Miracle-dz/UniFAR-FTUs huggingface dataset:
Going Beyond AER: An Extensive Analysis of Word Alignments and Their Impact on MT

This paper presents an extensive evaluation of five different alignments and investigates their impact on the corresponding MT system output. We introduce new measures for intrinsic evaluations and examine the distribution of phrases and untranslated words during decoding to identify which characteristics of different alignments affect translation. We show that precision-oriented alignments yield better MT output (translating more words and using longer phrases) than recall-oriented alignments.
One-mode Projection-based Multilevel Approach for Community Detection in Bipartite Networks

Interest in algorithms for community detection in networked systems has increased over the last decade, mostly motivated by a search for scalable solutions capable of handling large-scale networks. Multilevel approaches provide a potential solution to scalability, as they reduce the cost of a community detection algorithm by applying it to a coarsened version of the original network. The small-scale solution thus obtained is then projected back to the original large-scale model to obtain the desired solution. However, standard multilevel methods are not directly applicable to bipartite network models and the literature lacks studies on multilevel optimization applied to such networks. This article addresses this gap and introduces a novel multilevel method based on onemode projection that allows executing traditional multilevel methods in bipartite network models. The approach has been validated with an algorithm that solves the Barber’s modularity problem. It attained improved runtime performance, whilst solution accuracy is shown to be statistically equivalent to that of the standard method.
Anti-Inflammatory and Immunoregulatory Functions of Artemisinin and Its Derivatives

Artemisinin and its derivatives are widely used in the world as the first-line antimalarial drug. Recently, growing evidences reveal that artemisinin and its derivatives also possess potent anti-inflammatory and immunoregulatory properties. Meanwhile, researchers around the world are still exploring the unknown bioactivities of artemisinin derivatives. In this review, we provide a comprehensive discussion on recent advances of artemisinin derivatives affecting inflammation and autoimmunity, the underlying molecular mechanisms, and also drug development of artemisinins beyond antimalarial functions.
A Universal Criteria Catalog for Evaluation of Heterogeneous Agent Development Artifacts

The research discipline of multi-agent systems is characterized by a high degree of heterogeneity. This heterogeneity leads to a vast amount of options (e.g. different architectures and languages) how to employ agent technology but is also one major source of difficulties for its adoption. People interested in using multi-agent systems depend on solid survey articles, which clarify and evaluate these different options and explain in which situations which choices should be made. A survey should also propose viable classification means for helping readers to understand which development artifacts broadly exhibit similar properties. To date, in most cases multi-agent system surveys do without classifications and only address one specific type of artifact such as agent languages or tools. Often, only the characteristics of the representatives are described without evaluating them. In this work a universal criteria catalog will be presented that has been defined abstractly enough for being usable for a wide variety of agent development artifacts. It will be shown how this abstract catalog can be further refined with respect to the chosen area of investigation. In addition to the catalog its general usage as part of a survey will be explained and a blueprint for survey conduction will be presented. To demonstrate its usefulness cutouts of extensive evaluations, performed in the areas of agent architectures, languages, methodologies, tools and platforms, will be presented.
Template-Based Continuous Speech Recognition

Despite their known weaknesses, hidden Markov models (HMMs) have been the dominant technique for acoustic modeling in speech recognition for over two decades. Still, the advances in the HMM framework have not solved its key problems: it discards information about time dependencies and is prone to overgeneralization. In this paper, we attempt to overcome these problems by relying on straightforward template matching. The basis for the recognizer is the well-known DTW algorithm. However, classical DTW continuous speech recognition results in an explosion of the search space. The traditional top-down search is therefore complemented with a data-driven selection of candidates for DTW alignment. We also extend the DTW framework with a flexible subword unit mechanism and a class sensitive distance measure-two components suggested by state-of-the-art HMM systems. The added flexibility of the unit selection in the template-based framework leads to new approaches to speaker and environment adaptation. The template matching system reaches a performance somewhat worse than the best published HMM results for the Resource Management benchmark, but thanks to complementarity of errors between the HMM and DTW systems, the combination of both leads to a decrease in word error rate with 17% compared to the HMM results
An interval programming approach for bilevel linear programming problem with fuzzy random coefficients

