nils-herrmann/clean_scirepeval huggingface dataset:
Attentional Multi-Reading Sarcasm Detection

Recognizing sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and real world facts. Most of the current sarcasm detection systems consider only the utterance in isolation. There are some limited attempts toward taking into account the conversational context. In this paper, we propose an interpretable end-to-end model that combines information from both the utterance and the conversational context to detect sarcasm, and demonstrate its effectiveness through empirical evaluations. We also study the behavior of the proposed model to provide explanations for the model’s decisions. Importantly, our model is capable of determining the impact of utterance and conversational context on the model’s decisions. Finally, we provide an ablation study to illustrate the impact of different components of the proposed model.
OPERATION OF ATTITUDES

■ Abstract This survey of attitude theory and research published between 1996 and 1999 covers the conceptualization of attitude, attitude formation and activation, attitude structure and function, and the attitude-behavior relation. Research regarding the expectancy-value model of attitude is considered, as are the roles of accessible beliefs and affective versus cognitive processes in the formation of attitudes. The survey reviews research on attitude strength and its antecedents and consequences, and covers progress made on the assessment of attitudinal ambivalence and its effects. Also considered is research on automatic attitude activation, attitude functions, and the relation of attitudes to broader values. A large number of studies dealt with the relation between attitudes and behavior. Research revealing additional moderators of this relation is reviewed, as are theory and research on the link between intentions and actions. Most work in this context was devoted to issues raised by the theories of reasoned action and planned behavior. The present review highlights the nature of perceived behavioral control, the relative importance of attitudes and subjective norms, the utility of adding more predictors, and the roles of prior behavior and habit.
DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations

We participate in the two event extraction tasks of BioNLP 2016 Shared Task: binary relation extraction of SeeDev task and localization relations extraction of Bacteria Biotope task. Convolutional neural network (CNN) is employed to model the sentences by convolution and maxpooling operation from raw input with word embedding. Then, full connected neural network is used to learn senior and significant features automatically. The proposed model mainly contains two modules: distributive semantic representation building, such as word embedding, POS embedding, distance embedding and entity type embedding, and CNN model training. The results with F-score of 0.370 and 0.478 in our participant tasks, which were evaluated on the test data set, show that our proposed method contributes to binary relation extraction effectively and can reduce the impact of artificial feature engineering through automatically feature learning.
13 Developing Emotional Intelligence for Healthcare Leaders

Skills in emotional intelligence (EI) help healthcare leaders understand, engage and motivate their team. They are essential for dealing well with conflict and creating workable solutions to complex problems. EI skills are grounded in personal competence, upon which build the skills for social competence, including social awareness and relationship management. The leader’s EI skills strongly impact the culture of the organization. This article lists example strategies for building seventeen key emotional intelligence skills that are the foundations for personal and work success and provides examples of their appropriate use as well as their destructive under-use and over-use. Many examples are those incorporated into our healthcare-related leadership development institutes offered at the University of North Carolina’s Gillings School of Global Public Health.
Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting.
Learning What is Essential in Questions

Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies the notion of essential question terms with the goal of improving such QA solvers. We illustrate the importance of essential question terms by showing that humans’ ability to answer questions drops significantly when essential terms are eliminated from questions. We then develop a classifier that reliably (90% mean average precision) identifies and ranks essential terms in questions. Finally, we use the classifier to demonstrate that the notion of question term essentiality allows state-of-the-art QA solvers for elementary-level science questions to make better and more informed decisions, improving performance by up to 5%. We also introduce a new dataset of over 2,200 crowd-sourced essential terms annotated science questions.
Improving Self-Localization Efficiency in a Small Mobile Robot by Using a Hybrid Field of View Vision System

In this article a self-localization system for small mobile robots based on inexpensive cameras and unobtrusive, passive landmarks is presented and evaluated. The main contribution is the experimental evaluation of the hybrid field of view vision system for self-localization with artificial landmarks. The hybrid vision system consists of an omnidirectional, upward-looking camera with a mirror, and a typical, front-view camera. This configuration is inspired by the peripheral and foveal vision co-operation in animals. We demonstrate that the omnidirectional camera enables the robot to detect quickly landmark candidates and to track the already known landmarks in the environment. The front-view camera guided by the omnidirectional information enables precise measurements of the landmark position over extended distances. The passive landmarks are based on QR codes, which makes possible to easily include in the landmark pattern additional information relevant for navigation. We present evaluation of the positioning accuracy of the system mounted on a SanBot Mk II mobile robot. The experimental results demonstrate that the hybrid field of view vision system and the QR code landmarks enable the small mobile robot to navigate safely along extended paths in a typical home environment.
Dynamic quest plot generation using Petri net planning

