nils-herrmann/clean_scirepeval huggingface dataset:
Consciousness as integrated information: a provisional manifesto.

The integrated information theory (IIT) starts from phenomenology and makes use of thought experiments to claim that consciousness is integrated information. Specifically: (i) the quantity of consciousness corresponds to the amount of integrated information generated by a complex of elements; (ii) the quality of experience is specified by the set of informational relationships generated within that complex. Integrated information (Phi) is defined as the amount of information generated by a complex of elements, above and beyond the information generated by its parts. Qualia space (Q) is a space where each axis represents a possible state of the complex, each point is a probability distribution of its states, and arrows between points represent the informational relationships among its elements generated by causal mechanisms (connections). Together, the set of informational relationships within a complex constitute a shape in Q that completely and univocally specifies a particular experience. Several observations concerning the neural substrate of consciousness fall naturally into place within the IIT framework. Among them are the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the distinct role of different cortical architectures in affecting the quality of experience. Equating consciousness with integrated information carries several implications for our view of nature.
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations

This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al., 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, (Srinivas et al., 2010) proved that the regret vanishes at the approximate rate of O ( 1 √ t ) , where t is the number of observations. To complement their result, we attack the deterministic case and attain a much faster exponential convergence rate. Under some regularity assumptions, we show that the regret decreases asymptotically according to O ( e − τt (ln t)d/4 ) with high probability. Here, d is the dimension of the search space and τ is a constant that depends on the behaviour of the objective function near its global maximum.
A 65-fJ/Conversion-Step 0.9-V 200-kS/s Rail-to-Rail 8-bit Successive Approximation ADC

An 8-bit successive approximation (SA) analog-to- digital converter (ADC) in 0.18 mum CMOS dedicated for energy-limited applications is presented. The SA ADC achieves a wide effective resolution bandwidth (ERBW) by applying only one bootstrapped switch, thereby preserving the desired low power characteristic. Measurement results show that at a supply voltage of 0.9 V and an output rate of 200 kS/s, the SA ADC performs a peak signal-to-noise-and-distortion ratio of 47.4 dB and an ERBW up to its Nyquist bandwidth (100 kHz). It consumes 2.47 muW in the test, corresponding to a figure of merit of 65 f J/conversion-step.
The Sensitivity of Topic Coherence Evaluation to Topic Cardinality

When evaluating the quality of topics generated by a topic model, the convention is to score topic coherence — either manually or automatically — using the top-N topic words. This hyper-parameter N , or the cardinality of the topic, is often overlooked and selected arbitrarily. In this paper, we investigate the impact of this cardinality hyper-parameter on topic coherence evaluation. For two automatic topic coherence methodologies, we observe that the correlation with human ratings decreases systematically as the cardinality increases. More interestingly, we find that performance can be improved if the system scores and human ratings are aggregated over several topic cardinalities before computing the correlation. In contrast to the standard practice of using a fixed value of N (e.g. N = 5 or N = 10), our results suggest that calculating topic coherence over several different cardinalities and averaging results in a substantially more stable and robust evaluation. We release the code and the datasets used in this research, for
DATA REDUCTION TECHNIQUES TO ANALYZE NSL-KDD DATASET

In information field we have huge amount of data available that need to be turned into useful information. So we used Data reduction and its techniques. A process in which amount of data is minimized and that minimized data are stored in a data storage environment is known as data reduction. By this process of reducing data various advantages have been achieved in computer networks such as increasing storage efficiency and reduced computational costs. In this paper we have applied data reduction algorithms on NSL-KDD dataset. The output of each data reduction algorithm is given as an input to two classification algorithms i.e. J48 and Naïve Bayes. Our main is to find out which data reduction technique proves to be useful in enhancing the performance of the classification algorithm. Results are compared on the bases of accuracy, specificity and sensitivity.
Fast Adaptation in Generative Models with Generative Matching Networks

Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples. We develop a new generative model called Generative Matching Network which is inspired by the recently proposed matching networks for one-shot learning in discriminative tasks. By conditioning on the additional input dataset, our model can instantly learn new concepts that were not available in the training data but conform to a similar generative process. The proposed framework does not explicitly restrict diversity of the conditioning data and also does not require an extensive inference procedure for training or adaptation. Our experiments on the Omniglot dataset demonstrate that Generative Matching Networks significantly improve predictive performance on the fly as more additional data is available and outperform existing state of the art conditional generative models.
Critical factors of WAP services adoption: an empirical study

Mobile commerce is becoming increasingly important in business. This trend is particularly evident in the service industry. To cope with this demand, various platforms have been proposed to provide effective mobile commerce solutions. Among these solutions, wireless application protocol (WAP) is one of the most widespread technical standards for mobile commerce. Following continuous technical evolution, WAP has come to include various new features. However, WAP services continue to struggle for market share. Hence, understanding WAP service adoption is increasingly important for enterprises interested in developing mobile commerce. This study aims to (1) identify the critical factors of WAP service adoption; (2) explore the relative importance of each factor for users who adopt WAP and those who do not; (3) examine the causal relationships among variables on WAP service adoption behavior. This study conducts an empirical test of WAP service adoption in Taiwan, based on theory of planned behavior (TPB) and innovation diffusion theory (IDT). The results help clarify the critical factors influences on WAP service adoption in the Greater China economic region. The Greater China economic region is a rapidly growing market. Many western telecommunication enterprises are strongly interested in providing wireless services in Shanghai, Singapore, Hong Kong and Taipei. Since these cities share a similar culture and the same language, the analytical results and conclusions of this study may be a good reference for global telecommunication enterprises to establish the developing strategy for their eastern branches. From the analysis conducted in this study, the critical factors for influences on WAP service adoption include connection speed, service cost, user satisfaction, personal innovativeness, ease of use, peer influence, and facilitating condition. Therefore, this study proposes that strategies for marketing WAP services in the Greater China economic region can pay increased attention to these factors. Notably, this study also provides some suggestion for subsequent researchers and practitioners seeking to understand WAP service adoption behavior.
Pressure ulcer prevention and management.

This chapter reviews 218 published and unpublished research reports of pressure ulcer prevention and management by nurse researchers and researchers from other disciplines. The electronic databases MEDLINE (1966-July 2001), CINAHL (1982-June 2001), AMED (1985-July 2001), and EI Compedex Plus (1980-June 2001) were selected for the searches because of their focus on health and applied research. Moreover, evaluations of previous review articles and seminal studies that were published before 1966 are also included. Research conducted worldwide and published in English between 1930 and 2001 was included for review. Studies using descriptive, correlational, longitudinal, and randomized control trials were included. This review found that numerous gaps remain in our understanding of effective pressure ulcer prevention and management. Moreover, the majority of pressure ulcer care is derived from expert opinion rather than empirical evidence. Thus, additional research is needed to investigate pressure ulcer risk factors of ethnic minorities. Further studies are needed that examine the impact of specific preventive interventions (e.g., turning intervals based on risk stratification) and the cost-effectiveness of comprehensive prevention programs to prevent pressure ulcers. Finally, an evaluation is needed of various aspects of pressure ulcer management (e.g., use of support surfaces, use of adjunctive therapies) and healing of pressure ulcers.
Evaluation of HDR tone mapping methods using essential perceptual attributes

The problem of reproducing high dynamic range images on devices with restricted dynamic range has gained a lot of interest in the computer graphics community. There exist various approaches to this issue, which span several research areas including computer graphics, image processing, color vision, physiological aspects, etc. These approaches assume a thorough knowledge of both the objective and subjective attributes of an image. However, no comprehensive overview and analysis of such attributes has been published so far. In this contribution, we present an overview about the effects of basic image attributes in HDR tone mapping. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall image quality measure. We present results of subjective psychophysical experiments that we have performed to prove the proposed relationship scheme. Moreover, we also present an evaluation of existing tone mapping methods (operators) with regard to these attributes. Finally, the execution of with-reference and without a real reference perceptual experiments gave us the opportunity to relate the obtained subjective results. Our effort is not just useful to get into the tone mapping field or when implementing a tone mapping method, but it also sets the stage for well-founded quality comparisons between tone mapping methods. By providing good definitions of the different attributes, user-driven or fully automatic comparisons are made possible.
A dual-frequency wilkinson power divider

