Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Extraction of Entities and Relation from Legal Documents

In recent years, the journalists and computer sciences speak to each other to identify useful technologies which would help them in extracting useful information. This is called “computational Journalism”. In this paper, we present a method that will enable the journalists to automatically identifies and annotates entities such as names of people, organizations, role and functions of people in legal documents; the relationship between these entities are also explored. The system uses a combination of both statistical and rule based technique. The statistical method used is Conditional Random Fields and for the rule based technique, document and language specific regular expressions are used.
Making sense of nonce words

Abstract: The project „Wortwarte“ (www.wortwarte.de) aims at the collection and documentation of new words in German. The collection is based on German newspaper texts and enables lexicologists and lexicographers to describe lexical aspects of language change. One of the shortcomings of the approach is that only words can be detected on the basis of their form. New meanings of existing words are this invisible. In this paper it will be argued that a retrospective analysis of the collected data, in particular of the huge number of German compounds, allows insights into meaning changes of the parts of these compounds, in particular of the morphological head of such constructions. I will illustrate this point with three examples. The paper ends with some perspectives on the potential to automate parts of these analyses.
Enhanced Efficiency of Dye-Sensitized Solar Counter Electrodes Consisting of Two-Dimensional Nanostructural Molybdenum Disulfide Nanosheets Supported Pt Nanoparticles

This paper reports architecturally designed nanocomposites synthesized by hybridizing the two-dimensional (2D) nanostructure of molybdenum disulfide (MoS2) nanosheet (NS)-supported Pt nanoparticles (PtNPs) as counter electrodes (CEs) for dye-sensitized solar cells (DSSCs). MoS2 NSs were prepared using the hydrothermal method; PtNPs were subsequently reduced on the MoS2 NSs via the water–ethylene method to form PtNPs/MoS2 NSs hybrids. The nanostructures and chemical states of the PtNPs/MoS2 NSs hybrids were characterized by high-resolution transmission electron microscopy and X-ray photoelectron spectroscopy. Detailed electrochemical characterizations by electrochemical impedance spectroscopy, cyclic voltammetry, and Tafel-polarization measurement demonstrated that the PtNPs/MoS2 NSs exhibited excellent electrocatalytic activities, afforded a higher charge transfer rate, a decreased charge transfer resistance, and an improved exchange current density. The PtNPs/MoS2 NSs hybrids not only provided the exposed layers of 2D MoS2 NSs with a great deal of catalytically active sites, but also offered PtNPs anchored on the MoS2 NSs enhanced I3− reduction. Accordingly, the DSSCs that incorporated PtNPs/MoS2 NSs CE exhibited an outstanding photovoltaic conversion efficiency (PCE) of 7.52%, which was 8.7% higher than that of a device with conventional thermally-deposited platinum CE (PCE = 6.92%).
Human Motion Classification Based on Multi-Modal Sensor Data for Lower Limb Exoskeletons

Intuitive exoskeleton control is fundamental since it contributes to improved user acceptance and wearability comfort. This requires the detection of user's motion intention and its incorporation into the exoskeleton control system. In this work, we propose a classification system based on Hidden Markov Models (HMMs), which facilitates the online classification of multi-modal sensor data acquired from a lower-limb exoskeleton based on previously defined motion patterns. For classification of these motion patterns at each time step, we consider the most recent sensor measurements by using a sliding window approach. We collected a training data set from a total number of 10 subjects performing 13 different motions with a passive exoskeleton equipped with 7 3D-force sensors and 3 inertial measurement units (IMUs). Our evaluation includes an analysis of the time needed for correct classification (latency), a validation for a training set containing all subjects and a leave-one-out validation to assess the generalization performance of the approach. The results indicate that our approach can classify motions of subjects included in the training set with an average accuracy of 92.80% and is able to achieve a generalization performance of 84.46%. With the selected parameters an average latency of 368.97 ms is achieved.
FourEyes

