Miracle-dz/UniFAR-FTUs huggingface dataset:
Video compression and rate control methods based on the wavelet transform

Wavelet-based image and video compression techniques have become popular areas in the research community. In March of 2000, the Joint Pictures Expert Group (JPEG) released JPEG2000. JPEG2000 is a wavelet-based image compression standard and predicted to completely replace the original JPEG standard. In the video compression field, a compression technique called 3D wavelet compression shows promise. Thus, wavelet-based compression techniques have received more attention from the research community. This dissertation involves further investigation of the wavelet transform in the compression of image and video signals, and a rate control method for real-time transfer of wavelet-based compressed video. A pre-processing algorithm based on the wavelet transform is developed for the removal of noise in images prior to compression. The intelligent removal of noise reduces the entropy of the original signal, aiding in compressibility. The proposed wavelet-based denoising method shows a computational speedup of at least an order of magnitude than previously established image denoising methods and a higher peak signal-to-noise ratio (PSNR). A video denoising algorithm is also included which eliminates both intraand inter-frame noise. The inter-frame noise removal technique estimates the amount of motion in the image sequence. Using motion and noise level estimates, a video ii denoising technique is established which is robust to various levels of noise corruption and various levels of motion. A virtual-object video compression method is included. Object-based compression methods have come to the forefront of the research community with the adoption of the MPEG-4 (Motion Pictures Expert Group) standard. Object-based compression methods promise higher compression ratios without further cost in reconstructed quality. Results show that virtual-object compression outperforms 3D wavelet compression with an increase in compression ratio and higher PSNR. Finally, a rate-control method is developed for the real-time transmission of waveletbased compressed video. Wavelet compression schemes demand a rate-control algorithm for real-time video communication systems. Using a leaky-bucket design approach, the proposed rate-control method manages the uncertain factors in both the acquisition time of the group of frames (GoF), computation time of compression/decompression algorithms, and network delay. Results show good management and control of buffers and minimal variance in frame rate.
Data Mining Techniques in Database Systems

At the current stage the technologies for generating and collecting data have been advancing rapidly. The main problem is the extraction of valuable and accurate information from large data sets. One of the main techniques for solving this problem is Data Mining. Data mining (DM) is the process of identification and extraction of useful information in typically large databases. DM aims to automatically discover the knowledge that is not easily perceivable. It uses statistical analysis and artificial intelligence (AI) techniques together to address the issues. There are different types of tasks associated to data mining process. Each task can be thought of as a particular kind of problem to be solved by a data mining algorithm. The main types of tasks performed by DM algorithms are: Classification, Association, Clustering, Regression, Anomaly Detection, Feature Extraction, Time Series Analyses.
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (i) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert’s a priori belief about a word’s meaning can be incorrect – annotation should be performed based on the word’s contexts in the target domain instead.
A Study of Applying Automated Assessment in Teaching College English Writing Based on Juku Correction Network

With the continuous advancement of modern network technology, the drawbacks of the tradition-al English writing course teaching mode have become increasingly prominent, and the automated scoring system has gradually been used in the writing course. This paper proposes a college English writing teaching model based on Juku Correction Network, and conducts empirical re-search on the use of Juku Correction Network in college English writing teaching. The research results show that the teaching mode based on Juku Correction Network can effectively improve the overall level of students' English writing, and stimulate students' English writing motivation.
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports

Chest X-Ray (CXR) images are commonly used for clinical screening and diagnosis. Automatically writing reports for these images can considerably lighten the workload of radiologists for summarizing descriptive findings and conclusive impressions. The complex structures between and within sections of the reports pose a great challenge to the automatic report generation. Specifically, the section Impression is a diagnostic summarization over the section Findings; and the appearance of normality dominates each section over that of abnormality. Existing studies rarely explore and consider this fundamental structure information. In this work, we propose a novel framework which exploits the structure information between and within report sections for generating CXR imaging reports. First, we propose a two-stage strategy that explicitly models the relationship between Findings and Impression. Second, we design a novel co-operative multi-agent system that implicitly captures the imbalanced distribution between abnormality and normality. Experiments on two CXR report datasets show that our method achieves state-of-the-art performance in terms of various evaluation metrics. Our results expose that the proposed approach is able to generate high-quality medical reports through integrating the structure information.
An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics

We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics.
Introduction to the Mathematics of Medical Imaging, Second Edition

At the heart of every medical imaging technology is a sophisticated mathematical model of the measurement process and an algorithm to reconstruct an image from the measured data. This book provides a firm foundation in the mathematical tools used to model the measurements and derive the reconstruction algorithms used in most imaging modalities in current use. In the process, it also covers many important analytic concepts and techniques used in Fourier analysis, integral equations, sampling theory, and noise analysis. This text uses X-ray computed tomography as a "pedagogical machine" to illustrate important ideas and incorporates extensive discussions of background material making the more advanced mathematical topics accessible to readers with a less formal mathematical education. The mathematical concepts are illuminated with over 200 illustrations and numerous exercises. New to the second edition are a chapter on magnetic resonance imaging (MRI), a revised section on the relationship between the continuum and discrete Fourier transforms, a new section on Grangreat s formula, an improved description of the gridding method, and a new section on noise analysis in MRI. Audience The book is appropriate for one- or two-semester courses at the advanced undergraduate or beginning graduate level on the mathematical foundations of modern medical imaging technologies. The text assumes an understanding of calculus, linear algebra, and basic mathematical analysis. Contents Preface to the Second Edition; Preface; How to Use This Book; Notational Conventions; Chapter 1: Measurements and Modeling; Chapter 2: Linear Models and Linear Equations; Chapter 3: A Basic Model for Tomography; Chapter 4: Introduction to the Fourier Transform; Chapter 5: Convolution; Chapter 6: The Radon Transform; Chapter 7: Introduction to Fourier Series; Chapter 8: Sampling; Chapter 9: Filters; Chapter 10: Implementing Shift Invariant Filters; Chapter 11: Reconstruction in X-Ray Tomography; Chapter 12: Imaging Artifacts in X-Ray Tomography; Chapter 13: Algebraic Reconstruction Techniques; Chapter 14: Magnetic Resonance Imaging; Chapter 15: Probability and Random Variables; Chapter 16: Applications of Probability; Chapter 17: Random Processes; Appendix A: Background Material; Appendix B: Basic Analysis; Index.
IMPACT OF INFORMATION LITERACY AND LEARNER CHARACTERISTICS ON LEARNING BEHAVIOR OF JAPANESE STUDENTS IN ONLINE COURSES

This paper investigated the effects of information literacy on learning behavior, and measured various indices of learner characteristics in fully online and hybrid courses at a Japanese university. In fully online courses, some factor scores for information literacy were found to be significantly higher in face-to-face than hybrid courses. The information literacy scores of masters students were also found to be significantly higher than bachelor students. Results of causal analyses indicate that operational skills (component of information literacy) affect the online learning experience of bachelor students, while for masters students, attitudes affect learning habits in online learning experiences. This paper also examined the role of information literacy in fully online and hybrid courses.
Comparison on the Interpolating Schemes to the Fast Fourier Transform of Short Sinusoids

Abstract For eliminating the picket fence effect innate to the fast Fourier transform (FFT), several schemes of the interpolated FFT (IpFFT) have been developed. Most IpFFTs are biased significantly for a short sine wave, which is due to the interference from the negative frequency component. In this paper, five approaches are compared, including one recently proposed by authors that can take into account the negative frequency component. It is demonstrated that our approach is the best among the compared approaches when sampling is close (but not equal) to the coherent condition.
Analysis and Design of a Multi-Step Bias-Flip Rectifier for Piezoelectric Energy Harvesting

The full-wave rectifier is the most straightforward way of extracting energy from a piezoelectric source. Unfortunately, the inherent capacitance of the piezoelement significantly limits the efficiency of extraction. The bias-flip rectifier, which aims to mitigate this problem, not only needs a large inductor for efficient operation, but also needs the generation of pulses with a precisely defined ontime. A large inductor increases the overall volume of the system. We present the multi-stage bias-flip rectifier, which is a technique that achieves a high voltage-flip efficiency using a much smaller inductor, and relaxes timing-accuracy requirements. The rectifier, implemented in a 130-nm CMOS process, dissipates about 2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> and achieves a voltage-flip efficiency of 89.5% while using only a 47 <inline-formula> <tex-math notation="LaTeX">$\mu \text{H}$ </tex-math></inline-formula> inductor.
Detecting Phishing Attacks: A Comprehensive Approach

