nils-herrmann/clean_scirepeval huggingface dataset:
Learning to rank recommendations with the k-order statistic loss

Making recommendations by learning to rank is becoming an increasingly studied area. Approaches that use stochastic gradient descent scale well to large collaborative filtering datasets, and it has been shown how to approximately optimize the mean rank, or more recently the top of the ranked list. In this work we present a family of loss functions, the k-order statistic loss, that includes these previous approaches as special cases, and also derives new ones that we show to be useful. In particular, we present (i) a new variant that more accurately optimizes precision at k, and (ii) a novel procedure of optimizing the mean maximum rank, which we hypothesize is useful to more accurately cover all of the user's tastes. The general approach works by sampling N positive items, ordering them by the score assigned by the model, and then weighting the example as a function of this ordered set. Our approach is studied in two real-world systems, Google Music and YouTube video recommendations, where we obtain improvements for computable metrics, and in the YouTube case, increased user click through and watch duration when deployed live on www.youtube.com.
Midbrain Dopamine Neurons Encode a Quantitative Reward Prediction Error Signal

The midbrain dopamine neurons are hypothesized to provide a physiological correlate of the reward prediction error signal required by current models of reinforcement learning. We examined the activity of single dopamine neurons during a task in which subjects learned by trial and error when to make an eye movement for a juice reward. We found that these neurons encoded the difference between the current reward and a weighted average of previous rewards, a reward prediction error, but only for outcomes that were better than expected. Thus, the firing rate of midbrain dopamine neurons is quantitatively predicted by theoretical descriptions of the reward prediction error signal used in reinforcement learning models for circumstances in which this signal has a positive value. We also found that the dopamine system continued to compute the reward prediction error even when the behavioral policy of the animal was only weakly influenced by this computation.
A segmentation-free approach to Arabic and Urdu OCR

In this paper, we present a generic Optical Character Recognition system for Arabic script languages called Nabocr. Nabocr uses OCR approaches specific for Arabic script recognition. Performing recognition on Arabic script text is relatively more difficult than Latin text due to the nature of Arabic script, which is cursive and context sensitive. Moreover, Arabic script has different writing styles that vary in complexity. Nabocr is initially trained to recognize both Urdu Nastaleeq and Arabic Naskh fonts. However, it can be trained by users to be used for other Arabic script languages. We have evaluated our system’s performance for both Urdu and Arabic. In order to evaluate Urdu recognition, we have generated a dataset of Urdu text called UPTI (Urdu Printed Text Image Database), which measures different aspects of a recognition system. The performance of our system for Urdu clean text is 91%. For Arabic clean text, the performance is 86%. Moreover, we have compared the performance of our system against Tesseract’s newly released Arabic recognition, and the performance of both systems on clean images is almost the same.
Fuzzy sets and systems: theory and applications

This book effectively constitutes a detailed annotated bibliography in quasitextbook style of the some thousand contributions deemed by Messrs. Dubois and Prade to belong to the area of fuzzy set theory and its applications or interactions in a wide spectrum of scientific disciplines. The individual with an existing research commitment in this area will find the book competently written and an invaluable time-saver in developing an awareness of existing literature in English, French or German although the emphasis reflects, heavily at times, the authors' favorite topics. The mathematician wishing to precipitate out the mathematical and conceptual core of fuzzy set theory will find it frustrating reading, however. As I see it, the difficulty lies with how the subject is defined. Fuzzy set theory is not well delineated mathematically. It is determined by a set of papers which either have the word 'fuzzy' in the title or are authored by someone who has written such a paper. I am somewhat horrified that the authors include a bibhography specifically entitled 'nonfuzzy literature'. The dangerous insularity of the field has been noted in [1,17]. It seems justifiable, then, to direct this review in large part to fuzzy set theory generally.
Over 10W C-Ku band GaN MMIC non-uniform distributed power amplifier with broadband couplers

