Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Sequence to Sequence Learning for Morphological Inflection Generation: The BIU-MIT Systems for the SIGMORPHON 2016 Shared Task for Morphological Reinflection

Morphological reinflection is the task of generating a target form given a source form and the morpho-syntactic attributes of the target (and, optionally, of the source). This work presents the submission of Bar Ilan University and the Massachusetts Institute of Technology for the morphological reinflection shared task held at SIGMORPHON 2016. The submission includes two recurrent neural network architectures for learning morphological reinflection from incomplete inflection tables while using several novel ideas for this task: morpho-syntactic attribute embeddings, modeling the concept of templatic morphology, bidirectional input character representations and neural discriminative string transduction. The reported results for the proposed models over the ten languages in the shared task bring this submission to the second/third place (depending on the language) on all three sub-tasks out of eight participating teams, while training only on the Restricted category data.
Antiplasmodial activity of extracts of Tridax procumbens and Phyllanthus amarus in in vitro Plasmodium falciparum culture systems.

BACKGROUND Aqueous extracts of Tridax procumbens (TP) (Compositae) and Phyllanthus amarus (PA) (Euphorbiaceae) are used in traditional medicine in Ghana to treat malaria. Previous studies have demonstrated the anti-trypanosoma, anti-bacterial and anti-HIV effects of TP and PA. OBJECTIVE To assess the antiplasmodial activity of extracts of TP and PA. METHOD Aqueous extracts of TP and PA were prepared. A portion of each was freeze-dried and the remaining extracted sequentially with ethyl acetate and chloroform. Ethanolic extracts were also prepared. The antiplasmodial activity of the extracts was assessed with the 3H-hypoxanthine assay using chloroquine-resistant (Dd2) Plasmodium falciparum parasites. Chloroquine was used as the reference drug. The modified tetrazolium-based colorimetric assay was also used to evaluate the red blood cell (RBC)-protective/antiplasmodial activities and cytotoxicities of the extracts. RESULTS Results showed that TP and PA have antiplasmodial activities. The aqueous and ethanolic extracts of PA were the most active, yielding EC50 values of 34.9 µg/ml and 31.2 µg/ml, respectively in the tetrazolium-based assay. The TP and PA produced and IC50 values of 24.8 µg/ml and 11.7 µg/ml, respectively in the hypoxanthine assay. Protection of human RBCs against P. falciparum damage by the extracts highly correlated with their antiplasmodial activities. None of the extracts, within the concentration range (1.9-500 µg/ml) studied produced any overt toxicity to human RBCs. CONCLUSION The results indicate that both PA and TP have activities against chloroquine-resistant P. falciparum (Dd2) parasites. The antiplasmodial principles extracted into water and ethanol but not chloroform or ethyl acetate.
Towards robust cross-user hand tracking and shape recognition

Low-cost depth cameras create new opportunities for robust and ubiquitous vision-based interfaces. While much research has focused on full-body pose estimation and the interpretation of gross body movement, this work investigates skeleton-free hand detection, tracking, and shape classification. Our goal is to build a rich and reliable gestural interface by developing methods that recognize a broad set of hand shapes and which maintain high accuracy rates across a wide range of users. In this paper, we describe our approach to real-time hand detection and tracking using depth data from the Microsoft Kinect. We present quantitative shape recognition results for eight hand shapes collected from 16 users and then discuss physical configuration and interface design issues that help boost reliability and overall user experience.
PIK3CA and TP53 Gene Mutations in Human Breast Cancer Tumors Frequently Detected by Ion Torrent DNA Sequencing

Breast cancer is the most common malignancy and the leading cause of cancer deaths in women worldwide. While specific genetic mutations have been linked to 5–10% of breast cancer cases, other environmental and epigenetic factors influence the development and progression of the cancer. Since unique mutations patterns have been observed in individual cancer samples, identification and characterization of the distinctive breast cancer molecular profile is needed to develop more effective target therapies. Until recently, identifying genetic cancer mutations via personalized DNA sequencing was impractical and expensive. The recent technological advancements in next-generation DNA sequencing, such as the semiconductor-based Ion Torrent sequencing platform, has made DNA sequencing cost and time effective with more reliable results. Using the Ion Torrent Ampliseq Cancer Panel, we sequenced 737 loci from 45 cancer-related genes to identify genetic mutations in 105 human breast cancer samples. The sequencing analysis revealed missense mutations in PIK3CA, and TP53 genes in the breast cancer samples of various histologic types. Thus, this study demonstrates the necessity of sequencing individual human cancers in order to develop personalized drugs or combination therapies to effectively target individual, breast cancer-specific mutations.
An Autonomous Software Agent for Navy Personnel Work: a Case Study

