nils-herrmann/clean_scirepeval huggingface dataset:
Knowle: A semantic link network based system for organizing large scale online news events

An explosive growth in the volume, velocity, and variety of the data available on the Internet has been witnessed recently. The data originated frommultiple types of sources including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, health data has led to one of the most challenging research issues of the big data era. In this paper, Knowle—an online news management system upon semantic link network model is introduced. Knowle is a news event centrality data management system. The core elements of Knowle are news events on the Web, which are linked by their semantic relations. Knowle is a hierarchical data system, which has three different layers including the bottom layer (concepts), the middle layer (resources), and the top layer (events). The basic blocks of the Knowle system—news collection, resources representation, semantic relations mining, semantic linking news events are given. Knowle does not require data providers to follow semantic standards such as RDF or OWL, which is a semantics-rich self-organized network. It reflects various semantic relations of concepts, news, and events. Moreover, in the case study, Knowle is used for organizing andmining health news, which shows the potential on forming the basis of designing and developing big data analytics based innovation framework in the health domain.
A control perspective for centralized and distributed convex optimization

In this paper, we want to study how natural and engineered systems could perform complex optimizations with limited computational and communication capabilities. We adopt a continuous-time dynamical system view rooted in early work on optimization and more recently in network protocol design, and merge it with the dynamic view of distributed averaging systems. We obtain a general approach, based on the control system viewpoint, that allows to analyze and design (distributed) optimization systems converging to the solution of given convex optimization problems. The control system viewpoint provides many insights and new directions of research. We apply the framework to a distributed optimal location problem and demonstrate the natural tracking and adaptation capabilities of the system to changing constraints.
Implementing task-based language teaching in a Japanese EFL context

...although PPP lessons are often supplemented with skills lessons, most students taught mainly through conventional approaches such as PPP leave school unable to communicate effectively in English (Stern, 1983). This situation has prompted many ELT professionals to take note of... second language acquisition (SLA) studies... and turn towards holistic approaches where meaning is central and where opportunities for language use abound. Task-based learning is one such approach...
GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures

Drug discovery is the process by which new candidate medications are discovered. Developing a new drug is a lengthy, complex, and expensive process. Here, in this paper, we propose a biomedical knowledge graph embedding-based recurrent neural network method called GrEDeL, which discovers potential drugs for diseases by mining published biomedical literature. GrEDeL first builds a biomedical knowledge graph by exploiting the relations extracted from biomedical abstracts. Then, the graph data are converted into a low dimensional space by leveraging the knowledge graph embedding methods. After that, a recurrent neural network model is trained by the known drug therapies which are represented by graph embeddings. Finally, it uses the learned model to discover candidate drugs for diseases of interest from biomedical literature. The experimental results show that our method could not only effectively discover new drugs by mining literature, but also could provide the corresponding mechanism of actions for the candidate drugs. It could be a supplementary method for the current traditional drug discovery methods.
Incentive mechanisms in mobile delay tolerant network

Mobile delay tolerant network (MDTN) is a kind of no stabilized end-to-end connection network and has the characteristics of long time delay and intermittent interruption. To forward a network packet, MDTN relies on the relay nodes, using the “store — carry — forwards” routing method. However, nodes will be selfish and unwilling to forward messages for others due to the limited resources such as energy, storage space and bandwidth. Therefore, it is necessary to bring in incentive mechanism to motivate the selfish nodes to cooperatively forward messages. In this paper, we divide present incentive mechanisms into three categories: reputation-based scheme, tit-for-tat (TFT)-based incentive scheme and credit-based incentive scheme. Then we qualitatively analyze and compare typical incentive mechanisms have been proposed. Finally, we make a conclusion and point out the inadequacies in present incentive mechanisms under MDTN.
A non-reward attractor theory of depression