In the real world, many decision making problems often need to be modeled as a class of bilevel programming problems where fuzzy random coefficients are contained in both objective functions and constraint functions. To deal with these problems, an interval programming approach based on the α-level set is proposed to determine the optimal value range containing the best and worst optimal values so as to provide more information for decision makers. Furthermore, by incorporating expectation optimization model into probabilistic chance constraints, the best and worst optimal problems are transformed into deterministic ones. In addition, an estimation of distribution algorithm is designed to derive the best and worst Stackelberg solutions. Finally, a numerical example is given to show the application of the proposed models and algorithm.
sNASP and ASF1A function through both competitive and compatible modes of histone binding

Histone chaperones are proteins that interact with histones to regulate the thermodynamic process of nucleosome assembly. sNASP and ASF1 are conserved histone chaperones that interact with histones H3 and H4 and are found in a multi-chaperoning complex in vivo. Previously we identified a short peptide motif within H3 that binds to the TPR domain of sNASP with nanomolar affinity. Interestingly, this peptide motif is sequestered within the known ASF1–H3–H4 interface, raising the question of how these two proteins are found in complex together with histones when they share the same binding site. Here, we show that sNASP contains at least two additional histone interaction sites that, unlike the TPR–H3 peptide interaction, are compatible with ASF1A binding. These surfaces allow ASF1A to form a quaternary complex with both sNASP and H3–H4. Furthermore, we demonstrate that sNASP makes a specific complex with H3 on its own in vitro, but not with H4, suggesting that it could work upstream of ASF1A. Further, we show that sNASP and ASF1A are capable of folding an H3–H4 dimer in vitro under native conditions. These findings reveal a network of binding events that may promote the entry of histones H3 and H4 into the nucleosome assembly pathway.
Molecular profiling including epidermal growth factor receptor and p21 expression in high-risk breast cancer patients as indicators of outcome.

BACKGROUND Patients with high-risk primary breast cancer remain at high risk for relapse. More precise prognostic and predictive tools are needed to improve treatment of such patients. PATIENTS AND METHODS Formalin-fixed, paraffin-embedded tumors from 239 high-risk breast cancer patients were examined for expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), estrogen receptor, progesterone receptor, Ki-67, p16, p21, p27, and p53 by immunohistochemistry. Gene expression of EGFR, HER2, glutathione S-transferase-Pi (GSTP1), excision repair cross complementation1 (ERCC1), p21, beta-tubulin-3, multidurg resistance (MDR1), cyclooxygenase2 (COX2), and cyclin-E was measured by RT-PCR. RESULTS Eighty percent of patients presented with locally advanced, or > or =10 axillary nodal metastasis, and 20% with inflammatory breast cancer. The median age was 46 years (26-62 years) and the median number of involved axillary lymph nodes was 12 (0-42). At a median follow-up of 86 months, relapse-free survival (RFS) and overall survival for the entire group were 50% (95% CI 43% to 57%) and 62% (95% CI 56% to 69%). Multivariate Cox stepwise analysis resulted in a simple model for RFS consisting only of p21 expression, EGFR expression assessed by RT-PCR, and number of axillary nodal metastases. CONCLUSION A prognostic model on the basis of the expression of a limited number of proteins and genes may help to guide target-specific therapies in patients with high-risk breast cancer.
Double-Rotor Switched Reluctance Machine (DRSRM)

With the era of modern vehicle electrification, electric machines with high traction torque-speed output and compact volume are highly desired. This paper presents a family of switched reluctance machine configurations that are composed of double rotors and one stator integrated in one machine housing. The machines are potentially more compact and lower cost, while providing two independent mechanical outputs suitable for hybrid electric vehicle transmissions. The detailed design process of a double-rotor switched reluctance machine (DRSRM) is presented, comprising of analytical calculations, finite-element field analysis, and full-drive system simulation. Additionally, some optimizations are applied to maximize the machine performance and minimize the machine weight and volume. A scaled prototype machine is designed and built according to chosen vehicle drive cycles to evaluate and validate the DRSRM prototype.
Predicting UNIX Command Lines: Adjusting to User Patterns