A 6–18 GHz monolithic microwave integrated circuit (MMIC) power amplifier (PA) was successfully developed using a quarter wavelength short stub and a monolithic broadband coupler for a non-uniform distributed power amplifier (NDPA) topology. This topology improved the output power at 18 GHz and attained a flat output power profile over 6–18 GHz. It also achieved filtering characteristics for both lower and higher cut-off frequencies. A fabricated MMIC PA with 0.25µm GaN HEMTs delivered an output power of more than 10 W with average power added efficiency (PAE) of 18% over 6 to 18 GHz. To the best of our knowledge, this is the best combination of output power and bandwidth for any solid-state MMIC amplifier operating up to the full Ku-band.
On surveillance for safety critical events: In-vehicle video networks for predictive driver assistance systems

We study techniques for monitoring and understanding real-world human activities, in particular of drivers, from distributed vision sensors. Real-time and early prediction of maneuvers is emphasized, specifically overtake and brake events. Study this particular domain is motivated by the fact that early knowledge of driver behavior, in concert with the dynamics of the vehicle and surrounding agents, can help to recognize dangerous situations. Furthermore, it can assist in developing effective warning and driver assistance systems. Multiple perspectives and modalities are captured and fused in order to achieve a comprehensive representation of the scene. Temporal activities are learned from a multi-camera head pose estimation module, hand and foot tracking, ego-vehicle parameters, lane and road geometry analysis, and surround vehicle trajectories. The system is evaluated on a challenging dataset of naturalistic driving in real-world settings.
An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering

In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art approches to recommendations consider the recommendation process from a “missing value prediction” perspective. This approach simplifies the model validation phase that is based on the minimization of standard error metrics such as RMSE. However, recent studies have pointed several limitations of this approach, showing that a lower RMSE does not necessarily imply improvements in terms of specific recommendations. We demonstrate that the underlying probabilistic framework offers several advantages over traditional methods, in terms of flexibility in the generation of the recommendation list and consequently in the accuracy of recommendation.
A character-based convolutional neural network for language-agnostic Twitter sentiment analysis

Most work on tweet sentiment analysis is mono-lingual and the models that are generated by machine learning strategies do not generalize across multiple languages. Cross-language sentiment analysis is usually performed through machine translation approaches that translate a given source language into the target language of choice. Machine translation is expensive and the results that are provided by theses strategies are limited by the quality of the translation that is performed. In this paper, we propose a language-agnostic translation-free method for Twitter sentiment analysis, which makes use of deep convolutional neural networks with character-level embeddings for pointing to the proper polarity of tweets that may be written in distinct (or multiple) languages. The proposed method is more accurate than several other deep neural architectures while requiring substantially less learnable parameters. The resulting model is capable of learning latent features from all languages that are employed during the training process in a straightforward fashion and it does not require any translation process to be performed whatsoever. We empirically evaluate the efficiency and effectiveness of the proposed approach in tweet corpora based on tweets from four different languages, showing that our approach comfortably outperforms the baselines. Moreover, we visualize the knowledge that is learned by our method to qualitatively validate its effectiveness for tweet sentiment classification.
Determinants of accepting wireless mobile data services in China

Data services via wireless networks and mobile devices have experienced rapid growth worldwide. We investigated the factors influencing adoption of wireless mobile data services (WMDS) in China and tested our model for explaining adoption intentions there. We argued that individuals form their intention to adopt WMDS under the influence of wireless mobile technology, the social environment, personal innovativeness of IT, trust awareness, and the facilitating conditions. We examined the simultaneous effects of these five influences on beliefs in the context of wireless Internet data services via mobile phones. Survey data were collected from 1432 participants in several metro cities across China. The findings suggest that WMDS adoption intention in China is determined by consumers’ perceived usefulness and perceived ease of use of WMDS. Theoretical and practical implications are included in our paper.  (2008) 52–64
Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles.

In the 1980s, exosomes were described as vesicles of endosomal origin secreted from reticulocytes. Interest increased around these extracellular vesicles, as they appeared to participate in several cellular processes. Exosomes bear proteins, lipids, and RNAs, mediating intercellular communication between different cell types in the body, and thus affecting normal and pathological conditions. Only recently, scientists acknowledged the difficulty of separating exosomes from other types of extracellular vesicles, which precludes a clear attribution of a particular function to the different types of secreted vesicles. To shed light into this complex but expanding field of science, this review focuses on the definition of exosomes and other secreted extracellular vesicles. Their biogenesis, their secretion, and their subsequent fate are discussed, as their functions rely on these important processes.
Towards real-time Speech Emotion Recognition using deep neural networks

