Miracle-dz/UniFAR-FTUs huggingface dataset:
Switching characteristics of generalized array multiplier architectures and their applications to low power design

This paper presents several new array multiplier architectures for reducing switching activity in general digital signal processing (DSP) applications. A cellular structure is described which can be used to obtain any array multiplier suitable for a given application. The switching activity at the output nodes of the cells in this structure is analyzed and compared with a tree multiplier based on 4:2 compressors. It is shown that the relative improvement in power is a function of statistical properties of the signal and most structures out-perform all others for specific signal conditions. It is also shown that selection of appropriate array architecture can give up to 49% reduction in switching activity compared to a tree multiplier, and more than 3 times less switching activity compared to the widely used least-significant-bit-first array multiplier for commonly occurring situations. We also outline applications of the proposed structures to the areas of low power quantization, reconfigurable computing and high-level synthesis for low power.
Behavioural and Evoked Potential Measures of Auditory Processing In Adults and Children with Unilateral Hearing Loss (UHL)

ii Acknowledgments . iv List of Tables . xi List of Figures . xii Co-Authorships Forms . xv CHAPTER 1: Introduction . 1 CHAPTER 2: Consequences of Unilateral Hearing Loss in Adults and Children . 1
Spatially resolved spectroscopy of passive spiral galaxies

Passive spiral galaxies, despite their spiral morphological appearance, do not have any emission lines indicative of ongoing star formation in their optical spectra. Previous studies have suggested that passive spiral galaxies preferentially exist in infall regions of galaxy clusters, suggesting that the cluster environment is likely to be responsible for creating these galaxies. By carrying out spatially resolved long-slit spectroscopy on four nearby passive spiral galaxies with the Apache Point Observatory 3.5-m telescope, we investigated the stellar populations of passive spiral galaxies separately for their inner and outer regions. In the two unambiguously passive spiral galaxies among the four observed galaxies, H$\delta$ absorption lines are more prominent in the outer regions of the galaxies, whereas the 4000-{\AA} breaks (D$_{4000}$) are strongest in the inner regions of the galaxies. A comparison with a simple stellar population model for the two passive spiral galaxies indicates that the outer regions of the galaxies tend to harbour younger populations of stars. The strong H$\delta$ absorption observed in the outer regions of the sample galaxies is consistent with that of galaxies whose star formation ceased a few Gyrs ago. Because of the large uncertainty in the absorption indices in our samples, further observations are needed in order to place constraints on the mechanisms that quench star formation in passive spiral galaxies.
A New Coupling Matrix Extracting Method From the Frequency Response

Coupled resonators are widely used in many applications, especially for filters. It is necessary to know the coupling matrix of those resonators in order to further modify and/or optimize the existing design. In recent years, many efforts have been made to generate a coupling matrix. In this paper, a new method is proposed, which can be applied for both the synchronous and asynchronous coupled resonators. With this method, the eigenvalues and eigenvectors of the coupling matrix can be generated directly from the simulated frequency responses, and the coupling matrix can then be determined. Typical examples are illustrated for the cases such as a quadruplet with an image, real, or complex transmission zeros. The impact of loss is also discussed. As a matter of fact, this method has been successfully used in the design of high-temperature superconducting filters
Clustering for Improved Learning in Maze Traversal Problem

The maze traversal problem (finding the shortest distance to the goal from any position in a maze) has been an interesting challenge in computational intelligence. Recent work has shown that the cellular simultaneous recurrent neural network (CSRN) can solve this problem for simple mazes. This thesis focuses on exploiting relevant information about the maze to improve learning and decrease the training time for the CSRN to solve mazes. Appropriate variables are identified to create useful clusters using relevant information. The CSRN was next modified to allow for an additional external input. With this additional input, several methods were tested and results show that clustering the mazes improves the overall learning of the traversal problem for the CSRN.
Content Redundancy in BitTorrent

The BitTorrent Peer-to-Peer (P2P) file sharing protocol is a popular way to distribute digital content. It's a very scalable protocol, where the entry of new peers increases the total capacity of the network, especially after peers have finished their download and remain just as uploaders (known as seeders). Through the monitoring of live Internet swarms we have discovered that there is a significant amount of repeated content being shared. Various publishers tend to publish the same content through different torrent files, creating independent swarms that end up having the exact same content or a large number of common parts. As such, the size of each swarm is smaller than it could be, should there be only one swarm for that content. This affects the performance of BitTorrent and diminishes the opportunities for exploiting locality. By analyzing 3067 swarms, we concluded that there is a significant amount of common content between different swarms. Through our measurement results, we show that this redundancy can be exploited in order to increase data availability and source diversity. By increasing the swarm size through the aggregation of different swarms sharing common content, the swarm's locality properties are also improved. These results suggest that there can be many advantages for both peers and ISPs if this redundant content is exploited by BitTorrent.
The Effects of Social Media Tone on Engagement: Evidence from Indian General Election 2014

