Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Extrapolation for Faster Lasso Solvers

Convex sparsity-inducing regularizations are ubiquitous in high-dimension machine learning, but their non-differentiability requires the use of iterative solvers. To accelerate such solvers, state-of-the-art approaches consist in reducing the size of the optimization problem at hand. In the context of regression, this can be achieved either by discarding irrelevant features (screening techniques) or by prioritizing features likely to be included in the support of the solution (working set techniques). Duality comes into play at several steps in these techniques. Here, we propose an extrapolation technique starting from a sequence of iterates in the dual that leads to the construction of an improved dual point. This enables a tighter control of optimality as used in stopping criterion, as well as better screening performance of Gap Safe rules. Finally, we propose a working set strategy based on an aggressive use of Gap Safe rules and our new dual point construction, which improves state-of-the-art time performance on Lasso problems.
A Review of the Development and Performance of the ARPANET Routing Algorithm

This paper presents a comprehensive review of the ARPANET routing algorithm, from its original implementation to our plans for future modifications. We hope that by collecting this information, and by providing considerable details, we can provide others with a useful reference document concerning some of the practical problems of network algorithm design. Much of the discussion below assumes a basic familiarity with the principles of packet switching, the ARPANET implementation, and some of the relevant terminology, information which can be found, for example, in [4]. Sections 1 and 2 give a brief summary of basic routing concepts and of the original routing algorithm, respectively. The following two sections describe in detail subsequent modifications and the actual implementation currently in use. Section 5 then discusses some problems that have developed over the past few years, as network usage has grown considerably. The final sections outline some explanations for these problems and some mechanisms for improving performance. We are in the process of implementing these and other changes to the routing algorithm.
There's an app for that: The impact of reminder apps on student learning and anxiety

ABSTRACT Today's students experience increased anxiety around school and have difficulty keeping track of course assignments. The authors conceptually develop and empirically test a model of the impact of a homework reminder mobile application (app) on executive function skills and learning outcomes with undergraduate business students. The findings suggest that perceived usefulness of Homework Suite Planner app positively predicted students' executive functioning by increasing their organization and time management. Increases in executive functioning due to the Homework Suite app led to more completed assignments, lower anxiety about forgetting homework assignments, and greater overall learning satisfaction with the course. Future research and implications for higher education instructors are discussed.
Intelligent scheduling optimization using a rule-based artificial neural network

Requirements for greater precision and reduced rejection/acceptance errors demand improved inspection methods that can be provided by implementing increased automation into the quality control (QC) process. Automated inspection planners can require significant human interface work deciphering the generated output, often requiring more work than the service they provide. The output of an automated inspection planner needs to produce a process plan that would resemble one created by an expert inspector. This paper discusses the implementation of an artificial neural network (ANN) to optimize the sequence of inspection points based upon inspection rule criteria. Using features from a feature-based CAD, the automated inspection planner uses the inspection rules to create "rule matrices" that the ANN will use to sequence the inspection points to be probed by a coordinate measurement machine (CMM). This is an extension of the familiar traveling salesman problem (TSP) solved by a Hopfield neural network. <<ETX>>
Image-based localization for mobile and vehicular applications

Image-based localization for mobile and vehicular applications Hamed Sadeghi Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2017 We study the image-based localization problem in mobile and vehicular applications. The problem can be stated as ‘given a query image at an unknown location with some side information available regarding the camera and environment, how can one estimate the camera location? ’. The side information could be cameras parameters, database of location-tagged images and/or measurements of some 3D dimensions. The problem has been well investigated in a number of literature works. We focus on the localization accuracy and complexity in this thesis and propose methods to improve them. Some of the proposed methods increase the location recognition rate, while others improve the localization accuracy. We also propose a robust method for outdoor scenarios. The majority of the image-based localization methods can be classified into two categories, i.e. landmark-based and image retrieval-based methods. We propose a semisupervised formulation for the landmark-based problems, which results in a considerable decrease in the median and variance of the localization error compared to the state-ofthe-art, at the cost of higher complexity. For the image retrieval-based approach, we propose an Epipolar geometry-based method to refine the localization estimate. The proposed method demonstrates greater accuracy, higher robustness to database sparsity as well as extrapolation capability. For the indoor scenarios, where both category of methods fail to localize the user accurately, we propose two methods utilizing OCR, called OCRAPOSE I and OCRAPOSE II. The proposed methods improve the localization accuracy, provide simplified training and lower complexity.
Measuring and managing the remote client perceived response time for web transactions using server-side techniques

