Miracle-dz/UniFAR-FTUs huggingface dataset:
Connectionist speaker normalization and adaptation

In a speaker-independent, large-vocabulary continuous speech recognition systems, recognition accuracy varies considerably from speaker to speaker, and performance may be significantly degraded for outlier speakers such as nonnative talkers. In this paper, we explore supervised speaker adaptation and normalization in the MLP component of a hybrid hidden Markov model/ multilayer perceptron version of SRI's DECIPHERTM speech recognition system. Normalization is implemented through an additional transformation network that preprocesses the cepstral input to the MLP. Adaptation is accomplished through incremental retraining of the MLP weights on adaptation data. Our approach combines both adaptation and normalization in a single, consistent manner, works with limited adaptation data, and is text-independent. We show significant improvement in recognition accuracy.
THE EFFECT OF OIL PALM ASH INCORPORATION IN FOAMED CONCRETE

This paper investigates the effect of replacing different portions of cement by fine oil palm ash (FOPA). A target density of 1000 kg/m 3 was used for the foamed concrete mixes. A foamed concrete mix of 1 part binder, 2 parts filler and 0.45 part of water has been used. Cement was replaced at levels of 25, 35, 45, 55 and 65% by weight of binder. The compressive strength, density, water absorption, drying shrinkage and sorptivity were tested at different ages. The mix containing 25% of fine OPA showed enhanced properties in comparison to the control mix at the age of 90 days. The mixed showed higher compressive strength, less water absorption, increased density and lesser sorptivity. However, the same mix showed higher shrinkage readings than that of the control mix.
Chemical and microstructural diversity of steel grades 355

The aim of the paper is to show, using theoretical and practical analyses, chemical and microstructural differences among individual types of steel grades 355 found in the market. The mechanical properties required for these steels are achieved by alloying or thermomechanical treatment. It was found that the individual types of this steel are poorly weldable, particularly those of large thickness.
Joint spatial and spectral filter estimation for single trial detection of event related potentials

It is usually assumed that the main frequency components of ERPs are in some specific frequency bands. Therefore, predefined cutoff frequencies are used to filter the raw data in different applications. Here, an extension of the periodic spatial filter method is proposed that jointly learns spatial filters and the corresponding FIR filters to be used for single trial ERP detection. Experimental results confirm that the proposed spatio-spectral filtering method outperforms its predecessor spatial filter.
AR-Tracker: Track the Dynamics of Mobile Apps via User Review Mining

User-generated reviews on mobile applications (apps) are a valuable source of data for developers to improve the quality of their apps. But the reviews are usually massive in size and span over multiple topics, thus leading to great challenges for developers to efficiently identify the key reviews of interest. In recent studies, automatic user review mining has been recognized as a key solution to address this challenge. The existing methods, however, require extensive human efforts to manually label the training data. Besides, they only analyze the static characteristics over the whole set of collected reviews, while ignoring the dynamic information embedded in the reviews of different time periods. In this paper, we propose 'AR-Tracker', a new framework to mine user reviews without the need of human labeling and track the dynamics from the top-ranked reviews. Through extensive experiments on the reviews of four popular mobile apps collected over 7 months, we show that AR-Tracker can still achieve comparable accuracy with the state-of-the-art methods, e.g., AR-Miner. Additionally, a case study on Facebook reviews further validates the effectiveness of 'AR-Tracker' in tracking the dynamics.
Discovering Relevant Preferences in a Personalised Recommender System using Machine Learning Techniques

Personalised recommender systems learn about a user’s needs, and identify and suggest information items (news articles, images, videos, etc.) that meet those needs. User needs can be explicitly or implicitly defined either in the form of user tastes, interests and goals, or by system parameters and configurations. Most research efforts in the Recommender Systems field can be said to have been directed towards either defining and improving techniques that provide item recommendations from available preference data, or defining techniques for learning the latter. However, little research has focussed on learning which preferences are really relevant to provide accurate recommendations, and which ones imply anomalous behaviour of the recommendation mechanisms. We present a meta-evaluation methodology that applies Machine Learning techniques to analyse log information of a personalised news recommender system in order to discover (and rank) which user preferences and system settings are suitable for accurate recommendations. We also show how the proposed methodology can be used to ease the system
Calibrating Non-overlapping RGB-D Cameras

In this paper, we propose a novel method of calibrating non-overlapping RGB-D cameras using one chessboard fixed with a laser pointer. A laser pointer is fixed at one calibration board so that its pose at the coordinate system of the calibration board can be obtained easily. While one of the RGB-D cameras observes the calibration board fixed with the laser pointer, the laser pointer project a spot to the scene which is observed by the other. Thus, two 3D points, respectively located in the field of views of the two RGB-D cameras, are connected by a laser ray. The relative pose of two RGB-D cameras can be estimated through this collinear constraint. The experiment results show the effectiveness of the proposed method.
UvA-DARE (Digital Academic Repository) Incorporating clicks, attention and satisfaction into a search engine result page evaluation model