In this paper, a Wilkinson power divider operating at two arbitrary different frequencies is presented. The structure of this power divider and the formulas used to determine the design parameters have been given. Experimental results show that all the features of a conventional Wilkinson power divider, such as an equal power split, impedance matching at all ports, and a good isolation between the two output ports can be fulfilled at two arbitrary given frequencies simultaneously
Interpreting concept sketches

Concept sketches are popularly used by designers to convey pose and function of products. Understanding such sketches, however, requires special skills to form a mental 3D representation of the product geometry by linking parts across the different sketches and imagining the intermediate object configurations. Hence, the sketches can remain inaccessible to many, especially non-designers. We present a system to facilitate easy interpretation and exploration of concept sketches. Starting from crudely specified incomplete geometry, often inconsistent across the different views, we propose a globally-coupled analysis to extract part correspondence and inter-part junction information that best explain the different sketch views. The user can then interactively explore the abstracted object to gain better understanding of the product functions. Our key technical contribution is performing shape analysis without access to any coherent 3D geometric model by reasoning in the space of inter-part relations. We evaluate our system on various concept sketches obtained from popular product design books and websites.
Neurocinematics : The Neuroscience of Film

This article describes a new method for assessing the effect of a given film on viewers’ brain activity. Brain activity was measured using functional magnetic resonance imaging (fMRI) during free viewing of films, and inter-subject correlation analysis (ISC) was used to assess similarities in the spatiotemporal responses across viewers’ brains during movie watching. Our results demonstrate that some films can exert considerable control over brain activity and eye movements. However, this was not the case for all types of motion picture sequences, and the level of control over viewers’ brain activity differed as a function of movie content, editing, and directing style. We propose that ISC may be useful to film studies by providing a quantitative neuroscientific assessment of the impact of different styles of filmmaking on viewers’ brains, and a valuable method for the film industry to better assess its products. Finally, we suggest that this method brings together two separate and largely unrelated disciplines, cognitive neuroscience and film studies, and may open the way for a new interdisciplinary field of “neurocinematic” studies.
DKPro WSD: A Generalized UIMA-based Framework for Word Sense Disambiguation

Implementations of word sense disambiguation (WSD) algorithms tend to be tied to a particular test corpus format and sense inventory. This makes it difficult to test their performance on new data sets, or to compare them against past algorithms implemented for different data sets. In this paper we present DKPro WSD, a freely licensed, general-purpose framework for WSD which is both modular and extensible. DKPro WSD abstracts the WSD process in such a way that test corpora, sense inventories, and algorithms can be freely swapped. Its UIMA-based architecture makes it easy to add support for new resources and algorithms. Related tasks such as word sense induction and entity linking are also supported.
Learning to generalize to new compositions in image understanding

Recurrent neural networks have recently been used for learning to describe images using natural language. However, it has been observed that these models generalize poorly to scenes that were not observed during training, possibly depending too strongly on the statistics of the text in the training data. Here we propose to describe images using short structured representations, aiming to capture the crux of a description. These structured representations allow us to tease-out and evaluate separately two types of generalization: standard generalization to new images with similar scenes, and generalization to new combinations of known entities. We compare two learning approaches on the MS-COCO dataset: a state-of-the-art recurrent network based on an LSTM (Show, Attend and Tell), and a simple structured prediction model on top of a deep network. We find that the structured model generalizes to new compositions substantially better than the LSTM, ∼7 times the accuracy of predicting structured representations. By providing a concrete method to quantify generalization for unseen combinations, we argue that structured representations and compositional splits are a useful benchmark for image captioning, and advocate compositional models that capture linguistic and visual structure.
PI : a Parallel in-memory skip list based Index

