nils-herrmann/clean_scirepeval huggingface dataset:
Quality Assessment of Stereoscopic 3D Image Compression by Binocular Integration Behaviors

The objective approaches of 3D image quality assessment play a key role for the development of compression standards and various 3D multimedia applications. The quality assessment of 3D images faces more new challenges, such as asymmetric stereo compression, depth perception, and virtual view synthesis, than its 2D counterparts. In addition, the widely used 2D image quality metrics (e.g., PSNR and SSIM) cannot be directly applied to deal with these newly introduced challenges. This statement can be verified by the low correlation between the computed objective measures and the subjectively measured mean opinion scores (MOSs), when 3D images are the tested targets. In order to meet these newly introduced challenges, in this paper, besides traditional 2D image metrics, the binocular integration behaviors-the binocular combination and the binocular frequency integration, are utilized as the bases for measuring the quality of stereoscopic 3D images. The effectiveness of the proposed metrics is verified by conducting subjective evaluations on publicly available stereoscopic image databases. Experimental results show that significant consistency could be reached between the measured MOS and the proposed metrics, in which the correlation coefficient between them can go up to 0.88. Furthermore, we found that the proposed metrics can also address the quality assessment of the synthesized color-plus-depth 3D images well. Therefore, it is our belief that the binocular integration behaviors are important factors in the development of objective quality assessment for 3D images.
Energy-efficient motion planning for mobile robots

This paper presents a new approach to find energy-efficient motion plans for mobile robots. Motion planning has two goals: finding the routes and determining the velocities. We model the relationship of motors' speed and their power consumption with polynomials. The velocity of the robot is related to its wheels' velocities by performing a linear transformation. We compare the energy consumption of different routes at different velocities and consider the energy consumed for acceleration and turns. We use experiment-validated simulation to demonstrate up to 51% energy savings for searching an open area.
Examining the impact of off-task multi-tasking with technology on real-time classroom learning

The purpose of the present study was to examine the impact of multi-tasking with digital technologies while attempting to learn from real-time classroom lectures in a university setting. Four digitally-based multi-tasking activities (texting using a cell-phone, emailing, MSN messaging and Facebook ) were compared to 3 control groups (paper-and-pencil note-taking, word-processing note-taking and a natural use of technology condition) over three consecutive lectures. Comparisons indicated that participants in the Facebook and MSN conditions performed more poorly than those in the paper-and-pencil use control. Follow-up analyses were required to accommodate the substantial number of students who failed to comply with the limited use of technology specified by their assigned conditions. These analyses indicated that participants who did not use any technologies in the lectures outperformed students who used some form of technology. Consistent with the cognitive bottleneck theory of attention (Welford, 1967) and contrary to popular beliefs, attempting to attend to lectures and engage digital technologies for off-task activities can have a detrimental impact on learning.
Hybrid Buck–Boost Feedforward and Reduced Average Inductor Current Techniques in Fast Line Transient and High-Efficiency Buck–Boost Converter

This paper presents a buck-boost converter with high efficiency and small output ripple to extend the battery life of portable devices. Besides, the hybrid buck-boost feedforward (HBBFF) technique is integrated in this converter to achieve fast line response. The new control topology minimizes the switching and conduction losses at the same time even when four switches are used. Therefore, over a wide input voltage range, the proposed buck-boost converter with minimum switching loss like the buck or boost converter can reduce the conduction loss through the use of the reduced average inductor current (RAIC) technique. Moreover, the HBBFF technique minimizes the voltage variation at the output of error amplifier. Consequently, a fast line transient response can be achieved with small dropout voltage at the output. Especially, the converter can offer good line and load regulations to ensure a regulated output voltage without being affected by the decreasing battery voltage. Experimental results show that the output voltage is regulated over a wide battery lifetime, and the output ripple is minimized during mode transition. The peak efficiency is 97% and the transient dropout voltage can be improved substantially.
Risk Clusters , Hotspots , and Spatial Intelligence : Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies

The study reported here follows the suggestion by Caplan et al. (Justice Q, 2010) that risk terrain modeling (RTM) be developed by doing more work to elaborate, operationalize, and test variables that would provide added value to its application in police operations. Building on the ideas presented by Caplan et al., we address three important issues related to RTM that sets it apart from current approaches to spatial crime analysis. First, we address the selection criteria used in determining which risk layers to include in risk terrain models. Second, we compare the ‘‘best model’’ risk terrain derived from our analysis to the traditional hotspot density mapping technique by considering both the statistical power and overall usefulness of each approach. Third, we test for ‘‘risk clusters’’ in risk terrain maps to determine how they can be used to target police resources in a way that improves upon the current practice of using density maps of past crime in determining future locations of crime occurrence. This paper concludes with an in depth exploration of how one might develop strategies for incorporating risk terrains into police decisionmaking. RTM can be developed to the point where it may be more readily adopted by police crime analysts and enable police to be more effectively proactive and identify areas with the greatest probability of becoming locations for crime in the future. The targeting of police interventions that emerges would be based on a sound understanding of geographic attributes and qualities of space that connect to crime outcomes and would not be the result of identifying individuals from specific groups or characteristics of people as likely candidates for crime, a tactic that has led police agencies to be accused of profiling. In addition, place-based interventions may offer a more efficient method of impacting crime than efforts focused on individuals.
The socialbot network: when bots socialize for fame and money

Online Social Networks (OSNs) have become an integral part of today's Web. Politicians, celebrities, revolutionists, and others use OSNs as a podium to deliver their message to millions of active web users. Unfortunately, in the wrong hands, OSNs can be used to run astroturf campaigns to spread misinformation and propaganda. Such campaigns usually start off by infiltrating a targeted OSN on a large scale. In this paper, we evaluate how vulnerable OSNs are to a large-scale infiltration by socialbots: computer programs that control OSN accounts and mimic real users. We adopt a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are orchestrated in a command-and-control fashion. We operated such an SbN on Facebook---a 750 million user OSN---for about 8 weeks. We collected data related to users' behavior in response to a large-scale infiltration where socialbots were used to connect to a large number of Facebook users. Our results show that (1) OSNs, such as Facebook, can be infiltrated with a success rate of up to 80%, (2) depending on users' privacy settings, a successful infiltration can result in privacy breaches where even more users' data are exposed when compared to a purely public access, and (3) in practice, OSN security defenses, such as the Facebook Immune System, are not effective enough in detecting or stopping a large-scale infiltration as it occurs.
Type-2 fuzzy logic systems

We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on “output processing,” which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.
LIMTopic: A Framework of Incorporating Link Based Importance into Topic Modeling

Topic modeling has become a widely used tool for document management. However, there are few topic models distinguishing the importance of documents on different topics. In this paper, we propose a framework LIMTopic to incorporate link based importance into topic modeling. To instantiate the framework, RankTopic and HITSTopic are proposed by incorporating topical pagerank and topical HITS into topic modeling respectively. Specifically, ranking methods are first used to compute the topical importance of documents. Then, a generalized relation is built between link importance and topic modeling. We empirically show that LIMTopic converges after a small number of iterations in most experimental settings. The necessity of incorporating link importance into topic modeling is justified based on KL-Divergences between topic distributions converted from topical link importance and those computed by basic topic models. To investigate the document network summarization performance of topic models, we propose a novel measure called log-likelihood of ranking-integrated document-word matrix. Extensive experimental results show that LIMTopic performs better than baseline models in generalization performance, document clustering and classification, topic interpretability and document network summarization performance. Moreover, RankTopic has comparable performance with relational topic model (RTM) and HITSTopic performs much better than baseline models in document clustering and classification.
Runtime power estimation of mobile AMOLED displays

Modeling and estimating power consumption of OLED displays are necessary to understand the energy behavior of emerging mobile devices. Although previous study exists to model and estimate the power consumption of stationary display images, to the best of our knowledge, no prior work is found to deal with runtime power behavior of OLED display running real applications. This paper proposes a runtime power estimation scheme for OLED displays that involves monitoring kernel activities that capture the screen change events of running applications. The experiment results show that the proposed scheme estimates the display energy consumption of running applications with reasonable accuracy.
Surface plasmon resonance sensors for detection of chemical and biological species.

5.1. Detection Formats 475 5.2. Food Quality and Safety Analysis 477 5.2.1. Pathogens 477 5.2.2. Toxins 479 5.2.3.  Drugs 479 5.2.4. Vitamins 480 5.2.5. Hormones 480 5.2.6. Diagnostic Antibodies 480 5.2.7. Allergens 481 5.2.8. Proteins 481 5.2.9. Chemical Contaminants 481 5.3. Medical Diagnostics 481 5.3.1. Cancer Markers 481 5.3.2. Antibodies against Viral Pathogens 482 5.3.3. Drugs and Drug-Induced Antibodies 483 5.3.4. Hormones 483 5.3.5. Allergy Markers 483 5.3.6. Heart Attack Markers 484 5.3.7. Other Molecular Biomarkers 484 5.4. Environmental Monitoring 484 5.4.1. Pesticides 484 5.4.2. 2,4,6-Trinitrotoluene (TNT) 485 5.4.3. Aromatic Hydrocarbons 485 5.4.4. Heavy Metals 485 5.4.5. Phenols 485 5.4.6. Polychlorinated Biphenyls 487 5.4.7. Dioxins 487 5.5.  6.  7. Abbreviations 489 8. Acknowledgment 489 9.
Design of Last-Level On-Chip Cache Using Spin-Torque Transfer RAM (STT RAM)

