nils-herrmann/clean_scirepeval huggingface dataset:
Unconscious Emotion

Conscious feelings have traditionally been viewed as a central and necessary ingredient of emotion. Here we argue that emotion also can be genuinely unconscious. We describe evidence that positive and negative reactions can be elicited subliminally and remain inaccessible to introspection. Despite the absence of subjective feelings in such cases, subliminally induced affective reactions still influence people’s preference judgments and even the amount of beverage they consume. This evidence is consistent with evolutionary considerations suggesting that systems underlying basic affective reactions originated prior to systems for conscious awareness. The idea of unconscious emotion is also supported by evidence from affective neuroscience indicating that subcortical brain systems underlie basic ‘‘liking’’ reactions. More research is needed to clarify the relations and differences between conscious and unconscious emotion, and their underlying mechanisms. However, even under the current state of knowledge, it appears that processes underlying conscious feelings can become decoupled from processes underlying emotional reactions, resulting in genuinely unconscious emotion. affect; automaticity; consciousness; emotion; neuroscience To say that people are conscious of their own emotions sounds like a truism. After all, emotions are feelings, so how could one have feelings that are not felt? Of course, people sometimes may be mistaken about the cause of their emotion or may not know why they feel a particular emotion, as when they feel anxious for what seems no particular reason. On occasion, people may even incorrectly construe their own emotional state, as when they angrily deny that they are angry. But many psychologists presume that the emotion itself is intrinsically conscious, and that with proper motivation and attention, it can be brought into the full light of awareness. So, at least, goes the traditional view. Our view goes a bit further. We suggest that under some conditions an emotional process may remain entirely unconscious, even when the person is attentive and motivated to describe his or her feelings correctly (Berridge & Winkielman, 2003; Winkielman, Berridge, & Wilbarger, in press). Such an emotional process may nevertheless drive the person’s behavior and physiological reactions, even while remaining inaccessible to conscious awareness. In short, we propose the existence of genuinely unconscious emotions. THE TRADITIONAL VIEW: EMOTION AS A CONSCIOUS
Fraud Detection in Credit Card System UsingWeb Mining

Now a day the usage of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Various techniques like classification, clustering and apriori of web mining will be integrated to represent the sequence of operations in credit card transaction processing and show how it can be used for the detection of frauds. Initially, web mining techniques trained with the normal behaviour of a cardholder. If an incoming credit card transaction is not accepted by the web mining model with sufficiently high probability, it is considered to be fraudulent. At the same time, the system will try to ensure that genuine transactions will not be rejected. Using data from a credit card issuer, a web mining model based fraud detection system will be trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity. Web mining techniques can be trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, and mail-order fraud. The proposed system will be able to detect frauds by considering a cardholder‟s spending habit without its significance. Usually, the details of items purchased in individual transactions are not known to any Fraud Detection System. The proposed system will be an ideal choice for addressing this problem of current fraud detection system. Another important advantage of proposed system will be a drastic reduction in the number of False Positives transactions. FDS module of proposed system will receive the card details and the value of purchase to verify, whether the transaction is genuine or not. If the Fraud Detection System module will confirm the transaction to be of fraud, it will raise an alarm, and the transaction will be declined.
Stochastic Variational Inference for Bayesian Time Series Models

Bayesian models provide powerful tools for analyzing complex time series data, but performing inference with large datasets is a challenge. Stochastic variational inference (SVI) provides a new framework for approximating model posteriors with only a small number of passes through the data, enabling such models to be fit at scale. However, its application to time series models has not been studied. In this paper we develop SVI algorithms for several common Bayesian time series models, namely the hidden Markov model (HMM), hidden semi-Markov model (HSMM), and the nonparametric HDP-HMM and HDP-HSMM. In addition, because HSMM inference can be expensive even in the minibatch setting of SVI, we develop fast approximate updates for HSMMs with durations distributions that are negative binomials or mixtures of negative binomials.
The Anatomy of Large Facebook Cascades

When users post photos on Facebook, they have the option of allowing their friends, followers, or anyone at all to subsequently reshare the photo. A portion of the billions of photos posted to Facebook generates cascades of reshares, enabling many additional users to see, like, comment, and reshare the photos. In this paper we present characteristics of such cascades in aggregate, finding that a small fraction of photos account for a significant proportion of reshare activity and generate cascades of non-trivial size and depth. We also show that the true influence chains in such cascades can be much deeper than what is visible through direct attribution. To illuminate how large cascades can form, we study the diffusion trees of two widely distributed photos: one posted on President Barack Obama’s page following his reelection victory, and another posted by an individual Facebook user hoping to garner enough likes for a cause. We show that the two cascades, despite achieving comparable total sizes, are markedly different in their time evolution, reshare depth distribution, predictability of subcascade sizes, and the demographics of users who propagate them. The findings suggest not only that cascades can achieve considerable size but that they can do so in distinct ways.
Effects of a weighted vest on attention to task and self-stimulatory behaviors in preschoolers with pervasive developmental disorders.

