nils-herrmann/clean_scirepeval huggingface dataset:
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the 'PICO' elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.
Online Gradient Boosting

We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online learning algorithm with convex loss functions that competes with a larger class of regression functions. Our main result is an online gradient boosting algorithm which converts a weak online learning algorithm into a strong one where the larger class of functions is the linear span of the base class. We also give a simpler boosting algorithm that converts a weak online learning algorithm into a strong one where the larger class of functions is the convex hull of the base class, and prove its optimality.
Development of Wall-climbing Robots with Sliding Suction Cups

This paper investigates the development of wall-climbing robots (WCRs) with sliding suction cups (SSCs) based on the authors' recent research work. The merits and the challenges of the SSCs are analyzed in detail aiming at wheeled WCRs (WWCRs). The dynamic model of WWCRs is presented. It should be noted that the suction force derived from SSCs has great influence on the motion characteristics of WCRs. To utilize the merits, get over the weaknesses of SSCs and increase the service abilities of wall-cleaning robots, a series of tether supported wall sliding robots (WSRs) with large SSCs are designed. The control strategies of suction force are investigated to improve the adaptability to complex wall surfaces
Kinetic and Optimization Studies on Ethanol Production from Corn Flour

Studies on Simultaneous Saccharification and Fermentation (SSF) of corn flour, a major agricultural product as the substrate using starch digesting glucoamylase enzyme derived from Aspergillus niger and non starch digesting and sugar fermenting Saccharomyces cerevisiae in a batch fermentation. Experiments based on Central Composite Design (CCD) were conducted to study the effect of substrate concentration, pH, temperature, enzyme concentration on Ethanol Concentration and the above parameters were optimized using Response Surface Methodology (RSM). The optimum values of substrate concentration, pH, temperature and enzyme concentration were found to be 160 g/l, 5.5, 30°C and 50 IU respectively. The effect of inoculums age on ethanol concentration was also investigated. The corn flour solution equivalent to 16% initial starch concentration gave the highest ethanol concentration of 63.04 g/l after 48 h of fermentation at optimum conditions of pH and temperature. Monod model and Logistic model were used for growth kinetics and Leudeking – Piret model was used for product formation kinetics. Simultaneous Saccharification and Fermentation (SSF), Corn Starch, Ethanol, Logisitic Model.
Mental Health Mobile Apps: From Infusion to Diffusion in the Mental Health Social System

The roles of mental health educators and professionals in the diffusion of mental health mobile apps are addressed in this viewpoint article. Mental health mobile apps are emerging technologies that fit under the broad heading of mobile health (mHealth). mHealth, encompassed within electronic health (eHealth), reflects the use of mobile devices for the practice of public health. Well-designed mental health mobile apps that present content in interactive, engaging, and stimulating ways can promote cognitive learning, personal growth, and mental health enhancement. As key influencers in the mental health social system, counselor educators and professional associations may either help or hinder diffusion of beneficial mHealth technologies. As mental health mobile apps move towards ubiquity, research will continue to be conducted. The studies published thus far, combined with the potential of mental health mobile apps for learning and personal growth, offer enough evidence to compel mental health professionals to infuse these technologies into education and practice. Counselor educators and professional associations must use their influential leadership roles to train students and practitioners in how to research, evaluate, and integrate mental health mobile apps into practice. The objectives of this article are to (1) increase awareness of mHealth and mental health mobile apps, (2) demonstrate the potential for continued growth in mental health mobile apps based on technology use and acceptance theory, mHealth organizational initiatives, and evidence about how humans learn, (3) discuss evidence-based benefits of mental health mobile apps, (4) examine the current state of mHealth diffusion in the mental health profession, and (5) offer solutions for impelling innovation diffusion by infusing mental health mobile apps into education, training, and clinical settings. This discussion has implications for counselor educators, mental health practitioners, associations, continuing education providers, and app developers.
Learning to Rank for Question-Oriented Software Text Retrieval (T)

