Miracle-dz/UniFAR-FTUs huggingface dataset:
Blind reconstruction and automatic modulation classifier for non-uniform sampling based wideband communication receivers

Electromagnetic spectrum is a limited natural resource and needs to be used eﬃciently. However, various measurements conducted worldwide have observed poor spectrum utilization. Software deﬁned radios (SDRs) and cognitive radios (CRs) technologies allow eﬃcient utilization of spectrum by empowering mobile devices to change their transmission parameters like frequency band, sampling rate, modulation scheme, etc. to meet the desired quality of service for diﬀerent channel conditions. These mobile devices require smart receiver to detect transmission parameters of received signal. Such smart receivers, also known as multi-standard wireless communication receivers (MWCRs), must be capable of digitizing wideband signal ranging from 400 MHz to few GHz to support wide variety of data intensive services. Limited reconﬁgurability of analog front-end and unavailability of high rate analog to digital converters (ADCs) have generated signiﬁcant interest in non-uniform (sub-Nyquist) sampling (NUS) and digital reconstruction based MWCRs. Existing reconstruction approaches require prior knowledge of sparsity which may not be available in dynamic spectrum environment. To alleviate this problem, a blind adaptive orthogonal matching pursuit (AOMP) reconstruction approach has been proposed and is the ﬁrst contribution of this thesis. Novelty of AOMP is the use of online learning algorithm to ﬁnd spectrum occupancy (i.e. sparsity). Simulation results show that the average reconstruction error of AOMP is 29.7% lower than other approaches. To validate the usefulness of proposed approach in real life applications, performance of cumulant and machine learning based automatic modulation classiﬁer (AMC) is analyzed for the wideband signal digitized using the proposed approach. The simulation results are further veriﬁed on the proposed USRP testbed in real radio environment. Simulation and experimental results show that the accuracy of NUS based AMC approaches the accuracy of uniform sampling based AMC for higher values of SNR and proposed AOMP is superior to others. Also, the performance of AMC does not degrade signiﬁcantly with NUS given that wideband signal is sparse in frequency.
Models for Predicting Community-Specific Interest in News Articles

In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communities on reddit.com. We train and test models over three different time periods between 2015 and 2017 to demonstrate which features degrade in performance the most due to concept drift. Our models can classify news articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0 ROC AUC. However, while we can predict the community-specific popularity of news articles with high accuracy, practitioners should approach these models carefully. Predictions are both community-pair dependent and feature group dependent. Moreover, these feature groups generalize over time differently, with some only degrading slightly over time, but others degrading greatly. Therefore, we recommend that community-interest predictions are done in a hierarchical structure, where multiple binary classifiers can be used to separate community pairs, rather than a traditional multi-class model. Second, these models should be retrained over time based on accuracy goals and the availability of training data.
An automatic algorithm to exploit the symmetries of the system response matrix in positron emission tomography iterative reconstruction

Positron emission tomography (PET) iterative 3D reconstruction is a very computational demanding task. One of the main issues of the iterative reconstruction concerns the management of the system response matrix (SRM). The SRM models the relationship between the projection and the voxel space and its memory footprint can easily exceed hundreds of GB. Moreover, in order to make the reconstruction fast enough not to hinder its practical application, the SRM must be stored in the random access memory of the workstation used for the reconstruction. This issue is normally solved by implementing efficient storage schemes and by reducing the number of redundant patterns in the SRM through symmetries. However, finding a sufficient number of symmetries is often non-trivial and is typically performed using dedicated solutions that cannot be exported to different detectors and geometries. In this paper, an automatic approach to reduce the memory footprint of a pre-computed SRM is described. The proposed approach was named symmetry search algorithm (SSA) and consists in an algorithm that searches for some of the redundant patterns present in the SRM, leading to its lossy compression. This approach was built to detect translations, reflections and coordinates swap in voxel space. Therefore, it is particularly well suited for those scanners where some of the rotational symmetries are broken, e.g. small animal scanner where the modules are arranged in a polygonal ring made of few elements, and dual head planar PET systems. In order to validate this approach, the SSA is applied to the SRM of a preclinical scanner (the IRIS PET/CT). The data acquired by the scanner were reconstructed with a dedicated maximum likelihood estimation maximization algorithm with both the uncompressed and the compressed SRMs. The results achieved show that the information lost due to the SSA compression is negligible. Compression factors up to 52 when using the SSA together with manually inserted symmetries and up to 204 when using the SSA alone, can be obtained for the IRIS SRM. These results come without significant differences in the values and in the main quality metrics of the reconstructed images, i.e. spatial resolution and noise. Although the compression factors depend on the system considered, the SSA is applicable to any SRM and therefore it can be considered a general tool to reduce the footprint of a pre-computed SRM.
STUDYING NEAR FAULT ENDURANCE TIME ACCELERATION FUNCTION