The convenience of Internet communications and e-commerce has been exploited by consumers and criminals alike, and in recent years phishing has become a serious problem for information security as cyber criminals prey on individual Internet users as well as government organizations and businesses. Though public awareness to phishing attacks is generally on the rise, new and novel schemes or “zero-day attacks” that circumvent phishing filters and blacklists often still succeed in fooling even savvy users. Therefore, machine-learning-based approaches have been implemented for phishing detection. In this dissertation, we conduct a comprehensive study of phishing detection by investigating detecting phishing emails, URLs, and webpages, separately, using machine learning techniques. In addition, a simple rule-based system is developed for performance comparison against machine-learning-based systems. The key contribution of this dissertation is that it covers major aspects of phishing attack cycle and that it performance analyzes various methods for phishing detection. It is demonstrated that our proposed method of including a number of discriminative features extracted locally or from Web, employing a suitable feature selection algorithm, and then applying a batch or online learning classifier or ensemble of classifiers, results in highly accurate phishing detectors with respect to real-world data sets, thereby improving the state-of-the-art for this important problem. Further, preliminary analysis of a simple rule-based phishing detection engine also shows very promising results, thus indicating the tremendous potential for developing highly effective phishing detection methods using merely the expert systems approach.
Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection

Mammograms, which are X-ray images of the female breast, are used widely by radiologists to screen for breast cancer. The first stage of any computerized analysis of the digitised mammogram is to divide the image into anatomically distinct regions. The pectoral muscle is one of these regions and it appears on mediolateral oblique views of mammograms. In this paper, the rationale and algorithms for fully automatic, two-part segmentation of the pectoral muscle are presented. The algorithm consists of (a) estimation of the muscle edge by a straight line; and (b) refinement of the detected edge by surface smoothing and edge detection in a restricted neighbourhood derived from the first estimate.
Fault Tolerance Technique Offlining Faulty Blocks by Heap Memory Management

As dynamic random access memory (DRAM) cells continue to be scaled down for higher density and capacity, they have more faults. Thus, DRAM reliability becomes a major concern in computer systems. Previous studies have proposed many techniques preserving the reliability in various system components, such as DRAM internal, memory controller, caches, and operating systems. By reviewing the techniques, we identified the following two considerations: First, it is possible to recover faults with reasonable overhead at high fault rate only if the recovery unit is fine-grained. Second, since hardware modification requires additional cost in the employment of a technique, a pure software-based recovery technique is preferable. However, in the existing software-based recovery technique, the recovery unit is too coarse-grained to tolerate the high fault rate. In this article, we propose a pure software-based recovery technique with fine-granularity. Our key idea is based on heap segments being managed by the system library with variable-sized chunks to handle dynamic allocation in user applications. In our technique, faulty blocks in pages are offlined by marking them as allocated chunks. Thus, not only fault-free pages but also the remaining clean blocks in faulty pages are allowed to be usable space. Our technique is implemented by modifying the operating system and the system library. Since hardware assistance is unnecessary in the implementation, we evaluated our method on a real machine. Our evaluation results show that our technique has negligible performance overhead at high bit error rate (BER) 5.12e-5, which a hardware-based recovery technique could not tolerate without unacceptable area overhead. Also, at the same BER, our method provides 5.22× usable space, compared with page-offline, which is the state-of-the-art pure software-based technique.
Just “OneSeC” for Producing Multilingual Sense-Annotated Data

The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages. In this paper we formulate the assumption of One Sense per Wikipedia Category and present OneSeC, a language-independent method for the automatic extraction of hundreds of thousands of sentences in which a target word is tagged with its meaning. Our automatically-generated data consistently lead a supervised WSD model to state-of-the-art performance when compared with other automatic and semi-automatic methods. Moreover, our approach outperforms its competitors on multilingual and domain-specific settings, where it beats the existing state of the art on all languages and most domains. All the training data are available for research purposes at http://trainomatic.org/onesec.
Secure Load Balancing via Hierarchical Data Aggregation in Heterogeneous Sensor Networks

