nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Dropout for Supervised and Semi-Supervised Learning

Recently, training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has improved the generalization performance of neural networks. In contrast to the biased individual inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between 1) the training supervision and 2) the outputs from the network with the dropouts. The identified adversarial dropouts are used to automatically reconfigure the neural network in the training process, and we demonstrated that the simultaneous training on the original and the reconfigured network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST, SVHN, and CIFAR-10. We analyzed the trained model to find the performance improvement reasons. We found that adversarial dropout increases the sparsity of neural networks more than the standard dropout. Finally, we also proved that adversarial dropout is a regularization term with a rank-valued hyper parameter that is different from a continuous-valued parameter to specify the strength of the regularization.
Ethnic Classification Based on Iris Images

Iris texture is commonly thought to be highly discriminative between eyes and stable over individual lifetime, which makes iris particularly suitable for personal identification. However, iris texture also contains more information related to genes, which has been demonstrated by successful use of ethnic and gender classification based on iris. In this paper, we propose a novel ethnic classification method based on supervised codebook optimizing and Locality-constrained Linear Coding (LLC). The optimized codebook is composed of codes which are distinctive or mutual. Iris images from Asian and non-Asian are classified into two classes in experiments. Extensive experimental results show that the proposed method achieves encouraging classification rate and largely improves the ethnic classification performance comparing to existing algorithms.
Online Active Learning Methods for Fast Label-Efficient Spam Filtering

Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustworthy labels for messages may be costly to acquire. Past investigations of active learning in spam filtering have focused on the pool-based scenario, where there is assumed to be a large, unlabeled data set and the goal is to iteratively identify the best subset of examples for which to request labels. However, even with optimizations this is a costly approach. We investigate an online active learning scenario where the filter is exposed to a stream of messages which must be classified one at a time. The filter may only request a label for a given message immediately after it has been classified. The goal is to achieve strong online classification performance with few label requests. This is a novel scenario for low-cost active spam filtering, fitting for application in large-scale systems. We draw from the label efficient machine learning literature to investigate several approaches to selective sampling in this scenario using linear classifiers. We show that online active learning can dramatically reduce labeling and training costs with negligible additional overhead while maintaining high levels of classification performance.
Mitochondria in Ischemic Stroke: New Insight and Implications

Stroke is the leading cause of death and adult disability worldwide. Mitochondrial dysfunction has been regarded as one of the hallmarks of ischemia/reperfusion (I/R) induced neuronal death. Maintaining the function of mitochondria is crucial in promoting neuron survival and neurological improvement. In this article, we review current progress regarding the roles of mitochondria in the pathological process of cerebral I/R injury. In particular, we emphasize on the most critical mechanisms responsible for mitochondrial quality control, as well as the recent findings on mitochondrial transfer in acute stroke. We highlight the potential of mitochondria as therapeutic targets for stroke treatment and provide valuable insights for clinical strategies.
Digital Supply Chain Transformation toward Blockchain Integration

Digital supply chain integration is becoming increasingly dynamic. Access to customer demand needs to be shared effectively, and product and service deliveries must be tracked to provide visibility in the supply chain. Business process integration is based on standards and reference architectures, which should offer end-to-end integration of product data. Companies operating in supply chains establish process and data integration through the specialized intermediate companies, whose role is to establish interoperability by mapping and integrating companyspecific data for various organizations and systems. This has typically caused high integration costs, and diffusion is slow. This paper investigates the requirements and functionalities of supply chain integration. Cloud integration can be expected to offer a cost-effective business model for interoperable digital supply chains. We explain how supply chain integration through the blockchain technology can achieve disruptive transformation in digital supply chains and networks.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks.
Structure Preserving Video Prediction

