Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic Values of Free Triiodothyronine and Free Thyroxine and the Ratio of Free Triiodothyronine to Free Thyroxine in Thyrotoxicosis

Background The results of previous studies on the usefulness of free triiodothyronine (FT3) to free thyroxine (FT4) are controversial. We investigated the usefulness of FT3, FT4, and FT3/FT4 ratio in differentiating Graves' disease (GD) from destructive thyroiditis. Methods A total of 126 patients with untreated GD, 36 with painless thyroiditis, 18 with painful subacute thyroiditis, and 63 healthy controls, were recruited. The levels of FT3 and FT4 and the FT3/FT4 ratios for the different etiologies of thyrotoxicosis were evaluated separately by receiver operating characteristic (ROC) curve analysis. The expression levels of type 1 and type 2 deiodinase (DIO1 and DIO2) in thyroid tissues were also investigated. Results The optimal cut-off values were 7.215 pmol/L for FT3, 21.71 pmol/L for FT4, and 0.4056 for the FT3/FT4 ratio. The specificity and positive predictive value of the FT3/FT4 ratio were highest for values > 0.4056. DIO1 mRNA expression was significantly higher in the thyroid tissue of patients with GD (P = 0.013). Conclusions We demonstrated that the FT3/FT4 ratio was useful in differentiating GD from destructive thyroiditis. In addition, a relatively high expression of type 1 deiodinase in the thyroid might be responsible for the high FT3/FT4 ratio in patients with GD.
MiMicS: a multi-robot simulator for teaching, rapid prototyping and large scale evaluations

This paper presents MiMicS, a novel multi-robot simulator for teaching, rapid algorithm prototyping and large scale evaluation. Contrary to many other solutions, this simulator provides easy-to-use methods for defining new scenarios and an intuitive system to extensively test large batteries of simulations. MiMicS also incorporates a simple but efficient multi-thread and timing control mechanism. This reduces the need to use highly powerful computers to run long-term executions, alleviating problems caused by busy CPUs that can jeopardize the results of a simulation. The paper reviews the current state of the art on multi-robot simulators, describes the architecture and features of MiMicS, and evaluates its performance.
MetaAnchor: Learning to Detect Objects with Customized Anchors

We propose a novel and flexible anchor mechanism named MetaAnchor for object detection frameworks. Unlike many previous detectors model anchors via a predefined manner, in MetaAnchor anchor functions could be dynamically generated from the arbitrary customized prior boxes. Taking advantage of weight prediction, MetaAnchor is able to work with most of the anchor-based object detection systems such as RetinaNet. Compared with the predefined anchor scheme, we empirically find that MetaAnchor is more robust to anchor settings and bounding box distributions; in addition, it also shows the potential on the transfer task. Our experiment on COCO detection task shows MetaAnchor consistently outperforms the counterparts in various scenarios.
Diarrhea Prevalence and Sociodemographic Factors among Under-Five Children in Rural Areas of North Gondar Zone, Northwest Ethiopia

Background Diarrheal disease remains one of the principal causes of morbidity and mortality in infants and children in developing countries, including Ethiopia. Risk factors for diarrhea vary by settings and have important implications for developing intervention strategies to reduce the burden of the disease. Thus, the aim of this study was to assess diarrhea prevalence and sociodemographic factors among under-five children in rural areas of North Gondar Zone. Methods A community-based cross-sectional study was conducted from April to June 2016 among 736 randomly selected households with one child under five years old. A structured questionnaire was used for collecting information on sociodemographic characteristics and diarrheal occurrence. Data was analyzed using SPSS version 20. The bivariate and multivariable logistic regression analysis were used to determine the association between risk factors and diarrheal occurrence, and a p value < 0.05 was taken as statistically significant. Results A total of 736 under-five children and their respondents were enrolled during the study period. Almost all respondents were biological mothers 96.4% (709/736), married 94.2% (693/736), and house wives 86% (632/736). The overall prevalence of diarrheal disease among under-five children was 22.1% (163/743). Of these, children with age group of less than one year old, 7.7 % (57/736), were commonly infected with diarrheal diseases. Children less than or equal to one year [AOR=1.82, 95% CI= (1.39, 4.63)], guardians [AOR=4.37, 95% CI= (1.73, 11.1)], and children with no breast feeding practice [AOR=3.13, 95% CI= (1.62, 6.03)] were the major risk factors for the occurrence of diarrhea. Conclusion Childhood diarrhea remains an important health concern in the study area. Occurrence of diarrhea was statistically associated with child age less than or equal to one year, educational status of mother/guardians, and breast feeding. To minimize the magnitude childhood diarrhea, various designing and implementing strategies, such as health education, child care, breast feeding, and weaning practice, integrated with the existing national health extension are quite essential.
High abundance of diazotrophic picocyanobacteria (<3 µm) in a Southwest Pacific coral lagoon

