Miracle-dz/UniFAR-FTUs huggingface dataset:
UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity

We report results obtained by the UoW method in SemEval-2014’s Task 10 – Multilingual Semantic Textual Similarity. We propose to model Semantic Textual Similarity in the context of Multi-task Learning in order to deal with inherent challenges of the task such as unbalanced performance across domains and the lack of training data for some domains (i.e. unknown domains). We show that the Multi-task Learning approach outperforms previous work on the 2012 dataset, achieves a robust performance on the 2013 dataset and competitive results on the 2014 dataset. We highlight the importance of the challenge of unknown domains, as it affects overall performance substantially.
Dialectal Assamese vowel speech detection using acoustic phonetic features, KNN and RNN

The recognition of vowel phonemes plays an important role in the field of speech processing. Assamese is the major language of Assam and also the mother-tongue of the of the largest segment of the population of Assam. The standard Assamese language has four major dialects namely Central dialect, Eastern dialect, Goalpariya dialect and Kamrupi dialect. It has eight vowel phonemes which are /i/, /e/, /ε/, /a/, /n/, /c/, /o/ and /u/. In this paper, a comparative analysis between the Recurrent Neural Network (RNN) based algorithm and K-Nearest Neighbor (KNN) based algorithm is carried out for the recognition of the vowel sounds using the Acoustic Phonetic Features as the feature vector. Dialect wise recognition of the vowels is also carried out using the same feature vectors. A recognition rate of 97 % is obtained by using the KNN based algorithm for vowel recognition and an overall rate of 84.3% and 87% is obtained by RNN based algorithm and KNN based algorithm respectively for the dialectal Assamese vowel recognition. K-NN based approach gives better recognition rate than the ANN based approach.
Trace Ratio Linear Discriminant Analysis for Medical Diagnosis: A Case Study of Dementia

Dementia is one of the most common neurological disorders among the elderly. Identifying those who are of high risk suffering dementia is important to the administration of early treatment in order to slow down the progression of dementia symptoms. However, to achieve accurate classification, significant amount of subject feature information are involved. Hence identification of demented subjects can be transformed into a pattern recognition problem with high-dimensional nonlinear datasets. In this paper, we introduce trace ratio linear discriminant analysis (TR-LDA) for dementia diagnosis. An improved ITR algorithm (iITR) is developed to solve the TR-LDA problem. This novel method can be integrated with advanced missing value imputation method and utilized for the analysis of the nonlinear datasets in many real-world medical diagnosis problems. Finally, extensive simulations are conducted to show the effectiveness of the proposed method. The results demonstrate that our method can achieve higher accuracies for identifying the demented patients than other state-of-art algorithms.
Momentum space anisotropy of electronic correlations in Fe and Ni: An analysis of magnetic Compton profiles

The total and magnetically resolved Compton profiles are analyzed within the combined density functional and dynamical mean-field theory for the transition-metal elements Fe and Ni. A rather good agreement between the measured and computed magnetic Compton profiles of Fe and Ni is obtained with the standard local spin-density approximation (LSDA). By including local but dynamic many-body correlations captured by dynamical mean-field theory (DMFT), the calculated magnetic Compton profile is further improved when compared with experiment. The second moment of the difference of the total Compton profiles between the LSDA and DMFT, along the same momentum direction, has been used to discuss the strength of electronic correlations in Fe and Ni.
Fully dynamic semantic word clouds

Word clouds are popular graphical representations used to effectively summarize the core content of a document, or of a set of documents. In particular, in a semantic word cloud the positions of the words also reflect their semantic correlation. Several algorithms have been proposed in the literature to compute semantic word clouds, both in a static and in a dynamic setting. However, so far, solutions proposed to generate dynamic word clouds assume that all text changes are known in advance. In this paper we propose a new dynamic scenario. We assume to receive a text stream in a given time period, and we compute, in a sort of real-time fashion, a dynamic word cloud that shows the evolution of the stream. The visualization should preserve both the semantic relationships between words and the user's mental map. We present an algorithmic framework and the results of an experimental study that compares different solutions for the steps of its algorithmic pipeline.
The Outcome of NGOs’ Activism in Developing Countries Under Visibility Constraint

Many Developing Countries ratified ILO Fundamental Conventions and authorized local labour unions. Multinational companies producing in these countries pay more when NGOs campaigns take place and reputation counts. However, whether this external pressure from NGOs benefit local workers outside MNEs affiliates in host countries remains an open issue. Segmented and weak local labour unions often rely on external funding from the North and technical assistance by labour NGOs. They need to increase their visibility in the labour intensive sectors targeted by Northern donations and activism. To address these issues we develop a bargaining model adapted to peculiarities of labour market institutions in developing countries, i.e. external funding and the complementarity with labour NGOs. This model is estimated on data on Indonesian manufacturing firms, before and after the authorisation of labour unions, in sensitive and non sensitive sectors. We find that, in sectors with visibility for labour unions, the net outcome on wages of the presence of NGOs is negative. The external fundings imply a distortion in the objective of labour unions, confronted with the constraint of increasing the employment in the formal sector.
Session Types as Generic Process Types

