Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Questions, Talk Abstracts, and Summary of Discussions

Participants took a critical look at the results, choice of problems, guiding philosophies, research methodology , and engineering projects that currently absorb much of the eeort of people working in \cryptography" and \computer system security. " This report summarizes both the formal presentations and the informal discussions that took place. Section 1 contains our account of the group discussions and statements of open questions, both general and speciic, that we think are important. This report on the workshop is based on our recollections, our notes, and notes taken by the graduate-student participants; we assume responsibility for any inaccuracies in our account. Section 2 contains abstracts of the talks presented at the workshop; whenever possible, these abstracts are accompanied by references to complete papers or extended abstracts that the speaker has published. Names and addresses of participants are given in Section 3. The part of the theoretical computer science community that studies cryptography has created a large amount of terminology. Many of these terms were used in Wednesday morn-ing's talks.
Studies on Application of Support Vector Machine in Diagnose of Coronary Heart Disease

Right now, the diagnosis of coronary heart disease is mostly from experienced physician's judgment. How to use computer intelligent algorithms to aid in diagnosing of coronary heart disease has been a hot research of machine learning. This article will apply support vector machine (SVM) method which is based on the statistical learning theory to the diagnosis of coronary heart disease. On the basis of original data pre-processing and feature extraction, classifiers with different kernel are selected to classify the test data, followed by a comparison of classification results which show that the accuracy of classifiers with radial basis function is the highest. To select the best parameters of kernel function and penalty factor, Grid Search Method of optimizing parameters is used, which makes the classifier achieve the highest classification accuracy.
Citation Analysis with Neural Attention Models

Automated citation analysis (ACA) can be important for many applications including author ranking and literature based information retrieval, extraction, summarization and question answering. In this study, we developed a new compositional attention network (CAN) model to integrate local and global attention representations with a hierarchical attention mechanism. Training on a new benchmark corpus we built, our evaluation shows that the CAN model performs consistently well on both citation classiﬁcation and sentiment analysis tasks.
THE USE OF PULMONARY FUNCTION TEST TO PREDICT OCCUPATIONAL LUNG DISEASE AMONG WORKERS IN BASRAH STEEL INDUSTRY

One hundred and eighty asymptomatic workers in the steel industry of Basrah whose duration of employment ranges from 5-15 year were randomly selected and examined at Al Sader teaching hospital during the period (2000-2002). Workers was assessed clinically and the function of their respiratory system were studied by measuring FEV1 (Forced Expiratory Volume in the first second) and FVC (Forced Vital Capacity). The results showed that the majority of the examined workers 45.55% had restrictive pulmonary defect, 20% had obstructive pulmonary defect while 7.22% had combined obstructive and restrictive defects.
Tags Meet Ratings: Improving Collaborative Filtering with Tag-Based Neighborhood Method

Collaborative flltering (CF) is a method for personalized recommendation. The sparsity of rating data seriously impairs the quality of CF’s recommendation. Meanwhile, there is more and more tag information generated by online users that implies their preferences. Exploiting these tag data is a promising means to alleviate the sparsity problem. Although the intention is straight-forward, there’s no existed solution that makes full use of tags to improve the recommendation quality of traditional rating-based collaborative flltering approaches. In this paper, we propose a novel approach to fuse a tag-based neighborhood method into the traditional rating-based CF. Tag-based neighborhood method is employed to flnd similar users and items. These neighborhood information helps the sequent CF procedure produce higher quality recommendations. The experiments show that our approach outperforms the state-of-the-art ones.
Generalizing Scales