Question-oriented text retrieval, aka natural language-based text retrieval, has been widely used in software engineering. Earlier work has concluded that questions with the same keywords but different interrogatives (such as how, what) should result in different answers. But what is the difference? How to identify the right answers to a question? In this paper, we propose to investigate the "answer style" of software questions with different interrogatives. Towards this end, we build classifiers in a software text repository and propose a re-ranking approach to refine search results. The classifiers are trained by over 16,000 answers from the StackOverflow forum. Each answer is labeled accurately by its question's explicit or implicit interrogatives. We have evaluated the performance of our classifiers and the refinement of our re-ranking approach in software text retrieval. Our approach results in 13.1% and 12.6% respectively improvement with respect to text retrieval criteria nDCG@1 and nDCG@10 compared to the baseline. We also apply our approach to FAQs of 7 open source projects and show 13.2% improvement with respect to nDCG@1. The results of our experiments suggest that our approach could find answers to FAQs more precisely.
Discovering Adversarial Examples with Momentum

Machine learning models, especially Deep Neural Networks, are vulnerable to adversarial examples—malicious inputs crafted by adding small noises to real examples, but fool the models. Adversarial examples transfer from one model to another, enabling black-box attacks to real-world applications. In this paper, we propose a strong attack algorithm named momentum iterative fast gradient sign method (MI-FGSM) to discover adversarial examples. MI-FGSM is an extension of iterative fast gradient sign method (I-FGSM) but improves the transferability significantly. Besides, we study how to attack an ensemble of models efficiently. Experiments demonstrate the effectiveness of the proposed algorithm. We hope that MI-FGSM can serve as a benchmark attack algorithm for evaluating the robustness of various models and defense methods.
Building Online Trust through Socially Rich Web Interfaces

This paper investigates how human warmth and sociability (social presence) can be integrated through the Web interface to positively impact online trust. An empirical study was undertaken to explore the impact of various levels of socially-rich text and picture design elements on the perception of online social presence and its subsequent effect on online trust and its antecedents. Higher levels of social presence are shown to positively impact the perceived usefulness, enjoyment and trust of commercial Websites. Implications of these finding for practitioners and future research are outlined.
Dermatitis by Tropical Rat Mite, Ornithonyssus bacoti (Mesostigmata, Macronyssidae) in Italian city-dwellers: a diagnostic challenge.

References 1 Ishii N, Teye K, Fukuda S et al. Anti-desmocollin autoantibodies in non-classical pemphigus.    2 Chorzelski TP, Hashimoto T, Nishikawa T et al. Unusual acantholytic bullous dermatosis associated with neoplasia and IgG and IgA antibodies against bovine desmocollins I and II.  351–355. 3 Nakajima K, Hashimoto T, Nakajima H, Yokogawa M, Ikeda M, Kodama H. IgG/IgA pemphigus with dyskeratotic acantholysis and intraepidermal neutrophilic microabscesses. J Dermatol 2007; 34: 757–760. 4 Santiago-et-Sanchez-Mateos D, Juarez Martin A, Gonzalez De Arriba A et al. IgG/IgA pemphigus with IgA and IgG antidesmoglein 1 antibodies detected by enzyme-linked immunosorbent assay: presentation of two cases.  2011; 25: 110–112. 5 Watkins C, West C, Kosari P, Ali S, Sangüeza O, Huang W. IgG/IgA Pemphigus: report of a Rare Variant of Atypical Pemphigus and a Review of the Literature.  1011–1015. 6 Bruckner AL, Fitzpatrick JE, Hashimoto T, Weston WL, Morelli JG. Atypical IgA/IgG pemphigus involving the skin, oral mucosa, and colon in a child: a novel variant of IgA pemphigus? Pediatr Dermatol 2005; 22: 321–327. 7 Saruta H, Ishii N, Teye K et al. Two cases of pemphigus vegetans with IgG anti-desmocollin 3 antibodies. JAMA Dermatol 2013;  1209–1213.
Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images

In this work, we present an application of domain randomization and generative adversarial networks (GAN) to train a near real-time object detector for industrial electric parts, entirely in a simulated environment. Large scale availability of labelled real world data is typically rare and difficult to obtain in many industrial settings. As such here, only a few hundred of unlabelled real images are used to train a Cyclic-GAN network, in combination with various degree of domain randomization procedures. We demonstrate that this enables robust translation of synthetic images to the real world domain. We show that a combination of the original synthetic (simulation) and GAN translated images, when used for training a Mask-RCNN object detection network achieves greater than 0.95 mean average precision in detecting and classifying a collection of industrial electric parts. We evaluate the performance across different combinations of training data.
Grand average ERP-image plotting and statistics: A method for comparing variability in event-related single-trial EEG activities across subjects and conditions

BACKGROUD
With the advent of modern computing methods, modeling trial-to-trial variability in biophysical recordings including electroencephalography (EEG) has become of increasingly interest. Yet no widely used method exists for comparing variability in ordered collections of single-trial data epochs across conditions and subjects.


