nils-herrmann/clean_scirepeval huggingface dataset:
Predictive Collaborative Filtering with Side Information

Recommender systems have been widely studied in the literature as they have real world impacts in many E-commerce platforms and social networks. Most previous systems are based on the user-item recommendation matrix, which contains users’ history recommendation activities on items. In this paper, we propose a novel predictive collaborative filtering approach that exploits both the partially observed user-item recommendation matrix and the item-based side information to produce top-N recommender systems. The proposed approach automatically identifies the most interesting items for each user from his or her non-recommended item pool by aggregating over his or her recommended items via a low-rank coefficient matrix. Moreover, it also simultaneously builds linear regression models from the item-based side information such as item reviews to predict the item recommendation scores for the users. The proposed approach is formulated as a rank constrained joint minimization problem with integrated least squares losses, for which an efficient analytical solution can be derived. To evaluate the proposed learning technique, empirical evaluations on five recommendation tasks are conducted. The experimental results demonstrate the efficacy of the proposed approach comparing to the competing methods.
ADADELTA: An Adaptive Learning Rate Method

We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters. We show promising results compared to other methods on the MNIST digit classification task using a single machine and on a large scale voice dataset in a distributed cluster environment.
L2-constrained Softmax Loss for Discriminative Face Verification

In recent years, the performance of face verification systems has significantly improved using deep convolutional neural networks (DCNNs). A typical pipeline for face verification includes training a deep network for subject classification with softmax loss, using the penultimate layer output as the feature descriptor, and generating a cosine similarity score given a pair of face images. The softmax loss function does not optimize the features to have higher similarity score for positive pairs and lower similarity score for negative pairs, which leads to a performance gap. In this paper, we add an L2-constraint to the feature descriptors which restricts them to lie on a hypersphere of a fixed radius. This module can be easily implemented using existing deep learning frameworks. We show that integrating this simple step in the training pipeline significantly boosts the performance of face verification. Specifically, we achieve state-of-the-art results on the challenging IJB-A dataset, achieving True Accept Rates of 0.863 and 0.910 at False Accept Rates 0.0001 and 0.001 respectively on the face verification protocol.
Anatomically accurate, finite model eye for optical modeling.

There is a need for a schematic eye that models vision accurately under various conditions such as refractive surgical procedures, contact lens and spectacle wear, and near vision. Here we propose a new model eye close to anatomical, biometric, and optical realities. This is a finite model with four aspheric refracting surfaces and a gradient-index lens. It has an equivalent power of 60.35 D and an axial length of 23.95 mm. The new model eye provides spherical aberration values within the limits of empirical results and predicts chromatic aberration for wavelengths between 380 and 750 nm. It provides a model for calculating optical transfer functions and predicting optical performance of the eye.
Brain Mechanisms in Early Language Acquisition

The last decade has produced an explosion in neuroscience research examining young children's early processing of language. Noninvasive, safe functional brain measurements have now been proven feasible for use with children starting at birth. The phonetic level of language is especially accessible to experimental studies that document the innate state and the effect of learning on the brain. The neural signatures of learning at the phonetic level can be documented at a remarkably early point in development. Continuity in linguistic development from infants' earliest brain responses to phonetic stimuli is reflected in their language and prereading abilities in the second, third, and fifth year of life, a finding with theoretical and clinical impact. There is evidence that early mastery of the phonetic units of language requires learning in a social context. Neuroscience on early language learning is beginning to reveal the multiple brain systems that underlie the human language faculty.
The Development of Phased-Array Radar Technology

■ Lincoln Laboratory has been involved in the development of phased-array radar technology since the late 1950s. Radar research activities have included theoretical analysis, application studies, hardware design, device fabrication, and system testing. Early phased-array research was centered on improving the national capability in phased-array radars. The Laboratory has developed several test-bed phased arrays, which have been used to demonstrate and evaluate components, beamforming techniques, calibration, and testing methodologies. The Laboratory has also contributed significantly in the area of phased-array antenna radiating elements, phase-shifter technology, solid-state transmit-andreceive modules, and monolithic microwave integrated circuit (MMIC) technology. A number of developmental phased-array radar systems have resulted from this research, as discussed in other articles in this issue. A wide variety of processing techniques and system components have also been developed. This article provides an overview of more than forty years of this phased-array radar research activity.
Generative Representations for Computer-Automated Evolutionary Design

With the increasing computational power of computers, software design systems are progressing from being tools enabling architects and designers to express their ideas, to tools capable of creating designs under human guidance. One of the main limitations for these computer-automated design systems is the representation with which they encode designs. If the representation cannot encode a certain design, then the design system cannot produce it. To be able to produce new types of designs, and not just optimize pre-defined parameterizations, evolutionary design systems must use generative representations. Generative representations are assembly procedures, or algorithms, for constructing a design, thereby allowing for truly novel design solutions to be encoded. In addition, by enabling modularity, regularity and hierarchy, the level of sophistication that can be evolved is increased. We demonstrate the advantages of generative representations on two different design domains: the evolution of spacecraft antennas and the evolution of 3D solid objects.
Key Enabling Technologies for Secure and Scalable Future Fog-IoT Architecture: A Survey

