Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Series Analysis for Bitcoin Transactions: The Case of Pirate@40's HYIP Scheme

Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a "static" manner and none of them analyzes transaction history "dynamically", i.e. without taking into account the "time variation of how Bitcoin is transferred". The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.
Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis

In this letter, a sparse representation (SR) model named convolutional sparsity based morphological component analysis (CS-MCA) is introduced for pixel-level medical image fusion. Unlike the standard SR model, which is based on single image component and overlapping patches, the CS-MCA model can simultaneously achieve multi-component and global SRs of source images, by integrating MCA and convolutional sparse representation (CSR) into a unified optimization framework. For each source image, in the proposed fusion method, the CSRs of its cartoon and texture components are first obtained by the CS-MCA model using pre-learned dictionaries. Then, for each image component, the sparse coefficients of all the source images are merged and the fused component is accordingly reconstructed using the corresponding dictionary. Finally, the fused image is calculated as the superposition of the fused cartoon and texture components. Experimental results demonstrate that the proposed method can outperform some benchmarking and state-of-the-art SR-based fusion methods in terms of both visual perception and objective assessment.
A Deep Learning Approach for Data Driven Vocal Tract Area Function Estimation

In this paper we present a data driven vocal tract area function (VTAF) estimation using Deep Neural Networks (DNN). We approach the VTAF estimation problem based on sequence to sequence learning neural networks, where regression over a sliding window is used to learn arbitrary non-linear one-to-many mapping from the input feature sequence to the target articulatory sequence. We propose two schemes for efficient estimation of the VTAF; (1) a direct estimation of the area function values and (2) an indirect estimation via predicting the vocal tract boundaries. We consider acoustic speech and phone sequence as two possible input modalities for the DNN estimators. Experimental evaluations are performed over a large data comprising acoustic and phonetic features with parallel articulatory information from the USC-TIMIT database. Our results show that the proposed direct and indirect schemes perform the VTAF estimation with mean absolute error (MAE) rates lower than 1.65 mm, where the direct estimation scheme is observed to perform better than the indirect scheme.
Validation of the Design of an 11 kW Inductive Charging Prototype on a New Test Bench for WPT-Systems

In the future, different types of e-mobility will dominate the mobility. For a gain in comfort and range electrical vehicles (EV) can be equipped with an inductive charging system. For the development of inductive charging systems environments for testing are needed. In this paper a new designed test bench for an entire EV with an integrated inductive charging system is presented. Especially the testing of misalignment and the consideration of the vehicles underfloor can be realized very easy. With the aid of the new test bench, an 11kW prototype which was designed in a former paper will be measured and parameters will be compared with the designed parameters. The progress in testing infrastructure will lead to a faster and easier development of new prototypes.
Brain's Tumor Edge Detection on Low Contrast Medical Images

Medical image processing is the most challenging and emerging field nowadays. Edge detection of MRI images is one of the most important elements of this field. This paper describes the proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain. This method incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using BCET. The result of second stage is subjected to image segmentation by using Fuzzy c-Means (FCM) clustering method. Finally, canny edge detection method is applied to detect the fine edges. For the experimental study we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done by using MATLAB software. The result of studies of the experimental material with usage of the proposed methodology demonstrates some resistivity to a noise. Also, an increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.
Design of Microstrip Dual-Mode Impedance Transformers

new four-port component, dual-mode impedance transformer for wideband matching of two microstrip lines with equal characteristic impedance and a coupled microstrip line with general even- and odd-mode impedances, is proposed. Practical realization problems are discussed and a new dimension correction approach is derived. Two transformers between 50-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> microstrips and microstrip coupled lines with even/odd impedances <inline-formula> <tex-math notation="LaTeX">$100~\Omega /68~\Omega $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$36~\Omega /27~\Omega $ </tex-math></inline-formula>, respectively, were built. The concept was experimentally verified at the frequency band 0.01–16 GHz.
Choquet Integral-Based Fusion of Multiple Patterns for Improving EIT Spatial Resolution