NEW METHOD
We have developed a method based on an ERP-image visualization tool in which potential, spectral power, or some other measure at each time point in a set of event-related single-trial data epochs are represented as color coded horizontal lines that are then stacked to form a 2-D colored image. Moving-window smoothing across trial epochs can make otherwise hidden event-related features in the data more perceptible. Stacking trials in different orders, for example ordered by subject reaction time, by context-related information such as inter-stimulus interval, or some other characteristic of the data (e.g., latency-window mean power or phase of some EEG source) can reveal aspects of the multifold complexities of trial-to-trial EEG data variability.



This study demonstrates new methods for computing and visualizing 'grand' ERP-image plots across subjects and for performing robust statistical testing on the resulting images. These methods have been implemented and made freely available in the EEGLAB signal-processing environment that we maintain and distribute.
Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future interactions. In this paper, we propose a computer Go system that follows experts way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of 253, 233 professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on
Cachier: Edge-Caching for Recognition Applications

Recognition and perception based mobile applications, such as image recognition, are on the rise. These applications recognize the user's surroundings and augment it with information and/or media. These applications are latency-sensitive. They have a soft-realtime nature - late results are potentially meaningless. On the one hand, given the compute-intensive nature of the tasks performed by such applications, execution is typically offloaded to the cloud. On the other hand, offloading such applications to the cloud incurs network latency, which can increase the user-perceived latency. Consequently, edge computing has been proposed to let devices offload intensive tasks to edge servers instead of the cloud, to reduce latency. In this paper, we propose a different model for using edge servers. We propose to use the edge as a specialized cache for recognition applications and formulate the expected latency for such a cache. We show that using an edge server like a typical web cache, for recognition applications, can lead to higher latencies. We propose Cachier, a system that uses the caching model along with novel optimizations to minimize latency by adaptively balancing load between the edge and the cloud, by leveraging spatiotemporal locality of requests, using offline analysis of applications, and online estimates of network conditions. We evaluate Cachier for image-recognition applications and show that our techniques yield 3x speedup in responsiveness, and perform accurately over a range of operating conditions. To the best of our knowledge, this is the first work that models edge servers as caches for compute-intensive recognition applications, and Cachier is the first system that uses this model to minimize latency for these applications.
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches

Due to cellular heterogeneity, cell nuclei classification, segmentation, and detection from pathological images are challenging tasks. In the last few years, Deep Convolutional Neural Networks (DCNN) approaches have been shown state-of-the-art (SOTA) performance on histopathological imaging in different studies. In this work, we have proposed different advanced DCNN models and evaluated for nuclei classification, segmentation, and detection. First, the Densely Connected Recurrent Convolutional Network (DCRN) model is used for nuclei classification. Second, Recurrent Residual U-Net (R2U-Net) is applied for nuclei segmentation. Third, the R2U-Net regression model which is named UD-Net is used for nuclei detection from pathological images. The experiments are conducted with different datasets including Routine Colon Cancer(RCC) classification and detection dataset, and Nuclei Segmentation Challenge 2018 dataset. The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks. The results are evaluated with different performance metrics including precision, recall, Dice Coefficient (DC), Means Squared Errors (MSE), F1-score, and overall accuracy. We have achieved around 3.4% and 4.5% better F-1 score for nuclei classification and detection tasks compared to recently published DCNN based method. In addition, R2U-Net shows around 92.15% testing accuracy in term of DC. These improved methods will help for pathological practices for better quantitative analysis of nuclei in Whole Slide Images(WSI) which ultimately will help for better understanding of different types of cancer in clinical workflow.
Arnor: Modeling Social Intelligence via Norms to Engineer Privacy-Aware Personal Agents