As businesses continue to grow their dependence on the World Wide Web, it is increasingly vital for them to accurately measure and manage response time of their Web services. This dissertation shows that it is possible to determine the remote client perceived response time for Web transactions using only server-side techniques and that doing so is useful and essential for the management of latency based service level agreements. First, we present Certes, a novel modeling algorithm, that accurately estimates connection establishment latencies as perceived by the remote clients, even in the presence of admission control drops. We present a non-linear optimization that models this effect and then we present an O(c) time and space online approximation algorithm. Second, we present ksniffer, an intelligent traffic monitor which accurately determines the pageview response times experienced by a remote client without any changes to existing systems or Web content. Novel algorithms for inferring the remote client perceived response time on a per pageview basis are presented which take into account network loss, RTT, and incomplete information. Third, we present Remote Latency-based Management (RLM), a system that controls the latencies experienced by the remote client by manipulating the packet traffic into and out of the Web server complex. RLM tracks the progress of each pageview download in real-time, as each embedded object is requested, making fine grained decisions on the processing of each request as it pertains to the overall pageview latency. RLM introduces fast SYN and SYN/ACID retransmission and embedded object rewrite and removal techniques to control the latency perceived by the remote client. We have implemented these mechanisms in Linux and demonstrated their effectiveness across a wide-range of realistic workloads. Our experimental results demonstrate for the first time that server-side response time measurements can be done in real-time at giga-bit traffic rates to within 5This is an order of magnitude better than common application-level techniques run at the Web server. Our results also demonstrate for the first time how both the mean and the shape of the per pageview client perceived response time distribution can be dynamically controlled at the server complex.
Acoustic Features Modelling for Statistical Parametric Speech Synthesis: A Review

ABSTRACT The objective of this paper is to present a detailed review of modelling various acoustic features employed in statistical parametric speech synthesis (SPSS). As reported in the literature, many acoustic features have been modelled in SPSS to enhance the synthesis quality. This work studies those approaches that add to the quality of SPSS by including such acoustic features. In particular, several categories of acoustic features that improve the perceptual quality of synthetic speech are discussed. The acoustic features modelling reported in the literature can be broadly classified as F0, vocal-tract, and source features, which primarily represent the prosody, intelligibility, and naturalness of speech, respectively. Besides, SPSS techniques to synthesize speech from these acoustic features and recent advancement in synthesis based on direct waveform generation are also studied in the paper. Finally, the paper concludes with a brief discussion and a mention on the scope in SPSS.
PID Controller Tuning using Soft Computing Methodologies for Industrial Process- A Comparative Approach

Industrial Processes are non-linear, time variant and sensitive to disturbances. They are extremely complex and highly resistant to control. The dynamics of process offers great difficulty in controller design. Proportional, Integral, Derivative controller has been used in the process. In this context Z-N method has been used for controller tuning. Some disadvantages of Z-N method control technique for tuning a PID controller are: excessive number of rules to set the gain, inadequate dynamics of closed loop responses, and difficulty to deal with non-linear responses. The aim of this study is to develop a soft computing based PID control tuning methodology for optimizing the controller for the fitness function. In this approach, the transfer function of the marine processes has been taken for a simulation using MATLAB. From this simulation, outputs have proved their excellence in giving better results by improving the steady state characteristics and performance indices.
Operational fault detection in network infrastructure

Network infrastructure security concerns the protection of a network instead of the confidentiality and integrity of packet content. Most of the existing methods require the modifications of routers or require them to run background processes. These kinds of methods are less reliable because they rely on the cooperation of routers in the network to detect any malicious router in the same network. In this paper, we propose to protect the network infrastructure by running a set of detection routines in the boundary firewalls of the network infrastructure. These routines can detect various kinds of network attacks without the assistance of routers. The routines use the hop-by-hop technique to send a number of testing packets to the routers in the network one by one. By observing the packet responds, the routines are able to identify the attacks to a network infrastructure (if there is any), namely, packet drop, packet misroute, or routing loop.
Up-link analytical outage evaluation for slow frequency hopping mobile radio systems