Most existing Speech Emotion Recognition (SER) systems rely on turn-wise processing, which aims at recognizing emotions from complete utterances and an overly-complicated pipeline marred by many preprocessing steps and hand-engineered features. To overcome both drawbacks, we propose a real-time SER system based on end-to-end deep learning. Namely, a Deep Neural Network (DNN) that recognizes emotions from a one second frame of raw speech spectrograms is presented and investigated. This is achievable due to a deep hierarchical architecture, data augmentation, and sensible regularization. Promising results are reported on two databases which are the eNTERFACE database and the Surrey Audio-Visual Expressed Emotion (SAVEE) database.
Visual Question Answering as a Meta Learning Task

The predominant approach to Visual Question Answering (VQA) demands that the model represents within its weights all of the information required to answer any question about any image. Learning this information from any real training set seems unlikely, and representing it in a reasonable number of weights doubly so. We propose instead to approach VQA as a meta learning task, thus separating the question answering method from the information required. At test time, the method is provided with a support set of example questions/answers, over which it reasons to resolve the given question. The support set is not fixed and can be extended without retraining, thereby expanding the capabilities of the model. To exploit this dynamically provided information, we adapt a state-of-the-art VQA model with two techniques from the recent meta learning literature, namely prototypical networks and meta networks. Experiments demonstrate the capability of the system to learn to produce completely novel answers (i.e. never seen during training) from examples provided at test time. In comparison to the existing state of the art, the proposed method produces qualitatively distinct results with higher recall of rare answers, and a better sample efficiency that allows training with little initial data. More importantly, it represents an important step towards vision-and-language methods that can learn and reason on-the-fly.
Metadata Extraction from PDF Papers for Digital Library Ingest

In this paper we analyze our recent research on the use of document analysis techniques for metadata extraction from PDF papers. We describe a package that is designed to extract basic metadata from these documents. The package is used in combination with a digital library software suite to easily build personal digital libraries. The proposed software is based on a suitable combination of several techniques that include PDF parsing, low level document image processing, and layout analysis. In addition, we use the information gathered from a widely known citation database (DBLP) to assist the tool in the difficult task of author identification. The system is tested on some paper collections selected from recent conference proceedings.
Multimedia content processing through cross-modal association

Multimodal information processing has received considerable attention in recent years. The focus of existing research in this area has been predominantly on the use of fusion technology. In this paper, we suggest that cross-modal association can provide a new set of powerful solutions in this area. We investigate different cross-modal association methods using the linear correlation model. We also introduce a novel method for cross-modal association called Cross-modal Factor Analysis (CFA). Our earlier work on Latent Semantic Indexing (LSI) is extended for applications that use off-line supervised training. As a promising research direction and practical application of cross-modal association, cross-modal information retrieval where queries from one modality are used to search for content in another modality using low-level features is then discussed in detail. Different association methods are tested and compared using the proposed cross-modal retrieval system. All these methods achieve significant dimensionality reduction. Among them CFA gives the best retrieval performance. Finally, this paper addresses the use of cross-modal association to detect talking heads. The CFA method achieves 91.1% detection accuracy, while LSI and Canonical Correlation Analysis (CCA) achieve 66.1% and 73.9% accuracy, respectively. As shown by experiments, cross-modal association provides many useful benefits, such as robust noise resistance and effective feature selection. Compared to CCA and LSI, the proposed CFA shows several advantages in analysis performance and feature usage. Its capability in feature selection and noise resistance also makes CFA a promising tool for many multimedia analysis applications.
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning

We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can be applied to both reinforcement and supervised learning. The key idea is to learn a meta-critic: an action-value function neural network that learns to criticise any actor trying to solve any specified task. For supervised learning, this corresponds to the novel idea of a trainable task-parametrised loss generator. This meta-critic approach provides a route to knowledge transfer that can flexibly deal with few-shot and semi-supervised conditions for both reinforcement and supervised learning. Promising results are shown on both reinforcement and supervised learning problems.
Word2Bits - Quantized Word Vectors