Modern search engine result pages often provide immediate value to users and organize information in such a way that it is easy to navigate. The core ranking function contributes to this and so do result snippets, smart organization of result blocks and extensive use of one-box answers or side panels. While they are useful to the user and help search engines to stand out, such features present two big challenges for evaluation. First, the presence of such elements on a search engine result page (SERP) may lead to the absence of clicks, which is, however, not related to dissatisfaction, so-called “good abandonments. ” Second, the non-linear layout and visual diﬀerence of SERP items may lead to non-trivial patterns of user attention, which is not captured by existing evaluation metrics.Inthis paper we propose a model of user behavior on a SERP that jointly captures click behavior, user attention and satisfaction, the CAS model, and demonstrate that it gives more accurate predictions of user actions and self-reported satisfaction than existing models based on clicks alone. We use the CAS model to build a novel evaluation metric that can be applied to non-linear SERP layouts and that can account for the utility that users obtain directly on a SERP. We demonstrate that this metric shows better agreement with user-reported satisfaction than conventional evaluation metrics.
Interactive reconstruction of virtual environments from photographs, with application to scene-of-crime analysis

There are many real-world applications of Virtual Reality that require the construction of complex and accurate three-dimensional models, suitably structured for interactive manipulation. In this paper, we present semi-automatic methods that allow such environments to be quickly and easily built from photographs taken with uncalibrated cameras, and illustrate the techniques by application to the real-world problem of scene-of-crime reconstruction.
in Computer Science and Software Engineering

— Many applications including face verification use face image retrieval method. It’s a challenging technique since all the faces and the faces will be similar due to its similar geometrical configuration of face structure. Face recognition system is a computer application for automatically identifying or verifying a person.Establishing identity is becoming critical in our vastly interconnected society. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. This paper does a research and comparative study of face recognition by using near set theory.
Structure-Based Design of Human TLR8-Specific Agonists with Augmented Potency and Adjuvanticity

Human Toll-like receptor 8 (hTLR8) is expressed in myeloid dendritic cells, monocytes, and monocyte-derived dendritic cells. Engagement by TLR8 agonists evokes a distinct cytokine profile which favors the development of type 1 helper T cells. Crystal structures of the ectodomain of hTLR8 cocrystallized with two regioisomers of a dual TLR7/8-agonistic N1-substituted imidazoquinolines showed subtle differences in their interactions in the binding site of hTLR8. We hypothesized that the potency of a previously reported best-in-class pure TLR8 agonist, 3-pentylquinoline-2-amine, could be further enhanced by “designing in” functional groups that would mimic key intermolecular interactions that we had observed in the crystal structures. We performed a focused exploration of decorating the quinoline core with alkylamino groups at all possible positions. These studies have led to the identification of a novel TLR8 agonist that was ∼20-fold more potent than the parent compound and displays prominent adjuvantic activity in a rabbit model of immunization.
Multi-criteria Vertical Handoff Decision Algorithm Using Hierarchy Modeling and Additive Weighting in an Integrated WiFi/WiMAX/UMTS Environment- A Case Study

Multi-criteria decision making (MCDM) algorithms play an important role in ensuring quality of service in an integrated HetNets (Heterogeneous Networks). The primary objective of this paper is to develop a multi-criteria vertical handoff decision algorithm (VHDA) for best access network selection in an integrated Wireless Local Area Network (WLAN)/ Universal Mobile Telecommunications System (UMTS)/ Worldwide Interoperability for Microwave Access (WiMAX) system. The proposed design consists of two parts, the first part is the evaluation of an Analytic Hierarchy Process (AHP) to decide the relative weights of handoff decision criteria and the second part computes the final score of the weights to rank network alternatives using Simple Additive Weighting (SAW). SAW ranks the network alternatives in a faster and simpler manner than AHP. The AHP-SAW mathematical model has been designed, evaluated and simulated for streaming video type of traffic. For other traffic type, such as conversational, background and interactive, only simulation results have been discussed and presented in brief. Simulation results reveal that the hierarchical modelling and computing provides optimum solution for access network selection in an integrated environment as obtained results prove to be an acceptable solution to what could be expected in real life scenarios.
Compliant and adaptive control of a planar monopod hopper in rough terrain