In most cases, the story of popular RPG games is designed by professional designers as a main content. However, manual design of game content has limitation in the quantitative aspect. Manual story generation requires a large amount of time and effort. Because gamers want more diverse and rich content, so it is not easy to satisfy the needs with manual design. PCG (Procedural Content Generation) is to automatically generate the content of the game. In this paper, we propose a quest generation engine using Petri net planning. As a combination of Petri-net modules a quest, a quest plot is created. The proposed method is applied to a commercial game platform to show the feasibility.
Differentiation of Chara intermedia and C. baltica compared to C. hispida based on morphology and amplified fragment length polymorphism

Charophytes are macrophytic green algae, occurring in standing and running waters throughout the world. Species descriptions of charophytes are contradictive and different determination keys use various morphologic characters for species discrimination. Chara intermedia Braun, C. baltica Bruzelius and C. hispida Hartman are treated as three species by most existing determination keys, though their morphologic differentiation is based on different characteristics. Amplified fragment length polymorphism (AFLP) was used to detect genetically homogenous groups within the C. intermedia-C. baltica-C. hispida-cluster, by the analysis of 122 C. intermedia, C. baltica and C. hispida individuals from central and northern Europe. C. hispida clustered in a distinct genetic group in the AFLP analysis and could be determined morphologically by its aulacanthous cortification. However, for C. intermedia and C. baltica no single morphologic character was found that differentiated the two genetic groups, thus C. intermedia and C. baltica are considered as cryptic species. All C. intermedia specimen examined came from freshwater habitats, whereas the second group, C. baltica, grew in brackish water. We conclude that the species differentiation between C. intermedia and C. baltica, which is assumed to be reflected by the genetic discrimination groups, corresponds more with ecological (salinity preference) than morphologic characteristics. Based on the genetic analysis three differing colonization models of the Baltic Sea and the Swedish lakes with C. baltica and C. intermedia were discussed. As samples of C. intermedia and C. baltica have approximately the same Jaccard coefficient for genetic similarity, we suggest that C. baltica colonized the Baltic Sea after the last glacial maximum from refugia along the Atlantic and North Sea coasts. Based on the similarity of C. intermedia intermediate individuals of Central Europe and Sweden we assume a colonization of the Swedish lakes from central Europe.
The (in)security of proprietary cryptography

Proprietary cryptography is a term used to describe custom encryption techniques that are kept secret by its designers to add additional security. It is questionable if such an approach increases the cryptographic strength of the underlying mathematical algorithms. The security of proprietary encryption techniques relies entirely on the competence of the semi-conductor companies, which keep the technical description strictly confidential after designing. It is difficult to give a public and independent security assessment of the cryptography, without having access to the detailed information of the design. Proprietary cryptography is currently deployed in many products which are used on a daily basis by the majority of people world-wide. It is embedded in the computational core of many wireless and contactless devices used in access control systems and vehicle immobilizers. Contactless access control cards are used in various security systems. Examples include the use in public transport, payment terminals, office buildings and even in highly secure facilities such as ministries, banks, nuclear power plants and prisons. Many of these access control cards are based on proprietary encryption techniques. Prominent examples are the widely deployed contactless access control systems that use the MIFARE Classic, iClass and Cryptomemory technology. A vehicle immobilizer is an electronic device that prevents the engine of the vehicle from starting when the corresponding transponder is not present. This transponder is a wireless radio frequency chip which is typically embedded in the plastic casing of the car key. When the driver tries to start the vehicle, the car authenticates the transponder before starting the engine, thus preventing hot-wiring. According to European Commission directive (95/56/EC) it is mandatory that all cars, sold in the EU from 1995 onwards, are fitted with an electronic immobilizer. In practice, almost all recently sold cars in Europe are protected by transponders that embed one of the two proprietary encryption techniques Hitag2 or Megamos Crypto. In this doctoral thesis well-known techniques are combined with novel methods
Detecting predation and scavenging by DNA gut-content analysis: a case study using a soil insect predator-prey system