Instead of considering scales to be linearly ordered structures, it is proposed that scales are better conceived of as metrics (dissimilarity matrices). Further, to be considered a scale of typological interest, there should be a significant correlation between a meaning-scale and a form-scale. This conceptualisation allows for a fruitful generalization of the concept "scale". As a hands-on example of the proposals put forward in this paper, the "scale of likelihood of spontaneous occurrence" (Haspelmath 1993) is reanalyzed. This scale describes the prototypical agentivity of the subject of a predicate. 1. Scales as restrictions on form-function mapping Scales of linguistic structure are one of the more promising avenues of research into the unification of the worldwide linguistic diversity. Although our growing understanding of the diversity of the world’s languages seems to put more and more doubt on many grandiose attempts on universally valid generalizations, the significance of scales for human languages (like the well-known animacy scale) still appears to stand strong. So, what actually is a scale? A scale seems to be mostly thought of as an asymmetrical one-dimensional arrangement (a “total order” in mathematical parlance) on certain cross-linguistic categories/functions. Put differently, a scale is a linear ordering of functions with a “high end” and a “low end”. To be a considered an interesting scale, the formal encoding of these functions in actual languages should be related to this linear ordering. In this paper, I will argue that this concept of a scale can be fruitfully generalized. In a very general sense, all linguistic structure consists of forms expressing particular functions. If we find restrictions—across languages— on the kind of forms that are used to express certain functions, then this The term “scale” is used here synonymously to what is also known as an “implicational hierarchy”, “markedness hierarchy” or simply “hierarchy” in linguistics. Scales, 379-396 Marc Richards & Andrej L. Malchukov (eds.) LINGUISTISCHE ARBEITS BERICHTE 86, Universität Leipzig 2008
Dynamix: anonymity on dynamic social structures

In this paper we advance communication using social networks in two directions by considering dynamics of social graphs. First, we formally define the problem of routing on dynamic graphs and show an interesting and intuitive connection between graph dynamics and random walks on weighted graphs; graphs in which weights summarize history of edge dynamics and allow for future dynamics to be used as weight adjustment. Second, we present several measurements of our proposed model on dynamic graphs extracted from real-world social networks and compare them to static structures driven from the same graphs. We show several interesting trade-offs and highlight the potential of our model to capture dynamics, enrich graph structure, and improves the quantitative sender anonymity when compared to the case of static graphs.
Loop closure detection on a suburban road network using a continuous appearance-based trajectory

This paper presents a novel technique for performing SLAM along a continuous trajectory of appearance. Derived from components of FastSLAM and FAB-MAP, the new system dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM) augments appearancebased place recognition with particle-filter based ‘pose filtering’ within a probabilistic framework, without calculating global feature geometry or performing 3D map construction. For loop closure detection CAT-SLAM updates in constant time regardless of map size. We evaluate the effectiveness of CAT-SLAM on a 16km outdoor road network and determine its loop closure performance relative to FAB-MAP. CAT-SLAM recognizes 3 times the number of loop closures for the case where no false positives occur, demonstrating its potential use for robust loop closure detection in large environments.
Conceptual modeling of multimedia documents

An approach to the document-retrieval problem that aims to increase the efficiency and effectiveness of document-retrieval systems by exploiting the semantic contents of the documents is presented. The document retrieval problem is delineated, and conceptual document modeling basics and requirements are discussed. An experimental system, the Multimedia Office Server (Multos), which implements some of the document-model concepts described, is presented. <<ETX>>
The Multicast Capacity of Finite Field Multiple-Access Networks

We study the problem of multicasting over a network of multiple-access channels (MACs). The separation-based solution to this problem is to reduce each MAC to a set of noiseless bit pipes via a channel code and then employ network coding. Sometimes, however, the physical-layer structure of the MAC can be exploited more advantageously. In many cases of interest, the MAC output is a (deterministic) function of its inputs, corrupted by noise. We develop structured codes to exploit the natural function of a MAC to reliably compute functions as part of a network code and show that in many scenarios of interest our scheme outperforms the separation-based solution. If each MAC can be written as a sum over some finite field plus noise, then our achievable rate coincides with the max-flow min-cut bound.
The prosody of modern Hebrew - a quantitative study