Because of its high storage density with superior scalability, low integration cost and reasonably high access speed, spin-torque transfer random access memory (STT RAM) appears to have a promising potential to replace SRAM as last-level on-chip cache (e.g., L2 or L3 cache) for microprocessors. Due to unique operational characteristics of its storage device magnetic tunneling junction (MTJ), STT RAM is inherently subject to a write latency versus read latency tradeoff that is determined by the memory cell size. This paper first quantitatively studies how different memory cell sizing may impact the overall computing system performance, and shows that different computing workloads may have conflicting expectations on memory cell sizing. Leveraging MTJ device switching characteristics, we further propose an STT RAM architecture design method that can make STT RAM cache with relatively small memory cell size perform well over a wide spectrum of computing benchmarks. This has been well demonstrated using CACTI-based memory modeling and computing system performance simulations using SimpleScalar. Moreover, we show that this design method can also reduce STT RAM cache energy consumption by up to 30% over a variety of benchmarks.
Image convolution processing: A GPU versus FPGA comparison

Convolution is one of the most important operators used in image processing. With the constant need to increase the performance in high-end applications and the rise and popularity of parallel architectures, such as GPUs and the ones implemented in FPGAs, comes the necessity to compare these architectures in order to determine which of them performs better and in what scenario. In this article, convolution was implemented in each of the aforementioned architectures with the following languages: CUDA for GPUs and Verilog for FPGAs. In addition, the same algorithms were also implemented in MATLAB, using predefined operations and in C using a regular x86 quad-core processor. Comparative performance measures, considering the execution time and the clock ratio, were taken and commented in the paper. Overall, it was possible to achieve a CUDA speedup of roughly 200× in comparison to C, 70× in comparison to Matlab and 20× in comparison to FPGA.
Towards Characterisation of Music via Rhythmic Patterns

A central problem in music information retrieval is finding suitable representations which enable efficient and accurate computation of musical similarity and identity. Low level audio features are ideal for calculating identity, but are of limited use for similarity measures, as many aspects of music can only be captured by considering high level features. We present a new method of characterising music by typical bar-length rhythmic patterns which are automatically extracted from the audio signal, and demonstrate the usefulness of this representation by its application in a genre classification task. Recent work has shown the importance of tempo and periodicity features for genre recognition, and we extend this research by employing the extracted temporal patterns as features. Standard classification algorithms are utilised to discriminate 8 classes of Standard and Latin ballroom dance music (698 pieces). Although pattern extraction is error-prone, and patterns are not always unique to a genre, classification by rhythmic pattern alone achieves up to 50% correctness (baseline 16%), and by combining with other features, a classification rate of 96% is obtained.
Medical image captioning : learning to describe medical image findings using multitask-loss CNN

Automatic detection and classification of lesions in medical images remains one of the most important and challenging problems. In this paper, we present a new multi-task convolutional neural network (CNN) approach for detection and semantic description of lesions in diagnostic images. The proposed CNN-based architecture is trained to generate and rank rectangular regions of interests (ROI’s) surrounding suspicious areas. The highest score candidates are fed into the subsequent network layers. These layers are trained to generate semantic description of suspicious findings. During the training stage, our method uses rectangular ground truth boxes; it does not require accurately delineated lesion contours. It has a clear advantage for supervised training on large datasets. Our system learns discriminative features which are shared in the detection and the description stages. This eliminates the need for hand-crafted features, and allows a minimal-overhead application of the method to new modalities and organs. The proposed approach estimates values of the standard radiological lexicon descriptors from the image data. These descriptors represent the basis for a diagnosis. Based on the descriptor values, the method generates a structured medical report. The proposed approach should help radiologists to understand a diagnostic decision of a computer aided diagnosis (CADx) system. We test the proposed method on proprietary and publicly available breast databases, and show that our method outperforms the competing approaches.
Deep contextual language understanding in spoken dialogue systems

We describe a unified multi-turn multi-task spoken language understanding (SLU) solution capable of handling multiple context sensitive classification (intent determination) and sequence labeling (slot filling) tasks simultaneously. The proposed architecture is based on recurrent convolutional neural networks (RCNN) with shared feature layers and globally normalized sequence modeling components. The temporal dependencies within and across different tasks are encoded succinctly as recurrent connections. The dialog system responses beyond SLU component are also exploited as effective external features. We show with extensive experiments on a number of datasets that the proposed joint learning framework generates state-of-the-art results for both classification and tagging, and the contextual modeling based on recurrent and external features significantly improves the context sensitivity of SLU models.
Fast Linear Discriminant Analysis using QR Decomposition and Regularization

