Miracle-dz/UniFAR-FTUs huggingface dataset:
A Character n-gram Based Approach for Improved Recall in Indian Language NER

Named Entity Recognition (NER) is the task of identifying and classifying all proper nouns in a document as person names, organization names, location names, date & time expressions and miscellaneous. Previous work (Cucerzan and Yarowsky, 1999) was done using the complete words as features which suffers from a low recall problem. Character n-gram based approach (Klein et al., 2003) using generative models, was experimented on English language and it proved to be useful over the word based models. Applying the same technique on Indian Languages, we experimented with Conditional Random Fields (CRFs), a discriminative model, and evaluated our system on two Indian Languages Telugu and Hindi. The character n-gram based models showed considerable improvement over the word based models. This paper describes the features used and experiments to increase the recall of Named Entity Recognition Systems which is also language independent.
Tree Optimization In Overlay Multicast Based on Location and Bandwidth

One of the main parts of peer-to-peer multicast is the structure of overlay network. Most of the existing works utilize some common parameters for constructing overlay tree. These parameters are bandwidth and delay. The mismatch between current overlay structure and underlying network topology causes severe link stress and then leads to high end to end latency. According to these problems, we propose a new overlay construction algorithm called OLT in this paper that finds the location of peers and tries to connect peers that are as close as possible. OLT exploits a number of heuristic techniques to reach an optimized tree even in case of peer heterogeneity and high churn rate. Our simulations show that OLT reduces link stress and latency because it constructs a logical tree more compatible to physical network than the existing algorithms.
TRANSACTIVE MEMORY THEORY: A REVIEW

In this paper, we analyze extant literature to assess the state of transactive memory theory research and report the preliminary findings of our research in progress. Based on prevalent transactive memory themes identified in the literature, we content analyze 52 articles at the individual, group, and organizational levels of analysis, and identify several future MIS research opportunities. Our findings show that themes such as knowledge directories, transferring the knowledge didactically, and pooling knowledge centrally, have received the majority of research focus. Future research may investigate how information systems can enhance human abilities in the transactive memory system to relieve cognitive strain and enhance individual memory capacity. Additional research could examine the effects of information technologies on the relationship between transactive memory and performance outcomes in large groups or organizations. Future investigation of these topics will serve to both extend transactive memory theory and operationalize the theory in MIS research settings.
Supported Employment Research: Expanding Competitive Employment Opportunities for Persons with Significant Disabilities.

Supported employment was initiated through the Rehabilitation Act Amendments of 1986 specifically to assist persons with the most significant disabilities successfully achieve and retain competitive employment. This study reports the results of a ten year effort to chart the growth of supported employment in areas such as the number and disability profile of participants, consumer outcomes, funding mechanisms and program expenditures. The costs and outcomes for supported employment and sheltered employment are also compared. Strategies are presented to expand the utilization of supported employment and thereby increase employment opportunities available for persons with the most significant disabilities. SUPPORTED EMPLOYMENT: A DECADE OF RAPID GROWTH
Self-organization framework for mobile ad hoc networks

Mobile ad hoc networks (MANETs) face a number of challenges, in particular due to dynamic network topology and large variable number of mobile nodes. To overcome these problems an effective solution is to define a self-organizing architecture that facilitates the network management task and permits to deploy wide panoply of services. One method to organize such networks is to define a virtual backbone that covers all the nodes in the network. In this paper we propose new self-organization architecture based on an optimized CDS construction. In this approach, the neighbors select CDS nodes intelligibly. In the selection processes, a new weight parameter depending on energy, link quality and connectivity is introduced in order to increase the CDS lifetime and hence the network performances. To study the benefits of this new architecture, we propose a new routing protocol called "PROC" (Proactive Routing based on Optimized CDS). Simulation results, using NS2, show that PROC minimizes the overhead and the energy consumption compared to the well-known OLSR routing protocol.
Willingness-to-Pay for Oyster Consumption Mortality Risk Reductions

In this paper we use data from an internet-based survey and estimate the benefits of an oyster consumption safety policy with the contingent valuation method. In addition to providing a context specific estimate of willingness-to-pay for oyster safety, we consider two unresolved issues in the contingent valuation health risk literature. First, a number of studies in the mortality risk reduction literature find that willingness-to-pay is not sensitive to the scope of the risk change. We present the scope test as a difference in the number of lives saved by the program, instead of small changes in risk, and that referendum votes are responsive to scope. Second, we identify those at risk respondents who would actually benefit from the policy and decompose willingness-to-pay into personal mortality risk reduction values and altruistic willingness-to-pay. We find that respondents are sensitive to the scope of the policy and most at-risk respondents are willing to pay even more. We find that willingness-to-pay per life saved is $1.28 million for the pure private good of own-risk reduction. Willingness-to-per per life saved including private values and public, altruistic nonuse values is $5.92 million. Altruistic values are the major component of willingness-to-pay. Key Words: contingent valuation, scope test, altruism, seafood safety, health risk, oyster
Numerical approximation for a visibility based pursuit-evasion game

