nils-herrmann/clean_scirepeval huggingface dataset:
Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data

Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a detailed taxonomy of the work in this area, including research efforts as well as commercial offerings. An up-to-date survey of the existing work is also provided. Finally, a brief overview of the WebSIFT system as an example of a prototypical Web usage mining system is given.
Multi-Branch Fully Convolutional Network for Face Detection

Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 × 480 with no assumption on the minimum detectable face size.
A Model of Factor Influences on Electronic Commerce Adoption and Diffusion in Small-and Medium-sized Enterprises

To survive in today’s global marketplace, businesses need to be able to deliver products on time, maintain market credibility and introduce new products and services faster than competitors. This is especially crucial to the Smalland Medium-sized Enterprises (SMEs). Since the emergence of the Internet, it has allowed SMEs to compete effectively and efficiently in both domestic and international market. Unfortunately, such leverage is often impeded by the resistance and mismanagement of SMEs to adopt Electronic Commerce (EC) proficiently. Consequently, this research aims to investigate how SMEs can adopt and implement EC successfully to achieve competitive advantage. Building on an examination of current technology diffusion literature, a model of EC diffusion has been developed. It investigates the factors that influence SMEs in the adoption of EC, followed by an examination in the diffusion process, which SMEs adopt to integrate EC into their business systems.
Layer-Specific Adaptive Learning Rates for Deep Networks

The increasing complexity of deep learning architectures is resulting in training time requiring weeks or even months. This slow training is due in part to "vanishing gradients," in which the gradients used by back-propagation are extremely large for weights connecting deep layers (layers near the output layer), and extremely small for shallow layers (near the input layer), this results in slow learning in the shallow layers. Additionally, it has also been shown that in highly non-convex problems, such as deep neural networks, there is a proliferation of high-error low curvature saddle points, which slows down learning dramatically . In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points. This enables us to speed up learning in the shallow layers of the network and quickly escape high-error low curvature saddle points. We test our method on standard image classification datasets such as MNIST, CIFAR10 and ImageNet, and demonstrate that our method increases accuracy as well as reduces the required training time over standard algorithms.
Diagnosing Advanced Persistent Threats: A Position Paper

When a computer system is hacked, analyzing the root-cause (for example entry-point of penetration) is a diagnostic process. An audit trail, as defined in the National Information Assurance Glossary, is a securityrelevant chronological (set of) record(s), and/or destination and source of records that provide evidence of the sequence of activities that have affected, at any time, a specific operation, procedure, or event. After detecting an intrusion, system administrators manually analyze audit trails to both isolate the root-cause and perform damage impact assessment of the attack. Due to the sheer volume of information and low-level activities in the audit trails, this task is rather cumbersome and time intensive. In this position paper, we discuss our ideas to automate the analysis of audit trails using machine learning and model-based reasoning techniques. Our approach classifies audit trails into the high-level activities they represent, and then reasons about those activities and their threat potential in real-time and forensically. We argue that, by using the outcome of this reasoning to explain complex evidence of malicious behavior, we are equipping system administrators with the proper tools to promptly react to, stop, and mitigate attacks.
Learning to Learn Second-Order Back-Propagation for CNNs Using LSTMs

Convolutional neural networks (CNNs) typically suffer from slow convergence rates in training, which limits their wider application. This paper presents a new CNN learning approach, based on second-order methods, aimed at improving: a) Convergence rates of existing gradient-based methods, and b) Robustness to the choice of learning hyper-parameters (e.g., learning rate). We derive an efficient back-propagation algorithm for simultaneously computing both gradients and second derivatives of the CNN's learning objective. These are then input to a Long Short Term Memory (LSTM) to predict optimal updates of CNN parameters in each learning iteration. Both meta-learning of the LSTM and learning of the CNN are conducted jointly. Evaluation on image classification demonstrates that our second-order backpropagation has faster convergences rates than standard gradient-based learning for the same CNN, and that it converges to better optima leading to better performance under a budgeted time for learning. We also show that an LSTM learned to learn a small CNN network can be readily used for learning a larger network.
Nachdiplom Lecture: Statistics Meets Optimization 4.1 Problem Set up and Motivation

Last time, we proved that a sketch dimension m % 1 δ2W (AK(xLS) ∩ Sn−1) is sufficient to ensure this property. A related but slightly different notion of approximation is that of solution approximation, in which we measure the quality in terms of some norm between x̂ and xLS. Defining the (semi)-norm ‖u‖A : = √ uTATAu/n, let us say that x̂ is a δ-accurate solution approximation if ‖xLS − x̂‖A ≤ δ.
Factor analysis and scale revision.