Crowdsourcing is a common means of collecting image segmentation training data for use in a variety of computer vision applications. However, designing accurate crowd-powered image segmentation systems is challenging, because defining object boundaries in an image requires significant fine motor skills and hand-eye coordination, which makes these tasks error-prone. Typically, special segmentation tools are created and then answers from multiple workers are aggregated to generate more accurate results. However, individual tool designs can bias how and where people make mistakes, resulting in shared errors that remain even after aggregation. In this article, we introduce a novel crowdsourcing approach that leverages tool diversity as a means of improving aggregate crowd performance. Our idea is that given a diverse set of tools, answer aggregation done across tools can help improve the collective performance by offsetting systematic biases induced by the individual tools themselves. To demonstrate the effectiveness of the proposed approach, we design four different tools and present FourEyes, a crowd-powered image segmentation system that uses aggregation across different tools. We then conduct a series of studies that evaluate different aggregation conditions and show that using multiple tools can significantly improve aggregate accuracy. Furthermore, we investigate the idea of applying post-processing for multi-tool aggregation in terms of correction mechanism. We introduce a novel region-based method for synthesizing more accurate bounds for image segmentation tasks through averaging surrounding annotations. In addition, we explore the effect of adjusting the threshold parameter of an EM-based aggregation method. Our results suggest that not only the individual tool’s design, but also the correction mechanism, can affect the performance of multi-tool aggregation. This article extends a work presented at ACM IUI 2018 [46] by providing a novel region-based error-correction method and additional in-depth evaluation of the proposed approach.
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model

In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.
Suicide an Alarming Need to Go Beyond Medication - An Exploration of Primary Care Scenario

Background: In the current globalized, technologically advanced society, suicide is one of the serious public health issues. The health system has an important role in assessing and preventing the suicidal act. General practitioners are the first contact for the community to seek health care. The aim of the study is to explore knowledge of general practitionersâ€™ regarding suicide prevention and the extent of risk assessment and management in the primary health care setting. Methods: Twelve in-depth interviews were conducted among general practitioners working in the primary health care setup both the public and private sectors in Urban Bangalore, Karnataka. A qualitative research design was adopted. Data was analyzed using thematic analysis approach. Results: This study reveals the general practitioners who are working in the primary health care set up, had inadequate knowledge about the causation of suicide, warning signs, risk and protective factors of suicide and also lacked skills in risk assessment and prevention strategies in the primary health care set up. Conclusion: Viewing suicide as a public health issue, it is very important to relate to the primary health care set up â€“where; people directly coming, approaching and being in contact with the health care system. The study indicates to focus psycho-social aspect with the medical management of the suicidal behaviour and also highlights the importance of risk assessment of suicidal behaviour in the primary health care setup by enhancing the knowledge of general practitioners regarding suicide prevention in a bio-psycho-social framework. Keywords: General practitioner, knowledge, suicide prevention
Phylogeographic analysis of the brown alga Cutleria multifida (Tilopteridales, Phaeophyceae) suggests a complicated introduction history

In depth genetic comparisons of populations of Cutleria multifida (Tilopteridales, Phaeophyceae) collected from Europe, the northwestern Pacific Ocean, Australia and New Zealand using the DNA sequences of four gene regions (the mitochondrial cox2 and cox3 genes, the intergeneric spacer region adjacent to cox3, and the open reading frame) suggested that the northwestern European and Japanese populations were considerably greater in terms of their genetic divergence than Mediterranean, Australian or New Zealand populations. The haplotypes of the populations in northwestern European (distribution range including the type locality, seven haplotypes) and Japanese populations (seven haplotypes) were unique except for one shared haplotype. There were weak but positive correlations between the geographical distance and the genetic divergence among northwestern European and Japanese populations. Moreover, both female and male gametophytes occurred in eight of the nine Japanese localities, suggesting Japanese populations showed normal sexual heteromorphic life history of the species. In light of these results, it appears that Japanese populations were native to the area despite earlier hypothesis. In contrast, Australian and New Zealand populations were composed of only one haplotype that is very close to those found in northwestern Europe and Japan, suggesting a recent introduction history from Europe (or from northeastern Asia via Europe) by ship transport to Australia and New Zealand. The Mediterranean populations included two haplotypes identical to those found in northwestern Europe and Japan, and it is suggestive of transoceanic introductions of some populations between Mediterranean and Japanese coasts.
Optimal Treatments in Cancer Immunotherapy Involving CD4+ T Cells

We apply optimal control theory to a model of interactions between cancer cells, CD4+ T cells, cytokines and host cells to devise best immunotherapies for treating cancer. The CD4+ T cells cannot kill cancer cells directly but use the cytokines produced to suppress tumor growth. The immunotherapy implemented is modeled as a control agent and it can be either transferring of CD4+ T cells, cytokines or both. We establish existence and uniqueness of the optimal control. The optimal treatment strategy is then solved numerically under different scenarios. Our numerical results provide best protocols in terms of strengths and timing of the treatments.
Risk for Exercise Addiction: A Comparison of Triathletes Training for Sprint-, Olympic-, Half- Ironman-, and Ironman-distance Triathlons