In wireless sensor networks, sensing driven nature of data generation and uneven cluster sizes result in unbalanced data traffic load among clusters. Clusters in which the event generation rate is high and/or clusters that have more members than others suffer from congestion and data loss which negatively affect the accuracy of the collected data. In addition, the cluster heads of such clusters exhaust their energy earlier than others, thereby reducing the network lifetime. Hence, the data load among clusters must be balanced to preserve the data accuracy and prolog the network lifetime. This paper presents Secure Load Balancing (SLB) protocol and introduces pseudo-sinks in order to improve data accuracy and bandwidth utilization of wireless sensor networks while still providing secure communication. Simulation results show that, in comparison with traditional cluster based networks, SLB protocol improves the data accuracy and increases the data delivery rate in the presence of security constraints.
Supervisor Localization: A Top‐Down Approach to Distributed Control of Discrete‐Event Systems

A purely distributed control paradigm is proposed for discrete‐event systems (DES). In contrast to control by one or more external supervisors, distributed control aims to design built‐in strategies for individual agents. First a distributed optimal nonblocking control problem is formulated. To solve it, a top‐down localization procedure is developed which systematically decomposes an external supervisor into local controllers while preserving optimality and nonblockingness. An efficient localization algorithm is provided to carry out the computation, and an automated guided vehicles (AGV) example presented for illustration. Finally, the ‘easiest’ and ‘hardest’ boundary cases of localization are discussed.
Statistics-Assisted Beam Training for MmWave Massive MIMO Systems

Combined with massive multiple-input multiple-output technology, beamforming can effectively compensate the high path-loss of millimeter-wave signals and hence improve the received power at user equipment (UE). In order to establish the beam alignment between base station (BS) and UE, beam training (BT) is required to select appropriate beamforming vectors at both BS and UE sides. In general, a good trade-off between beamforming gain and BT overhead is critical to enhance the system performance. In this letter, a statistics-assisted beam training (SA-BT) scheme is proposed to reduce the BT overhead. Specifically, BS scans the candidate beams according to the descending order of the utilization frequency of each beam. Meanwhile, the BT procedure is finished when the beamforming gain at UE exceeds the pre-defined threshold. The theoretical analyses and the numerical simulations prove that the proposed scheme can reduce the BT overhead and hence improve the spectral efficiency significantly compared with the existing methodologies.
Effective use of analytic DSS and job performance: Looking beyond technology acceptance

ABSTRACT This study extends and test the work done on effective use of information systems (IS) by developing measures for this construct which expands the conceptualization of use beyond the popular technology acceptance model (TAM). Effective use posits dimensions of use (informed action, representational fidelity, and transparent interaction) which are intended to measure a user’s ability to effectively use an IS in a given context. Moving beyond simple adoption and use contexts is an important distinction that expands researcher’s and practitioner’s ability to assess usage effectiveness. In a work context, it is more important to have high performance and effective usage of systems in comparison to just having high usage rates. Therefore this study uses the context of analytic decision support systems (DSS) and tests the relationship between the proposed dimensions of effective use and job performance. A survey (N = 265) of executives with work experience using analytic DSS was conducted to maximize realism and generalizability. This study is the first to empirically test the proposed relationships of effective use to performance. Results indicate that informed action is a significant predictor of individual job performance. However, representational fidelity and transparent interaction are not significant predictors of job performance.
BAD SLAM: Bundle Adjusted Direct RGB-D SLAM

A key component of Simultaneous Localization and Mapping (SLAM) systems is the joint optimization of the estimated 3D map and camera trajectory. Bundle adjustment (BA) is the gold standard for this. Due to the large number of variables in dense RGB-D SLAM, previous work has focused on approximating BA. In contrast, in this paper we present a novel, fast direct BA formulation which we implement in a real-time dense RGB-D SLAM algorithm. In addition, we show that direct RGB-D SLAM systems are highly sensitive to rolling shutter, RGB and depth sensor synchronization, and calibration errors. In order to facilitate state-of-the-art research on direct RGB-D SLAM, we propose a novel, well-calibrated benchmark for this task that uses synchronized global shutter RGB and depth cameras. It includes a training set, a test set without public ground truth, and an online evaluation service. We observe that the ranking of methods changes on this dataset compared to existing ones, and our proposed algorithm outperforms all other evaluated SLAM methods. Our benchmark and our open source SLAM algorithm are available at: www.eth3d. net