The current paper presents a preliminary study of Modern Hebrew prosody using a quantitative model. In a small corpus of segmentally identical utterances the place of focus and sentence mode were systematically varied. Following the rationale of MFGI, the Mixdorff-Fujisaki Model of German intonation, the phonetic properties of the contrast lending intonemes were examined. These properties are remarkably similar to those of German: The place of focus is marked by high accent command amplitudes and reduction of post-focal accents whereas pre-focal accents remain almost unaffected. Questions and non-terminal utterances are marked by rising intonation on the last accented item.
Using SPIDER: an experience report

At AT&T Labs-Research, we have been developing a prototype system called SPIDER to efficiently support flexible string matching of attribute values in large databases. SPIDER has been used in AT&T, both as a key component of an operational portal for matching customer names and addresses, and for a variety of ad hoc data quality analyses. In this talk, we report on experiences with SPIDER.
Blind multi-user MMSE detection of CDMA signals

The recovery of code-division multiple access (CDMA) information signals in a frequency selective fading channel is a problem of great theoretical and practical interest. This paper addresses the estimation of an optimal (within the class of linear detectors) multi-user CDMA receiver. A novel approach is introduced that enables the estimation of the minimum mean-square error (MMSE) detector in a blind setting. The MMSE detector is obtained through a double subspace projection that exploits the subspace structure associated with both the code of the desired user and the estimated signal subspace of the covariance matrix for the observed signals. The technique allows for interference rejection without requiring the knowledge of the codes for the interferers.
Real-Time L 1 Adaptive Control Algorithm in Uncertain Networked Control Systems

This paper studies the real-time implementation of output-feedback L1 adaptive controller over real-time networks. Event-triggering schedules the data transmission dependent upon errors exceeding certain threshold. Continuous-time and discrete-time L1 adaptive control algorithms are provided. We show that with the proposed event-triggering schemes the states and the input in the networked system can be arbitrarily close to those of a stable reference system by increasing the sampling frequency and the transmission frequency. Stability conditions, in terms of event threshold and allowable transmission delays, are also provided, which can serve as the guidance in real-time scheduling.
Large-scale millimeter-wave phased arrays for 5G systems

The construction of large phased arrays, composed of 256-1024 elements, with relatively low-cost is one of the main problems of 5G systems. In this talk, we will present our effort in this area and show 256 element and 1024 element phased arrays operating at 60GHz. This is done using highly complex and integrated silicon (SiGe) chips, capable of phase shifting and frequency translation functions, and operation up to 100C. Measured patterns and communication links capable of 2-4 Gbps over hundreds of meters will be shown.
A Data-Driven Hybrid Optimization Model for Short-Term Residential Load Forecasting

Analyzing the characteristic of residential load and forecasting are beneficial for electric power companies to provide higher quality, more reliable electricity services to the customers. This paper is focusing on the research of short-term load forecasting based on the residential load data of Shanghai, China. Firstly, the time series mining technology was studied and also its disadvantages such as the meteorological conditions were not taken into consideration were pointed out. Secondly, we analyzed the most competitive method of the existing forecasting methods that taking meteorological conditions into account, namely the similar day method. In order to consider comprehensively the effect of meteorological conditions and fully utilize the advantages of the time series mining technology, this paper proposed a hybrid optimization model that combines the time series method and the similar day method. The extensive experimental results show that the hybrid optimization method can significantly improve the accuracy and robustness of short-term residential load forecasting.
Identification of Sequence Motifs That Target Neuronal Nicotinic Receptors to Dendrites and Axons