Researchers have noted that when taken to an excessive level, exercise may become addictive. This study investigated the risk of exercise addiction for triathletes using the Exercise Addiction Inventory (EAI; Terry, Szabo, & Griffths, 2004). The sample consisted of 1,285 male and female triathletes, ranging in age from 18–70 years old. Results indicated that approximately 20% of triathletes are at risk for exercise addiction, and that training for longer distance races (i.e., Olympic, Half-Ironman, and Ironman) puts triathletes at greater risk for exercise addiction than training for shorter races (i.e., Sprint). No significant association was found between the risk for exercise addiction and the number of years of participating. However, as the number of weekly training hours increased, so did a triathlete’s risk for exercise addiction. At-risk triathletes need greater clinical attention, and further research should be conducted to help clinicians develop awareness and appropriate interventions.
Computational methods for the identification of statistically significant genes: applications to gene expression data of various human diseases

In this dissertation, we address the problem of gene selection from ranked gene lists. We propose a new hybrid feature selection method (mAP-KL) that combines successfully multiple hypothesis testing and affinity propagation clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. We subject our method across a variety of validation tests on simulated microarray data as well as on real microarray data. The overall evaluation results suggest that mAP-KL generates concise yet biologically relevant and informative n-gene expression signatures, which can serve as a valuable discrimination tool for diagnostic and prognostic purposes, by identifying potential disease biomarkers in a broad range of diseases. Finally, to provide the research community with the capability to apply mAP-KL in any given gene expression dataset, we have implemented this methodology to a Bioconductor/R-package accompanied with extra functionalities.
Genomic Analysis of Intestinal Responses to Gut Flora Altered by Antimicrobials in Human Flora-Associated ( HFA ) Mice

Genetic expression in colonic epithelial cells, responding to the changes of human intestinal flora altered by exposure to drugs or contaminants, provides valuable health information. Gut flora are important in: (1) Metabolic fermentation and energy salvage, (2) Intestinal cell proliferation and differentiation, and (3) Development and homoeostasis of immune system and protection against pathogen invasion. Many drugs orally delivered or present in food may disturb normal flora and cause human health concerns. Here we report a short, but integral method to understand host-microbial relationships in the intestine, as well as to identify the genetic basis for the impact of drugs on intestinal functions. This is achieved by comparing gene expression in colonic epithelial cells between germ-free (GF) mice, human flora-associated (HFA) mice and drug-treated HFA mice using laser capture microdissection, RiboAmp® HS RNA Amplification Kit and cDNA microarrays.
1st International Workshop on Machine Learning Methods for Recommender Systems

Aspect-base Sentiment Analysis is a core component in Review Recommendation System. With the booming of customers’ reviews online, an efficient sentiment analysis algorithm will substantially enhance a review recommendation system’s performance, providing users with more helpful and informative reviews. Recently, two kinds of LDA derived models, namely Word Model and Phrase Model, take the dominant positions in this field. However, the requirement of exact aspect number underlies the usability and flexibility of these LDA extended models. Although, Dirichlet Process(DP), which can help to automatically generate the number of aspects, has been on trial, its random word assignment mechanism makes the result unsatisfying. This paper proposes a model named Similarity Dependency Dirichlet Process(SDDP) to cope with the above problems. SDDP inherits the merits of DP to automatically determine the number of aspects, but it exploits the semantic similarities between words to infer aspects and sentiments, alleviating the random assignment problem in DP. Furthermore, based on SDDP, this paper builds a word model W-SDDP and a phrase model P-SDDP respectively to detect aspects and sentiments from two different perspectives. Finally we experiment both our two models on 6 datasets, and compare with other 6 currently most popular models. The result shows that both W-SDDP and P-SDDP outperform the other 6 models, indicating SDDP is a promising model for sentiment analysis.
ELIGIBILITY FROM DEVELOPMENTAL ASSESSMENT OF YOUNG CHILDREN ( DAYC ) SCORES by

This quantitative non-experimental correlational study used logistic regression and archival data to examine the relationship between scores obtained by children at age three on the Developmental Assessment of Young Children (DAYC) rating scale and later special education eligibility status. The purpose of this study was to determine if DAYC scores can predict future special education eligibility as defined by the criteria of the State of Tennessee Department of Education and thereby provide a tactic for early identification and intervention. The 74 participants were students from a small, rural, high-poverty school district who were initially evaluated at age three using the DAYC and who were subsequently re-evaluated three years later. Students were originally rated by parents and teachers and later evaluated by licensed school psychologists using state-approved testing procedures and special education eligibility standards.
Asymptotic Analysis of Large Auctions with Risk-Averse