Despite recent emergence of adversarial based methods for video prediction, existing algorithms often produce unsatisfied results in image regions with rich structural information (i.e., object boundary) and detailed motion (i.e., articulated body movement). To this end, we present a structure preserving video prediction framework to explicitly address above issues and enhance video prediction quality. On one hand, our framework contains a two-stream generation architecture which deals with high frequency video content (i.e., detailed object or articulated motion structure) and low frequency video content (i.e., location or moving directions) in two separate streams. On the other hand, we propose a RNN structure for video prediction, which employs temporal-adaptive convolutional kernels to capture time-varying motion patterns as well as tiny objects within a scene. Extensive experiments on diverse scenes, ranging from human motion to semantic layout prediction, demonstrate the effectiveness of the proposed video prediction approach.
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking

We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features. We present a novel neural network architecture based on a combination of recurrent neural networks that are used to encode questions and answers, and a multilayer perceptron. We show how this approach can be combined with additional features, in particular, the discourse features presented by Jansen et al. (2014). Our neural approach achieves state-of-the-art performance on a public dataset from Yahoo! Answers and its performance is further improved by incorporating the discourse features. Additionally, we present a new dataset of Ask Ubuntu questions where the hybrid approach also achieves good results.
A survey on deep learning in medical image analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.
An Ontology for Video Event Representation

Representation and recognition of events in a video is important for a number of tasks such as video surveillance, video browsing and content based video indexing. This paper describes the results of a "Challenge Project on Video Event Taxonomy" sponsored by the Advanced Research and Development Activity (ARDA) of the U.S. Government in the summer and fall of 2003. The project brought together more than 30 researchers in computer vision and knowledge representation and representatives of the user community. It resulted in the development of a formal language for describing an ontology of events, which we call VERL (Video Event Representation Language) and a companion language called VEML (Video Event Markup Language) to annotate instances of the events described in VERL. This paper provides a summary of VERL and VEML as well as the considerations associated with the specific design choices.
Planning for Autonomous Cars that Leverage Effects on Human Actions

Traditionally, autonomous cars make predictions about other drivers’ future trajectories, and plan to stay out of their way. This tends to result in defensive and opaque behaviors. Our key insight is that an autonomous car’s actions will actually affect what other cars will do in response, whether the car is aware of it or not. Our thesis is that we can leverage these responses to plan more efficient and communicative behaviors. We model the interaction between an autonomous car and a human driver as a dynamical system, in which the robot’s actions have immediate consequences on the state of the car, but also on human actions. We model these consequences by approximating the human as an optimal planner, with a reward function that we acquire through Inverse Reinforcement Learning. When the robot plans with this reward function in this dynamical system, it comes up with actions that purposefully change human state: it merges in front of a human to get them to slow down or to reach its own goal faster; it blocks two lanes to get them to switch to a third lane; or it backs up slightly at an intersection to get them to proceed first. Such behaviors arise from the optimization, without relying on hand-coded signaling strategies and without ever explicitly modeling communication. Our user study results suggest that the robot is indeed capable of eliciting desired changes in human state by planning using this dynamical system.
Convergence of Software Science and Computational Intelligence: A New Transdisciplinary Research Field

Software Science is a discipline that studies the theoretical framework of software as instructive and behavioral information, which can be embodied and executed by generic computers in order to create expected system behaviors and machine intelligence. Intelligence science is a discipline that studies the mechanisms and theories of abstract intelligence and its paradigms such as natural, artificial, machinable, and computational intelligence. The convergence of software and intelligent sciences forms the transdisciplinary field of computational intelligence, which provides a coherent set of fundamental theories, contemporary denotational mathematics, and engineering applications. This editorial addresses the objectives of the International Journal of Software Science and Computational Intelligence (IJSSCI), and explores the domain of the emerging discipline. The historical evolvement of software and intelligence sciences and their theoretical foundations are elucidated. The coverage of this inaugural issue and recent advances in software and intelligence sciences are reviewed. This editorial demonstrates that the investigation into software and intelligence sciences will result in fundamental findings toward the development of future generation computing theories, methodologies, and technologies, as well as novel mathematical structures.
Evolving Artificial Neural Networks

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANN’s) in recent years.  reviews different combinations between ANN’s and evolutionary algorithms (EA’s), including using EA’s to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EA’s; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANN’s and EA’s can lead to significantly better intelligent systems than relying on ANN’s or EA’s alone.
A positive association found between autism prevalence and childhood vaccination uptake across the U.S. population.