Linear Discriminant Analysis (LDA) is among the most optimal dimension reduction methods for classification, which provides a high degree of class separability for numerous applications from science and engineering. However, problems arise with this classical method when one or both of the scatter matrices is singular. Singular scatter matrices are not unusual in many applications, especially for highdimensional data. For high-dimensional undersampled and oversampled problems, the classical LDA requires modification in order to solve a wider range of problems. In recent work the generalized singular value decomposition (GSVD) has been shown to mitigate the issue of singular scatter matrices, and a new algorithm, LDA/GSVD, has been shown to be very robust for many applications in machine learning. However, the GSVD inherently has a considerable computational overhead. In this paper, we propose fast algorithms based on the QR decomposition and regularization that solve the LDA/GSVD computational bottleneck. In addition, we present fast algorithms for classical LDA and regularized LDA utilizing the framework based on LDA/GSVD and preprocessing by the Cholesky decomposition. Experimental results are presented that demonstrate substantial speedup in all of classical LDA, regularized LDA, and LDA/GSVD algorithms without any sacrifice in classification performance for a wide range of machine learning applications.
Networks and the best approximation property

Networks can be considered as approximation schemes. Multilayer networks of the perceptron type can approximate arbitrarily well continuous functions (Cybenko 1988, 1989; Funahashi 1989; Stinchcombe and White 1989). We prove that networks derived from regularization theory and including Radial Basis Functions (Poggio and Girosi 1989), have a similar property. From the point of view of approximation theory, however, the property of approximating continuous functions arbitrarily well is not sufficient for characterizing good approximation schemes. More critical is the property ofbest approximation. The main result of this paper is that multilayer perceptron networks, of the type used in backpropagation, do not have the best approximation property. For regularization networks (in particular Radial Basis Function networks) we prove existence and uniqueness of best approximation.
Evolving Novel Cellular Automaton Seeds Using Compositional Pattern Producing Networks (CPPN)

The aim of this study is to evolve novel seeds for John Conway's Game of Life cellular automaton (CA) with Compositional Pattern Producing Networks (CPPNs), a variation of artificial neural networks known to evolve organic patterns when used to process visual data. CPPNs were evolved using both objective search (implemented with NeuroEvolution of Augmenting Topologies) and novelty search, which focuses on finding novel solutions rather than objectively "fitter" solutions. Objective search quickly evolved game of life solutions that converged to trivial combinations of previously known solutions. However, novelty search produced non-trivial symmetries and complex high period oscillators such as the period 15 pentadecathlon. Regardless, neither approach evolved purely novel or undocumented seeds. Despite this failure, the complex evolved solutions demonstrate that CPPNs can serve as a powerful encoding for cellular automata seeds. As such, these results stand as the first baseline for further exploration into encoding cellular automata using CPPN.
Computer Based Assessment Systems Evaluation via the ISO9126 Quality Model

Many commercial products, as well as freeware and shareware tools, are the result of studies and research in this field made by companies and public institutions. This noteworthy growth in the market raises the problem of identifying a set of criteria that may be useful to an educational team wishing to select the most appropriate tool for their assessment needs. The scientific literature is very poor in respect of this issue. An important help is provided in this direction, by a number of research studies in the field of Software Engineering providing general criteria that may be used to evaluate software systems. Furthermore, a relevant effort has been made in this field by the International Standard Organization that in 1991 defined the ISO9126 standard for “Information Technology – Software Quality Characteristics and Sub-characteristics” (ISO, 1991). It is important to note that a typical CBA system is composed by:
Sleepless in Chicago: tracking the effects of adolescent sleep loss during the middle school years.

The influence of the sleep patterns of 2,259 students, aged 11 to 14 years, on trajectories of depressive symptoms, self-esteem, and grades was longitudinally examined using latent growth cross-domain models. Consistent with previous research, sleep decreased over time. Students who obtained less sleep in sixth grade exhibited lower initial self-esteem and grades and higher initial levels of depressive symptoms. Similarly, students who obtained less sleep over time reported heightened levels of depressive symptoms and decreased self-esteem. Sex of the student played a strong role as a predictor of hours of sleep, self-esteem, and grades. This study underscores the role of sleep in predicting adolescents' psychosocial outcomes and highlights the importance of using idiographic methodologies in the study of developmental processes.