In this paper, a method is proposed for controlling a hopping monopod. It takes dynamics of the robot into account to have better nominal tracking of desired trajectories and more compliant environmental interactions at the same time. We have incorporated also natural dynamics of the robot into the system by using off-line gaits extracted from optimizations on energy. The main control loop consists of the projected inverse dynamics that generates actuator torques given desired trajectories and also a feedback loop designed and tuned specifically for the structure of the robot. A trajectory generator uses known optimal trajectories together with some stabilizing control laws that modify these trajectories to have better robustness in different situations. The average speed of the robot is also regulated by means of a self-organizing controller. We apply soft transitions in trajectories from phase to phase to avoid sharp actuator input profiles. Our method is successfully tested on a monopod hopper robot in simulation. It can handle slightly rough or sloped terrains while maintaining a given average speed. Simulation results suggest that our method is a promising candidate to control a real robot under construction.
Determination of refraction nonlinear index, for effect thermal, of solutions with nanoparticles of gold

Research of nonlinear optical properties of materials for manufacturing opto-electronic devices, had a great growth in the last years. The solutions with nanoparticle metals present nonlinear optical properties. In this work we present the results of characterizing, analyzing and determining the magnitude and sign of the nonlinear refractive index, using the z-scan technique in solutions with nanoparticles of gold, lipoic acid and sodium chloride. We used a continuous Argon laser at 514 nm with variable power, an 18 cms lens, and a chopper. We determined the nonlinear refractive index in the order of 10-9. These materials have potential applications mainly as optical limiters.
Dynamic workload driven data integration in tableau

Tableau is a commercial business intelligence (BI) software tool that supports interactive, visual analysis of data. Armed with a visual interface to data and a focus on usability, Tableau enables a wide audience of end-users to gain insight into their datasets. The user experience is a fluid process of interaction in which exploring and visualizing data takes just a few simple drag-and-drop operations (no programming or DB experience necessary). In this context of exploratory, ad-hoc visual analysis, we describe a novel approach to integrating large, heterogeneous data sources. We present a new feature in Tableau called data blending, which gives users the ability to create data visualization mashups from structured, heterogeneous data sources dynamically without any upfront integration effort. Users can author visualizations that automatically integrate data from a variety of sources, including data warehouses, data marts, text files, spreadsheets, and data cubes. Because our data blending system is workload driven, we are able to bypass many of the pain-points and uncertainty in creating mediated schemas and schema-mappings in current pay-as-you-go integration systems.
Modeling study of the mobility in FDSOI devices with a focus on near-spacer-region

We have studied the mobility in the FDSOI devices as a function of silicon thickness, doping, surface orientation and applying different back biases. This study is also done in the near-spacer-region that is partially inverted. Simulations have been obtained with a self-consistent Poisson-Schrödinger which provides a precise energy distribution of carriers and, allied to a Kubo-Greenwood carrier mobility solver, performs a quantum corrected drift diffusion (QCDD) model, capable of capturing non local effects on transport (tunneling) and mobility (influence of geometry).
Polynomial Optimization Methods for Matrix Factorization

Matrix factorization is a core technique in many machine learning problems, yet also presents a nonconvex and often difficult-to-optimize problem. In this paper we present an approach based upon polynomial optimization techniques that both improves the convergence time of matrix factorization algorithms and helps them escape from local optima. Our method is based on the realization that given a joint search direction in a matrix factorization task, we can solve the ``subspace search'' problem (the task of jointly finding the steps to take in each direction) by solving a bivariate quartic polynomial optimization problem. We derive two methods for solving this problem based upon sum of squares moment relaxations and the Durand-Kerner method, then apply these techniques on matrix factorization to derive a direct coordinate descent approach and a method for speeding up existing approaches. On three benchmark datasets we show the method substantially improves convergence speed over state-of-the-art approaches, while also attaining lower objective value.
Broadcast Cooperation Strategies for Two Colocated Users

This work considers the problem of communication between a remote single transmitter and a destined user, with helping colocated users, over an independent block Rayleigh-fading channel. The colocation nature of the users allows cooperation, which increases the overall achievable rate, from transmitter to destination. The transmitter is ignorant of the fading coefficients, while receivers have access to perfect channel state information (CSI). We propose, for this setting, a multilayer broadcast transmission approach. The broadcast approach enables enhanced cooperation between the colocated users. That is due to the nature of broadcasting, where the better the channel quality, the more layers that can reliably be decoded. The cooperation between the users is performed over additive white Gaussian noise (AWGN) channels, with a relaying power constraint, and unlimited bandwidth. Three commonly used cooperation techniques are studied: amplify-and-forward (AF), compress-and-forward (CF), and decode-and-forward (DF). These techniques are extended by using the broadcast approach for the case of relaxed decoding delay constraint. For this case, a separate processing of the layers, which includes multisession cooperation is shown to be beneficial. Further, closed-form expressions for infinitely many AF sessions and recursive expressions for the more complex CF are given. Numerical results for the various cooperation strategies demonstrate how the multisession cooperation outperforms conventional relaying techniques.
BITUMINOUS ROADS IN AUSTRALIA