Behavioural type systems ensure more than the usual safety guarantees of static analysis. They are based on the idea of "types-as-processes", providing dedicated type algebras for particular properties, ranging from protocol compatibility to race-freedom, lock-freedom, or even responsiveness. Two successful, although rather different, approaches, are session types and process types. The former allows to specify and verify (distributed) communication protocols using specific type (proof) systems; the latter allows to infer from a system specification a process abstraction on which it is simpler to verify properties, using a generic type (proof) system. What is the relationship between these approaches? Can the generic one subsume the specific one? At what price? And can the former be used as a compiler for the latter? The work presented herein is a step towards answers to such questions. Concretely, we define a stepwise encoding of a pi-calculus with sessions and session types (the system of Gay and Hole) into a pi-calculus with process types (the Generic Type System of Igarashi and Kobayashi). We encode session type environments, polarities (which distinguish session channels end-points), and labelled sums. We show forward and reverse operational correspondences for the encodings, as well as typing correspondences. To faithfully encode session subtyping in process types subtyping, one needs to add to the target language record constructors and new subtyping rules. In conclusion, the programming convenience of session types as protocol abstractions can be combined with the simplicity and power of the pi-calculus, taking advantage in particular of the framework provided by the Generic Type System.
Teleoperated versus open needle driving: Kinematic analysis of experienced surgeons and novice users

During robotic teleoperation, the dynamics of the master manipulators and the control of remote-side instruments impose challenges on the motor system of the human operator, and may impact performance and learning. In teleoperated robot-assisted minimally invasive surgery, there is a clear correlation between patient outcomes and the surgeon's case experience. However, the effect of the teleoperator on human motor skills and the relationship between these motor skills and patient outcomes are unknown. We used the da Vinci Research Kit, a custom research version of the da Vinci Surgical System, to compare teleoperated and open needle-driving movements of experienced da Vinci surgeons and novices. The experimental protocol consisted of structured but unconstrained needle driving trials repeated 80 times to allow for computational modeling of movement coordination and learning. Kinematic analysis showed that teleoperation increases trial time but reduces path length, that the trial times and path lengths of experienced surgeons are smaller than those of novices. In addition, there are significant differences in learning between experienced surgeons and novice users. Modeling of the movements and learning processes of experienced and novice surgeons may be used in the design of novel controllers that will expand robotic surgery capabilities and improve robot-assisted surgical skill acquisition.
Internal models for motion control

This paper is a review of different approaches and models underlying the voluntary control of human hand-arm movement. These models, dedicated to artificial movement simulation with application to motor control, robotics and computer animation, are categorized along at least three axis: Direct vs. Inverse models, Dynamics vs. Kinematics models, Global vs. Local models. We focus on sensory-motor models which have a biologically relevant control scheme for hand-arm reaching movements. Different methods are proposed with various points of view, related to kinematics, dynamics, theory of control, optimization or learning theory.
Derlin-dependent accumulation of integral membrane proteins at cell surfaces

Quality-control mechanisms of protein folding of transmembrane and secreted proteins is mediated by endoplasmic-reticulum-associated degradation (ERAD), which is used to detect and to degrade misfolded proteins in the ER. The ERAD machinery consists of chaperones, transmembrane proteins and ubiquitin-associated enzymes that detect, modify, and retro-translocate the misfolded proteins to the cytoplasm for degradation by the proteasome. In contrast to ERAD, little is known about the fates of integral membrane and secreted proteins that become misfolded at the plasma membrane or in the extracellular space. Derlin proteins are a family of proteins that are conserved in all eukaryotes, where they function in ERAD. Here, we show that loss of Derlin function in Caenorhabditis elegans and in mouse macrophages results in the accumulation of integral membrane proteins at the plasma membrane. Induction of LDL receptor misfolding at the plasma membrane results in a sharp decrease in its half-life, which can be rescued by proteasomal inhibitors or by reduction of Derlin-1 levels. We also show that Derlin proteins localize to endosomes as well as to the ER. Our data are consistent with a model where Derlin proteins function in a spatially segregated quality control pathway that is used for the recognition and degradation of transmembrane proteins that become misfolded at the plasma membrane and/or in endosomes.
Binding of Silver(I) Ions by Alfalfa Biomass (Medicago Sativa): Batch PH, Time, Temperature, and Ionic Strength Studies