In a shadowing-free environment, the improvement introduced by slow frequency hopping (SFH) on a TDMA-based mobile radio system can be taken into account by properly re-defining the minimum carrier-to-interference protection ratio. This protection ratio, with SFH, is dependent on the transmission system, channel model, traffic and frequency reuse parameters. The approach is used in order to analytically investigate the up-link capacity of a SFH mobile radio system, by taking into account a complete scenario, i.e. shadowing, fast fading, power control, antenna diversity, discontinuous transmission and forward error correction (FEC) with non-ideal interleaving and sectorization.
A field study of performance improvements in HMM-based speaker verification

This study reports our findings on speaker verification (SV) performance improvements using random 4-digit utterances collected over a single microphone type. The databases used in this study are the result of an ongoing field trial of SV access to automatic teller machines (ATMs) for secure unattended banking services. The SV system uses continuous density HMM models trained on 18 connected 4-digit utterances and has a baseline equal-error-rate (EER) of between 5.5 and 11% for different sets of data. Because of the limited training data, estimates for the mixture variances are most often poor. By calculating average mixture variances using all of the training data for a given speaker and then setting all of the model variances for that speaker to these speaker dependent values and using cohort normalization, the EER decreases consistently to between 2.5 and 6.5%. <<ETX>>
A tone recognition of polysyllabic Chinese words using an approximation model of four tone pitch patterns

Presents an approximate model based on the tangent function as a simple approximate method for recognition of four tones of Chinese speech. In order to carry out the segmentation of the syllabic unit, the authors propose a novel segmentation scheme, in which, on the basis of analyzing the sound linking characteristic between syllables, the phrases are classified into two groups as non-linked syllabic and linked syllabic. The pitch information may be directly employed for the former, and for the latter, two parameters, sound power and average magnitude differential function, are used. The authors carried out recognition experiments using three syllable phrases and received satisfactory results. <<ETX>>
Working Memory Training and Implicit Learning 1

Recent studies have shown that working memory can be improved through training and this improvement generalizes to other cognitive measures. Working memory studies typically focus on the retention of random sequences; however, much of what working memory is used for is not random. This study investigated what effect probabilistic structure has on adaptive training of working memory and how differing levels of structure affect generalization to other cognitive tasks. Participants received four days of working memory training that was either adaptive or non adaptive with sequences that were constrained or probabilistic. A battery of cognitive measures was taken at preand posttest. Probabilistic structure provided beneficial effects on adaptive training of working memory in as little as four hours of training. Moreover, these benefits carried over to improvements on measures of non-verbal reasoning and executive function.
Two-level Indoor Navigation using WiFi and MEMS Sensors

In this paper, the indoor pedestrian location algorithm is investigated, in which we proposes a novel WiFi/MEMS integration structure for indoor navigation to fuse the information from WiFi and MEMS sensors. In WiFi part, aiming at the problem of large amount of calculation in the algorithm, a partition solution called “moving partition” is originally proposed. The test results from indoor experiments indicate that with “moving partition”, the computation time decreased by almost half and the proposed WiFi/MEMS solution works well, of which the mean positioning accuracy has been greatly improved compared with MEMS alone and WiFi alone. Keywords—indoor navigation, MEMS sensors, WiFi, extended kalman filter
A Template Metaprogramming approach to Support Parallel Programs for Multicores

In advent of multicore era, plain C/C++ programming language can not fully reflect computer architectures. Source-to-source transformation helps tailor programs close to contemporary hardwares. In this paper, we propose template-based approach to perform transformation for programs with rich static information. We present C++ template metaprogramming techniques to conduct parallelization for specific multicores. Parallel pattern and execution model are provided in the form of template classes and oraganized as library. We implement a prototype template library ‐ libvina, to demonstrate the idea. It enables programmers to utilize new architectural features and add parallelization strategies by extending template library. Finally, we evaluate our template library on commodity x86 and GPU platforms by a variety of typical applications in multimedia and scientific fields. In experiments, we show that our approach is flexible to support multiple parallel models and capable of transforming sequential code to parallel equivalence according to specific multicore architectures. Moreover, the cost of programmability using our approach to adapt to more than one multicore platform is manageable.
Design of wavelength-routed optical networks for circuit switched traffic