The reason for the rapid rise of autism in the United States that began in the 1990s is a mystery. Although individuals probably have a genetic predisposition to develop autism, researchers suspect that one or more environmental triggers are also needed. One of those triggers might be the battery of vaccinations that young children receive. Using regression analysis and controlling for family income and ethnicity, the relationship between the proportion of children who received the recommended vaccines by age 2 years and the prevalence of autism (AUT) or speech or language impairment (SLI) in each U.S. state from 2001 and 2007 was determined. A positive and statistically significant relationship was found: The higher the proportion of children receiving recommended vaccinations, the higher was the prevalence of AUT or SLI. A 1% increase in vaccination was associated with an additional 680 children having AUT or SLI. Neither parental behavior nor access to care affected the results, since vaccination proportions were not significantly related (statistically) to any other disability or to the number of pediatricians in a U.S. state. The results suggest that although mercury has been removed from many vaccines, other culprits may link vaccines to autism. Further study into the relationship between vaccines and autism is warranted.
Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items

Clothing recognition is an extremely challenging problem due to wide variation in clothing item appearance, layering, and style. In this paper, we tackle the clothing parsing problem using a retrieval based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to parse the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse masks (paper doll item transfer) from retrieved examples. Experimental evaluation shows that our approach significantly outperforms state of the art in parsing accuracy.
Light Ears: Information Leakage via Smart Lights

Modern Internet-enabled smart lights promise energy efficiency and many additional capabilities over traditional lamps. However, these connected lights create a new attack surface, which can be maliciously used to violate users’ privacy and security. In this paper, we design and evaluate novel attacks that take advantage of light emitted by modern smart bulbs in order to infer users’ private data and preferences. The first two attacks are designed to infer users’ audio and video playback by a systematic observation and analysis of the multimediavisualization functionality of smart light bulbs. The third attack utilizes the infrared capabilities of such smart light bulbs to create a covert-channel, which can be used as a gateway to exfiltrate user’s private data out of their secured home or office network. A comprehensive evaluation of these attacks in various real-life settings confirms their feasibility and affirms the need for new privacy protection mechanisms.
Performance considerations of network functions virtualization using containers

The network performance of virtual machines plays a critical role in Network Functions Virtualization (NFV), and several technologies have been developed to address hardware-level virtualization shortcomings. Recent advances in operating system level virtualization and deployment platforms such as Docker have made containers an ideal candidate for high performance application encapsulation and deployment. However, Docker and other solutions typically use lower-performing networking mechanisms. In this paper, we explore the feasibility of using technologies designed to accelerate virtual machine networking with containers, in addition to quantifying the network performance of container-based VNFs compared to the state-of-the-art virtual machine solutions. Our results show that containerized applications can provide lower latency and delay variation, and can take advantage of high performance networking technologies previously only used for hardware virtualization.
Compassion in palliative care: a review.

Compassion has been recognized as a key aspect of high-quality healthcare, particularly in palliative care. This article provides a general review of the current understanding of compassion in palliative care and summarizes emergent compassionate initiatives in palliative care at three interdependent levels: compassion for patients, compassion in healthcare professionals, and compassionate communities at the end of life.



Compassion is a constructive response to suffering that enhances treatment outcomes, fosters the dignity of the recipient, and provides self-care for the giver. Patients and healthcare professionals value compassion and perceive a general lack of compassion in healthcare systems. Compassion for patients and for professionals' self-care can be trained and implemented top-down (institutional policies) and bottom-up (compassion training). 'Compassionate communities' is an important emerging movement that complements regular healthcare and social services with a community-level approach to offer compassionate care for people at the end of life.