This book is for civil engineers concerned with the planning, design and construction of pavements who wish to increase their knowledge of a specialist area. The book first deals with the origin and manufacture of the mineral aggregate and binder constituents of a bituminous structure and the assessment of their quality by current specifications. It describes the sprayed binder and coated aggregate construction practices and indicates the methods used to control these processes. It describes the important environmental effects encountered by bituminous structure or surfacing in service and the various forms of distress which can be obtained both shortly after application and in the long term. The second part of the book deals with the basic behaviour of bituminous materials in handling and service: deformation andd flow response of the binder under load; deformation behaviour of dense (compacted) minearal aggregate/binder mixtures; factors affecting the adhesion of binders to aggregates, particularly in the presence of water; binder performance in long-term pavement service; current developments in the improvement of binder performance; and current knowledge of substitute binders for petroleum bitumen which can be derived from coal or oil bearing shale. The ISBN of the microfiche version is 0-86910-149-8. (TRRL)
How to avoid herd: a novel stochastic algorithm in grid scheduling

Grid technologies promise to bring the grid users high performance. Consequently, scheduling is being becoming a crucial problem. Herd behavior is a common phenomenon, which causes the severe performance decrease in grid environment with respect to bad scheduling behaviors. In this paper, on the basis of the theoretical results of the homogeneous balls and bins model, we proposed a novel stochastic algorithm to avoid herd behavior. Our experiments address that the multi-choice strategy, combined with the advantages of DHT, can decrease herd behavior in large-scale sharing environment, at the same time, providing better schedule performance while burdening much less scheduling overhead than greedy algorithms. In the case of 1000 resources, the simulations show that, for the heavy load(i.e. system utilization rate 0.5), the multi-choice algorithm reduces the number of incurred herds by a factor of 36, the average job waiting time by a factor of 8, and the average job turn-around time by 12% compared to the greedy algorithms
Statistical Translation of English Texts to API Code Templates

We develop T2API, a context-sensitive, graph-based statisticaltranslation approach that takes as input an English description of aprogramming task and synthesizes the corresponding API code templatefor the task. We train T2API to statistically learn the alignmentsbetween English and APIs and determine the relevant API elements. Thetraining is done on StackOverflow, which is a bilingual corpus onwhich developers discuss programming problems in two types oflanguage: English and programming language. T2API considers both thecontext of the words in the input query and the context of APIelements that often go together in the corpus. The derived APIelements with their relevance scores are assembled into an API usageby GraSyn, our novel graph-based API synthesis algorithm thatgenerates a graph representing an API usage from a large codecorpus. Importantly, T2API is capable of generating new API usagesfrom previously seen sub-usages.
A Modified Ultimate Failure Criterion and Material Degradation Scheme in Bridging Model Prediction for Biaxial Strength of Laminates

The first worldwide failure exercise (WWFE-I) indicated that the bridging micromechanics model exhibited a unique feature in calculating internal thermal stresses in the constituent fiber and resin materials of a laminated composite. Once applied to laminate strength prediction, however, the model only gave moderate correlation with the experiments. In this article, a material degradation scheme and a modified ultimate failure criterion for laminates are incorporated into the bridging model to improve its predictive capacity. Pure resin inter-layers are also introduced to simulate the effect of interfaces in between the lamina plies. With these modifications, much more accurate predictions have been obtained for the majority of the exercise problems, especially for the ultimate strengths and deformations of the multi-directional laminates. The correlation of the present theory predictions with the experiments is even better than that of any other theory predictions made in the exercise.
Sorting in the V-BLAST Algorithm and Loading

In this paper we consider transmission over MIMO channels with channel state information available at the transmitter. This enables the use of precoding as well as an optimization of rate and power distribution over the parallel subchannels in the transmission system. We show that in the case of so-called loading, equalization by the V-BLAST algorithm does not lead to the optimum performance. A revised detection algorithm is given, which performs an optimized sorting for systems with loading. Simulation results are presented that underline the performance improvements possible.
FPGA based fault emulation of synchronous sequential circuits

This paper describes a feasibility study of accelerating fault simulation by emulation on FPGA. Fault simulation b an important subtask in test pattern generation and it is frequently used throughout the test generation process. In order to further speed up simulation, we propose to make use of reconfigurable hardware by emulating circuit together with fault insertion structures on FPGA. Experiments showed that it is beneficial to use emulation for circuits/methods that require large numbers of test vectors, e.g., sequential circuits and/or genetic algorithms.