As every user has his own idiosyncrasies and preferences, an interface that is honed for one user may be problematic for another. To accommodate a diverse range of users, many computer applications therefore include an interface that can be customized — e.g., by adjusting parameters, or defining macros. This allows each user to have his “own” version of the interface, honed to his specific preferences. However, most such interfaces require the user to perform this customization by hand — a tedious process that requires the user to be aware of his personal preferences. We are therefore exploring adaptive interfaces, that can autonomously determine the user’s preference, and adjust the interface appropriately. This paper describes such an adaptive system — here a UNIXshell that can predict the user’s next command, and then use this prediction to simplify the user’s future interactions. We present a relatively simple model here, then explore a variety of techniques to improve its accuracy, including a “mixture of experts” model. In a series of experiments, on real-world data, we demonstrate that the simple system can correctly predict the user’s next command almost of the time, and can do so robustly — across a range of different users; and that it is extremely difficult to further improve this result.
Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.
Antibodies Trap Tissue Migrating Helminth Larvae and Prevent Tissue Damage by Driving IL-4Rα-Independent Alternative Differentiation of Macrophages

Approximately one-third of the world's population suffers from chronic helminth infections with no effective vaccines currently available. Antibodies and alternatively activated macrophages (AAM) form crucial components of protective immunity against challenge infections with intestinal helminths. However, the mechanisms by which antibodies target these large multi-cellular parasites remain obscure. Alternative activation of macrophages during helminth infection has been linked to signaling through the IL-4 receptor alpha chain (IL-4Rα), but the potential effects of antibodies on macrophage differentiation have not been explored. We demonstrate that helminth-specific antibodies induce the rapid trapping of tissue migrating helminth larvae and prevent tissue necrosis following challenge infection with the natural murine parasite Heligmosomoides polygyrus bakeri (Hp). Mice lacking antibodies (JH −/−) or activating Fc receptors (FcRγ−/−) harbored highly motile larvae, developed extensive tissue damage and accumulated less Arginase-1 expressing macrophages around the larvae. Moreover, Hp-specific antibodies induced FcRγ- and complement-dependent adherence of macrophages to larvae in vitro, resulting in complete larval immobilization. Antibodies together with helminth larvae reprogrammed macrophages to express wound-healing associated genes, including Arginase-1, and the Arginase-1 product L-ornithine directly impaired larval motility. Antibody-induced expression of Arginase-1 in vitro and in vivo occurred independently of IL-4Rα signaling. In summary, we present a novel IL-4Rα-independent mechanism of alternative macrophage activation that is antibody-dependent and which both mediates anti-helminth immunity and prevents tissue disruption caused by migrating larvae.
Influence of Hearing Loss on Children’s Identification of Spondee Words in a Speech-Shaped Noise or a Two-Talker Masker

Objective: This study evaluated the influence of hearing loss on children’s speech-perception abilities in a speech-shaped noise or a two-talker masker. For both masker conditions, it was predicted that children with hearing loss would require a more advantageous signal to noise ratio (SNR) than children with normal hearing to achieve the same criterion level of performance. However, it was hypothesized that the performance gap between children with hearing loss and children with normal hearing would be larger in the two-talker than in the speech-shaped noise masker. Design: A repeated-measures design compared the spondee identification performance of two age groups of children with hearing loss (9–11 and 13–17 years of age) and a group of children with normal hearing (9–11 years of age) in continuous speech-shaped noise or a two-talker masker. Estimates of the SNR required for 70.7% correct spondee identification were obtained using an adaptive, four-alternative, forced-choice procedure. Children were tested in the sound field. Children with hearing loss wore their personal hearing aids at their regular settings during testing. Results: Both groups of children with hearing loss performed more poorly than children with normal hearing in the speech-shaped noise masker. Younger children required an additional 2.7 dB SNR and older children required an additional 4.7 dB SNR to achieve the same level of performance as children with normal hearing. This disadvantage decreased to 8.1 dB for both age groups of children with hearing loss in the two-talker masker. For children with hearing loss, degree of hearing loss was significantly correlated with performance in the speech-shaped noise masker, but not in the two-talker masker. Conclusions: A larger performance gap was observed between children with hearing loss and children with normal hearing in competing speech than in steady state noise. These results are consistent with the hypothesis that hearing loss influenced children’s perceptual processing abilities.
Automatic cerebral microbleeds detection from MR images via Independent Subspace Analysis based hierarchical features

