nils-herrmann/clean_scirepeval huggingface dataset:
A framework for mesencephalic dopamine systems based on predictive Hebbian learning.

We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.
Web Application Testing: A Review on Techniques, Tools and State of Art

Web applications are meant to be viewed by human user. While this implies that quality of web application has importance in our daily life. Web application quality is our prime concern. To ensure the quality of web application, web testing is having a dandy role in Software Testing as well as Web Community. Web Applications are erring because of features provided for rising of web application. In the last years, various web testing problems have been addressed by research work. Several tools, techniques and methods have been determined to test w eb application eff icaciously. This paper w ill present the contribution of researchers in the f ield of web application in previous years and state of art of web testing and challenges primarily because of distributed and heterogeneous nature of web
On the Role of Lexical and World Knowledge in RTE3

To score well in RTE3, and even more so to create good justifications for entailments, substantial lexical and world knowledge is needed. With this in mind, we present an analysis of a sample of the RTE3 positive entailment pairs, to identify where and what kinds of world knowledge are needed to fully identify and justify the entailment, and discuss several existing resources and their capacity for supplying that knowledge. We also briefly sketch the path we are following to build an RTE system (Our implementation is very preliminary, scoring 50.9% at the time of RTE). The contribution of this paper is thus a framework for discussing the knowledge requirements posed by RTE and some exploration of how these requirements can be met.
Evaluating Reinforcement Learning Algorithms in Observational Health Settings

Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez Paulson School of Engineering and Applied Sciences, Harvard University Institute for Medical Engineering and Science, MIT T.H. Chan School of Public Health, Harvard University Department of Statistics, Harvard University Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT Department of Surgery and Cancer, Faculty of Medicine, Imperial College London Department of Bioengineering, Imperial College London Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center MIT Critical Data
Fashionable shape switching: explorations in outfit-centric design

We present a design exercise illustrating how fashion practices and the fashion design process can be used to create new opportunities both in the mobile domain and in product design, as well as in wearable computing. We investigate the concept of outfit-centric design by extending the support for social and visual interaction with digital devices beyond the currently available shells and stickers, and drawing on the ways in which people vary their dress ensembles. We designed a set of mock-up samples in a local fashion style, as a first step in understanding possible applications of the emerging technology of organic interfaces. Initial user feedback shows how fashion-conscious participants creatively experimented with the set's variations of shape and color in outfits created from their personal wardrobes, which revealed the importance of the objects' size and location on the body. It also points out that a lack of integration with the fashion system's processes reduces the attractiveness of the samples.
Restoring an Image Taken through a Window Covered with Dirt or Rain

Photographs taken through a window are often compromised by dirt or rain present on the window surface. Common cases of this include pictures taken from inside a vehicle, or outdoor security cameras mounted inside a protective enclosure. At capture time, defocus can be used to remove the artifacts, but this relies on achieving a shallow depth-of-field and placement of the camera close to the window. Instead, we present a post-capture image processing solution that can remove localized rain and dirt artifacts from a single image. We collect a dataset of clean/corrupted image pairs which are then used to train a specialized form of convolutional neural network. This learns how to map corrupted image patches to clean ones, implicitly capturing the characteristic appearance of dirt and water droplets in natural images. Our models demonstrate effective removal of dirt and rain in outdoor test conditions.
Predicting India Volatility Index: An Application of Artificial Neural Network

Forecasting has always been an area of interest for the researchers in various realms of finance especially in the stock market e. g. stock index, return on a stock, etc. Stock market volatility is one such area. Since the inception of implied volatility index (VIX) by the Chicago Board of Options Exchange (CBOE) in 1993, VIX index has generated a lot of interest. This study examines the predicting ability of several technical indicators related to VIX index to forecast the next trading day&apos;s volatility. There is a wide set of methods available for forecasting in finance. In this study, Artificial neural network (ANN) modeling technique has been employed to forecast the upwards or downwards movement in next trading day&apos;s volatility using India VIX (a volatility index based on the NIFTY Index Option prices) based indicators. The results of the study reveal that ANN models can be real handy in forecasting the downwards movement in VIX. The knowledge about a more probable downwards movement in volatility might be significant value add for the investors and help them in making decisions related to trading.
Language as a Latent Variable: Discrete Generative Models for Sentence Compression

In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a variational auto-encoder for inference in this model and apply it to the task of compressing sentences. In this application the generative model first draws a latent summary sentence from a background language model, and then subsequently draws the observed sentence conditioned on this latent summary. In our empirical evaluation we show that generative formulations of both abstractive and extractive compression yield state-of-the-art results when trained on a large amount of supervised data. Further, we explore semi-supervised compression scenarios where we show that it is possible to achieve performance competitive with previously proposed supervised models while training on a fraction of the supervised data.
Contextual Video Recommendation by Multimodal Relevance and User Feedback