IDA is an autonomous software agent whose task is to assign a sailor to a new tour of duty at the end of the old. Her task input is most often initiated by an email from the sailor; thereafter she acts completely autonomously. The task requires IDA to access databases, to deliberate, to perform complex constraint satisfaction, and to negotiate with the sailor in natural language. IDA’s architecture and mechanisms are motivated by a variety of computational paradigms and implement a number of cognitive models including the Global Workspace Theory of consciousness. IDA is able to interact in depth with sailors in a relatively complex real-world environment involving job requirements, Navy policy, location, travel, training, dates, costs, preferences, etc. Here we briefly describe the IDA technology and her interactions as a case study of the interaction of sophisticated software agents with humans.
Qualitative Analysis on Matchmaking Techniques for Web Service Discovery

The ample adoption of web services raises the challenging problem in web service discovery. Web service discovery is the process of finding appropriate services for the user defined tasks. The big challenge in web service discovery is to discover the services that suit the user requirements and to enhance the efficiency and accuracy in matchmaking procedures. This poster focuses on matchmaking in web service discovery. The objective of matchmaking is to match the service requests with the web service advertisements and to offer appropriate services. This paper is focus on the matchmaking architecture, its information flow and types of matchmaking techniques (functional based matchmaking, non-functional based matchmaking and hybrid matchmaking). It sets the basis for analysis of each matchmaking technique, the existing amenity and snag of it as well as the amendment of each technique.
Unifying Cloud Management: Towards Overall Governance of Business Level Objectives

We address the challenge of providing unified cloud resource management towards an overall business level objective, given the multitude of managerial tasks to be performed and the complexity of any architecture to support them. Resource level management tasks include elasticity control, virtual machine and data placement, autonomous fault management, etc, which are intrinsically difficult problems since services normally have unknown lifetime and capacity demands that varies largely over time. To unify the management of these problems, (for optimization with respect to some higher level business level objective, like optimizing revenue while breaking no more than a certain percentage of service level agreements)becomes even more challenging as the resource level managerial challenges are far from independent. After providing the general problem formulation, we review recent approaches taken by the research community, including mainly general autonomic computing technology for large-scale environments and resource level management tools equipped with some business oriented or otherwise qualitative features. We propose and illustrate a policy-driven approach where a high-level management system monitors overall system and services behavior and adjusts lower level policies (e.g., thresholds for admission control, elasticity control, server consolidation level, etc) for optimization towards the measurable business level objectives.
A Framework of Privacy Decision Recommendation for Image Sharing in Online Social Networks

Image sharing in Online Social Networks (OSNs) faces potential risks of exposing users' private or sensitive information to others. In this paper, we develop a framework of calculating the privacy level of a digital image based on perceptual hashing and semantic privacy rules. In particular, we design two privacy preserving perception hashing methods: the first one is based on the SIFT features which focus on the description of sensitive objects in the image and the second one is based on the LBP features which focus on the description of faces in the image. Both methods use the secret key to protect hashes from adversaries or untrusted servers. The recommendation of a privacy level for a given image is based on two types of privacy rules, user-defined specific rules based on the logic combination of the semantic tags of image objects retrieved from the perceptual hash database, and general rules based on machine learning. The privacy level, as the measurement of the degree of sensitivity of a digital image, can be directly fed to the user before he shares the image to OSNs. It can also be fed to the user's access control component. Experimental results show the effectiveness and usability of the proposed framework.
The Relationship between Complementary and Alternative Medicine Use and Breast Cancer Early Detection: A Critical Review

Objective. Complementary and alternative medicine (CAM) use is prevalent. Concurrently, breast cancer is the most common cancer in women worldwide, with early detection techniques widely available. This paper examined the overlap between participation in allopathic breast cancer early detection activities and CAM use. Methods. A systematic review examined the association between breast screening behaviors and CAM use. Searches were conducted on the PubMed, Embase, CINAHL, and NCCAM databases and gray literature between 1990 and 2011. STROBE criteria were used to assess study quality. Results. Nine studies met the search criteria. Four focused on CAM use in women at high breast cancer risk and five on average risk women. CAM use in women ranged from 22% to 82% and was high regardless of breast cancer risk. Correlations between CAM use and breast cancer early detection were not strong or consistent but significant relationships that did emerge were positive. Conclusions. Populations surveyed, and measures used to assess CAM, breast cancer screening, and correlates, varied widely. Many women who obtained allopathic screening also sought out CAM. This provides a foundation for future interventions and research to build on women's motivation to enhance health and develop ways to increase the connections between CAM and allopathic care.
Identification of genes and transcription factors associated with glucocorticoid response in lens epithelial cells