Neuronal nicotinic acetylcholine receptors (nAChRs) belong to a family of ligand-gated ion channels that play important roles in central and peripheral nervous systems. The subcellular distribution of neuronal nAChRs has important implications for function and is not well understood. Here, we analyzed the targeting of two major types of neuronal nAChRs by expressing epitope-tagged subunits in cultured hippocampal neurons. Surprisingly, the α7 nAChR (α7) and α4/β2 nAChR (α4β2) displayed distinct patterns of expression, with α7 targeted preferentially to the somatodendritic compartments, whereas α4β2 was localized to both axonal and dendritic domains. When fused to CD4 or IL2RA (interleukin 2 receptor α subunit) proteins, which are normally distributed ubiquitously, the M3–M4 intracellular loop from the α7 subunit promoted dendritic expression, whereas the homologous M3–M4 loop from the α4 subunit led to surface axonal expression. Systemic screening and alanine substitution further identified a 25-residue leucine motif ([DE]XXXL[LI]) containing an axonal targeting sequence within the α4 loop and a 48-residue dileucine and tyrosine motif (YXXØ) containing a dendritic targeting sequence from the α7 loop. These results provide valuable information in understanding diverse roles of neuronal nAChRs in mediating and modulating synaptic transmission, synaptic plasticity, and nicotine addiction.
Jenga: Harnessing Heterogeneous Memories through Reconfigurable Cache Hierarchies

Conventional memory systems are organized as a rigid hierarchy, with multiple levels of progressively larger and slower memories. Hierarchy allows a simple, fixed design to benefit a wide range of applications, because working sets settle at the smallest (and fastest) level they fit in. However, rigid hierarchies also cause significant overheads, because each level adds latency and energy even when it does not capture the working set. In emerging systems with heterogeneous memory technologies such as stacked DRAM, these overheads often limit performance and efficiency. We propose Jenga, a reconfigurable cache hierarchy that avoids these pathologies and approaches the performance of a hierarchy optimized for each application. Jenga monitors application behavior and dynamically builds virtual cache hierarchies out of heterogeneous, distributed cache banks. Jenga uses simple hardware support and a novel software runtime to configure virtual cache hierarchies. On a 36-core CMP with a 1 GB stacked-DRAM cache, Jenga outperforms a combination of state-of-the-art techniques by 10% on average and by up to 36%, and does so while saving energy, improving system-wide energy-delay product by 29% on average and by up to 96%.
A Taxonomy and Performance Model of Data Skew Effects in Parallel Joins

Recent work on parallel joins and data skew has concentrated on algorithm design without considering the causes and chara.cteristics of data. skew itself. Existming ana.lyt,ic models of skew do not cont.ain enough informat,ion to fully describe data skew in parallel implementations. Because the assumptions made about the nature of skew vary between authors, it is almost impossible to make valid comparisons of parallel algorithms. In t,his paper, a taxonomy of skew effects is developed, and a. new performance model is introduced. The model is used to compare the performance of two parallel join algorithms.
Laboratory Investigation:Automated Instrument Tracking in Robotically Assisted Laparoscopic Surgery

This paper describes a practical and reliable image analysis and tracking algorithm to achieve automated instrument localization and scope maneuvering in robotically assisted laparoscopic surgery. Laparoscopy is a minimally invasive surgical procedure that utilizes multiple small incisions on the patient's body through which the surgeon inserts tools and a videoscope in order to conduct an operation. The scope relays images of internal organs to a camera, and the images are displayed on a video screen. The surgeon performs the operation by viewing the scope images rather than performing the traditional “open” procedure, where a large incision is made on the patient's body for direct viewing.The current mode of laparoscopy employs an assistant to hold the scope and position it in response to the surgeon's verbal commands. However, this results in suboptimal visual feedback, because the scope is often aimed incorrectly and vibrates due to hand trembling. We have developed a robotic laparoscope positioner to.
Deep Attributes from Context-Aware Regional Neural Codes