With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users according to their historical and current viewings or preferences, has become one of most pervasive online video services. This article presents a novel contextual video recommendation system, called VideoReach, based on multimodal content relevance and user feedback. We consider an online video usually consists of different modalities (i.e., visual and audio track, as well as associated texts such as query, keywords, and surrounding text). Therefore, the recommended videos should be relevant to current viewing in terms of multimodal relevance. We also consider that different parts of videos are with different degrees of interest to a user, as well as different features and modalities have different contributions to the overall relevance. As a result, the recommended videos should also be relevant to current users in terms of user feedback (i.e., user click-through). We then design a unified framework for VideoReach which can seamlessly integrate both multimodal relevance and user feedback by relevance feedback and attention fusion. VideoReach represents one of the first attempts toward contextual recommendation driven by video content and user click-through, without assuming a sufficient collection of user profiles available. We conducted experiments over a large-scale real-world video data and reported the effectiveness of VideoReach.
A Dual Attentive Neural Network Framework with Community Metadata for Answer Selection

Nowadays the community-based question answering (cQA) sites become popular Web service, which have accumulated millions of questions and their associated answers over time. Thus, the answer selection component plays an important role in a cQA system, which ranks the relevant answers to the given question. With the development of this area, problems of noise prevalence and data sparsity become more tough. In our paper, we consider the task of answer selection from two aspects including deep semantic matching and user community metadata representation. We propose a novel dual attentive neural network framework (DANN) to embed question topics and user network structures for answer selection. The representation of questions and answers are first learned by convolutional neural networks (CNNs). Then the DANN learns interactions of questions and answers, which is guided via user network structures and semantic matching of question topics with double attention. We evaluate the performance of our method on the well-known question answering site Stack exchange. The experiments show that our framework outperforms other state-of-the-art solutions to the problem.
Computer Models of Creativity

goal for AI workers. Indeed, the proposal that led to the famous 1956 Dartmouth Summer School often remembered as the time of AI’s birth mentioned “creativity,” “invention,” and “discovery” as key aims for the newly named discipline (McCarthy et al. 1955, 45, 49ff.). And, 50 years later, Herb Simon—in an email discussion between AAAI Fellows (quoted in Boden 2006, 1101)—cited a paper on creativity (Simon 1995) in answer to the challenge of whether AI is a science, as opposed to “mere” engineering. But if its status as an AI goal is obvious, its credibility as a potential AI achievement is not. Many people, including many otherwise hard-headed scientists, doubt—or even deny outright—the possibility of a computer’s ever being creative. Sometimes, such people are saying that, irrespective of its performance (which might even match superlative human examples), no computer could “really” be creative: the creativity lies entirely in the programmer. That’s a philosophical question that needn’t detain us here (but see the final section, below). More to the point, these people typically claim that a computer simply could not generate apparently creative performance. That’s a factual claim—which, in effect, dismisses AI research on creativity as a waste of time. However, it is mistaken: AI scientists working in this area aren’t doomed to disappointment. It doesn’t follow that they will ever, in practice, be able to engineer a new Shakespeare or a neo-Mozart (although the latter goal has been approached more nearly than most people imagine). But lesser examples of AI creativity already abound. And, crucially, they help us to understand how human creativity is possible.
An ontology for representing financial headline news

This paper presents the development of an ontology to represent financial headline news. This ontology is developed using the On-To-Knowledge methodology where the focus is on the design steps of the Knowledge Meta Process. This development is part of an ongoing project which aims to design a virtual stock market simulator based on multi-agent systems. The proposed ontology has 31 concepts and includes 201 attributes. The testing results conducted on reliable headline news show that 99% of these headline news can be properly represented by the attributes of the right category in the ontology. Unreliable headline news characterized by news having uncertainties, incompleteness, ill-definition, or imprecision cannot be represented by the proposed ontology. Approaches for representing these unreliable headline
Template-Free Construction of Poems with Thematic Cohesion and Enjambment

Existing poetry generation systems usually focus on particular features of poetry (such as rhythm, rhyme, metaphor) and specific techniques to achieve them. They often resort to template-base solutions, in which it is not always clear how many of the alleged features of the outputs were already present in the template employed. The present paper considers two specific features – thematic consistency, and enjambment – and presents an ngram based construction method that achieves these features without the use of templates. The construction procedure is not intended to produce poetry of high quality, only to achieve the features considered specifically in its design. A set of metrics is defined to capture these features in quantitative terms, and the metrics are applied to system outputs and to samples of both human and computer generated poetry. The results of these tests are discussed in terms of the danger of to ignoring certain features when designing construction procedures but valuing them during evaluation even if they arise from hard-wiring in the resources or serendipitous emergence, and the fundamental need for poets to develop a personal voice – fundamental for human poets and inconsistent with the application of Turing tests.
Dynamics of B cells in germinal centres

Humoral immunity depends on the germinal centre (GC) reaction during which somatically mutated high-affinity memory B cells and plasma cells are generated. Recent studies have uncovered crucial cues that are required for the formation and the maintenance of GCs and for the selection of high-affinity antibody mutants. In addition, it is now clear that these events are promoted by the dynamic movements of cells within and between GCs. These findings have resolved the complexities of the GC reaction in greater detail than ever before. This Review focuses on these recent advances and discusses their implications for the establishment of humoral immunity.
Peer-to-Peer Botnets: Overview and Case Study