This work addresses a vision-based target tracking problem between a mobile observer and a target in the presence of a circular obstacle. The task of keeping the target in the observer's field-of-view is modeled as a pursuit-evasion game by assuming that the target is adversarial in nature. Due to the presence of obstacles, this is formulated as a game with state constraints. The objective of the observer is to maintain a line-of-sight with the target at all times. The objective of the target is to break the line-of-sight in finite amount of time. First, we establish that the value of the game exists in this setting. Then we reduce the dimension of the problem by formulating the game in relative coordinates, and present a discretization in time and space for the reduced game. Based on this discretization, we use a fully discrete semi-Lagrangian scheme to compute the Kružkov transform of the value function numerically, and show that the scheme converges for our problem. Finally, we compute the optimal control action of the players from the Kružkov transform of the value function, and demonstrate the performance of the numerical scheme by numerous simulations.
Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association

In this paper, we study the challenging problem of multiobject tracking in a complex scene captured by a single camera. Different from the existing tracklet associationbased tracking methods, we propose a novel and efficient way to obtain discriminative appearance-based tracklet affinity models. Our proposed method jointly learns the convolutional neural networks (CNNs) and temporally constrained metrics. In our method, a siamese convolutional neural network (CNN) is first pre-trained on the auxiliary data. Then the siamese CNN and temporally constrained metrics are jointly learned online to construct the appearance-based tracklet affinity models. The proposed method can jointly learn the hierarchical deep features and temporally constrained segment-wise metrics under a unified framework. For reliable association between tracklets, a novel loss function incorporating temporally constrained multi-task learning mechanism is proposed. By employing the proposed method, tracklet association can be accomplished even in challenging situations. Moreover, a largescale dataset with 40 fully annotated sequences is created to facilitate the tracking evaluation. Experimental results on five public datasets and the new large-scale dataset show that our method outperforms several state-of-the-art approaches in multi-object tracking.
Prediction of the Pharmacokinetics, Pharmacodynamics, and Efficacy of a Monoclonal Antibody, Using a Physiologically Based Pharmacokinetic FcRn Model

Although advantages of physiologically based pharmacokinetic models (PBPK) are now well established, PBPK models that are linked to pharmacodynamic (PD) models to predict pharmacokinetics (PK), PD, and efficacy of monoclonal antibodies (mAbs) in humans are uncommon. The aim of this study was to develop a PD model that could be linked to a physiologically based mechanistic FcRn model to predict PK, PD, and efficacy of efalizumab. The mechanistic FcRn model for mAbs with target-mediated drug disposition within the Simcyp population-based simulator was used to simulate the pharmacokinetic profiles for three different single doses and two multiple doses of efalizumab administered to virtual Caucasian healthy volunteers. The elimination of efalizumab was modeled with both a target-mediated component (specific) and catabolism in the endosome (non-specific). This model accounted for the binding between neonatal Fc receptor (FcRn) and efalizumab (protective against elimination) and for changes in CD11a target concentration. An integrated response model was then developed to predict the changes in mean Psoriasis Area and Severity Index (PASI) scores that were measured in a clinical study as an efficacy marker for efalizumab treatment. PASI scores were approximated as continuous and following a first-order asymptotic progression model. The reported steady state asymptote (Y ss) and baseline score [Y (0)] was applied and parameter estimation was used to determine the half-life of progression (Tp) of psoriasis. Results suggested that simulations using this model were able to recover the changes in PASI scores (indicating efficacy) observed during clinical studies. Simulations of both single dose and multiple doses of efalizumab concentration-time profiles as well as suppression of CD11a concentrations recovered clinical data reasonably well. It can be concluded that the developed PBPK FcRn model linked to a PD model adequately predicted PK, PD, and efficacy of efalizumab.
Facial Expression Recognition with sEMG Method

Facial expression recognition has broad application prospects in the fields of psychological study, nursing care, Human Computer Interaction as well as affective computing. The method with surface Electromyogram (sEMG), which is one of vital bio-signals, has its superiority in several aspects such as high temporal resolution and data processing efficiency over other methods. Researches regarding EMG signal to study emotional expression have started since the second half of last century. Meanwhile, studies on myoelectrical control systems focusing on the computation of bio-signal processing and data analysis have been blooming in the recent twenty years. To have a comprehensive view of utilizing facial sEMG method, a systematic review is presented in this paper for facial expression recognition from experiment design to measurement systems, and data analysis steps.
Multilingualism and trait emotional intelligence: an exploratory investigation