Recently, many researches employ middle-layer output of convolutional neural network models (CNN) as features for different visual recognition tasks. Although promising results have been achieved in some empirical studies, such type of representations still suffer from the well-known issue of semantic gap. This paper proposes so-called deep attribute framework to alleviate this issue from three aspects. First, we introduce object region proposals as intermedia to represent target images, and extract features from region proposals. Second, we study aggregating features from different CNN layers for all region proposals. The aggregation yields a holistic yet compact representation of input images. Results show that cross-region max-pooling of soft-max layer output outperform all other layers. As soft-max layer directly corresponds to semantic concepts, this representation is named "deep attributes". Third, we observe that only a small portion of generated regions by object proposals algorithm are correlated to classification target. Therefore, we introduce context-aware region refining algorithm to pick out contextual regions and build context-aware classifiers. We apply the proposed deep attributes framework for various vision tasks. Extensive experiments are conducted on standard benchmarks for three visual recognition tasks, i.e., image classification, fine-grained recognition and visual instance retrieval. Results show that deep attribute approaches achieve state-of-the-art results, and outperforms existing peer methods with a significant margin, even though some benchmarks have little overlap of concepts with the pre-trained CNN models.
Examining How Wi-Fi Affects Customers Loyalty at Different Restaurants: An Examination from South Korea

We have read this thesis and recommend that it be approved. iv DEDICATION It would have been impossible to finish my thesis without my committee members, friends, and family. I appreciate their support. v ACKNOWLEDGMENTS It would have been impossible to complete my thesis if my committee members had not supported me. Dr. Carolin Lusby helped me to process thesis and her guidance touched many facets of my thesis. Not only did Dr. Luisby serve as my committee chair, but also she gave me to adjust to my graduate school life. My committee members have also supported me with their valuable time and efforts. To Dr. Yoo, she supported me with statistics and analysis as random sampling. Dr. Yoo, she helped me set up the idea of customer loyalty. To Dr. Kitterlin, your willingness to help students with warm words of encourage always impressed me. Thank you for your knowledgeable guidance and for serving on my committee. I appreciate my parents' and my older sister's help. My family has always prayed for me and supported me with their best wishes. Finally, I would like to thank God most of all. The main research objective of this study was to determine whether Wi-Fi affects customer's loyalty and how its impact differs depending on the type of restaurants. For the study I designed a primary field survey to collect data and performed multiple linear regression analysis to test the study hypotheses. Findings show that Wi-Fi service factors turned out to be significant for all types of restaurants for both attitudinal and behavioral loyalty. However, not all of the Wi-Fi usage factors were significant consistently. Findings are expected to assist hospitality marketers to utilize Wi-Fi service as a point of service towards their customers.
Design of Low Power 5-Bit Hybrid Flash ADC

In this paper, a low power 5-bit hybrid flash architecture is proposed. The proposed analog-to-digital converter (ADC) uses appropriate combination of both conventional double-tail comparators and standard cell comparators. Standard cell comparators are used to reduce power consumption. Thus, the proposed hybrid architecture results in extended dynamic range when compared to standard cell and thresholdinverter quantization (TIQ) based flash ADC. The proposedhybrid architecture is implemented in CMOS 180 nm N-welltechnology with 1.8 V supply. Simulation results show thepower reduction upto 48.47% when compared to conventionalarchitecture. The simulated spurious-free dynamic range (SFDR) is 38.54 dB and effective number of bits (ENOB) is 4.7 bits witha 1.22 MHz input at a sampling rate of 250 MS/s. The hybridADC consumes about 7.11 mW of power with figures of merit(FOM) of 1094.18 fJ/conversion-step.
Evolutionary Optimization of Three-Photon Absorption in Molecular Iodine

We report on the application of an evolutionary algorithm to a noisy, dynamic optimization problem in chemistry: the maximization of three-photon absorption in molecular iodine. An evolution strategy is used in real-time in a closed loop experiment to search the space of physically realizable phase-modulated femtosecond laser pulses. The probability of three-photon absorption is estimated by measuring UV fluorescence. With the evolutionary search it is possible to enhance the UV fluorescence by a factor of 3.4 compared to the most intense pulse.