We consider the problem of routing and assignment of wavelength (RAW) in wavelength-routed optical networks. Given a set of requests for all-optical connections (or lightpaths), the problem is to find wavelength-continuous routes from the source nodes to their respective destination nodes. Since the number of wavelengths available per link is limited, lightpaths cannot be established between every source-destination node pairs. Moreover, a common wavelength might not be available on all the segments of a chosen route. Thus, routing and wavelength assignment are to be considered concurrently for the best possible performance. Several heuristic algorithms have already been proposed for the RAW problem, however, most of these algorithms are based on the traditional model of circuit-switched networks where the routing and wavelength assignment steps are decoupled. A new RAW scheme is developed which unifies the routing and wavelength assignment subproblems. This unified approach is found to yield significantly better solutions to the RAW problem than the earlier ones. The paper also introduces an analytical model for predicting the average number of lightpaths requests a given physical network can support.
Learning Design Implementation in SCORM E-Learning Environment

Please, cite this publication as: Shoikova, L. , & Ivanova, E. (2006). Learning Design Implementation in SCORM E-Learning Environment. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks. org
USING MULTIPLE SEMANTIC MEASURES FOR COREFERENCE RESOLUTION IN ONTOLOGY POPULATION

The problem of populating an ontology consists in adding to it some new, domain-specific content from an input expressed, in particular, in a natural language. We focus on an important aspect in the ontology population process – finding and resolving coreferences, i.e., similar mentions of entities in the input text. Our contribution is a novel formal framework that extends the state-of-the-art approaches to coreference resolution by using multiple semantic similarity properties in the resolution process, i.e., we extend the list of the ontological properties used for coreference resolution with additional properties such as inverse, symmetry, intersection, union, etc. We use the proposed framework to improve our previously proposed algorithm for coreference resolution used in our general approach to text analysis and information extraction for populating subject domain ontologies. We describe a multi-agent implementation of our information extraction system and we show that using additional semantic similarity measures for evaluating coreferential candidates improves the quality of the coreference resolution process, especially for complex objects whose coreferencing has not been yet studied in detail.
THE INTRINSIC HAND - A 22 DEGREE-OF-FREEDOM ARTIFICIAL HAND-WRIST REPLACEMENT

1.0 INTRODUCTION Now is an exciting time to be in the field of Upper-Limb Prosthetics. Due to the limited function of current commercially available upper-limb prosthetics and the increased incidence of amputation injuries being seen in Operation Enduring Freedom (OEF Afghanistan) and Operation Iraqi Freedom (OIF) a number of new initiatives have been put into place to develop replacement arm/hand systems capable of replicating the function of the human arm and hand. In particular, the Defense Advanced Research Projects Agency (DARPA) has a four year initiative, “Revolutionizing prosthetics 2009”. The goal of this program is to develop a replacement arm/hand system capable of enabling soldiers with limb loss from OEF/OIF the capability of returning to service similar to the way those with lower ankle-foot disarticulation have been able to do for years. Led by the Johns Hopkins University Applied Physics Laboratory, the goal of this project is to design a fully functional biomechatronic analog to the human hand and arm. The device must be capable of duplicating the function of the original limb and withstand the rigors of daily living. Our involvement on this team is to develop robust and anthropomorphic hand-wrist prostheses capable of dexterous manipulation suitable for use by persons with all levels of trans-radial limb loss. Where as to date most anthropomorphic high degree of freedom hand manipulators place their actuators in the forearm [8, 9], this paper details the results of placing the actuators intrinsic to the hand.
Real Time Intrusion Detection With Fuzzy, Genetic and Apriori Algorithm.

A B S T R A C T This paper represents the approach of intrusion detection in network using Genetic, Fuzzy and Apriori Algorithm. With widespread use of internet service, nowadays data is always under the threat of intrusions when communicated over them and misused therefore Intrusion Detection system has became part and parcel of or let’s say most vital component of computer and its network security. Such IDS system have gathered attention by maintaining high security levels ensuring trusted and safe communication of the information between different organizations. Many perspectives for intrusion detection have been proposed before but none shows satisfactory results so the research for better outcome in this field yet continues. Here in this approach we proposed a method which is combination of genetic rule, fuzzy rule and association rule for better outcome of intrusion detection. Fuzzy rule can classify network attack data for being a machine learning algorithm whereas genetic algorithm provides best optimal solution by finding proper fuzzy rule and apriori algorithm provides best association rule to detect attack. Our approach can be implemented on both well known dataset i.e. KDD cup 1999 as well as on own network dataset.IDS will be evaluated in terms of detection rate, detection speed, false alarm rate and attack types .