Prolonged glucocorticoids (GCs) treatment may lead to the formation of posterior subcapsular cataracts. The present study aimed to investigate differential gene expression in lens epithelial cells (LECs) in response to GCs using DNA microarray profiling. The gene expression profile of GSE13040 was downloaded from the Gene Expression Omnibus database, which includes 12 human LECs treated with vehicle or dexamethasone (Dex) for 4 or 16 h with six samples at each time period, of which three samples were treated with vehicle (control group) and three samples were treated with Dex (Dex group) at each time point. The differentially expressed genes (DEGs) were identified between the control group and the Dex group at each time period with the thresholds of P<0.05 and |logFC|>1. The DEGs were further analyzed using bioinformatics methods. Firstly, DEGs were subject to a hierarchical cluster analysis. Subsequently, the functional enrichment analysis was performed for the common DEGs between the two time periods. Finally, the transcription factors and binding sites of DEGs associated with response to GC stimulus were analyzed. A total of 696 and 949 DEGs were identified at 4 h and 16 h, respectively. Hierarchical cluster analysis revealed that DEG expression was higher in the Dex group than in the control group (P<0.05). A total of 13 significant functions were enriched for the 72 common DEGs at the two time periods. Chemokine (C-C motif) ligand 2 (CCL2), dual-specificity phosphatase-1 (DUSP1) and FAS were associated with the response to GC stimulus and the transcription factor c-Jun bound to promoter regulation regions of CCL2, DUSP1 and FAS. In conclusion, the transcription factors and binding sites of DEGs associated with the response of LECs to GCs may provide potential gene targets for designing and developing drugs to protect against GC-induced cataract formation.
BetaRun 2017 Team Description Paper: Variety, Complexity, and Learning

RoboCup offers a set of benchmark problems for Artificial Intelligence in form of official world championships since 1997. The most tactical advanced and richest in terms of behavioural complexity of these is the 2D Soccer Simulation League, a simulated robotic soccer competition. BetaRun is a new attempt combining both machine learning and manual programming approaches, with the ultimate goal to arrive at a team that is trained entirely from observing and playing games, and a successor of the World Champion team Gliders 2016.
EVIDENCE FOR THE EURO AREA

This paper investigates the empirical determinants of corporate cash holdings in the euro area as a function of firm size. The results show that there are significant differences in investment in liquid assets for firms of different size. More specifically, liquid assets for smaller firms in the euro area are more strongly linked to firm cash flow and its variability than cash holdings for larger firms, possibly as a result of their more restricted access to external funds and the need to provide for future investment needs. Likewise, results show that the link between cash holdings and tangible assets, which facilitate access to external finance, is stronger for small and medium-sized firms than for large firms. In contrast, cash holding sensitivity to variations in the spread between the return on liquid assets and alternative uses of these funds (debt repayment, in the empirical specification presented in this paper) is higher for larger firms, something that might be linked to their better access to capital markets and their lower need to keep a cash buffer for precautionary reasons.
Explaining individual changes in moral values and moral emotions among adolescent boys and girls: A fixed-effects analysis

In this study we examine to what extent within-individual changes in parental monitoring, bonds with parents and school, and rule-breaking peers can explain within-individual changes in morality. We distinguish between three key dimensions of morality: moral values, anticipated shame, and anticipated guilt. We use data from the SPAN project, a two-wave panel study among 616 adolescents (ages 12–19) from secondary schools in The Hague, The Netherlands. Employing a fixed-effects model, we found that within-individual changes in parental monitoring, bonds with parents and school, and rule-breaking peers are significantly related to within-individual changes in moral values, anticipated shame, and anticipated guilt. These findings emphasize the important role of family, school, and the peer group in the development of morality during adolescence. Implications of these findings are discussed.
Avaliação da imagem corporal de estudantes do curso de Nutrição de um centro universitário particular de Fortaleza

Objectives Assessing body image and identifying the various types of body image distortion. Methods This research is quantitative, with cross-sectional design and it was held in a University Center of Fortaleza (Ceara). Data collection was conducted from April to May of 2015. The sample consisted of 300 undergraduate students of Nutrition, female, aged 17-51 years. Body Shape Questionnaire (BSQ) and an evaluation sheet were used to measured current weight and height. The students reported their weight and height that they craved, if they were physically active and the frequency of this activity as a way to identify other possible disturbances. Results The sample consisted of 300 students, with the mean age 25.45 years (±6.60). The average score of the BSQ was 81.43 points (±30.45), corresponding to mild concern with body image. The current average BMI was 22.90 kg/m2 (±3.15). It was observed that the students, although they are mostly with suitable weight and height, they wanted to be thinner and taller (desired weight = 57.39 kg, desired height = 1.63 m). Conclusion The results evidence that the group would like to be thinner and taller even presenting eutrophic nutritional status. When it comes from Nutrition students this study has an important impact because they will become healthcare professionals.
Evaluating UML : A Practical Application of a Framework for the Understanding of Quality in Requirements Specifications and Conceptual Modeling