In the present study we detected unicellular diazotrophic cyanobacteria along a nutri- ent gradient in New Caledonia's coral lagoon (24 000 km 2 ) using whole-cell hybridization of specific Nitro821 probes (TSA-FISH, Tyramide Signal Amplification of Fluorescent In Situ Hybridization). The specificity of this probe was confirmed on cultures and in the natural environment. Surprisingly, the community of Nitro821-targeted cells was numerically dominated by picocyanobacteria (97%, 1 to 1.5 µm). These organisms were either free living (63%), recovered with the 0.2 to 3 µm size frac- tion or associated (37%) to particles or planktonic dinoflagellates from larger size fractions (3 to 10 µm and >10 µm). Diazotrophic picocyanobacterial abundance ranged from 3 to 140 cells ml -1 along the nutrient gradient and was highest at the oligotrophic lagoonal station. These cells may con- tribute to N2 fixation from the 10 µm size fraction (0 to 1.9 nmol N -1 24 h -1 ). In addition, in the middle of the lagoon day- light 15 N2 fixation could explain 78% of 15 N2 fixation over 24 h. The results presented in the present study reveal for the first time a significant concentration of photosynthetic diazotrophs within the marine picoplankton community.
Sentinel: Generating GUI Tests for Android Sensor Leaks

Due to the widespread use of Android devices and apps, it is important to develop tools and techniques to improve app quality and performance. Our work focuses on a problem related to hardware sensors on Android devices: the failure to disable unneeded sensors, which leads to sensor leaks and thus battery drain. We propose the Sentinel testing tool to uncover such leaks. The tool performs static analysis of app code and produces a model which maps GUI events to callback methods that a ect sensor behavior. The model is traversed to identify paths that are likely to exhibit sensor leaks during run-time execution. The reported paths are then used to generate test cases. The execution of each test case tracks the run-time behavior of sensors and reports observed leaks. Our experimental results indicate that Sentinel effectively detects sensor leaks, while focusing the testing efforts on a very small subset of possible GUI event sequences.
Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study

Neural sequence-to-sequence (seq2seq) approaches have proven to be successful in grammatical error correction (GEC). Based on the seq2seq framework, we propose a novel fluency boost learning and inference mechanism. Fluency boosting learning generates diverse error-corrected sentence pairs during training, enabling the error correction model to learn how to improve a sentence's fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally with multiple inference steps. Combining fluency boost learning and inference with convolutional seq2seq models, our approach achieves the state-of-the-art performance: 75.72 (F_{0.5}) on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set respectively, becoming the first GEC system that reaches human-level performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks.
Impact of colors on advertisement and packaging on buying behavior

Article history: Received April 15, 2012 Accepted 10 June 2012 Available online June 11 2012 Purpose of this research paper is to find out relationship between varying color scheming in advertising and buying behavior. Data for this purpose were collected from 200 people through questionnaire. Results indicated that buying behavior is greatly influenced by color scheming of a product and its advertisement. Strategies discussing how to enhance color scheming of product and use of colors in ads are also discussed. © 2012 Growing Science Ltd. All rights reserved.
Turbulence modification of particle-laden flow in model of rocket motor

Experiments with two-phase flow were investigated in a model of rocket engine using Particle Image Velocimetry (PIV). The high mass loading (up to 120) of dispersed inertial alumina particles (St≈10·103) creates two way coupling flow regimes. In particle-laden flows the Turbulence Kinetic Energy (TKE) can increase or decrease in presents of dispersed particle phase. The results showed that with increasing of mass loading the TKE increasing respectively. The magnitude of TKE modification has high impact on the design and performance of the rocket motor.
Coordination Event Detection and Initiator Identification in Time Series Data

Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the Coordination Initiator Inference Problem and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time series data from interacting entities.
Corporate Takeovers and Labor Restructuring