Individual celebrities or “human brands” in fields ranging from sports to art to politics use social media platforms to connect and engage with their audience. We analyze the effects of the content generated by human brands (referred to as human brand generated content) on the popular social media platform, Twitter, on audience engagement. The first objective of this study is to examine the effect of the tone of human generated content on social media engagement. Building on the arguments of perception spillover effects, we investigate the content generated by human brands belonging to the same group as the focal human brand on the engagement of the focal human brand. Set in the context of the 2014 Indian general election, we find that both positive toned content and negative toned content have a positive effect on social media engagement. We find that the popularity of a human brand negatively moderates the relationship between positive tone content and engagement. Our findings provide evidence of interesting spillover effects of content created by related brands on the effectiveness of content created by a focal human brand. In particular, we find evidence for the presence of reinforcement effect, i.e., the effect of negative tone on engagement is greater when the tone of related human brands is negative, and the engagement is higher when the tones of both the focal human brand and related brands are positive. However, we find that the effect of negative toned tweets of a focal human brand is lower when the tone of related human brands is positive. Our analyses account for endogeneity issues, correlated unobservables, and reflection problem. We supplement our core results with additional analyses that include alternative model specifications, alternative samples, estimation strategies, and engagement metrics. We present the implications of this study to practice as well as the literature on social media content.
A semantic model for authentication protocols

The authors specify authentication protocols as formal objects with precise syntax and semantics, and define a semantic model that characterizes protocol executions. They have identified two basic types of correctness properties, namely, correspondence and secrecy; that underlie the correctness concerns of authentication protocols. Assertions for specifying these properties, and a formal semantics for their satisfaction in the semantic model are defined. The Otway-Rees protocol is used to illustrate the semantic model and the basic correctness properties. <<ETX>>
International Conference on Information Systems ( ICIS ) 1-1-2008 Knowledge Based Diffusion-A Case Study Experience

This paper builds upon the previous research of Beynon-Davies and Williams (2003) and their knowledge-based model of the diffusion of Information Systems Development Methods (ISDMs). Through the lens of the implementation of a UK government IS development project we consider how a Rapid Application Development approach was unbundled and contextualized and highlight the problems experienced. We further reflect how evidence from this empirical case confirms the efficacy of many of the elements of the diffusion model and suggest a number of directions for extending this framework, enabling better understanding of the practical application of ISDMs within
Is there a relationship between Parkinson's disease and Chlamydia pneumoniae?

OBJECTIVES The aim was to investigate a possible relationship between Chlamydia pneumoniae and Parkinson's disease (PD). STUDY DESIGN Serum samples obtained from a cohort of 51 patients with PD and from 37 age- and sex-matched controls were assessed for the presence of antibodies. The control group was selected from healthy people. In both groups, 5 mL of blood was taken and after centrifugation frozen at -80°C. Presence and concentration for C. pneumoniae IgM and IgG were determined by the enzyme linked immunosorbent assay (ELISA) and immunofluorescence (IFA), using C. pneumoniae IgG and IgM kit (Euroimmun, Germany). RESULTS Chlamydia pneumoniae IgG was positive in 50 (98%) patients in ELISA study. C. pneumoniae IgG was positive in 34 (92%) control subjects in ELISA study. C. pneumoniae IgG positivity in patients was slightly higher, but the difference did not reach statistical significance (P = 0.17). No statistically significant difference was found between the patient and the control groups in IFA study (P ≥ 0.5). C. pneumoniae IgM results (both ELISA and IFA study) was negative in the both PD group and control group.
UICE: A High-Performance Cryptographic Module for SoC and RFID Applications

In order to overcome proprietary algorithms with respect to the system manufacturers, a free cryptographic module, the Universal Immobilizer Crypto Engine (UICE), will be proposed. This UICE algorithm is tailored to 8-bit microprocessor architectures and is therefore very fast in software and hardware. The dedicated hardware implementation leads to a small gate count, because the registers for input and output are shared. The important non-linear function, here an 8 x 8 SBox, may be built as a gate array or small ROM with the advantage of flexibility. Several tests – statistical and random-number tests - have been performed in order to analyze the strength properties of the algorithm. So far no weakness was found after ten rounds of encryption. Although this cryptographic module was intentionally developed for RadioFrequency Identification (RFID) systems, it is a proper choice for all systems needing embedded cryptography such as SoC with bus encryption or firmware to be secured. RFID-Systems have become commonplace in access control and security applications, the usage and importance of cryptographic co-processors in RFID transponder devices has grown significantly. Improved vehicle security systems, also known as immobilizers, are required due to increased vehicle theft worldwide. Such devices provide high security at low cost and low power.
A Research Agenda on Managerial Intention to Green IT Adoption: from norm activation Perspective