Fog or Edge computing has recently attracted broad attention from both industry and academia. It is deemed as a paradigm shift from the current centralized cloud computing model and could potentially bring a “Fog-IoT” architecture that would significantly benefit the future ubiquitous Internet of Things (IoT) systems and applications. However, it takes a series of key enabling technologies including emerging technologies to realize such a vision. In this article, we will survey these key enabling technologies with specific focuses on security and scalability, which are two very important and much-needed characteristics for future large-scale deployment. We aim to draw an overall big picture of the future for the research and development in these areas.
From context to content: leveraging context to infer media metadata

The recent popularity of mobile camera phones allows for new opportunities to gather important metadata at the point of capture. This paper describes a method for generating metadata for photos using spatial, temporal, and social context. We describe a system we implemented for inferring location information for pictures taken with camera phones and its performance evaluation. We propose that leveraging contextual metadata at the point of capture can address the problems of the semantic and sensory gaps. In particular, combining and sharing spatial, temporal, and social contextual metadata from a given user and across users allows us to make inferences about media content.
Polyphenols in Fruits and Vegetables and Its Effect on Human Health

Polyphenols represent a group of chemical substances common in plants, structurally characterized by the presence of one or more phenol units. Polyphenols are the most abundant antioxidants in human diets and the largest and best studied class of polyphenols is flavonoids, which include several thousand compounds. Numerous studies confirm that they exert a protective action on human health and are key components of a healthy and balanced diet. Epidemiological studies correlate flavonoid intake with a reduced incidence of chronic diseases, such as cardiovascular disease, diabetes and cancer. The involvement of reactive oxygen species (ROS) in the etiology of these degenerative conditions has suggested that phytochemicals showing antioxidant activity may contribute to the prevention of these pathologies. The present review deals with phenolic compounds in plants and reports on recent studies. Moreover, the present work includes information on the relationships between the consumption of these compounds, via feeding, and risk of disease occurrence, i.e. the effect on human health. Results obtained on herbs, essential oils, from plants grown in tropical, subtropical and temperate regions, were also reported.
Minimal Gated Unit for Recurrent Neural Networks

Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN’s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU’s properties theoretically and empirically.
Synthetic Data Generation Capabilties for Testing Data Mining Tools

Recently, due to commercial success of data mining tools, there has been much attention to extracting hidden information from large databases to predict security problems and terrorist threats. The security applications are somewhat more complicated than commercial applications due to (i) lack of sufficient specific knowledge on what to look for, (ii) R&D labs developing these tools are not able to easily obtain sensitive information due to security, privacy or cost issues. Tools developed for security applications require substantially more testing and revisions in order to prevent costly errors. This paper describes a platform for the generation of realistic synthetic data that can facilitate the development and testing of data mining tools. The original applications for this platform were people information and credit card transaction data sets. In this paper, we introduce a new shipping container application that can support the testing of data mining tools developed for port security
A Survey of Binary Similarity and Distance Measures

The binary feature vector is one of the most common representations of patterns and measuring similarity and distance measures play a critical role in many problems such as clustering, classification, etc. Ever since Jaccard proposed a similarity measure to classify ecological species in 1901, numerous binary similarity and distance measures have been proposed in various fields. Applying appropriate measures results in more accurate data analysis. Notwithstanding, few comprehensive surveys on binary measures have been conducted. Hence we collected 76 binary similarity and distance measures used over the last century and reveal their correlations through the hierarchical clustering technique.
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.
Investigations into resting-state connectivity using independent component analysis.

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.
QA-Driven Relation Extraction

Information Extraction (IE) is concerned with mining factual structures from unstructured text data, including entity and relation extraction. For example, identifying Donald Trump as “person” and Washington D.C. as “location”, and understand the relationship between them (say, Donald Trump spoke at Washington D.C.), from a specific sentence. Typically, IE systems rely on large amount of training data, primarily acquired via human annotation, to achieve the best performance. But since human annotation is costly and non-scalable, the focus has shifted to adoption of a new strategy Distant Supervision . Distant supervision is a technique that can automatically extract labeled training data from existing knowledge bases without human efforts. However the training data generated by distant supervision is context-agnostic and can be very noisy. Moreover, we also observe the difference between the quality of training examples in terms of to what extent it infers the target entity/relation type. In this project, we focus on removing the noise and identifying the quality difference in the training data generated by distant supervision, by leveraging the feedback signals from one of IE’s downstream applications, QA, to improve the performance of one of the state-of-the-art IE framework, CoType . Data Mining, Relation Extraction, Question Answering.
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we investigate interactive semantic parsing, where the agent can ask the user clarification questions to resolve ambiguities via a multi-turn dialogue, on an important type of programs called “If-Then recipes.” We develop a hierarchical reinforcement learning (HRL) based agent that significantly improves the parsing performance with minimal questions to the user. Results under both simulation and human evaluation show that our agent substantially outperforms non-interactive semantic parsers and rule-based agents.
Acoustic Scene Classification Using a CNN-SuperVector System Trained with Auditory and Spectrogram Image Features