The Endurance Time (ET) method is a time-history based dynamic pushover procedure. In this method, the structures are subjected to gradually intensifying acceleration called an acceleration function. Then, their performances are assessed from linear to collapse level based on the interval time through which they can satisfy the required objectives. ET Acceleration Functions (ETAFs) are calibrated upon actual earthquake spectra compatible with the Iranian National Building Code (standard no. 2800). In this code, there is no distinction between far and near fault regions. ETAFs scale based spectrum increases uniformly at all periods as the time passes which is why this uniform increment is not desirable in near fault earthquakes. As the rupture propagation is oriented towards a site, near fault earthquakes have further spectral acceleration in the period over 0.6 sec compared to far fault earthquakes. The shape of the spectrum becomes richer over long periods as its level increases. In this research, ETAFs are presented for near fault target spectra, provided by Abrahamson-Silva attenuation relations. These relations have been modi ed by Somerville near fault correction coecients. According to the results obtained in this research, while the new acceleration function can model directivity eff ects at higher periods, it also satis es endurance time concepts.
SERVICE RECOMMENDATION FOR INDIVIDUAL AND PROCESS USE

Web services have been developed as an attractive paradigm for publishing, discovering and consuming services. They are loosely-coupled applications that can be run alone or be composed to create new value-added services. They can be consumed as individual services which provide a unique interface to receive inputs and return outputs; or they can be consumed as components to be integrated into business processes. We call the first consumption case individual use and the second case business process use. The requirement of specific tools to assist consumers in the two service consumption cases involves many researches in both academics and industry. On the one hand, many service portals and service crawlers have been developed as specific tools to assist users to search and invoke Web services for individual use. However, current approaches take mainly into account explicit knowledge presented by service descriptions. They make recommendations without considering data that reflect user interest and may require additional information from users. On the other hand, some business process mechanisms to search for similar business process models or to use reference models have been developed. These mechanisms are used to assist process analysts to facilitate business process design. However, they are labor-intense, error-prone, time-consuming, and may make business analyst confused. In our work, we aim at facilitating the service consumption for individual use and business process use using recommendation techniques. We target to recommend users services that are close to their interest and to recommend business analysts services that are relevant to an ongoing designed business process. To recommend services for individual use, we take into account the user's usage data which reflect the user's interest. We apply well-known collaborative filtering techniques which are developed for making recommendations. We propose five algorithms and develop a web-based application that allows users to use services. To recommend services for business process use, we take into account the relations between services in business processes. We target to recommend relevant services to selected positions in a business process. We define the neighborhood context of a service. We make recommendations based on the neighborhood context matching. Besides, we develop a query language to allow business analysts to formally express constraints to filter services. We also propose an approach to extract the service's neighborhood context from business process logs. Finally, we develop three applications to validate our approach. We perform experiments on the data collected by our applications and on two large public datasets. Experimental results show that our approach is feasible, accurate and has good performance in real use-cases
OFDM and MC-CDMA: A Primer

About the Authors. Other Wiley and IEEE Press Books on Related Topics. Acknowledgements. 1. Introduction. 2. Introduction to Orthogonal Frequency Division Multiplexing. 3. OFDM Transmission over Gaussian Channels 4. OFDM Transmission over Wideband Channels. 5. OFDM Time and Frequency Domain Synchronisation. 6. Adaptive Single- and Multi-user OFDM Techniques. II. OFDM versus MC-CDMA Systems. 7. OFDM versus MC-CDMA. 8. Basic Spreading Sequences. 9. MC-CDMA Performance in Synchronous Environments. III. Advanced Topics: Multi-User OFDM Systems. 10 Maximum-Likelihood Enhanced Sphere Decoding of MIMO-OFDM. 11. Genetic Algorithm Aided Joint Channel Estimation and MUD for SDMA OFDM. 12. Multi-User OFDM Employing Genetic Algorithm Aided Minimum Bit Error RateMulti-User Detection. 13. Conclusion and Further Research Problems. Glossary. Bibliography. Subject Index. Author Index.
Managing Business Improvement and Quality: Implementing Key Tools and Techniques