ABSTRACT Recent research suggests that multilingualism, advanced knowledge of several languages and frequent use of them can – to some extent- shape personality traits and psychological dimensions. The present study focuses on the effect of multilingualism on emotional intelligence which has been linked to increased emotional granularity, i.e. a greater store of emotion concepts and an increased ability to know how and when to use them [Feldman-Barrett, L. (2017a). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(1), 1–23. doi:10.1093/scan/nsw154; 2017b. How emotions are made. The secret life of the brain. Boston, NY: Houghton Mifflin Harcourt]. Using three databases collected for previous studies to which a total of 1278 participants contributed, the present study explores whether individuals knowing more languages, and knowing them to a higher level scored higher on trait Emotional Intelligence. Statistical analyses revealed that, with one exception, no such relationship existed. Possible reasons for this overall null result is that Trait EI is less susceptible than other personality dimensions to be shaped by social environmental factors such as multilingualism or that the instruments used were too blunt.
Reliable Minimum Energy CMOS Circuit Design

—Low voltage operation can markedly reduce the energy requirements of digital circuits; however, in the face of variability it also greatly reduces the reliability and yield . In order to mitigate this effect, device dimensions can be increased and adaptive body biasing can be employed, but at the cost of potentially increasing energy per operation. This paper presents a new framework for determining the minimum energy operating point of digital CMOS circuits with precise guarantees on reliability and parametric yield
Underpricing in Market Games with a Distinguished Player

We generalize asset pricing theory by merging it with ideas taken from corporate governance. We do this by introducing a so called distinguished player de ned by the ability to enhance the value of a company facing private e¤ort costs. The current fundamental value of the company hinges then on the distinguished players prospective e¤ort which is determined by his ex post holdings and hence by the outcome of the trading game. In this article we characterize underpricing trade equilibria for a general class of market mechanisms. They display interesting theoretical properties. Shares of the company are traded below their correctly anticipated value in order to discourage the distinguished player to sell and withdraw from the company. In particular, this implies a substantial generalization and reinterpretation of the traditional no-arbitrage condition towards a game-theoretic understanding. No rational investor can gain by deviating even though shares of the company are traded below their true value. An intriguing empirically testable prediction of this model are excess-returns for companies with a publicly visible distinguished player which if con rmed would falsify models based on the traditional no-arbitrage condition. JEL Classi cation Numbers: G12, G32, C72, D43, D46 Johann Wolfgang Goethe Universität Frankfurt, 60054 Frankfurt am Main, Germany; e-mail correspondence: blonski@wiwi.uni-frankfurt.de yJohann Wolfgang Goethe Universität Frankfurt, 60054 Frankfurt am Main, Germany; e-mail correspondence: lilienfeld@wiwi.uni-frankfurt.de
Wormhole routing on multichannel ring networks

Distributed computing systems which rely on message passing for communication between computers, require low latency in the communications network in order to be effective. The multichannel ring network was previously proposed as a local area network topology for use in such distributed systems. In this paper we describe how wormhole routing can be used in the multichannel ring to decrease the communication latency of the network. Wormhole routing is first discussed in detail, and then the implementation of this technique is described. <<ETX>>
An Efficient Path Computation Model for Hierarchically Structured Topographical Road Maps

In this paper, we have developed a HiTi (Hierarchical MulTi) graph model for structuring large topographical road maps to speed up the minimum cost route computation. The HiTi graph model provides a novel approach to abstracting and structuring a topographical road map in a hierarchical fashion. We propose a new shortest path algorithm named SPAH, which utilizes HiTi graph model of a topographical road map for its computation. We give the proof for the optimality of SPAH. Our performance analysis of SPAH on grid graphs showed that it significantly reduces the search space over existing methods. We also present an in-depth experimental analysis of HiTi graph method by comparing it with other similar works on grid graphs. Within the HiTi graph framework, we also propose a parallel shortest path algorithm named ISPAH. Experimental results show that inter query shortest path problem provides more opportunity for scalable parallelism than the intra query shortest path problem.
Monitoring the Upsurge of Biofuels in Commodity Futures Markets

This paper investigates interaction between energy and agriculture futures prices shedding lights on the recent upsurge of biofuels. We propose the correlation model of price returns between energy and agriculture taking into account biofuels. The empirical studies on the DJ-AIG commodity indices document that the increase of correlations between energy and grain price returns occurs during high energy price periods, which supports the correlation model. By using petroleum as energy and both soybean and soybean oil as biofuels, we show the sharp rise in the correlations after year 2004, which may correspond to the recent upsurge of biofuels. In addition, we examine the relationship between petroleum and three agricultural sources of bioethanol: sugar, wheat, and corn. It is shown that except corn there exist the increasing correlations of the agriculturals with petroleum. The exception may stem from the different profiles of corn comparing to sugar and wheat such as well-diversified use of corn and low production efficiency of corn as bioethanol. Finally, we offer an illustrative example that the increasing correlation between energy and agriculture is useful for generating profits by applying pairs trading to commodity futures markets.
Convolutional neural network-based image representation for visual loop closure detection