Electrical impedance tomography (EIT) is a promising visualization technology with advantages of low cost, nonradiation, and rapid response. The most used EIT pattern leads to relatively limited measurements by which the EIT spatial resolution is low. However, multiple EIT patterns can generate more measurements, thus fusing multiple EIT patterns is helpful to improve the EIT spatial resolution. But the most challenging task of fusing multiple EIT patterns is how to correctly reflect the interaction among them. In this paper, eight typical EIT patterns are employed to obtain more measurements, and Choquet integral method is used to represent the interaction among EIT patterns. Consequently, the optimal combination of measurements from eight EIT patterns is selected to reconstruct the EIT image. Simulations and experiments were carried out and the results indicated that the proposed method can effectively improve the spatial resolution of reconstruction images.
Cleaning Behavior Estimation for Self-Supported Cognitive Rehabilitation System

People suffering from brain injury can present complications such as attention disorder, memory disorder, functional disorder, and aphasia, which limit their capacity to perform activities of daily living. We aim to implement a system for cognitive rehabilitation based on daily routines. For instance, we previously reported the benefits of cognitive rehabilitation through cooking. In this paper, we focus on cognitive rehabilitation based on cleaning tasks, which are as important as cooking for daily living. We tested the system with the help of a patient from the Osaka Prefecture Self-Reliance Center for People with Disabilities on November 9, 16 and 30, 2017. We propose and test a system that detects some cleaning tools and a human skeleton to assess the patients' behavior while cleaning, and rehabilitation is guided using indoor navigation. Object recognition allowed to integrate information about the cleaning tools to determine different cleaning behaviors. In addition, we tracked the patient to determine her state from a library of five states for the recognized skeleton in different cleaning areas. By detecting the cleaning tools and person's state, we estimated the cleaning behavior. As state estimation may retrieve a high rate of misidentification of the person's state, we implemented an error correction algorithm for estimating the cleaning behavior based on previous states, thus improving the system accuracy. We evaluated the university environment before testing the system, and determined the accuracy of both object recognition and person's state for estimating the cleaning behavior.
RIDIRE. Corpus and tools for the acquisition of Italian L2

This paper introduces the RIDIRE corpus, built by means of an open source tool (RIDIRE-CPI) for creating specifically designed web corpora through a targeted crawling strategy. The RIDIRE-CPI architecture combines existing open source tools with specifically developed modules, comprising a robust crawler, a user friendly web interface, several conversion and cleaning tools, an anti-duplicate filter, a language guesser, and a PoS-tagger. The RIDIRE corpus is a balanced Italian web corpus (1.5 billion tokens) designed for enhancing the study of Italian as a second language, while also being exploitable for lexicographic purposes. The targeted crawling was performed through content selection, metadata assignment, and validation procedures. These features allowed the construction of a large corpus with a specific design, covering a variety of language usage domains (News, Business, Administration and Legislation, Literature, Fiction, Design, Cookery, Sport, Tourism, Religion, Fine Arts, Cinema, Music). The RIDIRE query system allows research to be carried out on the whole corpus itself or on the sub-corpora. Specifically, available queries comprehend all the functions usually exploited in corpus-based lexicography: frequency lists, concordances and patterns, collocations, Sketches, and Sketch Differences.
High Selective Balanced Bandpass Filters Using End-Connected Conductor-Backed Coplanar Waveguide

In this paper, an end-connected conductor-backed coplanar waveguide (CBCPW) balanced bandpass filter and a size reduction one with adjustable multiple transmission zeros have been developed by using the separated electric and magnetic coupling, and quarter-wavelength short-circuited stubs. The differential mode (DM) and the common mode (CM) equivalent transmission line circuits have been modeled and verified, and the DM/CM parts can be designed individually. The proposed CBCPW balanced bandpass filters have been fabricated and measured, and their advantages of multiple transmission zeros, high out-of-band suppression, and high common mode suppression characteristics have been demonstrated.
Space Charge Analysis of Polyethylene with Chemical Defects Based on Density Function Theory