With the development of susceptibility weighted imaging (SWI) technology, cerebral microbleed (CMB) detection is increasingly essential in cerebrovascular diseases diagnosis and cognitive impairment assessment. Clinical CMB detection is based on manual rating which is subjective and time-consuming with limited reproducibility. In this paper, we propose a computer-aided system for automatic detection of CMBs from brain SWI images. Our approach detects the CMBs within three stages: (i) candidates screening based on intensity values (ii) compact 3D hierarchical features extraction via a stacked convolutional Independent Subspace Analysis (ISA) network (iii) false positive candidates removal with a support vector machine (SVM) classifier based on the learned representation features from ISA. Experimental results on 19 subjects (161 CMBs) achieve a high sensitivity of 89.44% with an average of 7.7 and 0.9 false positives per subject and per CMB, respectively, which validate the efficacy of our approach.
CR: Capability Information for Routing of Wireless Ad Hoc Networks in the Real Environment

In greedy routing, each relay node forwards the message to a neighbor (also called successor) that is closer to the destination. However, the successor candidate set (SCS) is different every time when the relative location of relay node to the destination changes. The configuration in the entire network when all the succeeding paths from a relay node are blocked by local minima is irregular and its concern region cannot be determined unless the routing actually initiates. In this paper, we introduce a new information model to determine the pattern of SCS under the impact of local minima by sacrificing little routing flexibility. As a result, each 1-hop advance can avoid those unsafe situations in order to stay along a non-detour path. In our model, each node prepares the information in a proactive model, but can use it for all different paths passing through, saving the cost and delay in the reactive model. We focus on an ``everyone" model, in which each node will apply the same generic process in a fully distributed manner, in order to achieve a reliable solution in real applications where the communication link is constituted irregularly and its quality changes dynamically. In details, we discuss how in a sample realistic environment the pattern of SCS can be interpreted in a single safety descriptor in [0,1] at each node. It indicates the maximum probability of a successful non-detour path from this node to the edge of networks. The larger value the more likely the non-detour routing will be successful and the more reliable the path will be. We illustrate the effectiveness of this indirect reference information in the corresponding routing, in terms of the cost of information construction and update propagation, and the success of non-detour path constitution, compared with the best results known to date.
Optimization in a Health Care System: a Liver Transplantation Example

This text presents a study in modeling a waiting list of liver transplantation, and the use of the resources that affects the system efficiency. The approach uses the graph theory to modeling the system as a workflow. The objective is to know how the system is, and how it will work after changing some parameters. The focus is the planning and the system management. The work encloses aspects of knowledge representation and optimization. An experiment is described and some conclusions are extracted
Acoustic model merging using acoustic models from multilingual speakers for automatic speech recognition

Many studies have explored on the usage of existing multilingual speech corpora to build an acoustic model for a target language. These works on multilingual acoustic modeling often use multilingual acoustic models to create an initial model. This initial model created is often suboptimal in decoding speech of the target language. Some speech of the target language is then used to adapt and improve the initial model. In this paper however, we investigate multilingual acoustic modeling in enhancing an acoustic model of the target language for automatic speech recognition system. The proposed approach employs context dependent acoustic model merging of a source language to adapt acoustic model of a target language. The source and target language speech are spoken by speakers from the same country. Our experiments on Malay and English automatic speech recognition shows relative improvement in WER from 2% to about 10% when multilingual acoustic model was employed.
A Wearable Assistant for Gait Training for Parkinson’s Disease with Freezing of Gait in Out-of-the-Lab Environments

People with Parkinson’s disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user’s clinical condition by transmitting sensing data and statistics to a telemedicine service. We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users’ feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home.