Due to the coarse granularity of data accesses and the heavy use of latches, indices in the B-tree family are not efficient for in-memory databases, especially in the context of today’s multi-core architecture. In this paper, we present PI, a Parallel in-memory skip list based Index that lends itself naturally to the parallel and concurrent environment, particularly with non-uniform memory access. In PI, incoming queries are collected, and disjointly distributed among multiple threads for processing to avoid the use of latches. For each query, PI traverses the index in a Breadth-First-Search (BFS) manner to find the list node with the matching key, exploiting SIMD processing to speed up the search process. In order for query processing to be latch-free, PI employs a light-weight communication protocol that enables threads to re-distribute the query workload among themselves such that each list node that will be modified as a result of query processing will be accessed by exactly one thread. We conducted extensive experiments, and the results show that PI can be up to three times as fast as the Masstree, a state-of-the-art B-tree based index.
Information and communications technologies for elderly ubiquitous healthcare in a smart home

Over the past century, in most countries, there has been a continual increase in life expectancy primarily due to improvements in public health, nutrition, personal hygiene and medicine. However, these improvements are now coupled with aging population demographics and falling birth rates, which, when combined, are expected to significantly burden the socioeconomic well-being of many of these countries. In fact, never before in human history have we been confronted with such a large aging population, nor have we developed solid, cost-effective solutions for the well-being, healthcare and social needs of the elderly. One efficient and cost-effective solution to the problem of elderly/patient care is remote healthcare monitoring so they can continue to live at home rather than in nursing homes or hospitals that are very expensive and with limited spaces. These remote monitoring systems will allow medical personnel to keep track of important physiological signs with reduced human resources, at less cost and in real time. This paper introduces several low-cost, noninvasive, user-friendly sensing and actuating systems using information and communication technologies. Such systems can be used to create engineering solutions to some of the pressing healthcare problems in our society, especially as it pertains to the elderly. One example is the integration of sensors, wireless communications, low-power electronics and intelligent computing to determine health-related information using signals from walking patterns. Such a sensing system will be suitable for prolonged use in a home environment. It will be wearable, noninvasive and non-intrusive, similar to smart socks, smart wrist-bands or smart belts. Other examples such as a smart joint monitor and a smart sleeping environment will be discussed, and future perspectives and research challenges in smart home technologies will be described.
DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%−81.7, Sleep-EDF: 82.0%−76.9) compared with the state-of-the-art methods (MASS: 85.9%−80.5, Sleep-EDF: 78.9%−73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.
A Secure Image Steganography Based on RSA Algorithm and Hash-LSB Technique

Steganography is a method of hiding secret messages in a cover object while communication takes place between sender and receiver. Security of confidential information has always been a major issue from the past times to the present time. It has always been the interested topic for researchers to develop secure techniques to send data without revealing it to anyone other than the receiver. Therefore from time to time researchers have developed many techniques to fulfill secure transfer of data and steganography is one of them. In this paper we have proposed a new technique of image steganography i.e. Hash-LSB with RSA algorithm for providing more security to data as well as our data hiding method. The proposed technique uses a hash function to generate a pattern for hiding data bits into LSB of RGB pixel values of the cover image. This technique makes sure that the message has been encrypted before hiding it into a cover image. If in any case the cipher text got revealed from the cover image, the intermediate person other than receiver can't access the message as it is in encrypted form.
Text Simplification using Typed Dependencies: A Comparision of the Robustness of Different Generation Strategies

We present a framework for text simplification based on applying transformation rules to a typed dependency representation produced by the Stanford parser. We test two approaches to regeneration from typed dependencies: (a) gen-light, where the transformed dependency graphs are linearised using the word order and morphology of the original sentence, with any changes coded into the transformation rules, and (b)gen-heavy, where the Stanford dependencies are reduced to a DSyntS representation and sentences are generating formally using the RealPro surface realiser. The main contribution of this paper is to compare the robustness of these approaches in the presence of parsing errors, using both a single parse and an n-best parse setting in an overgenerate and rank approach. We find that the gen-light approach is robust to parser error, particularly in the n-best parse setting. On the other hand, parsing errors cause the realiser in the genheavy approach to order words and phrases in ways that are disliked by our evaluators.