In recent years, first principle calculation has been widely used in various fields including condensed matter physics, theoretical chemical to get a microscopic and intrinsic understanding of different material and chemical reaction, leading to the formation of a brand new subject, quantum chemistry. In the field of high voltage and insulation, first principle calculation or quantum chemistry have been applied to areas of charge trapping, charge injection and voltage stabilizer. In this paper, in order to clarify charge trapping mechanism of polyethylene, several alkanes with different chemical defects such as carbonyl, alcoholic group have been established with the help of quantum chemistry. The results show that the trapping sites have a strong relationship with the structure of the molecular. The frontier orbitals are both localized around the defects structures and the partial density of states(PDOS) of defect fragments accounts for the vast majority of total density of states(TDOS) at frontier orbital levels. Besides, the electrostatic potential around the defects structures on the van der Waals (VDW) surface are both negative which means these places tend to be holes traps.
Group Sparsity based Signal Detection for Massive Multi User Spatial Modulation Cyclic Prefix Single Carrier Systems

Massive spatial modulation (MSM) is considered as an appealing technique for multi antenna wireless communications. Massive SM-MIMO utilizes multiple transmit antennas (TAs) for every user with one radio frequency (RF) chain and hundreds of antennas at base station (BS) with little number of RF chain. Owing to a big number of TAs at the user and little number of RF chains at BS, signal detection turns into challenging issue. To resolve this issue, a joint grouped SM transmission scheme at users and signal detection based on group subspace pursuit (GSP) at BS can be suggested to get better the performance of signal detection. In addition to, the cyclic prefix single carrier (CPSC) is utilized to withstand the multipath channels. Simulation results prove that BER performance of the suggested signal detection based on GSP outperforms classical signal detection based on SP by 3dB SNR gain at BER=10-4.
Integrated optimization of supplier selection and service scheduling in cloud manufacturing environment

Cloud Manufacturing environment enables enterprises to share their resources together for rental profits and customized production. The fact that most manufacturing devices are organized and controlled in a particular production line hinders the flexible use of the corresponding services in a cloud platform. Therefore, the collaborative selection of upper layer suppliers and the underlying manufacturing services is of great importance, but rarely considered. To address the problem, a two-layer integrated model of supplier selection and service scheduling is proposed to provide feasible solutions for different kinds of production requirements. The decomposition-based multi-objective evolutionary algorithm, i.e., MOEA/D, is applied to minimize the production time, the rental cost and the transportation cost for producing a customized product. We empirically demonstrate that the integrated optimization way is more efficient than the original two-step decision way in practice.
Challenging the Boundaries of Unsupervised Learning for Semantic Similarity

The semantic analysis field has a crucial role to play in the research related to text analytics. Calculating the semantic similarity between sentences is a long-standing problem in the area of natural language processing, and it differs significantly as the domain of operation differs. In this paper, we present a methodology that can be applied across multiple domains by incorporating corpora-based statistics into a standardized semantic similarity algorithm. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. When tested on both benchmark standards and mean human similarity dataset, the methodology achieves a high correlation value for both word (<inline-formula> <tex-math notation="LaTeX">$r=0.8753$ </tex-math></inline-formula>) and sentence similarity (<inline-formula> <tex-math notation="LaTeX">$r=0.8793$ </tex-math></inline-formula>) concerning <italic>Rubenstein and Goodenough</italic> standard and the <italic>SICK</italic> dataset (<inline-formula> <tex-math notation="LaTeX">$r=0.8324 $ </tex-math></inline-formula><xref ref-type="fn" rid="fn1"><sup>1</sup></xref>) outperforming other unsupervised models. <fn id="fn1"><label><sup>1</sup></label><p>Eliminating the outliers which constitutes to 3.75% of 4927 statement pairs</p></fn>
Use of 3D VR environment for educational administration efficiency purposes

People involved in education often associate 3D VR with interactive, augmented reality-based novel teaching methods. But 3D VR environment isn’t limited to teaching only; it makes educational planning and administration more efficient as well. This study presents an example of workflow in 3D VR space related to self-assessment required for the institutional accreditation process that higher education institutions have to face regularly. Tasks requiring cooperation and collaboration, as well as MaxWhere features that support them are going to be summarized. Among the findings of the study, it should be emphasized that the use of the 3D VR environment is an excellent opportunity to coordinate the staff involved in compiling complex documentation, to organize their work in a flexible and efficient manner, resulting in up to 25% of reduction in working time spent for the accomplishment of the process.
Performance Analysis of Improved Energy Detector With Hardware Impairments for Accurate Spectrum Sensing