We seek to address the challenge of engineering socially intelligent personal agents that are privacy-aware. We propose Arnor, a method, including a metamodel based on social constructs. Arnor incorporates social norms and goes beyond existing agent-oriented software engineering (AOSE) methods by systematically capturing how a personal agent’s actions influence the social experience it delivers. We conduct two empirical studies to evaluate Arnor. First, via a multiphase developer study, we show that Arnor simplifies application development. Second, via simulation experiments, we show that Arnor provides improved privacy-preserving social experience to end users than personal agents engineered using a traditional AOSE method.
Adaptive Patient-Cooperative Control of a Compliant Ankle Rehabilitation Robot (CARR) With Enhanced Training Safety

This paper proposes a new adaptive patient-cooperative control strategy for improving the effectiveness and safety of robot-assisted ankle rehabilitation. This control strategy has been developed and implemented on a compliant ankle rehabilitation robot (CARR). The CARR is actuated by four Festo Fluidic muscles located to the calf in parallel, has three rotational degrees of freedom. The control scheme consists of a position controller implemented in joint space and a high-level admittance controller in task space. The admittance controller adaptively modifies the predefined trajectory based on real-time ankle measurement, which enhances the training safety of the robot. Experiments were carried out using different modes to validate the proposed control strategy on the CARR. Three training modes include: 1) a passive mode using a joint-space position controller, 2) a patient–robot cooperative mode using a fixed-parameter admittance controller, and 3) a cooperative mode using a variable-parameter admittance controller. Results demonstrate satisfactory trajectory tracking accuracy, even when externally disturbed, with a maximum normalized root mean square deviation less than 5.4%. These experimental findings suggest the potential of this new patient-cooperative control strategy as a safe and engaging control solution for rehabilitation robots.
Use of Hurdle Technology in Food Preservation

In industrialized countries there is a rapidly increasing demand from the consumer for fresh-like, minimally processed food products and in developing countries, food storable without refrigeration are of special interest, because refrigeration is costly and not continuously available. Hurdle technology is an intelligent mix of hurdles which works synergistically and provides safe, healthy products of high quality. The present review focuses on hurdle technology: different types of hurdles used, how these hurdles work together (mechanism), its effect on the microorganism and ultimately it explains how it gives a quality food product. A literature search was carried out and a total of 11 food products and their effect after the application of hurdle technology were studied. It was found that the application of this technology will result in a safe product with optimal shelf life, improved microbial stability, sensory quality, nutritional & economic properties, convenient to use, incur in less transportation and storage costs. Thus, hurdle technology is a technology which aims to improve the total quality of foods by the application of an intelligent mix of hurdles. Hurdle technology, hurdles, inactivation, quality, stability
Effectiveness and safety of artemether–lumefantrine versus artesunate–amodiaquine for unsupervised treatment of uncomplicated falciparum malaria in patients of all age groups in Nanoro, Burkina Faso: a randomized open label trial

Several studies have reported high efficacy and safety of artemisinin-based combination therapy (ACT) mostly under strict supervision of drug intake and limited to children less than 5 years of age. Patients over 5 years of age are usually not involved in such studies. Thus, the findings do not fully reflect the reality in the field. This study aimed to assess the effectiveness and safety of ACT in routine treatment of uncomplicated malaria among patients of all age groups in Nanoro, Burkina Faso. A randomized open label trial comparing artesunate–amodiaquine (ASAQ) and artemether–lumefantrine (AL) was carried out from September 2010 to October 2012 at two primary health centres (Nanoro and Nazoanga) of Nanoro health district. A total of 680 patients were randomized to receive either ASAQ or AL without any distinction by age. Drug intake was not supervised as pertains in routine practice in the field. Patients or their parents/guardians were advised on the time and mode of administration for the 3 days treatment unobserved at home. Follow-up visits were performed on days 3, 7, 14, 21, and 28 to evaluate clinical and parasitological resolution of their malaria episode as well as adverse events. PCR genotyping of merozoite surface proteins 1 and 2 (msp-1, msp-2) was used to differentiate recrudescence and new infection. By day 28, the PCR corrected adequate clinical and parasitological response was 84.1 and 77.8 % respectively for ASAQ and AL. The cure rate was higher in older patients than in children under 5 years old. The risk of re-infection by day 28 was higher in AL treated patients compared with those receiving ASAQ (p < 0.00001). Both AL and ASAQ treatments were well tolerated. This study shows a lowering of the efficacy when drug intake is not directly supervised. This is worrying as both rates are lower than the critical threshold of 90 % required by the WHO to recommend the use of an anti-malarial drug in a treatment policy.