We study private-value auctions with n risk-averse bidders, where n is a large number. We first use asymptotic techniques to calculate explicit approximations of the equilibrium bids and of the seller’s revenue in any k-price auction (k = 1, 2, . . . ), and use these explicit approximations to show that all large k-price auctions with risk-averse bidders are O(1/n) revenue equivalent. We then prove that there exist auction mechanisms for which the limiting revenue as n −→ ∞ in the case of risk-averse bidders is strictly below the risk-neutral limit. Therefore, these auction mechanisms are not revenue equivalent to large k-price auctions even in the limit as n −→ ∞. Finally, we formulate a general condition under which the limiting revenue with risk-averse bidders is equal to the risk-neutral limit. School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, fibich@tau.ac.il Department of Industrial Engineering and Management, Faculty of Engineering Sciences, Ben-Gurion University, P.O. Box 653, Beer-Sheva 84105, Israel, ariehg@bgumail.bgu. ac.il 1
Dissociation of sensory facilitation and decision bias in statistical learning

ABSTRACT The detection of regularities in the sensory environment, known as statistical learning, is an important brain function that has been observed in many experimental contexts. In these experiments, statistical learning of patterned sensory stimulation leads to improvements in the speed and/or accuracy with which subsequent stimuli are recognized. That is, statistical learning facilitates the transformation of sensory stimuli into motor responses, but the mechanism by which this occurs is unclear. Statistical learning could improve the efficiency of sensory processing, or it could bias responses toward particular outcomes. The distinction is important, as these different hypotheses imply different functions and different neural substrates for statistical learning. Here we address this problem by studying statistical learning as a decision-making process, which allows us to leverage the extensive computational literature on this topic. Specifically we describe a method for applying the Diffusion Decision Model (DDM) to isolate different sensory and cognitive processes associated with decision-making. The results indicate that statistical learning improves performance on a visual learning task in two distinct ways: by altering the efficiency of sensory processing and by introducing biases in the decision-making process. By fitting the parameters of the DDM to data from individual subjects, we find that the prominence of these two factors differed substantially across the population, and that these differences were predictive of individual performance on the psychophysical task. Overall, these results indicate that different cognitive processes can be recruited by statistical learning, and that the DDM is a powerful framework for detecting these influences.
Sparse denoising : aggregation versus global optimization

Introduction: Denoising is often addressed via sparse coding with respect to an overcomplete translation-invariant dictionary. There are two main approaches for dictionaries composed of translates of an orthonormal basis. The classical approach is cycle spinning [1], which aggregates partial estimates, each of which is sparse with respect to a different shift of the orthonormal basis. An alternative is offered by convolutional sparse representations [2] [3, Sec. II], which perform a global optimization over the entire dictionary. It is tempting to view the former approach as providing a suboptimal solution of the latter. Here we compare the two approaches and show that, while the global optimization produces estimates with lower bias than the corresponding aggregation procedure, these are also characterized by a higher variance. In practice, the computationally demanding global optimization outperforms the simpler aggregation of partial estimates only when images admit an extremely sparse representation w.r. t. the dictionary, while they perform similarly on natural images. Denoising: The input signal s∈RN is modeled as s = y + η, where η∼N (0, σ2) and y∈RN denotes the unknown noise-free signal. We consider denoising methods that approximate y as a sparse linear combination of atoms from an overcomplete translation-invariant dictionary D ∈ RN×N , formed as the union of all shifted copies Di , i ∈ {1, . , N }, of an orthonormal basis D1 ∈ RN×N , i.e. ŷ = Dx̂ where D= ( D1 · · · DN ) ∈ RN×N 2 and x̂∈RN . Cycle spinning [1] separately seeks sparsity separately with respect to each orthonormal basis Di , by solving the optimization problems x̂i = arg min u∈RN 1 2 Diu − s 2 2 + λR (u) , i ∈ {1, . , N } , (1)
DNN Multimodal Fusion Techniques for Predicting Video Sentiment

We present our work on sentiment prediction using the benchmark MOSI dataset from the CMU-MultimodalDataSDK. Previous work on multimodal sentiment analysis have been focused on input-level feature fusion or decision-level fusion for multimodal fusion. Here, we propose an intermediate-level feature fusion, which merges weights from each modality (audio, video, and text) during training with subsequent additional training. Moreover, we tested principle component analysis (PCA) for feature selection. We found that applying PCA increases unimodal performance, and multimodal fusion outperforms unimodal models. Our experiments show that our proposed intermediate-level feature fusion outperforms other fusion techniques, and it achieves the best performance with an overall binary accuracy of 74.0% on video+text modalities. Our work also improves feature selection for unimodal sentiment analysis, while proposing a novel and effective multimodal fusion architecture for this task.