The impact of transceiver hardware impairments on the accuracy of spectrum sensing cannot be ignored in low-cost and high data rate cognitive radio systems. Nevertheless, ideal hardware for spectrum sensing is widely assumed in the technical literature. This paper presents a novel method for evaluating the improved energy detector (IED) statistics using <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> distribution over additive white Gaussian noise (AWGN) and Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel by considering transceiver hardware imperfections. Moreover, the performance of the IED over AWGN channel is highlighted by the area under the receiver operating curve. Furthermore, the average probability of detection is evaluated for both fading and non-fading environments. An asymptotic analysis studies detection probability over fading channels at a low average signal-to-noise-ratio region. Moreover, <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-order law combining and <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-order law selecting diversity techniques are proposed to increase the performance of the detector. Our simulation results demonstrate that the diversity techniques significantly improve the detector performance.
Multi-Band Processing With Gabor Filters and Time Delay Neural Nets for Noise Robust Speech Recognition

Spectro-temporal feature extraction and multi-band processing were both invented with the goal of increasing the robustness of speech recognisers. However, although these methods have been in use for a long time now, and they are evidently compatible, few attempts have been made to combine them. This is why here we investigate the combination of multi-band processing with the use of spectro-temporal Gabor filters. First, based on the TIMIT corpus, we optimise their meta-parameters like the overlap, and the number of bands. Then we verify the cross-corpus viability of our multi-band processing approach on the Aurora-4 corpus. Lastly, we combine our method with the recently proposed channel dropout method. Our results show that this combination not only leads to lower error rates than those got using either multi-band processing or channel dropout, but these results compare favourably to those recently reported for the clean training scenario on the Aurora-4 corpus.
IaaS-Aided Access Control for Information-Centric IoT

Information-Centric Networking (ICN) is a novel network architecture designed to replace the current IP-based architecture. In ICN, contents are identified based on unique names and can be cached in-network in order to immediately serve subsequent requests. These characteristics are particularly important in the context of the Internet of Things (IoT), where they allow to heavily reduce loads on the things, as well as increase content availability and decrease energy consumption. In this paper we address the open problem of providing access control for IoT deployments in ICN. Our protocol takes advantage of the reliance of IoT on Infrastructure-as-a-Service and provides confidentiality, traitor tracing and revocation of an unbound number of users. At the same time, our protocol preserves both network caching and location-independent content retrieval. To the best of our knowledge, our protocol is the first access control protocol for ICN providing all these advantages. We provide an extensive analysis of the security properties of our protocol, as well as a thorough experimental evaluation. Our evaluation shows that our protocol does not pose significant over-head with respect to published content size nor computational time required. Considering a popular IoT camera, our evaluation shows that our protocol introduces 0.27% size overhead and 150ms of computational overhead for a 7 seconds stream.
Sub-Grid Partitioning Algorithm for Distributed Outlier Detection on Big Data

Anomaly detection or outlier detection has become a major research problem in the era of big data. It is used in many applications, remove noise from signals and in credit card fraud detection. One type of outlier detection is Density-based outlier detection. Its major uniqueness is in detecting outlier points in different densities. One of the algorithms that are based on density based outlier detection is Local Outlier Factor (LOF). LOF gives every point a score that identifies its outlierness compared to other points. In this paper, we propose a new algorithm called sub-Grid partition (SGP) algorithm. SGP algorithm helps in calculating the LOF for Big Data in a distributed environment. SGP algorithm splits the tuples into small grids each grid is splitted into sub-grids. Sub-grids in the border are duplicated in every processing node for calculating the LOF for every tuple in these grids. Duplication of sub-grids lead to increase in the number of tuples that will be processed but in the other hand reduces the network overhead required for communication between processing nodes and reducing processing node idle time waiting for the requested tuple. In the end, we evaluate the performance of the SGP algorithm through a series of simulation experiments over real data sets.
Multiword expression identification using deep learning

Multiword expressions combine words in various ways to produce phrases that have properties that are not predictable from the properties of their individual words or their normal mode of combination. There are many types of multiword expressions including proverbs, named entities, and verb noun combinations. In this thesis, we propose various deep learning models to identify multiword expressions and compare their performance to more traditional machine learning models and current multiword expression identification systems. We show that convolutional neural networks are able to perform better than state-of-the-art with the three hidden layer convolutional neural network performing best. To our knowledge, this is the first work that applies deep learning models for broad multiword expression identification.