Botnets have recently been identified as one of the most important threats to the security of the Internet. Traditionally, botnets organize themselves in an hierarchical manner with a central command and control location. This location can be statically defined in the bot, or it can be dynamically defined based on a directory server. Presently, the centralized characteristic of botnets is us eful to security professionals because it offers a central point of failure for the botnet. In the near future, we believe attackers will move to more resilient architectures. In particular, one class of botnet structure that has entered initial stages of development is peer-to-peer based architectures. In this paper, we present an overview of peer-to-peer botnets. We also present a case study of a Kademlia-based Trojan.Peacomm bot.
End of Moore ’ s law : thermal ( noise ) death of integration in micro and nano electronics

The exponential growth of memory size and clock frequency in computers has a great impact on everyday life. The growth is empirically described by Moore’s law of miniaturization. Physical limitations of this growth would have a serious impact on technology and economy. A thermodynamical effect, the increasing thermal noise voltage (Johnson–Nyquist noise) on decreasing characteristic capacitances, together with the constrain of using lower supply voltages to keep power dissipation manageable on the contrary of increasing clock frequency, has the potential to break abruptly Moore’s law within 6–8 years, or earlier.
A survey of appearance models in visual object tracking

Visual object tracking is a significant computer vision task which can be applied to many domains, such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models.
 To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic.
 The contributions of this survey are fourfold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source codes and video datasets) are examined in this survey.
ESD protection circuit design for ultra-sensitive IO applications in advanced sub-90nm CMOS technologies

This paper presents a protection strategy for ultra-sensitive I/O containing thin gate oxides, while combining two complementary ESD design approaches: (1) low-voltage diode-chain triggered SCR clamps that allow for efficient voltage clamping; (2) active-source-pump circuits applied for effective expansion of narrow ESD design windows for ultra-thin GOX protection. The focus of the paper is on the ASP schemes while some RF aspects are covered as well.
Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind

Thunderstorms in theUnited States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-line (nontornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind ($50 kt or 25.7m s) for each storm cell in the continental United States, at distances up to 10 km outside the storm cell and lead times up to 90min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the nearstorm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the receiver operating characteristic (ROC) curve ranges from 0.88 to 0.95, the critical success index (CSI) ranges from 0.27 to 0.91, and the Brier skill score (BSS) ranges from 0.19 to 0.65 (.0 is better than climatology). For all three scores, the best value occurs for the smallest distance (inside storm cell) and/or lead time (0–15min), while the worst value occurs for the greatest distance (5–10 km outside storm cell) and/or lead time (60–90min). The system was deployed during the 2017 Hazardous Weather Testbed.
Big Data Analytics as a Service for Affective Humanoid Service Robots

This paper identifies and analyses the advanced capability requirements for humanoid service robots to serve in highly complicated and intelligence demanding applications, such as children education and home care, in future smart home environments. In particular, a Distributed Collaboration and Continuous Learning (DCCL) mechanism is identified as the key capability of a humanoid service robot to succeed in these applications. Based on the latest Big Data Analytics tools with distributed machine learning technologies integrated as services, a novel DCCL middleware platform is developed to facilitate the realisation of the DCCL mechanism. A user preference based children toy recommendation application is introduced as a use case study of the DCCL mechanism and platform.
An empirical study of the naive Bayes classifier

The naive Bayesclassifiergreatly simplify learning byassumingthatfeaturesareindependent given class. Although independenceis generallya poor assumption, in practicenaiveBayesoftencompetes well with moresophisticatedclassifiers. Our broadgoal is to understandthedatacharacter isticswhichaffect theperformanceof naiveBayes. OurapproachusesMonteCarlosimulationsthatallow a systematicstudy of classificationaccuracy for several classesof randomly generatedproblems. We analyzethe impact of the distribution entropy on the classificationerror, showing that low-entropy featuredistributions yield good performanceof naive Bayes. We also demonstrate that naive Bayes works well for certain nearlyfunctional featuredependencies, thus reachingits bestperformancein twooppositecases:completely independentfeatures(as expected)and functionally dependent features(which is surprising).Anothersurprisingresultis that theaccuracy of naive Bayes is not directly correlatedwith the degree of feature dependenciesmeasuredas the classconditional mutual information betweenthe features.Instead,a betterpredictorof naiveBayesaccuracy is theamountof informationabouttheclass that is lost becauseof the independenceassumption.
Interactive Data Visualization - Foundations, Techniques, and Applications

In this course, you will learn the fundamental algorithmic and design principles of visualizing and exploring complex data. The course will cover multiple aspects of data presentation including human perception and design theory; algorithms for exploring patterns in data such as topic modeling, clustering, and dimensionality reduction; as well as a wide range of statistical graphics and information visualization techniques. We will explore numerical data, relational data, temporal data, spatial data, graphs and text. Hands-on projects will be based on Python or JavaScript with D3. 3 credits.