White grubs (larvae of Coleoptera: Scarabaeidae) are abundant in below-ground systems and can cause considerable damage to a wide variety of crops by feeding on roots. White grub populations may be controlled by natural enemies, but the predator guild of the European species is barely known. Trophic interactions within soil food webs are difficult to study with conventional methods. Therefore, a polymerase chain reaction (PCR)-based approach was developed to investigate, for the first time, a soil insect predator-prey system. Can, however, highly sensitive detection methods identify carrion prey in predators, as has been shown for fresh prey? Fresh Melolontha melolontha (L.) larvae and 1- to 9-day-old carcasses were presented to Poecilus versicolor Sturm larvae. Mitochondrial cytochrome oxidase subunit I fragments of the prey, 175, 327 and 387 bp long, were detectable in 50% of the predators 32 h after feeding. Detectability decreased to 18% when a 585 bp sequence was amplified. Meal size and digestion capacity of individual predators had no influence on prey detection. Although prey consumption was negatively correlated with cadaver age, carrion prey could be detected by PCR as efficiently as fresh prey irrespective of carrion age. This is the first proof that PCR-based techniques are highly efficient and sensitive, both in fresh and carrion prey detection. Thus, if active predation has to be distinguished from scavenging, then additional approaches are needed to interpret the picture of prey choice derived by highly sensitive detection methods.
Generic anomalous vertices detection utilizing a link prediction algorithm

In the past decade, graph-based structures have penetrated nearly every aspect of our lives. The detection of anomalies in these networks has become increasingly important, such as in exposing infected endpoints in computer networks or identifying socialbots. In this study, we present a novel unsupervised two-layered meta-classifier that can detect irregular vertices in complex networks solely by utilizing topology-based features. Following the reasoning that a vertex with many improbable links has a higher likelihood of being anomalous, we applied our method on 10 networks of various scales, from a network of several dozen students to online networks with millions of vertices. In every scenario, we succeeded in identifying anomalous vertices with lower false positive rates and higher AUCs compared to other prevalent methods. Moreover, we demonstrated that the presented algorithm is generic, and efficient both in revealing fake users and in disclosing the influential people in social networks.
Deadlock-Free Message Routing in Multiprocessor Interconnection Networks

A deadlock-free routing algorithm can be generated for arbitrary interconnection networks using the concept of virtual channels. A necessary and sufficient condition for deadlock-free routing is the absence of cycles in a channel dependency graph. Given an arbitrary network and a routing function, the cycles of the channel dependency graph can be removed by splitting physical channels into groups of virtual channels. This method is used to develop deadlock-free routing algorithms for k-ary n-cubes, for cube-connected cycles, and for shuffle-exchange networks.
A Survey on Automated Food Monitoring and Dietary Management Systems

Healthy diet with balanced nutrition is key to the prevention of life-threatening diseases such as obesity, cardiovascular disease, and cancer. Recent advances in smartphone and wearable sensor technologies have led to a proliferation of food monitoring applications based on automated food image processing and eating episode detection, with the goal to conquer drawbacks of the traditional manual food journaling that is time consuming, inaccurate, underreporting, and low adherent. In order to provide users feedback with nutritional information accompanied by insightful dietary advice, various techniques in light of the key computational learning principles have been explored. This survey presents a variety of methodologies and resources on this topic, along with unsolved problems, and closes with a perspective and boarder implications of this field.
Practitioners' expectations on automated fault localization

Software engineering practitioners often spend significant amount of time and effort to debug. To help practitioners perform this crucial task, hundreds of papers have proposed various fault localization techniques. Fault localization helps practitioners to find the location of a defect given its symptoms (e.g., program failures). These localization techniques have pinpointed the locations of bugs of various systems of diverse sizes, with varying degrees of success, and for various usage scenarios. Unfortunately, it is unclear whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by surveying 386 practitioners from more than 30 countries across 5 continents about their expectations of research in fault localization. In particular, we investigated a number of factors that impact practitioners' willingness to adopt a fault localization technique. We then compared what practitioners need and the current state-of-research by performing a literature review of papers on fault localization techniques published in ICSE, FSE, ESEC-FSE, ISSTA, TSE, and TOSEM in the last 5 years (2011-2015). From this comparison, we highlight the directions where researchers need to put effort to develop fault localization techniques that matter to practitioners.
A pilot study of home-based smoking cessation programs for rural, appalachian, pregnant smokers.