I examine the impact of M&As on employment dynamics and the composition of human capital in target firms. Using detailed employer-employee administrative data linked with hand-collected information on M&A activity in Brazil, I find that M&As are associated with a significant decline in employment, primarily through an increase in involuntary separations and a decline in hirings. This employment decline reflects efficiency-seeking restructuring. Target firms increase the share of high-skill and non-routine employees, while reducing costs through efficient consolidation and elimination of occupational overlap. The primary mechanism that explains these shifts is the adoption of innovation-intensive practices and investment in IT, particularly by acquirers that have the knowledge and ability to do so. Finally, post-merger compositional changes lead to an increase in wages and within-firm wage inequality.
Evaluating the Adaptive Selection of Classifiers for Cross-Project Bug Prediction

Bug prediction models are used to locate source code elements more likely to be defective. One of the key factors influencing their performances is related to the selection of a machine learning method (a.k.a., classifier) to use when discriminating buggy and non-buggy classes. Given the high complementarity of stand-alone classifiers, a recent trend is the definition of ensemble techniques, which try to effectively combine the predictions of different stand-alone machine learners. In a recent work we proposed ASCI, a technique that dynamically selects the right classifier to use based on the characteristics of the class on which the prediction has to be done. We tested it in a within-project scenario, showing its higher accuracy with respect to the Validation and Voting strategy. In this paper, we continue on the line of research, by (i) evaluating ASCI in a global and local cross-project setting and (ii) comparing its performances with those achieved by a stand-alone and an ensemble baselines, namely Naive Bayes and Validation and Voting, respectively. A key finding of our study shows that ASCI is able to perform better than the other techniques in the context of cross-project bug prediction. Moreover, despite local learning is not able to improve the performances of the corresponding models in most cases, it is able to improve the robustness of the models relying on ASCI.
Discovering Mobile Application Usage Patterns from a Large-Scale Dataset

The discovering of patterns regarding how, when, and where users interact with mobile applications reveals important insights for mobile service providers. In this work, we exploit for the first time a real and large-scale dataset representing the records of mobile application usage of 5,342 users during 2014. The data was collected by a software agent, installed at the users’ smartphones, which monitors detailed usage of applications. First, we look for general patterns of how users access some of the most popular mobile applications in terms of frequency, duration, diversity, and data traffic. Next, we mine the dataset looking for temporal patterns in terms of when and how often accesses occur. Finally, we exploit the location of each access to detect users’ points of interest and location-based communities. Based on the results, we derive a model to generate synthetic datasets of mobile application usage and evaluate solutions to predict the next application to be launched. We also discuss a series of implications of the findings regarding telecommunication services, mobile advertisements, and smart cities. This is the first time this dataset is used, and we also make it publicly available for other researchers.
Efficient, Scalable, and Resilient Vehicle-Centric Certificate Revocation List Distribution in VANETs

In spite of progress in securing Vehicular Communication (VC) systems, there is no consensus on how to distribute Certificate Revocation Lists (CRLs). The main challenges lie exactly in (i) crafting an efficient and timely distribution of CRLs for numerous anonymous credentials, pseudonyms, (ii) maintaining strong privacy for vehicles prior to revocation events, even with honest-but-curious system entities, (iii) and catering to computation and communication constraints of on-board units with intermittent connectivity to the infrastructure. Relying on peers to distribute the CRLs is a double-edged sword: abusive peers could "pollute" the process, thus degrading the timely CRLs distribution. In this paper, we propose a vehicle-centric solution that addresses all these challenges and thus closes a gap in the literature. Our scheme radically reduces CRL distribution overhead: each vehicle receives CRLs corresponding only to its region of operation and its actual trip duration. Moreover, a "fingerprint" of CRL 'pieces' is attached to a subset of (verifiable) pseudonyms for fast CRL 'piece' validation (while mitigating resource depletion attacks abusing the CRL distribution). Our experimental evaluation shows that our scheme is efficient, scalable, dependable, and practical: with no more than 25 KB/s of traffic load, the latest CRL can be delivered to 95% of the vehicles in a region (50x50 KM) within 15s, i.e., more than 40 times faster than the state-of-the-art. Overall, our scheme is a comprehensive solution that complements standards and can catalyze the deployment of secure and privacy-protecting VC systems.
Dynamic high-level requirements in self-adaptive systems