This article reviews methodological issues that arise in the application of exploratory factor analysis (EFA) to scale revision and refinement. The authors begin by discussing how the appropriate use of EFA in scale revision is influenced by both the hierarchical nature of psychological constructs and the motivations underlying the revision. Then they specifically address (a) important issues that arise prior to data collection (e.g., selecting an appropriate sample), (b) technical aspects of factor analysis (e.g., determining the number of factors to retain), and (c) procedures used to evaluate the outcome of the scale revision (e.g., determining whether the new measure functions equivalently for different populations).
Extracting relevant named entities for automated expense reimbursement

Expense reimbursement is a time-consuming and labor-intensive process across organizations. In this paper, we present a prototype expense reimbursement system that dramatically reduces the elapsed time and costs involved, by eliminating paper from the process life cycle. Our complete solution involves (1) an electronic submission infrastructure that provides multi- channel image capture, secure transport and centralized storage of paper documents; (2) an unconstrained data mining approach to extracting relevant named entities from un-structured document images; (3) automation of auditing procedures that enables automatic expense validation with minimum human interaction.
 Extracting relevant named entities robustly from document images with unconstrained layouts and diverse formatting is a fundamental technical challenge to image-based data mining, question answering, and other information retrieval tasks. In many applications that require such capability, applying traditional language modeling techniques to the stream of OCR text does not give satisfactory result due to the absence of linguistic context. We present an approach for extracting relevant named entities from document images by combining rich page layout features in the image space with language content in the OCR text using a discriminative conditional random field (CRF) framework. We integrate this named entity extraction engine into our expense reimbursement solution and evaluate the system performance on large collections of real-world receipt images provided by IBM World Wide Reimbursement Center.
Delaying the onset of Alzheimer disease: bilingualism as a form of cognitive reserve.

There is strong epidemiologic evidence to suggest that older adults who maintain an active lifestyle in terms of social, mental, and physical engagement are protected to some degree against the onset of dementia. Such factors are said to contribute to cognitive reserve, which acts to compensate for the accumulation of amyloid and other brain pathologies. We present evidence that lifelong bilingualism is a further factor contributing to cognitive reserve.



Data were collected from 211 consecutive patients diagnosed with probable Alzheimer disease (AD). Patients' age at onset of cognitive impairment was recorded, as was information on occupational history, education, and language history, including fluency in English and any other languages. Following this procedure, 102 patients were classified as bilingual and 109 as monolingual.



We found that the bilingual patients had been diagnosed 4.3 years later and had reported the onset of symptoms 5.1 years later than the monolingual patients. The groups were equivalent on measures of cognitive and occupational level, there was no apparent effect of immigration status, and the monolingual patients had received more formal education. There were no gender differences.



The present data confirm results from an earlier study, and thus we conclude that lifelong bilingualism confers protection against the onset of AD. The effect does not appear to be attributable to such possible confounding factors as education, occupational status, or immigration. Bilingualism thus appears to contribute to cognitive reserve, which acts to compensate for the effects of accumulated neuropathology.
Neural Music Synthesis for Flexible Timbre Control

The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process — creating audio samples from a score and instrument information — is modeled using generative neural networks. This paper describes a neural music synthesis model with flexible timbre controls, which consists of a recurrent neural network conditioned on a learned instrument embedding followed by a WaveNet vocoder. The learned embedding space successfully captures the diverse variations in timbres within a large dataset and enables timbre control and morphing by interpolating between instruments in the embedding space. The synthesis quality is evaluated both numerically and perceptually, and an interactive web demo is presented.
Modular Design of Mechatronic Systems with Function Modeling

