis annotated genes, gene variants, and metabolic networks and constitutes a useful source to enhance further investigations in gastroschisis.
On the Predictability of Network Robustness from Spectral Measures

Robustness against failure and attack is one of the essential properties of large-scale dynamical system such as power grids, transportation system, communication systems, and computer networks. Despite its popularity and intuitiveness, a major drawback of descriptive robustness metrics such as the size of the largest connected component and the diameter is its computational complexity. On the contrary, predictive metrics such as the spectral radius, the natural connectivity, and the algebraic connectivity are much easier to obtain than descriptive metrics, but the predictability of those measures against different levels and types of failures/attacks has not been well understood. In this paper, we therefore investigate how effectively predictive metrics (spectral measures) can estimate the robustness of a network against random node removal. Our finding includes that, among five types of spectral measures, the effective resistance is most suitable for predicting the largest cluster component size under low node removal ratio, and that the predictability of the effective resistance is stable for various networks generated with different network generation models.
Asymmetrical Magnet Shape Optimization Based on S-C Mapping for Torque Profile Mitigation in Unidirectional Application of SPMS Machine

Surface-mounted permanent magnet synchronous (SPMS) machines are widely used in applications that require low-torque ripple, reduced vibration, and acoustic noise. For those applications of SPMS machine (SPMSM) in which only unidirectional operation is needed, asymmetric design optimization shows to acquire significant advantages over symmetric designs. In this paper, an analytical method based on Schwarz–Christoffel mapping is presented for asymmetrical magnet shape optimization in SPMS machine. In this method, the permanent magnet is divided into finite cells so that each cell can be assigned with either air (off) or magnet (on). The optimization problem is solved using genetic algorithms (GAs). In order to remove nonmanufacturable shapes, the cluster of materials and cleaning method is used and applied to magnet and air cells, respectively. The minimum number of cells in a cluster ( $N_{\text {min}}$ ) is defined as an integer design parameter. The analytical method can significantly reduce the computational time used by the optimization process as compared with methods based on finite-element analysis (FEA). This method is applied to a 12-pole SPMS machine. Validation of the S-C mapping method and final results are carried out using 2-D and 3-D FEAs. The results obtained demonstrate that asymmetrical magnet shape improves the cogging torque and torque ripple performance of SPMS machine by a significant margin.
Dynamic Multiparty Authentication using Cryptographic Hardware for the Internet of Things

The rapid development of various emerging technologies such as cloud computing and the Internet of Things (IoT) presents significant security and privacy challenges. In particular, complex and challenging applications involving various business parties have the requirement to delegate access control mechanisms securely to one or more parties, who in turn can govern methods that enable multiple other parties to be authenticated in relation to the services that they wish to consume. Moreover, modern integrated design and manufacturing follows a business model where different third-party vendors provide hardware, software, and manufacturing services, making it hard to ensure the trustworthiness of the entire process. The emergence of IoT has led to our use of Field Programmable Gate Array (FPGA) devices with Dynamic Partial Reconfiguration (DPR) abilities, which allows for modifications to the hardware implemented to undergo changes and incorporate dynamic addition/deletion of access using security protocols. We use, Physically Unclonable Functions~(PUF), cryptographic hardware primitive which are based on a physical system having easy to evaluate, hard to predict and manufacturer resistant properties in developing a security protocol. In this paper, we combine the concepts of hardware security through PUFs and hardware evolution through DPR in providing dynamic secure multiparty authentication system.
Development of the Persian version of high-frequency emphasis quick speech in noise

Background and Aim: The quick speech in noise (Q-SIN) test shows the difficulty of spee­ch perception in noise by specifying signal to noise ratio (SNR) loss. Although the Persian version of Q-SIN has been already constructed, the high-frequency emphasis version of this test is not available. The present study aimed to construct six lists with high-frequency emphasis and implement it. Methods: We are going to prepare a high-frequ­ency emphasis version of Q-SIN and then test it on a small sample. First, researchers designed the relevant sentences; then experts examined their content and face validity. According to the criteria for developing the Q-SIN test, six lists with high-frequency emphasis were prepared. The test was examined on 26 (13 male and 13 female), 18−35 years old individuals with nor­mal hearing. To determine the test reliability, it was re-administered three weeks later with the same conditions. Results: Of 76 sentences prepared, 36 sentences received enough credit after determination of their content and face validity. These 36 senten­ces were used to make 6 lists. The mean value of SNR50 in the Persian language was obtained -4 dB. The mean values of SNR loss in 6 lists were -1.65, -1.8, -2.23, -1.61, -2.38 and -2.07. The results showed equivalency of lists 1, 2, 3, 4, and 6. Examination of test-retest reliability indicated that all lists except the list 2were reliable. Conclusion: The lists of 1, 3, 4, and 6 are reli­able and equivalent and can be used in clinical application.