Compassion can be enhanced through diverse methodologies at the organizational, professional, and community levels. This enhancement of compassion has the potential to improve quality of palliative care treatments, enhance healthcare providers' satisfaction, and reduce healthcare costs.
Modelling of Boil-Off Gas in LNG Tanks : A Case Study

This paper focuses on the effect of pressure and heat leakages on Boil-off Gas (BOG) in Liquefied Natural Gas (LNG) tanks. The Lee-Kesler-Plocker (LKP) and the Starling modified Benedict-Webb-Rubin (BWRS) empirical models were used to simulate the compressibility factor, enthalpy and hence heat leakage at various pressures to determine the factors that affect the BOG in typical LNG tanks of different capacities. Using a case study data the heat leakage of 140,000kl, 160,00kl, 180,000kl and 200,000kl LNG tanks were analyzed using the LKP and BWRS models. The heat leakage of LNG tanks depends on the structure of tanks, and the small tanks lose heat to the environment due to their large surface area to volume ratio. As the operation pressure was dropped to 200mbar, all four of the LNG tanks’ BOG levels reached 0.05vol%/day. In order to satisfy the BOG design requirement, the operating pressure of the four large LNG tanks in the case study was maintained above 200mbar. Thus, the operating pressure impacts BOG on LNG tanks, but this effect is limited under the extreme high operation pressure. An attempt was made to determine the relationship between the compositions of LNG and BOG; one been combustible and the other non-combustible gases. The main component of combustible gas was methane, and nitrogen was of non-combustible gases. The relationship between BOG and methane compositions was that, as the methane fraction increases in the LNG, the BOG volume also increases. In general, results showed a direct correlation between BOG and operating pressure. The study also found that larger LNG tanks have less BOG; however as the operation pressure is increased the differences in the quantity of BOG among the four tanks decreased.
A Content Management Perspective on Fact-Checking

Fact checking has captured the attention of the media and the public alike; it has also recently received strong attention from the computer science community, in particular from data and knowledge management, natural language processing and information retrieval; we denote these together under the term “content management”. In this paper, we identify the fact checking tasks which can be performed with the help of content management technologies, and survey the recent research works in this area, before laying out some perspectives for the future. We hope our work will provide interested researchers, journalists and fact checkers with an entry point in the existing literature as well as help develop a roadmap for future research and development work.
Computational Creativity: Automated Pun Generation

Humor is an integral part of our day-to-day communication making it interesting and plausible. The growing demand in robotics and the constant urge for machines to pass the Turing test to eliminate the thin line difference between human and machine behavior make it essential for machines to be able to use humor in communication. Moreover, Learning is a continuous process and very important at every stage of life. However sometimes merely reading from a book induces lassitude and lack of concentration may hamper strong foundation building. Children suffering from Autism Spectrum Disorder (ASD) suffer from slow learning and grasping issues. English being a funny language, a particular word has multiple meanings, making it difficult for children with ASD to cognize it. Solving riddles induces fast learning and sharpness in children including those affected by ASD. The existing systems however, are too far from being used in any practical application. This paper proposes a system that uses core ideas of JAPE to create puns for entertainment and vocabulary building purpose for children. General Terms Homophone: Two or more words having the same pronunciation but different meanings, origins, or spelling (e.g. new and knew) .  Two or more words having the same spelling or pronunciation but different meanings and origins (e.g. pole. and pole) . Rhyming words: Words that have the same ending sounds. E.g. are cat, hat, bat, mat, fat and rat .  A form of word play that suggests two or more meanings, by exploiting multiple meanings of words, or of similar-sounding words, for an intended humorous or rhetorical effect .  A system that uses punning words to generate riddles/jokes with an intention of making it humorous.