This paper develops a modularization scheme based on the functional model of a system. The modularization approach makes use of the function-behavior-state (FBS) model of the system to derive the entity relations. The design structure matrix (DSM) is automatically constructed based on the FBS model. In this way the tedious work of filling the DSM entries based on expert knowledge is avoided. The approach makes use of k-means clustering algorithm to allow the user to try different number of clusters in a fast way. The kmeans clustering is adopted for DSM based modularization by defining a proper entity representation, relation measure, and objective function. Two modularization schemes are performed, one based on the immediate relations and one on the deeper behavioral relations between the components. Considering the application on the shifting system of the Delft University of Technology (DUT) Formula Student car, the latter modularization resulted in more mechatronic behavior based modules, while the former resulted in modules based on mere disciplinary and spatial closeness.
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks

Lacking standardized extrinsic evaluation methods for vector representations of words, the NLP community has relied heavily onword similaritytasks as a proxy for intrinsic evaluation of word vectors. Word similarity evaluation, which correlates the distance between vectors and human judgments of “semantic similarity” is attractive, because it is computationally inexpensive and fast. In this paper we present several problems associated with the evaluation of word vectors on word similarity datasets, and summarize existing solutions. Our study suggests that the use of word similarity tasks for evaluation of word vectors is not sustainable and calls for further research on evaluation methods.
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN

In convolutional neural networks (CNNs), the filter grouping in convolution layers is known to be useful to reduce the network parameter size. In this paper, we propose a new logarithmic filter grouping which can capture the nonlinearity of filter distribution in CNNs. The proposed logarithmic filter grouping is installed in shallow CNNs applicable in a mobile application. Experiments were performed with the shallow CNNs for classification tasks. Our classification results on Multi-PIE dataset for facial expression recognition and CIFAR-10 dataset for object classification reveal that the compact CNN with the proposed logarithmic filter grouping scheme outperforms the same network with the uniform filter grouping in terms of accuracy and parameter efficiency. Our results indicate that the efficiency of shallow CNNs can be improved by the proposed logarithmic filter grouping.
The evolution of human mating: trade-offs and strategic pluralism.

During human evolutionary history, there were "trade-offs" between expending time and energy on child-rearing and mating, so both men and women evolved conditional mating strategies guided by cues signaling the circumstances. Many short-term matings might be successful for some men; others might try to find and keep a single mate, investing their effort in rearing her offspring. Recent evidence suggests that men with features signaling genetic benefits to offspring should be preferred by women as short-term mates, but there are trade-offs between a mate's genetic fitness and his willingness to help in child-rearing. It is these circumstances and the cues that signal them that underlie the variation in short- and long-term mating strategies between and within the sexes.
Instagram Use and It ' s Effect on Well-Being and Self-Esteem

Over the last ten years, social media has become an integral facet of modern society. In particular, image-based social networking sites such as Instagram have become increasingly popular among adolescents and young adults. However, despite this proliferation of use, the literature remains divided regarding the potential impacts of social media, particularly in regards to image-based platforms. The present study sought to analyze the relationship between social media usage patterns and its subsequent effects on user self-esteem and well-being. However, the study’s results show that, despite the existing literature, intensity of Instagram use serves as a mediating variable in this relationship. The study’s results show that it is intensity of use, not usage patterns, that determine user outcomes. Finally, the results show that users who engage with Instagram more intensely exhibit higher levels of self-esteem and well-being than users who do not use the application intensely.
28 GHz channel modeling using 3D ray-tracing in urban environments

In this paper, we analyze the radio channel characteristics at mmWave frequencies for 5G cellular communications in urban scenarios. 3D-ray tracing simulations in the downtown areas of Ottawa and Chicago are conducted in both the 2 GHz and 28 GHz bands. Each area has two different deployment scenarios, with different transmitter height and different density of buildings. Based on the observations of the ray-tracing experiments, important parameters of the radio channel model, such as path loss exponent, shadowing variance, delay spread and angle spread, are provided, forming the basis of a mmWave channel model. Based on the analysis and the 3GPP 3D-Spatial Channel Model (SCM) framework, we introduce a a preliminary mmWave channel model at 28 GHz.
DNA Fountain enables a robust and efficient storage architecture