Deep convolutional neural networks (CNN) have recently been shown in many computer vision and pattern recognition applications to outperform by a significant margin state-of-the-art solutions that use traditional hand-crafted features. However, this impressive performance is yet to be fully exploited in robotics. In this paper, we focus one specific problem that can benefit from the recent development of the CNN technology, i.e., we focus on using a pre-trained CNN model as a method of generating an image representation appropriate for visual loop closure detection in SLAM (simultaneous localization and mapping). We perform a comprehensive evaluation of the outputs at the intermediate layers of a CNN as image descriptors, in comparison with state-of-the-art image descriptors, in terms of their ability to match images for detecting loop closures. The main conclusions of our study include: (a) CNN-based image representations perform comparably to state-of-the-art hand-crafted competitors in environments without significant lighting change, (b) they outperform state-of-the-art competitors when lighting changes significantly, and (c) they are also significantly faster to extract than the state-of-the-art hand-crafted features even on a conventional CPU and are two orders of magnitude faster on an entry-level GPU.
Developer fluency: achieving true mastery in software projects

Outsourcing and offshoring lead to a rapid influx of new developers in software projects. That, in turn, manifests in lower productivity and project delays. To address this common problem we study how the developers become fluent in software projects. We found that developer productivity in terms of number of tasks per month increases with project tenure and plateaus within a few months in three small and medium projects and it takes up to 12 months in a large project. When adjusted for the task difficulty, developer productivity did not plateau but continued to increase over the entire three year measurement interval. We also discovered that tasks vary according to their importance(centrality) to a project. The increase in task centrality along four dimensions: customer, system-wide, team, and future impact was approximately linear over the entire period. By studying developer fluency we contribute by determining dimensions along which developer expertise is acquired, finding ways to measure them, and quantifying the trajectories of developer learning.
Efﬁcient Query Routing in RDF/S schema-based P2P Systems

P2P computing gains increasing attention lately, since it provides the means for realizing computing systems that scale to very large numbers of participating peers, while ensuring high autonomy and fault-tolerance. However, existing P2P systems oﬁer poor data management facilities, limited to IR-style keyword search using eventually a ﬂxed set of document attributes. Schema-based P2P systems have been proposed to support richer facilities in exchanging, querying and integrating structured and semi-structured data hosted by peers. In this paper, we address routing issues for expressive queries in schema-based P2P systems, where peers advertise their local bases using fragments of a community RDF/S schema (i.e., views). We then introduce an original encoding of RDF/S schema fragments, allowing to e–ciently check whether a peer view is subsumed by a query. We rely on this encoding to design an RDF/S view lookup service featuring a centralized and distributed execution over a DHT-based P2P system. We ﬂnally evaluate experimentally the proposed framework to demonstrate its scalability for large P2P networks and arbitrary RDF/S schema fragments, as well as, to estimate the number of routing hops required by the two versions of our lookup service.
Isolated digit recognition for Malayalam- An application perspective

Speech recognition is one of the promising technologies of the future. Voice user interfaces play an important role in many real world applications. This paper presents speaker independent isolated digit recognition for Malayalam language and reveals some application areas of digit recognition. Mel-Frequency Cepstral Coefficient (MFCC) is used as feature and Hidden Markov Model (HMM) is used as the signal processing algorithm for recognition. We also developed a computer application which uses the digit recognition system as the backbone structure.
Evaluation of a technique for improving the mapping of multiple speakers' vowel spaces in the F1~F2 plane

We evaluate a vowel formant normalisation technique that allows direct visual and statistical comparison of vowel triangles for multiple speakers of different sexes, by calculating for each speaker a ‘centre of gravity’ S in the F1 ~ F2 plane. S is calculated on the basis of formant frequency measurements taken for the so-called ‘point’ vowel [i], the average F1 and F2 for the vowel category with the highest average F1 (for English, usually the vowel of the TRAP or START lexical sets), and hypothetical minimal F1 and F2 values (coordinates we label [u'�]) extrapolated from the other two points. Expression of individual F1 and F2 measurements as ratios of the value of S for that formant permits direct mapping of different speakers’ vowel triangles onto one another, resulting in marked improvements in agreement in vowel triangle (a) area and (b) overlap, as compared to similar mappings attempted using linear Hz scales and the z (Bark) scale.