Green IT adoption is a plausible attempt for organizations to tackle the current environmental problem, and can also improve the economic performance of an organization. Therefore, organizational adoption of Green IT is beneficial to the society. Investigating how the decision maker in an organization would come to adopt Green IT is necessary to establish strategies in promoting organizational adoption of Green IT adoption because decision makers make decisions, rather than organizations. However, to our knowledge, studies in Green IT literature seldom investigate this area. In Green IT literature, emphasis has been given on the effects of institutional pressures and organizational resources on organizational adoption of Green IT. The current study adapts a Norm Activation Model in organizational context to investigate on how an intention to Green IT adoption is formed. The decision makers’ personal norm on environmental interpretation is argued to be activated when environmental preservation is interpreted as an opportunity, which may lead to intention to Green IT adoption. Perceived competitive advantage of Green IT will affect how environmental preservation is interpreted, as well as affect intention to Green IT adoption. Moreover, discretionary slack will also affect how environmental preservation is interpreted. All of these should be tested empirically to see if the results support our proposed hypotheses. Empirical data for this study will be collected via a survey.
A Novel Reputation Management System for volunteer clouds

Volunteer computing systems provide many thousands of Teraflop's of processor power for a wide range of scientific applications. There is a huge potential to use the contributed processor power for cloud computing applications. Cloud computing architectures have a robust design and are resilient to failures, whereas volunteer computing systems are highly distributed and prone to volunteer node failures. In order to deploy cloud services with high reliability in such volunteer clouds, we need a robust reputation management system to help estimate reputation of volunteer nodes for deciding on service deployment. This paper presents a Novel Reputation Management System for Volunteer Cloud. We have identified three basic trust parameters, namely, Performance in terms of time taken to do a job, Crashes of a node, and Correctness of returned results. We then define an overall reputation metric that combines the parameters to represent the overall reputation of the volunteer node. The paper also includes a discussion on possible strategies to implement the system in a distributed volunteer cloud.
Glucose Sensing in the Peritoneal Space Offers Faster Kinetics Than Sensing in the Subcutaneous Space

The paramount goal in the treatment of type 1 diabetes is the maintenance of normoglycemia. Continuous glucose monitoring (CGM) technologies enable frequent sensing of glucose to inform exogenous insulin delivery timing and dosages. The most commonly available CGMs are limited by the physiology of the subcutaneous space in which they reside. The very same advantages of this minimally invasive approach are disadvantages with respect to speed. Because subcutaneous blood flow is sensitive to local fluctuations (e.g., temperature, mechanical pressure), subcutaneous sensing can be slow and variable. We propose the use of a more central, physiologically stable body space for CGM: the intraperitoneal space. We compared the temporal response characteristics of simultaneously placed subcutaneous and intraperitoneal sensors during intravenous glucose tolerance tests in eight swine. Using compartmental modeling based on simultaneous intravenous sensing, blood draws, and intraarterial sensing, we found that intraperitoneal kinetics were more than twice as fast as subcutaneous kinetics (mean time constant of 5.6 min for intraperitoneal vs. 12.4 min for subcutaneous). Combined with the known faster kinetics of intraperitoneal insulin delivery over subcutaneous delivery, our findings suggest that artificial pancreas technologies may be optimized by sensing glucose and delivering insulin in the intraperitoneal space.
Human Motion Retrieval Based on Sparse Coding and Touchless Interactions

To search for a particular motion from a large database, a user-friendly and efficient retrieval mechanism is essential. In this paper, we propose a human motion retrieval system based on sparse coding and touch less interactions. Compared with existing methods that involve vector quantization, sparse coding leads to a more compact and discriminative representation. Motion comparison based on sparse representation greatly improves the effectiveness of motion retrieval in terms of accuracy and speed. With the recent advancement in human motion tracking hardware such as Kinect, our retrieval system allows the user to specify the query motion by performing it directly. Besides, the user interacts with the retrieval system interactively using gestures so no controller is required thus delivering a natural user interface.
Ray-Patch Intersection for Improving Rendering Quality of Per-pixel Displacement Mapping