DNA is an attractive medium to store digital information. Here we report a storage strategy, called DNA Fountain, that is highly robust and approaches the information capacity per nucleotide. Using our approach, we stored a full computer operating system, movie, and other files with a total of 2.14 × 106 bytes in DNA oligonucleotides and perfectly retrieved the information from a sequencing coverage equivalent to a single tile of Illumina sequencing. We also tested a process that can allow 2.18 × 1015 retrievals using the original DNA sample and were able to perfectly decode the data. Finally, we explored the limit of our architecture in terms of bytes per molecule and obtained a perfect retrieval from a density of 215 petabytes per gram of DNA, orders of magnitude higher than previous reports.
Scheduler technologies in support of high performance data analysis

Job schedulers are a key component of scalable computing infrastructures. They orchestrate all of the work executed on the computing infrastructure and directly impact the effectiveness of the system. Recently, job workloads have diversified from long-running, synchronously-parallel simulations to include short-duration, independently parallel high performance data analysis (HPDA) jobs. Each of these job types requires different features and scheduler tuning to run efficiently. A number of schedulers have been developed to address both job workload and computing system heterogeneity. High performance computing (HPC) schedulers were designed to schedule large-scale scientific modeling and simulations on supercomputers. Big Data schedulers were designed to schedule data processing and analytic jobs on clusters. This paper compares and contrasts the features of HPC and Big Data schedulers with a focus on accommodating both scientific computing and high performance data analytic workloads. Job latency is critical for the efficient utilization of scalable computing infrastructures, and this paper presents the results of job launch benchmarking of several current schedulers: Slurm, Son of Grid Engine, Mesos, and Yarn. We find that all of these schedulers have low utilization for short-running jobs. Furthermore, employing multilevel scheduling significantly improves the utilization across all schedulers.
The role of predictive biopharmaceutical modeling and simulation in drug development and regulatory evaluation.

Advances in predicting in vivo performance of drug products has the potential to change how drug products are developed and reviewed. Modeling and simulation methods are now more commonly used in drug product development and regulatory drug review. These applications include, but are not limited to: the development of biorelevant specifications, the determination of bioequivalence metrics for modified release products with rapid therapeutic onset, the design of in vitro-in vivo correlations in a mechanistic framework, and prediction of food effect. As new regulatory concepts such as quality by design require better application of biopharmaceutical modeling in drug product development, regulatory challenges in bioequivalence demonstration of complex drug products also present exciting opportunities for creative modeling and simulation approaches. A collaborative effort among academia, government and industry in modeling and simulation will result in improved safe and effective new/generic drugs to the American public.
[Use of infrared thermography in ophthalmology].

Thermoregulation disorders are associated with Body temperature fluctuation. Both hyper- and hypothermia are evidence of an ongoing pathological process. Contralateral symmetry in the Body heat spread is considered normal, while asymmetry, if above a certain level, implies an underlying pathology. Infrared thermography (IRT) is employed in many medical fields including ophthalmology. The earliest attempts of eye surface temperature evaluation were made in the 19th century. Over the last 50 years, different authors have been using this method to assess ocular adnexa, however, the technique remains insufficiently studied. The reported IRT data is often contradictory, which may be due to heterogeneity (in terms of severity) of patient groups and disparities between research parameters.
A survey on IDS alerts processing techniques

When an attacker tries to penetrate the network, there are many defensive systems, including intrusion detection systems (IDSs). Most IDSs are capable of detecting many attacks, but can not provide a clear idea to the analyst because of the huge number of false alerts generated by these systems. This weakness in the IDS has led to the emergence of many methods in which to deal with these alerts, minimize them and highlight the real attacks. It has come to a stage to take a stock of the research results a comprehensive view so that further research in this area will be motivated objectively to fulfill the gaps