1-1 Volume Four BINDING OF SILVER(I) IONS BY ALFALFA BIOMASS (MEDICAGO SATIVA): BATCH PH, TIME, TEMPERATURE, AND IONIC STRENGTH STUDIES 1 I. Herrera, 1,2 J.L. Gardea-Torresdey, 1 K.J. Tiemann, 2 J.R. Peralta-Videa, 1 V. Armendariz, and J.G. Parsons 1Department of Chemistry, 500 W. University Ave, University of Texas at El Paso, El Paso, TX 79968; Phone: (915)747-5359; Fax: (915)747-5748. 2Environmental Science and Engineering Ph.D. Program,University of Texas at El Paso, El Paso, TX 79968; Phone: (915)747-5359; Fax: (915)747-5748.
A duality between clause width and clause density for SAT

We consider the relationship between the complexities of k-SAT and those of SAT restricted to formulas of constant density. Let sk be the infimum of those c ges 0 such that k-SAT on n variables can be decided in time O(2cn) and dDelta be the infimum of those c ges 0 such that SAT on n variables and les Deltan clauses can be decided in time O(2cn). We show that limkrarrinfin sk = limDeltararrinfindDelta. So, for any epsi > 0, k-SAT can be solved in 2(1-epsi)n time independent of k if and only if the same is true for SAT with any fixed density of clauses to variables. We derive some interesting consequences from this. For example, assuming that 3-SAT is exponentially hard (that is, s3 > 0), SAT of any fixed density can be solved in time whose exponent is strictly less than that for general SAT. We also give an improvement to the sparsification lemma of Impagliazzo et al. (1998) showing that instances of k-SAT of density slightly more than exponential in k are almost the hardest instances of k-SAT. The previous result showed this for densities doubly exponential in k
Chordal sparsity, decomposing SDPs and the Lyapunov equation

Analysis questions in control theory are often formulated as Linear Matrix Inequalities and solved using convex optimisation algorithms. For large LMIs it is important to exploit structure and sparsity within the problem in order to solve the associated Semidefinite Programs efficiently. In this paper we decompose SDPs by taking advantage of chordal sparsity, and apply our method to the problem of constructing Lyapunov functions for linear systems. By choosing Lyapunov functions with a chordal graphical structure we convert the semidefinite constraint in the problem into an equivalent set of smaller semidefinite constraints, thereby facilitating the solution of the problem. The approach has the potential to be applied to other problems such as stabilising controller synthesis, model reduction and the KYP lemma.
Amplifying Tests to Validate Exception Handling Code: An Extended Study in the Mobile Application Domain

Validating code handling exceptional behavior is difficult, particularly when dealing with external resources that may be noisy and unreliable, as it requires (1) systematic exploration of the space of exceptions that may be thrown by the external resources, and (2) setup of the context to trigger specific patterns of exceptions. In this work, we first present a study quantifying the magnitude of the problem by inspecting the bug repositories of a set of popular applications in the increasingly relevant domain of Android mobile applications. The study revealed that 22% of the confirmed and fixed bugs have to do with poor exceptional handling code, and half of those correspond to interactions with external resources. We then present an approach that addresses this challenge by performing an systematic amplification of the program space explored by a test by manipulating the behavior of external resources. Each amplification attempts to expose a program's exception handling constructs to new behavior by mocking an external resource so that it returns normally or throws an exception following a predefined set of patterns. Our assessment of the approach indicates that it can be fully automated, is powerful enough to detect 67% of the faults reported in the bug reports of this kind, and is precise enough that 78% of the detected anomalies are fixed, and it has a great potential to assist developers.
WebWOZ: a wizard of oz prototyping framework

Language Technology (LT) based applications become more popular as technology improves. Prototyping early in the design process is critical for the development of high quality applications. It is difficult, however, to do low-fidelity prototyping (e.g. paper prototyping) of applications based on LT. One technique that has been used for this kind of prototyping is Wizard of Oz (WOZ). However, this generally involves the development of one-off user and wizard interfaces. A tool that facilitates the flexible integration of LT components into WOZ experiments is desirable. In this paper we explore the requirements for such a tool, drawing from the literature and a first WOZ experiment in which different wizards were observed and their behaviour was analysed.
A theory of induction and classification of tensor C*-categories