A non-reward attractor theory of depression is proposed based on the operation of the lateral orbitofrontal cortex and supracallosal cingulate cortex. The orbitofrontal cortex contains error neurons that respond to non-reward for many seconds in an attractor state that maintains a memory of the non-reward. The human lateral orbitofrontal cortex is activated by non-reward during reward reversal, and by a signal to stop a response that is now incorrect. Damage to the human orbitofrontal cortex impairs reward reversal learning. Not receiving reward can produce depression. The theory proposed is that in depression, this lateral orbitofrontal cortex non-reward system is more easily triggered, and maintains its attractor-related firing for longer. This triggers negative cognitive states, which in turn have positive feedback top-down effects on the orbitofrontal cortex non-reward system. Treatments for depression, including ketamine, may act in part by quashing this attractor. The mania of bipolar disorder is hypothesized to be associated with oversensitivity and overactivity in the reciprocally related reward system in the medial orbitofrontal cortex and pregenual cingulate cortex.
Summarization of Twitter Microblogs

Twitter1, the microblog site started in 2006, has become a social phenomenon. More than 340 million Tweets are sent out every day2. While a majority of posts are conversational or not particularly meaningful, about 3.6% of the posts concern topics of mainstream news3. Twitter has been credited with providing the most current news about many important events before traditional media, such as the attacks in Mumbai in November 2008. Twitter also played a prominent role in the unfolding of the troubles in Iran in 2009 subsequent to a disputed election, and the so-called Twitter Revolutions4 in Tunisia and Egypt in 2010-11. To help people who read Twitter posts or tweets, Twitter provides two interesting features: an API that allows users to search for posts that contain a topic phrase and a short list of popular topics called Trending Topics. A user can perform a search for a topic and retrieve a list of most recent posts that contain the topic phrase. The di culty in interpreting the results is that the returned posts are only sorted by recency, not relevancy. Therefore, the user is forced to manually read through the posts in order to understand what users are primarily saying about a particular topic. A website called WhatTheTrend5 attempts to provide definitions of trending topics by allowing users to manually enter descriptions of why a topic is trending. Here is an example of a definition from WhatTheTrend:
A Bluffer’s Guide to ... Sphericity

The use of repeated measures, where the same subjects are tested under a number of conditions, has numerous practical and statistical benefits. For one thing it reduces the error variance caused by between-group individual differences, however, this reduction of error comes at a price because repeated measures designs potentially introduce covariation between experimental conditions (this is because the same people are used in each condition and so there is likely to be some consistency in their behaviour across conditions). In between-group ANOVA we have to assume that the groups we test are independent for the test to be accurate (Scariano & Davenport, 1987, have documented some of the consequences of violating this assumption). As such, the relationship between treatments in a repeated measures design creates problems with the accuracy of the test statistic. The purpose of this article is to explain, as simply as possible, the issues that arise in analysing repeated measures data with ANOVA: specifically, what is sphericity and why is it important? What is Sphericity?
A novel multi-step reinforcement learning method for solving reward hacking

Reinforcement learning with appropriately designed reward signal could be used to solve many sequential learning problems. However, in practice, the reinforcement learning algorithms could be broken in unexpected, counterintuitive ways. One of the failure modes is reward hacking which usually happens when a reward function makes the agent obtain high return in an unexpected way. This unexpected way may subvert the designer’s intentions and lead to accidents during training. In this paper, a new multi-step state-action value algorithm is proposed to solve the problem of reward hacking. Unlike traditional algorithms, the proposed method uses a new return function, which alters the discount of future rewards and no longer stresses the immediate reward as the main influence when selecting the current state action. The performance of the proposed method is evaluated on two games, Mappy and Mountain Car. The empirical results demonstrate that the proposed method can alleviate the negative impact of reward hacking and greatly improve the performance of reinforcement learning algorithm. Moreover, the results illustrate that the proposed method could also be applied to the continuous state space problem successfully.
The Anatomy of the Facebook Social Graph

We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the ‘six degrees of separation’ phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which ‘your friends have more friends than you’. Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics.
AVID: Adversarial Visual Irregularity Detection

Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc. With the surge of deep learning methods in the recent years, researchers have tried a wide spectrum of methods for different applications. However, for the case of irregularity or anomaly detection in videos, training an end-to-end model is still an open challenge, since often irregularity is not well-defined and there are not enough irregular samples to use during training. In this paper, inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised or self-supervised settings, we propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images). Our proposed architecture is composed of two networks, which are trained in competing with each other while collaborating to find the irregularity. One network works as a pixel-level irregularity Inpainter, and the other works as a patch-level Detector. After an adversarial self-supervised training, in which I tries to fool D into accepting its inpainted output as regular (normal), the two networks collaborate to detect and fine-segment the irregularity in any given testing video. Our results on three different datasets show that our method can outperform the state-of-the-art and fine-segment the irregularity.
Classify, predict, detect, anticipate and synthesize: Hierarchical recurrent latent variable models for human activity modeling

Human activity modeling operates on two levels: highlevel action modeling, such as classification, prediction, detection and anticipation, and low-level motion trajectory prediction and synthesis. In this work, we propose a semisupervised generative latent variable model that addresses both of these levels by modeling continuous observations as well as semantic labels. We extend the model to capture the dependencies between different entities, such as a human and objects, and to represent hierarchical label structure, such as high-level actions and sub-activities. In the experiments we investigate our model’s capability to classify, predict, detect and anticipate semantic action and affordance labels and to predict and generate human motion. We train our models on data extracted from depth image streams from the Cornell Activity 120, the UTKinect-Action3D and the Stony Brook University Kinect Interaction Dataset. We observe that our model performs well in all of the tasks and often outperforms task-specific models.
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning

Many tasks in artificial intelligence require the collaboration of multiple agents. We exam deep reinforcement learning for multi-agent domains. Recent research efforts often take the form of two seemingly conflicting perspectives, the decentralized perspective, where each agent is supposed to have its own controller; and the centralized perspective, where one assumes there is a larger model controlling all agents. In this regard, we revisit the idea of the master-slave architecture by incorporating both perspectives within one framework. Such a hierarchical structure naturally leverages advantages from one another. The idea of combining both perspectives is intuitive and can be well motivated from many real world systems, however, out of a variety of possible realizations, we highlights three key ingredients, i.e. composed action representation, learnable communication and independent reasoning. With network designs to facilitate these explicitly, our proposal consistently outperforms latest competing methods both in synthetic experiments and when applied to challenging StarCraft1 micromanagement tasks.
Data Augmentation for Morphological Reinflection

This paper presents the submission of the Linguistics Department of the University of Colorado at Boulder for the 2017 CoNLL-SIGMORPHON Shared Task on Universal Morphological Reinflection. The system is implemented as an RNN Encoder-Decoder. It is specifically geared toward a low-resource setting. To this end, it employs data augmentation for counteracting overfitting and a copy symbol for processing characters unseen in the training data. The system is an ensemble of ten models combined using a weighted voting scheme. It delivers substantial improvement in accuracy compared to a non-neural baseline system in presence of varying amounts of training data.
Potential of compost tea for suppressing plant diseases

Numerous studies have demonstrated that water-based compost preparations, referred to as compost tea and compost-water extract, can suppress phytopathogens and plant diseases. Despite its potential, compost tea has generally been considered as inadequate for use as a biocontrol agent in conventional cropping systems but important to organic producers who have limited disease control options. The major impediments to the use of compost tea have been the lessthan-desirable and inconsistent levels of plant disease suppression as influenced by compost tea production and application factors including compost source and maturity, brewing time and aeration, dilution and application rate and application frequency. Although the mechanisms involved in disease suppression are not fully understood, sterilization of compost tea has generally resulted in a loss in disease suppressiveness. This indicates that the mechanisms of suppression are often, or predominantly, biological, although physico-chemical factors have also been implicated. Increasing the use of molecular approaches, such as metagenomics, metaproteomics, metatranscriptomics and metaproteogenomics should prove useful in better understanding the relationships between microbial abundance, diversity, functions and disease suppressive efficacy of compost tea. Such investigations are crucial in developing protocols for optimizing the compost tea production process so as to maximize disease suppressive effect without exposing the manufacturer or user to the risk of human pathogens. To this end, it is recommended that compost tea be used as part of an integrated disease management system.
Analysis of Transmedia Storytelling in Pokémon GO