Recent GPU advances have popularized pixel-shader based techniques for per-pixel displacement mapping. An important step in the process is to compute intersection of ray with a bilinear patch approximating the displacement map. Accurate ray-patch intersection can significantly enhance the quality of rendered images. However, the increase in quality comes at a significant computation cost resulting in a substantial drop in performance. This paper explores this trade-off between computation and quality. We propose several techniques for ray-patch intersection and compare it to exact ray-quadric/ray-bilinear intersection proposed previously. We show that our quadric based local-gradient-plane approximation achieves a comparable visual quality with a significant performance improvement.
Pseudomonas syringae from the rice pathogen De novo assembly using low-coverage short read sequence data

object identifier (DOIs) and date of initial publication. by PubMed from initial publication. Citations to Advance online articles must include the digital publication). Advance online articles are citable and establish publication priority; they are indexed appeared in the paper journal (edited, typeset versions may be posted when available prior to final Advance online articles have been peer reviewed and accepted for publication but have not yet
Automated Identification of Failure Causes in System Logs

Log files are commonly inspected by system administrators and developers to detect suspicious behaviors and diagnose failure causes. Since size of log files grows fast, thus making manual analysis impractical, different automatic techniques have been proposed to analyze log files. Unfortunately, accuracy and effectiveness of these techniques are often limited by the unstructured nature of logged messages and the variety of data that can be logged.This paper presents a technique to automatically analyze log files and retrieve important information to identify failure causes. The technique automatically identifies dependencies between events and values in logs corresponding to legal executions, generates models of legal behaviors and compares log files collected during failing executions with the generated models to detect anomalous event sequences that are presented to users. Experimental results show the effectiveness of the technique in supporting developers and testers to identify failure causes.
EU Regional Policy: Vertical Fiscal Externalities and Matching Grants

Although decision processes at the EU level are highly complex and often require unanimity, explicit side payments among member states are unknown. However, logrolling and redistributive policies are substitutes. This paper considers the EU regional policy as a substitute for explicit side payments and asks – from a normative and a positive point of view – why matching grants instead of unconditional grants are used although at first sight regional policy is a national and not a union-wide task. It argues that the EU system of own resources creates positive vertical fiscal externalities. Hence, matching grants are part of an efficient grant system and, in so far as bargaining in EU bodies is efficient, matching grants are the natural outcome.
Semantics of multi-viewpoints ontology alignments

In a previous paper, we defined a multi-viewpoints ontology alignment as a set of correspondences between multi-viewpoints elements. With a multi-viewpoints approach, the basic multi-viewpoints elements are classified into seven types: viewpoints, global concepts, local concepts, global roles, local roles, bridge rules, and individuals. A multi-viewpoints correspondence can be seen as an axiom that asserts a relation between global concepts, local concepts, global roles, local roles or individuals of two distinct multi-viewpoints ontologies. The main contribution of this paper is to provide formal semantic to formalize alignment based on description logic. Semantics of a multi-viewpoints alignment are determined by the semantics of its components. For that purpose, we first, give a formal definition of description logics extended by a stamping mechanism serve to describe multi-viewpoints ontologies and we provide its formal semantics. Then, we define the semantics of the multi-viewpoints ontology alignments.
Action recognition for human robot interaction in industrial applications

Human action recognition plays a vital role in the field of human-robot interaction and is widely researched for its potential applications. In this paper we propose a human action recognition framework for human robot interaction in industrial applications. First, a set of key descriptors are learned from a collection of weak spatio-temporal skeletal joint descriptors using random forests, which reduces the dimensionality and computational effort. We show that our approach reduces the descriptor dimensionality by 61 percent. The key descriptors are used with a multi-label one-versus-all binary random forest classifier for action classification. We propose an extension to the framework that allows recognizing multiple actions for a given time instant. This results in a low latency, flexible and re-configurable method that performs on par with other sophisticated approaches on challenging benchmarks like the MSR Action 3D dataset.
Attacks on and Countermeasures for USB Hardware Token Devices

This paper presents the methods used to attempt access to private data stored in Universal Serial Bus (USB) hardware tokens without having legitimate credentials. We look at the current state-of-theart products of the commercial world. Our research is based on an approach of using only common, off-the-shelf tools, yet we still succeed in defeating the security features and gaining access to private data. The focus is on three major attack categories: Mechanical, Electrical, and Software. We also examine other areas that may be susceptible to attack. Countermeasures and design changes that will enhance the security of such devices are proposed. Academic papers describing the design of secure hardware devices have existed for over two decades [1, 2, 3]. Applying these approaches into realworld products, however, requires an understanding of the threat model and security envelope of the product being designed. Improper implementation can lead to avenues of attack as described in this paper.