Enabling smart devices to infer about the environment using audio signals has been one of the several long-standing challenges in machine listening. The availability of public-domain datasets, e.g., Detection and Classification of Acoustic Scenes and Events (DCASE) 2016, enabled researchers to compare various algorithms on standard predefined tasks. Most of the current best performing individual acoustic scene classification systems utilize different spectrogram image based features with a Convolutional Neural Network (CNN) architecture. In this study, we first analyze the performance of a state-of-theart CNN system for different auditory image and spectrogram features, including Mel-scaled, logarithmically scaled, linearly scaled filterbank spectrograms, and Stabilized Auditory Image (SAI) features. Next, we benchmark an MFCC based Gaussian Mixture Model (GMM) SuperVector (SV) system for acoustic scene classification. Finally, we utilize the activations from the final layer of the CNN to form a SuperVector (SV) and use them as feature vectors for a Probabilistic Linear Discriminative Analysis (PLDA) classifier. Experimental evaluation on the DCASE 2016 database demonstrates the effectiveness of the proposed CNN-SV approach compared to conventional CNNs with a fully connected softmax output layer. Score fusion of individual systems provides up to 7% relative improvement in overall accuracy compared to the CNN baseline system.
A Novel Approach to Low Volume Generic Steganalysis

Generic Steganalysis aims to detect the presence of covert communication by identifying the given test data as stego / cover media. Thresholded adjacent pixel differences using different scan paths have been used to highlight feeble embedding artifacts created out of a low rate embedding process. The scan paths normally made use of in the embedding process have been utilized for a steganalytic scheme. A co occurrence matrix derived from thresholded adjacent pixel differences serves as the feature vector aiding detection of stego images carrying very minimal payloads.
Predicting seminal quality with artificial intelligence methods

0957-4174/$ see front matter 2012  A http://dx.doi.org/10.1016/j.eswa.2012.05.028 ⇑ Corresponding author. E-mail address: dgil@dtic.ua.es (D. Gil). 1 These authors equally contributed to this work. Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors, as well as life habits, may affect semen quality. Artificial intelligence techniques are now an emerging methodology as decision support systems in medicine. In this paper we compare three artificial intelligence techniques, decision trees, Multilayer Perceptron and Support Vector Machines, in order to evaluate their performance in the prediction of the seminal quality from the data of the environmental factors and lifestyle. To do that we collect data by a normalized questionnaire from young healthy volunteers and then, we use the results of a semen analysis to asses the accuracy in the prediction of the three classification methods mentioned above. The results show that Multilayer Perceptron and Support Vector Machines show the highest accuracy, with prediction accuracy values of 86% for some of the seminal parameters. In contrast decision trees provide a visual and illustrative approach that can compensate the slightly lower accuracy obtained. In conclusion artificial intelligence methods are a useful tool in order to predict the seminal profile of an individual from the environmental factors and life habits. From the studied methods, Multilayer Perceptron and Support Vector Machines are the most accurate in the prediction. Therefore these tools, together with the visual help that decision trees offer, are the suggested methods to be included in the evaluation of the infertile patient.
E-Learning: Strategies for Delivering Knowledge in the Digital Age

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TOWARDS A RESOURCE-BASED VIEW OF CORPORATE INCUBATORS

Corporate incubators for technology development are a recent phenomenon whose functioning and implications are not yet well understood. The resource-based view can offer an explanatory model on how corporate incubators function as specialised corporate units that hatch new businesses. While tangible resources, such as the financial, physical and even explicit knowledge flow, are all visible, and therefore easy to measure, intangible resources such as tacit knowledge and branding flow are harder to detect and localise. Managing the resource flow requires the initial allocation of resources to the corporate incubator during its set-up as well as a continuous resource flow to the technology venture and, during the harvest phase, also from it. Two levels of analysis need to be distinguished: (1) the resource flow between the corporate incubator and the technology venture and (2) the resource flow interface between the corporate incubator and the technology venture. Our empirical findings are based on two phases: First, in-depth case studies of 22 companies through 47 semi-structured interviews that were conducted with managers of large technology-intensive corporations’ corporate incubators in Europe and the U.S., and second, an analysis of the European Commission’s benchmarking survey of 77 incubators.