Preface. 1. The Role of Tools and Techniques in Total Quality Management. 2. Tools and Techniques: An Introduction. 3. Difficulties Encountered in the Use of Tools and Techniques. 4. Key Influences on the Use of Quality Management Tools and Techniques. 5. Implications for Education and Training. 6. An Overview of Common Tools. 7. An Overview of Common Techniques. 8. Tools and Techniques: An Assessment Methodology. References. Index.
Practical Flow Cytometry

List of Tables and Figures. Preface to the Fourth Edition: Why You Should Read the Book - Or Not. Foreword to the Third Edition. Preface to the Third Edition. Preface to the Second Edition. Foreword to the First Edition. Preface to the First Edition. 1. Overture. 2. Learning Flow Cytometry. 3. History. 4. How Flow Cytometers Work. 5. Data Analysis. 6. Flow Sorting. 7. Parameters and Probes. 8. Buying Flow Cytometers. 9. Building Flow Cytometers. 10. Using Flow Cytometers: Applications, Extensions, and Alternatives. 11. Sources of Supply. 12. Afterword.
Language and social networks

Editora s Preface to the Second Edition. Preface to the Second Edition. Acknowledgements. 1. Language, Class and Community. 2. Obtaining Data in the Speech Community: Major Principles. 3. Studying Language in the Community: The Fieldworker and the Social Network. 4. The Social Context of Speech Events. 5. The Quantitative Analysis of Linguistic Data. 6. The Language of the Individual Speaker: Patterns of Variation and Network Structure. 7. Conclusions and Theoretical Implications. Appendix. References. Index.
Information filtering by multiple examples

INFORMATION FILTERING BY MULTIPLE EXAMPLES by Mingzhu Zhu A key to successfully satisfy an information need lies in how users express it using keywords as queries. However, for many users, expressing their information needs using keywords is difficult, especially when the information need is complex. Search By Multiple Examples (SBME), a promising method for overcoming this problem, allows users to specify their information needs as a set of relevant documents rather than as a set of keywords. Most of the studies on SBME adopt the Positive Unlabeled learning (PU learning) techniques by treating the user’s provided examples (denoted as query examples) as positive set and the entire data collection in the database as unlabeled set. User’s information need is then represented as a query vector, which is obtained from the query examples or further augmented with unlabeled data as negative examples, in which the documents are ranked according to their degree of similarity to the query vector. The query examples are treated as being relevant to a single topic to build the query vector, but it is often the case that they belong to multiple topics. New methods are needed to deal with such a topic diversity issue. Furthermore, there are many PU learning algorithms available, but it is still unknown which methods perform most effectively for SBME, as the experiments conducted in the previous studies have not taken into account the user search situation, where the size of the query examples varies and is much smaller than the size of the unlabeled data. When the query examples are much fewer than the unlabeled data, the system effectiveness may downgrade dramatically because of the class imbalance problem. Thus, it is important to identify the most effective PU learning algorithms for SBME and explore how to improve the system effectiveness further. In the previous studies on SBME, a document is usually treated as a vector, of which the features are terms in the collections. Such a term-vector based document representation brings high dimensionality problems when the collection is large; or even worse, some noisy features seriously degrade the performance of the learning algorithms. Feature selection is necessary for solving the high dimensionality problem. This research proposes a framework named Information Filtering by Multiple Examples (IFME) to explore how to improve SBME by: (1) solving the topic diversity issue by adopting probabilistic topic models to predict user’s information need from the query examples; (2) tackling the class imbalance problem by adopting machine learning techniques; (3) identifying the most effective PU learning algorithms for SBME, (4) adopting ensemble learning techniques to improve the effectiveness of the PU learning algorithms for SBME further; and (5) adopting topic model for feature dimension reduction. The experimental results show that the proposed framework addressed the research questions successfully. INFORMATION FILTERING BY MULTIPLE EXAMPLES
Control of the amount and functionality of the eEF 1 A 1 and eEF 1 A 2 isoforms in mammalian cells

B. S. Negrutskii, O. V. Novosylna, L.V. Porubleva © 2018 B. S. Negrutskii et al.; Published by the Institute of Molecular Biology and Genetics, NAS of Ukraine on behalf of Biopolymers and Cell. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited UDC 577
No Effect of Interstimulus Interval on Acoustic Reflex Thresholds