This paper addresses the problem of describing the structure of tensor C*-categories M with conjugates and irreducible tensor unit. No assumption on the existence of a braided symmetry or on amenability is made. Our assumptions are motivated by the remark that these categories often contain non-full tensor C*-subcategories with conjugates and the same objects admitting an embedding into the Hilbert spaces. Such an embedding defines a compact quantum group by Woronowicz duality. An important example is the Temperley--Lieb category canonically contained in a tensor C*-category generated by a single real or pseudoreal object of dimension bigger than 2. The associated quantum groups are the universal orthogonal quantum groups of Wang and Van Daele. Our main result asserts that there is a full and faithful tensor functor from M to a category of Hilbert bimodule representations of the compact quantum group. In the classical case, these bimodule representations reduce to the G-equivariant Hermitian bundles over compact homogeneous G-spaces, with G a compact group. Our structural results shed light on the problem of whether there is an embedding functor of M into the Hilbert spaces. We show that this is related to the problem of whether a classical compact Lie group can act ergodically on a non-type I von Neumann algebra. In particular, combining this with a result of Wassermann shows that an embedding exists if M is generated by a pseudoreal object of dimension 2.
New receiver architecture for localisation system

We propose in this paper a simple receiver structure in which the radio frequency signal is directly digitized by the analog-to-digital converters (ADC) with a sampling frequency smaller than signal frequency. This structure is used for a direction finding system by means of an antenna array. The direction of arrival (DOA) of RF signal is estimated using the proposed receiver architecture and the high resolution method MUSIC (multiple signal classification). The measurement system composed of a 4 quasi-Yagi antenna element uniform linear array and of 4 parallel digital receivers using a digital oscilloscope is built for validating this idea. The simulation results using ADS (advanced design system) software and the measurement results in anechoic chamber show that the DOA is accurately determined by this structure
A Multi-Agent Approach to Distributed Rendering Optimization

Physically based rendering is the process of generating a 2D image from the abstract description of a 3D Scene. Despite the development of various new techniques and algorithms, the computational requirements of generating photorealistic images still do not allow to render in real time. Moreover, the configuration of good render quality parameters is very difficult and often too complex to be done by nonexpert users. This paper describes a novel approach called MAgarRO (standing for "Multi-Agent AppRoach to Rendering Optimization") which utilizes principles and techniques known from the field of multi-agent systems to optimize the rendering process. Experimental results are presented which show the benefits of MAgarRO -based rendering optimization.
Privacy Preserving Trust Negotiation for Pervasive Healthcare

Healthcare systems are being extended to monitor patients with body sensors wirelessly linked to a mobile phone that interacts with remote healthcare services and staff. As such systems become more widespread, with multiple healthcare providers and security domains, the establishment of trust between users, providers and medical staff will become important. In this paper we implement the ETTG privacy-preserving trust negotiation protocol and show how it can be used to automatically establish mutual trust between interacting parties in compliance with access-control and disclosure policies. The protocol is implemented in Java and can be run on J2ME platforms. The trust negotiation steps are logged and the resulting trust graphs can be visualised to show how policy compliance was achieved. We also develop a new easier-to-understand syntax for ETTG and use it to define access-control and disclosure policies for a small pervasive healthcare scenario
Building well-performing classifier ensembles : model and decision level combination

There is a continuing drive for better, more robust generalisation performance from classification systems, and prediction systems in general. Ensemble methods, or the combining of multiple classifiers, have become an accepted and successful tool for doing this, though the reasons for success are not always entirely understood. In this thesis, we review the multiple classifier literature and consider the properties an ensemble of classifiers - or collection of subsets - should have in order to be combined successfully. We find that the framework of Stochastic Discrimination provides a well-defined account of these properties, which are shown to be strongly encouraged in a number of the most popular/successful methods in the literature via differing algorithmic devices. This uncovers some interesting and basic links between these methods, and aids understanding of their success and operation in terms of a kernel induced on the training data, with form particularly well suited to classification. One property that is desirable in both the SD framework and in a regression context, the ambiguity decomposition of the error, is de-correlation of individuals. This motivates the introduction of the Negative Correlation Learning method, in which neural networks are trained in parallel in a way designed to encourage de-correlation of the individual networks. The training is controlled by a parameter λ governing the extent to which correlations are penalised. Theoretical analysis of the dynamics of training results in an exact expression for the interval in which we can choose λ while ensuring stability of the training, and a value λ∗ for which the training has some interesting optimality properties. These values depend only on the size N of the ensemble. Decision level combination methods often result in a difficult to interpret model, and NCL is no exception. However in some applications, there is a need for understandable decisions and interpretable models. In response to this, we depart from the standard decision level combination paradigm to introduce a number of model level combination methods. As decision trees are one of the most interpretable model structures used in classification, we chose to combine structure from multiple individual trees to build a single combined model. We show that extremely compact, well performing models can be built in this way. In particular, a generalisation of bottom-up pruning to a multiple-tree context produces good results in this regard. Finally, we develop a classification system for a real-world churn prediction problem, illustrating some of the concepts introduced in the thesis, and a number of more practical considerations which are of importance when developing a prediction system for a specific problem.