This study examined the effectiveness of using a weighted vest for increasing attention to a fine motor task and decreasing self-stimulatory behaviors in preschool children with pervasive developmental disorders (PDD).



Using an ABA single-subject design, the duration of attention to task and self-stimulatory behaviors and the number of distractions were measured in five preschool children with PDD over a period of 6 weeks.



During the intervention phase, all participants displayed a decrease in the number of distractions and an increase in the duration of focused attention while wearing the weighted vest. All but 1 participant demonstrated a decrease in the duration of self-stimulatory behaviors while wearing a weighted vest; however, the type of self-stimulatory behaviors changed and became less self-abusive for this child while she wore the vest. During the intervention withdrawal phase, 3 participants experienced an increase in the duration of self-stimulatory behaviors, and all participants experienced an increase in the number of distractions and a decrease in the duration of focused attention. The increase or decrease, however, never returned to baseline levels for these behaviors.



The findings suggest that for these 5 children with PDD, the use of a weighted vest resulted in an increase in attention to task and decrease in self-stimulatory behaviors. The most consistent improvement observed was the decreased number of distractions. Additional research is necessary to build consensus about the effectiveness of wearing a weighted vest to increase attention to task and decrease self-stimulatory behaviors for children with PDD.
Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering

Community Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this, the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question. Current AQP systems either learn models using a) various hand-crafted features (HCF) or b) use deep learning (DL) techniques which automatically learn the required feature representations. In this paper, we propose a novel approach for AQP known as -“Deep Feature Fusion Network (DFFN)”which leverages the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, DFFN independently a) learns deep features using a Convolutional Neural Network (CNN) and b) computes hand-crafted features using various external resources and then combines them using a deep neural network trained to predict the final answer quality. DFFN achieves stateof-the-art performance on the standard SemEval-2015 and SemEval-2016 benchmark datasets and outperforms baseline approaches which individually employ either HCF or DL based techniques alone.
Agreement-Based Learning

Assume submodels are in exponential family: pm(x, z; θm) = exp { θ m ( φm(x)φ Z(z) ) −Am(θm) } for x ∈ X , z ∈ Zm and 0 otherwise Reformulation of Product EM: Aggregate parameters: b = ∑ m bm, bm = φ X m(x) θm E-step: compute expected sufficient statistics μ = E(b,∩mZm) def = Eq(z;b)φ(z) with support ∩mZm M-step: set θm to match moments φ X m(x)μ Exponential family formulation Two sources of intractability in the E-step: • Domain Z = ∩mZm is unwieldy (e.g., matchings) • Parameters b result in high tree-width graph New objective function: • A function of sufficient statistics μm and parameters θm for each submodel m = 1, . . . ,M  See paper for some preliminary bounds Algorithm: Aggregate parameters: b = ∑ m bm E-step: compute statistics μm = E(b′,Z ′) Aggregate statistics: μ̄ = 1 M ∑ m μm M-step: set θm to match moments φ X m(x)μ̄
Trends in Interactive Knowledge Discovery for Personalized Medicine: Cognitive Science meets Machine Learning

A grand goal of future medicine is in modelling the complexity of patients to tailor medical decisions, health practices and therapies to the individual patient. This trend towards personalized medicine produces unprecedented amounts of data, and even though the fact that human experts are excellent at pattern recognition in dimensions of ≤ 3, the problem is that most biomedical data is in dimensions much higher than 3, making manual analysis difficult and often impossible. Experts in daily medical routine are decreasingly capable of dealing with the complexity of such data. Moreover, they are not interested the data, they need knowledge and insight in order to support their work. Consequently, a big trend in computer science is to provide efficient, useable and useful computational methods, algorithms and tools to discover knowledge and to interactively gain insight into high-dimensional data. A synergistic combination of methodologies of two areas may be of great help here: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning. A trend in both disciplines is the acquisition and adaptation of representations that support efficient learning. Mapping higher dimensional data into lower dimensions is a major task in HCI, and a concerted effort of computational methods including recent advances from graphtheory and algebraic topology may contribute to finding solutions. Moreover, much biomedical data is sparse, noisy and timedependent, hence entropy is also amongst promising topics. This paper provides a rough overview of the HCI-KDD approach and focuses on three future trends: graph-based mining, topological data mining and entropy-based data mining.
Cuckoo Hashing with Pages

Although cuckoo hashing has significant applications in both theoretical and practical settings, a relevant downside is that it requires lookups to multiple locations. In many settings, where lookups are expensive, cuckoo hashing becomes a less compelling alternative. One such standard setting is when memory is arranged in large pages, and a major cost is the number of page accesses. We propose the study of cuckoo hashing with pages, advocating approaches where each key has several possible locations, or cells, on a single page, and additional choices on a second backup page. We show experimentally that with k cell choices on one page and a single backup cell choice, one can achieve nearly the same loads as when each key has k+1 random cells to choose from, with most lookups requiring just one page access, even when keys are placed online using a simple algorithm. While our results are currently experimental, they suggest several interesting new open theoretical questions for cuckoo hashing with pages.
A review on speaker diarization systems and approaches