Word vectors require significant amounts of memory and storage, posing issues to resource limited devices like mobile phones and GPUs. We show that high quality quantized word vectors using 1-2 bits per parameter can be learned by introducing a quantization function into Word2Vec. We furthermore show that training with the quantization function acts as a regularizer. We train word vectors on English Wikipedia (2017) and evaluate them on standard word similarity and analogy tasks and on question answering (SQuAD). Our quantized word vectors not only take 8-16x less space than full precision (32 bit) word vectors but also outperform them on word similarity tasks and question answering.
Monitoring moisture of soil using low cost homemade Soil moisture sensor and Arduino UNO

Persistent increase in population of world is demanding more and more supply of food. Hence there is a significant need of advancement in cultivation to meet up the future food needs. It is important to know moisture levels in soil to maximize the output. But most of farmers cannot afford high cost devices to measure soil moisture. Our research work in this paper focuses on home-made low cost moisture sensor with accuracy. In this paper we present a method to manufacture soil moisture sensor to estimate moisture content in soil hence by providing information about required water supply for good cultivation. This sensor is tested with several samples of soil and able to meet considerable accuracy. Measuring soil moisture is an effective way to determine condition of soil and get information about the quantity of water that need to be supplied for cultivation. Two separate methods are illustrated in this paper to determine soil moisture over an area and along the depth.
Accurate Angular Velocity Estimation With an Event Camera

We present an algorithm to estimate the rotational motion of an event camera. In contrast to traditional cameras, which produce images at a fixed rate, event cameras have independent pixels that respond asynchronously to brightness changes, with microsecond resolution. Our method leverages the type of information conveyed by these novel sensors (i.e., edges) to directly estimate the angular velocity of the camera, without requiring optical flow or image intensity estimation. The core of the method is a contrast maximization design. The method performs favorably against ground truth data and gyroscopic measurements from an Inertial Measurement Unit, even in the presence of very high-speed motions (close to 1000 deg/s).
An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs.
Evaluation of Stemming Techniques for Text Classification

Text mining is perceived as a process of extraction of meaningful information from textual document repositories. The stemming technique is one of the preprocessing techniques in text mining, which truncate inflectional and derivational endings, it reduces word to fetch common stem. It involves text processing task that includes text mining, information extraction and natural language processing. Usually, most of the document contains morphological variants, hence stemming involves removal of unwanted affixes from the document. In this paper, various stemming techniques are analysed and classified hence study shows the benefits and limitations of recent stemming techniques.  Text Mining, Stemming techniques, Decision based method, Statistical method.
Adaptive Algorithm Using Hot-Electron Injection for Programming Analog Computational Memory Elements Within 0.2% of Accuracy Over 3.5 Decades

This paper describes a new predictive algorithm that can be used for programming large arrays of analog computational memory elements within 0.2% of accuracy for 3.5 decades of currents. The average number of pulses required are 7-8 (20 mus each). This algorithm uses hot-electron injection for accurate programming and Fowler-Nordheim tunneling for global erase. This algorithm has been tested for programming 1024times16 and 96times16 floating-gate arrays in 0.25 mum and 0.5 mum n-well CMOS processes, respectively
A Text Summarization Approach under the Influence of Textual Entailment

This paper presents how text summarization can be influenced by textual entailment. We show that if we use textual entailment recognition together with text summarization approach, we achieve good results for final summaries, obtaining an improvement of 6.78% with respect to the summarization approach only. We also compare the performance of this combined approach to two baselines (the one provided in DUC 2002 and ours based on word-frequency technique) and we discuss the preliminary results obtained in order to infer conclusions that can be useful for future research.
A study on prediction of breast cancer recurrence using data mining techniques

Breast cancer is the most common cancer in women and thus the early stage detection in breast cancer can provide potential advantage in the treatment of this disease. Early treatment not only helps to cure cancer but also helps in its prevention of its recurrence. Data mining algorithms can provide great assistance in prediction of earl y stage breast cancer that always has been a challenging research problem. The main objective of this research is to find how precisely can these data mining algorithms predict the probability of recurrence of the disease among the patients on the basis of important stated parameters. The research highlights the performance of different clustering and classification algorithms on the dataset. Experiments show that classification algorithms are better predictors than clustering algorithms. The result indicates that the decision tree (C5.0) and SVM is the best predictor with 81% accuracy on the holdout sample and fuzzy c-means came with the lowest accuracy of37% among the algorithms used in this paper.