This study is part of a doctoral thesis on the topic of Hyperfiction: Past, Present and Future of Storytelling through Hypertext. It explores in depth the impact of transmedia storytelling and the role of hypertext in the realm of the currently popular social media phenomenon Pokémon GO. Storytelling is a powerful method to engage and unite people. Moreover, the technology progress adds a whole new angle to the method, with hypertext and cross-platform sharing that enhance the traditional storytelling so much that transmedia storytelling gives unlimited opportunities to affect the everyday life of people across the globe. This research aims at examining the transmedia storytelling approach in Pokémon GO, and explaining how that contributed to its establishment as a massive worldwide hit in less than a week. The social engagement is investigated in all major media platforms, including traditional and online media channels. Observation and content analyses are reported in this paper to form the conclusion that transmedia storytelling with the input of hypertext has a promising future as a method of establishing a productive and rewarding communication strategy. Communication, hypertext, Pokémon GO, storytelling, transmedia.
Thinking may be more than computing

The uncomputable parts of thinking (if there are any) can be studied in much the same spirit that Turing (1950) suggested for the study of its computable parts. We can develop precise accounts of cognitive processes that, although they involve more than computing, can still be modelled on the machines we call ‘computers’. In this paper, I want to suggest some ways that this might be done, using ideas from the mathematical theory of uncomputability (or Recursion Theory). And I want to suggest some uses to which the resulting models might be put. (The reader more interested in the models and their uses than the mathematics and its theorems, might want to skim or skip the mathematical parts.
Effects of stress on the development and progression of cardiovascular disease

Cardiovascular disease remains the leading cause of disease burden globally, which underlies the continuing need to identify new complementary targets for prevention. Over the past 5–10 years, the pooling of multiple data sets into 'mega-studies' has accelerated progress in research on stress as a risk and prognostic factor for cardiovascular disease. Severe stressful experiences in childhood, such as physical abuse and household substance abuse, can damage health and increase the risk of multiple chronic conditions in adulthood. Compared with childhood stress and adulthood classic risk factors, such as smoking, high blood pressure, and high serum cholesterol levels, the harmful effects of stress in adulthood are generally less marked. However, adulthood stress has an important role as a disease trigger in individuals who already have a high atherosclerotic plaque burden, and as a determinant of prognosis and outcome in those with pre-existing cardiovascular or cerebrovascular disease. In real-life settings, mechanistic studies have corroborated earlier laboratory-based observations on stress-related pathophysiological changes that underlie triggering, such as lowered arrhythmic threshold and increased sympathetic activation with related increases in blood pressure, as well as pro-inflammatory and procoagulant responses. In some clinical guidelines, stress is already acknowledged as a target for prevention for people at high overall risk of cardiovascular disease or with established cardiovascular disease. However, few scalable, evidence-based interventions are currently available.
Feasibility of patterned vertical CNT for dry electrode sensing of physiological parameters

Dry electrodes for impedimetric sensing of physiological parameters (such as ECG, EEG, and GSR) promise the ability for long duration monitoring. This paper describes the feasibility of a novel dry electrode interfacing using Patterned Vertical Carbon Nanotube (pvCNT) for physiological parameter sensing. The electrodes were fabricated on circular discs (φ = 10 mm) stainless steel substrate. Multiwalled electrically conductive carbon nanotubes were grown in pattered pillar formation of 100 μm squared with 50, 100, 200 and 500μm spacing. The heights of the pillars were between 1 to 1.5 mm. A comparative test with commercial ECG electrodes shows that pvCNT has lower electrical impedance, stable impedance over very long time, and comparable signal capture in vitro. Long duration study shows minimal degradation of impedance over 2 days period. The results demonstrate the feasibility of using pvCNT dry electrodes for physiological parameter sensing.
Review of electron beam therapy physics.

For over 50 years, electron beams have been an important modality for providing an accurate dose of radiation to superficial cancers and disease and for limiting the dose to underlying normal tissues and structures. This review looks at many of the important contributions of physics and dosimetry to the development and utilization of electron beam therapy, including electron treatment machines, dose specification and calibration, dose measurement, electron transport calculations, treatment and treatment-planning tools, and clinical utilization, including special procedures. Also, future changes in the practice of electron therapy resulting from challenges to its utilization and from potential future technology are discussed.