The acoustic reflex (AR), a longstanding component of the audiological test battery, has received renewed attention in the context of noise-induced cochlear synaptopathy—the destruction of synapses between inner hair cells and auditory nerve fibers. Noninvasive proxy measures of synaptopathy are widely sought, and AR thresholds (ARTs) correlate closely with synaptic survival in rodents. However, measurement in humans at high stimulus frequencies—likely important when testing for noise-induced pathology—can be challenging; reflexes at 4 kHz are frequently absent or occur only at high stimulus levels, even in young people with clinically normal audiograms. This phenomenon may partly reflect differences across stimulus frequency in the temporal characteristics of the response; later onset of the response, earlier onset of adaptation, and higher rate of adaptation have been observed at 4 kHz than at 1 kHz. One temporal aspect of the response that has received little attention is the interstimulus interval (ISI); inadequate duration of ISI might lead to incomplete recovery of the response between successive presentations and consequent response fatigue. This research aimed to test for effects of ISI on ARTs in normally hearing young humans, measured at 1 and 4 kHz. Contrary to our hypotheses, increasing ISIs from 2.5 to 8.5 s did not reduce ART level, nor raise ART reliability. Results confirm that clinically measured ARTs—including those at 4 kHz—can exhibit excellent reliability and that relatively short (2.5 s) ISIs are adequate for the measurement of sensitive and reliable ARTs.
Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information

Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language preprocessing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches. key words: text mining, software artifact, stop words, topic modeling, Pointwise Mutual Information (PMI)
Solving the contact map overlap problem via tree decomposition and a DEE-like pruning strategy

This paper proposes a new algorithm for protein structure alignment in the case where a protein structure is represented by a contact map graph and the sequential order of residues is strictly enforced in the alignment. The time complexity of the algorithm, O(n(2n+w)! /((2n)! w! )) where n is the maximum number of residues in two proteins and w = O((Du/Dl) n2/3 log n) is the treewidth of a contact graph. In particular, Du is the contact distance cutoff and Dc is the minimum inter-residue distance in a typical protein. If the distance between two matched residues after two proteins are superimposed is bounded by a parameter Dc, then the time complexity is O((n/epsivDc)6((n+w)! /(n! w! )Deltaw)) where e is a small constant and Delta = (1 + (2Dc/Dl))3. This algorithm consists of two major components: tree-decomposition and a DEE-like pruning strategy. The tree-decomposition method exploits the geometric feature of a protein structure and guarantees to terminate within subexponential time. The DEE-like pruning strategy exploits the sequential constraint and can quickly eliminate non-optimal alignments from a large search space. Based on these two techniques, we have developed a structure alignment program TreeAlign to find the globally optimal solution of the contact map overlap problem. The preliminary experimental results indicate that on a Linux PC with 2.5 GHz CPU, TreeAlign can align two similar proteins within seconds and two dissimilar proteins within minutes.
Collaborative Hotel Recommendation based on Topic and Sentiment of Review Comments

We considered a method for recommending hotels in this paper. Most of conventional hotel recommendation systems use a vector space model to represent hotels. A hotel recommendation system decides attributes such as service, location, price, etc. in the vector space. In such system-de(cid:12)ned (cid:12)xed vector space, it is diﬃcult to capture personal preference and evaluation viewpoint well, since each user has her/his preferences and viewpoints that are diﬀerent from axis of the system-de(cid:12)ned vector space. Therefore, we proposed a method that can automatically decide attributes of vector space. In most of systems, users can submit natural language comments, in which users can express various impressions about hotels. Therefore, in our method, we have utilized natural language comments that have submitted by users. We, (cid:12)rst, analyzed texts in comments by using Latent Dirichlet Allocation (LDA) and extracted representative topics about hotels from the tex ts automatically. We further analyzed the texts to (cid:12)nd sentiment for each extracted topic for each hotel. By using the topic and sentiment information, we generated our recommendations.
Machine Vision-Enabled Traffic Controller for Safer and Smoother Traffic Flow Around Construction Sites

With the continuous process of urbanization in many cities around the globe, construction works are increasingly interrupting traffic flow in urban roads. At construction sites, usually traffic control personnel are employed to instruct the traffic flow at the times obstruction. Employing manual labour flagmen for traffic control is costly and can potentially expose the workers to safety hazards. Alternative attempts such as using remotely controlled signs or timers are not ideal; the former still requires human input and the latter does not optimize traffic control based on traffic density. To tackle this problem, we propose a fully automated traffic controller solution that uses visual sensors and machine vision technology. This system can replace the human traffic controllers during lane closures on the event of a construction. The implementation of this system eliminates the requirement for conventional traffic controllers, reducing the overall health and safety risks to workers and drivers. Our solution can result in substantial cost savings by replacing the traffic controllers. In addition, with manual labour, traffic control performance can be affected by weather conditions or fatigue from long hour shifts. Thus, our solution can lead to improved road safety and robustness of traffic flow as well.