Many researchers have evaluated different parts of UML and have come up with suggestions for improvements to different parts of the language. This paper looks at UML (version 1.3) as a whole, and contains an overview evaluation of UML as a basis for creating models of high quality. The evaluation is done using a general framework for understanding quality of models and modeling languages in the information systems field. The evaluation is based on both practical experiences and evaluations of UML made by others. Based on the evaluation, we conclude that, although being an improvement over it is predecessors, UML still has many limitations and deficiencies.
Detection of Insiders Misuse in Database Systems

Almost all systems all over the world suffer from outsider and insider attacks. Outsider attacks are those that come from outside the system, however, insider attacks are those that are launched from insiders of the system. In this paper we concentrate on insider attacks detection on the application level; database is our focus. Insider attacks differ from outsider attacks in many ways; most importantly, insiders have more knowledge about the underlying systems. Because of their knowledge and their privileges of the system resources; their risk can be greater and more severe. In fact, insiders can find vulnerabilities in the system easily. Several techniques have been proposed that tackled the insider threat problem, but most of them concentrate on insider threat detection in computer system level. We describe a method for insider threat detection in database systems that handle entrants on the role of insiders for such attacks. Our simulation results show resistance against such attacks. Also, our results show good performance in terms of reducing false alarms to the minimum.
Large-Scale Examination of Academic Publications Using Statistical Models

We describe our experiences in three collaborative visual analytics projects. The projects center on large-scale examination of academic publications using statistical models. Each project involves a multidisciplinary team of social scientists, machine learning researchers, and experts in various academic fields. We highlight design guidelines learned from these experiences. In many cases, effective analysis results only after aligning the visualization with the analysis tasks, the capabilities of the modeling tools, and concepts meaningful to the domain experts. Substantiating findings from the analyses often requires verifying and modifying the underlying statistical models. In the examples below, we demonstrate that alignment allows analysts to verify model outputs by comparing them to domain knowledge and gain trust in the validity of findings derived from the models. Verification often leads to model modifications (changes in assumptions, parameter search, or sensitivity analysis); visualization can expose modeling abstractions, and help model builders better understand and alter the characteristics of the models. Displaying appropriate units of analysis also facilitates communication among collaborators with different backgrounds and expertise.
Effect of contaminations and surface preparation on the work function of single layer MoS2

Thinning out MoS2 crystals to atomically thin layers results in the transition from an indirect to a direct bandgap material. This makes single layer MoS2 an exciting new material for electronic devices. In MoS2 devices it has been observed that the choice of materials, in particular for contact and gate, is crucial for their performance. This makes it very important to study the interaction between ultrathin MoS2 layers and materials employed in electronic devices in order to optimize their performance. In this work we used NC-AFM in combination with quantitative KPFM to study the influence of the substrate material and the processing on single layer MoS2 during device fabrication. We find a strong influence of contaminations caused by the processing on the surface potential of MoS2. It is shown that the charge transfer from the substrate is able to change the work function of MoS2 by about 40 meV. Our findings suggest two things. First, the necessity to properly clean devices after processing as contaminations have a great impact on the surface potential. Second, that by choosing appropriate materials the work function can be modified to reduce contact resistance.
A Lasso-LS Optimization with a Frequency Variable Dictionary in a Multizone Sound System

This paper presents an approach for multizone wideband sound field generation using an efficient harmonic nested (EHN) dictionary for sparse loudspeakers' placement and weight. Effectively, the nested arrays provide a priori knowledge of prospective loudspeaker locations based on the frequency bands of interest. The nested arrays are then further optimized in the Lasso stage to form an efficient loudspeakers' location dictionary. The final loudspeaker locations and weightings are estimated by a two-stage Lasso-LS pressure matching optimization. In the first stage Lasso algorithm, the center band frequencies of octave bands from 1 kHz to 8 kHz were used to select active loudspeakers. A second stage then optimizes reproduction using all selected loudspeakers on the basis of a regularized LS algorithm. The results demonstrate that the proposed approach provides a solution for the multizone sound system with the mean squared error (MSE) under -30 dB across the targeted frequency range (500 Hz16 kHz) using a linear array e.g. 13 loudspeakers. While, the single-stage LS approach generates the MSE peaks of -10 dB and -9 dB at 9 kHz within the active and silent zones respectively using an identical number of loudspeakers and array length.