Non-functional requirements are the main drivers behind the design choices that shape a software architecture. Self-adaptive systems blur the boundary between design-time and run-time allowing the dynamic re-shaping of a running system to better accommodate changes in the execution context or in stakeholders' expectations. When these expectations are related to aspects that influence architectural decisions this imply the ability to reconsider these decisions at runtime without the need of re-deployment (and associated system downtime). Existing research works on non-functional requirements in self-adaptive systems usually focus on operationalizable requirements, which entails development activities refining these changes into their run-time counterparts (such as SLAs). In this paper we present an approach that allows the autonomic re-shaping of the architecture of a self-adaptive system when high-level nonfunctional non-operationalizable requirements change. Changes in the requirements while the system is operational can trigger a re-evaluation of specific design choices resulting in reconfiguration activities that modify the system architecture. Our approach is based on a (semantic) runtime requirements model that can be automatically refined from high-level representations using model-to-model (M2M) transformations.
To Call, or Not to Call: Contrasting Direct and Indirect Branch Coverage in Test Generation

While adequacy criteria offer an end-point for testing, they do not mandate how targets are covered. Branch Coverage may be attained through direct calls to methods, or through indirect calls between methods. Automated generation is biased towards the rapid gains offered by indirect coverage. Therefore, even with the same end-goal, humans and automation produce very different tests. Direct coverage may yield tests that are more understandable, and that detect faults missed by traditional approaches. However, the added burden for the generation framework may result in lower coverage and faults that emerge through method interactions may be missed. To compare the two approaches, we have generated test suites for both, judging efficacy against real faults. We have found that requir- ing direct coverage results in lower achieved coverage and likelihood of fault detection. However, both forms of Branch Coverage cover code and detect faults that the other does not. By isolating methods, Direct Branch Coverage is less constrained in the choice of input. However, traditional Branch Coverage is able to leverage method interactions to discover faults. Ultimately, both are situationally applicable within the context of a broader testing strategy.
Visual design checklist for graphical user interface (GUI) evaluation

The1 Graphical User Interface (GUI) design is a major process within the software development lifecycle, because the GUI is the portion of the interface that will be in direct contact with the end-user. There are resources to help make the interface meet recommended usability criteria, but tools focused specifically on the GUI design are lacking. In order to bridge this gap, this study presents the development of a visual design checklist for Graphical User Interface evaluation. The checklist was tested in a two-phase assessment process: the first with HCI professionals and experts, and the second in a make-believe development context. Results show good acceptance among professionals, and suggest the checklist can positively assist development teams meet usability criteria. In conclusion, it is possible to assume the checklist can be a valuable resource to evaluate the Graphical User Interface, thus preventing errors and improving the overall system acceptability by the end-user.
Comparison of BLSTM-Layer-Specific Affine Transformations for Speaker Adaptation

Bidirectional Long Short-Term Memory (BLSTM) Recurrent Neural Networks (RNN) acoustic models have demonstrated superior performance over Deep feed-forward Neural Networks (DNN) models in speech recognition and many other tasks. Although, a lot of work has been reported on DNN model adaptation, very little has been done on BLSTM model adaptation. This work presents a systematic study on the adaptation of BLSTM acoustic models by means of learning affine transformations within the neural network on small amounts of unsupervised adaptation data. Through a series of experiments on two major speech recognition benchmarks (Switchboard and CHiME-4), we investigate the significance of the position of the transformation in a BLSTM Network using a separate transformation for the forwardand backward-direction. We observe that applying affine transformations result in consistent relative word error rate reductions ranging from 6% to 11% depending on the task and the degree of mismatch between training and test data. Index Terms — Speaker Adaptation, DNN-BLSTM, Affine Transformation, Acoustic Modeling, Deep Neural Network
Usa-DSL: usability evaluation framework for domain-specific languages

Software quality is a constant concern for software engineers. Hence, they are increasingly taking advantage of new methods to evaluate their products quality. For instance, the evaluation of languages developed for specific domains, which in the literature are known as Domain-Specific Languages (DSLs), is a growing concern. DSLs are languages used by different developers to solve problems of specific domains. Regarding the evaluation of these languages, several experimental studies that subjectively evaluate usability can be found in the literature, but few of them have taken advantage of applying Human-Computer Interaction (HCI) techniques. Therefore, the goal of this paper is to present a usability evaluation framework for DSLs, called Usa-DSL. In order to produce a first evaluation of the framework, we use a Focus Group method, in which seven subjects met to discuss our proposed framework. This discussion resulted in modifications of our initial proposal, which were incorporated in the final framework presented in this paper.