To evaluate a web-based contingency management program (CM) and a phone-delivered cessation counseling program (Smoking Cessation for Healthy Births [SCHB]) with pregnant smokers in rural Appalachia who were ≤12 weeks gestation at enrollment.



Two group randomized design.



Home-based cessation programs in rural Appalachia Ohio and Kentucky.



A community sample of pregnant smokers (N = 17).



Participants completed demographic and smoking-related questionnaires and were assigned to CM (n = 7) or SCHB (n = 10) conditions. Smoking status was assessed monthly using breath carbon monoxide and urinary cotinine.



For CM, two of seven (28.57%) of the participants achieved abstinence, and three of 10 (30%) of those enrolled in SCHB were abstinent by late in pregnancy. Participants in CM attained abstinence more rapidly than those in SCHB. However, those in SCHB experienced less relapse to smoking, and a greater percentage of these participants reduced their smoking by at least 50%.



Based on this initial evaluation, the web-based CM and SCHB programs appeared to be feasible for use with rural pregnant smokers with acceptable program adherence for both approaches. Future researchers could explore combining these programs to capitalize on the strengths of each, for example, rapid smoking cessation based on CM incentives and better sustained cessation or reductions in smoking facilitated by the counseling support of SCHB.
Curiosity-driven optimization

The principle of artificial curiosity directs active exploration towards the most informative or most interesting data. We show its usefulness for global black box optimization when data point evaluations are expensive. Gaussian process regression is used to model the fitness function based on all available observations so far. For each candidate point this model estimates expected fitness reduction, and yields a novel closed-form expression of expected information gain. A new type of Pareto-front algorithm continually pushes the boundary of candidates not dominated by any other known data according to both criteria, using multi-objective evolutionary search. This makes the exploration-exploitation trade-off explicit, and permits maximally informed data selection. We illustrate the robustness of our approach in a number of experimental scenarios.
Efficacy of Grintuss® pediatric syrup in treating cough in children: a randomized, multicenter, double blind, placebo-controlled clinical trial

Cough is an extremely common problem in pediatrics, mostly triggered and perpetuated by inflammatory processes or mechanical irritation leading to viscous mucous production and increased sensitivity of the cough receptors. Protecting the mucosa might be very useful in limiting the contact with micro-organisms and irritants thus decreasing the inflammation and mucus production. Natural molecular complexes can act as a mechanical barrier limiting cough stimuli with a non pharmacological approach but with an indirect anti-inflammatory action.



Aim of the study was to assess the efficacy of a medical device containing natural functional components in the treatment of cough persisting more than 7 days.



In this randomized, parallel groups, double-blind vs. placebo study, children with cough persisting more than 7 days were enrolled. The clinical efficacy of the study product was assessed evaluating changes in day- and night-time cough scores after 4 and 8 days (t4 and t8) of product administration.



In the inter-group analysis, in the study product group compared with the placebo group, a significant difference (t4 study treatment vs. t4 placebo, p = 0.03) was observed at t4 in night-time cough score.Considering the intra-group analysis, only the study product group registered a significant improvement from t0 to t4 in both day-time (t0 vs. t4, p = 0.04) and night-time (t0 vs. t4, p = 0.003) cough scores.A significant difference, considering the study product, was also found in the following intra-group analyses: day-time scores at t4 vs. t8 (p =0.01) and at t0 vs. t8 (p = 0.001); night-time scores at t4 vs. t8 (p = 0.05), and at t0 vs. t8 (p = 0.005). Considering a subgroup of patients with higher cough (≥ 3) scores, 92.9% of them in the study product group improved at t0 vs. t4 day-time.



Grintuss® pediatric syrup showed to possess an interesting profile of efficacy and safety in the treatment of cough persisting more than 7 days.