Speaker indexing or diarization is an important task in audio processing and retrieval. Speaker diarization is the process of labeling a speech signal with labels corresponding to the identity of speakers. This paper includes a comprehensive review on the evolution of the technology and different approaches in speaker indexing and tries to offer a fully detailed discussion on these approaches and their contributions. This paper reviews the most common features for speaker diarization in addition to the most important approaches for speech activity detection (SAD) in diarization frameworks. Two main tasks of speaker indexing are speaker segmentation and speaker clustering. This paper includes a separate review on the approaches proposed for these subtasks. However, speaker diarization systems which combine the two tasks in a unified framework are also introduced in this paper. Another discussion concerns the approaches for online speaker indexing which has fundamental differences with traditional offline approaches. Other parts of this paper include an introduction on the most common performance measures and evaluation datasets. To conclude this paper, a complete framework for speaker indexing is proposed, which is aimed to be domain independent and parameter free and applicable for both online and offline applications.
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods.
Practical and Secure Solutions for Integer Comparison

Yao’s classical millionaires’ problem is about securely determining whether x > y, given two input values x, y, which are held as private inputs by two parties, respectively. The output x > y becomes known to both parties. In this paper, we consider a variant of Yao’s problem in which the inputs x, y as well as the output bit x > y are encrypted. Referring to the framework of secure n-party computation based on threshold homomorphic cryptosystems as put forth by Cramer, Damg̊ard, and Nielsen at Eurocrypt 2001, we develop solutions for integer comparison, which take as input two lists of encrypted bits representing x and y, respectively, and produce an encrypted bit indicating whether x > y as output. Secure integer comparison is an important building block for applications such as secure auctions. In this paper, our focus is on the two-party case, although most of our results extend to the multi-party case. We propose new logarithmic-round and constant-round protocols for this setting, which achieve simultaneously very low communication and computational complexities. We analyze the protocols in detail and show that our solutions compare favorably to other known solutions.
From music audio to chord tablature: Teaching deep convolutional networks toplay guitar

Automatic chord recognition is conventionally tackled as a general music audition task, where the desired output is a time-aligned sequence of discrete chord symbols, e.g. CMaj7, Esus2, etc. In practice, however, this presents two related challenges: one, the act of decoding a given chord sequence requires that the musician knows both the notes in the chord and how to play them on some instrument; and two, chord labeling systems do not degrade gracefully for users without significant musical training. Alternatively, we address both challenges by modeling the physical constraints of a guitar to produce human-readable representations of music audio, i.e guitar tablature via a deep convolutional network. Through training and evaluation as a standard chord recognition system, the model is able to yield representations that require minimal prior knowledge to interpret, while maintaining respectable performance compared to the state of the art.
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.
Automatic query generation for patent search

Patent search is the task of finding relevant existing patents, which is an important part of the patent's examiner's process of validating a patent application. In this paper, we studied how to transform a query patent (the application) into search queries. Three types of search features are explored for automatic query generation for patent search. Furthermore, different types of features are combined with a learning to rank method. Experiments based on a USPTO patent collection demonstrate that the single best search feature is the combination of words and noun-phrases from the summary field and the retrieval performance can be significantly improved by combining three types of search features.
Design Optimization of Mixed-Criticality Real-Time Embedded Systems

In this article, we are interested in implementing mixed-criticality real-time embedded applications on a given heterogeneous distributed architecture. Applications have different criticality levels, captured by their Safety-Integrity Level (SIL), and are scheduled using static-cyclic scheduling. According to certification standards, mixed-criticality tasks can be integrated onto the same architecture only if there is enough spatial and temporal separation among them. We consider that the separation is provided by partitioning, such that applications run in separate partitions, and each partition is allocated several time slots on a processor. Tasks of different SILs can share a partition only if they are all elevated to the highest SIL among them. Such elevation leads to increased development costs, which increase dramatically with each SIL. Tasks of higher SILs can be decomposed into redundant structures of lower SIL tasks. We are interested to determine (i) the mapping of tasks to processors, (ii) the assignment of tasks to partitions, (iii) the decomposition of tasks into redundant lower SIL tasks, (iv) the sequence and size of the partition time slots on each processor, and (v) the schedule tables, such that all the applications are schedulable and the development costs are minimized. We have proposed a Tabu Search-based approach to solve this optimization problem. The proposed algorithm has been evaluated using several synthetic and real-life benchmarks.
Plug-in martingales for testing exchangeability on-line

A standard assumption in machine learning is the exchangeability of data, which is equivalent to assuming that the examples are generated from the same probability distribution independently. This paper is devoted to testing the assumption of exchangeability on-line: the examples arrive one by one, and after receiving each example we would like to have a valid measure of the degree to which the assumption of exchangeability has been falsified. Such measures are provided by exchangeability martingales. We extend known techniques for constructing exchangeability martingales and show that our new method is competitive with the martingales introduced before. Finally we investigate the performance of our testing method on two benchmark datasets, USPS and Statlog Satellite data; for the former, the known techniques give satisfactory results, but for the latter our new more flexible method becomes necessary.