nils-herrmann/clean_scirepeval huggingface dataset:
Perception-Guided Multimodal Feature Fusion for Photo Aesthetics Assessment

Photo aesthetic quality evaluation is a challenging task in multimedia and computer vision fields. Conventional approaches suffer from the following three drawbacks: 1) the deemphasized role of semantic content that is many times more important than low-level visual features in photo aesthetics; 2) the difficulty to optimally fuse low-level and high-level visual cues in photo aesthetics evaluation; and 3) the absence of a sequential viewing path in the existing models, as humans perceive visually salient regions sequentially when viewing a photo.
 To solve these problems, we propose a new aesthetic descriptor that mimics humans sequentially perceiving visually/semantically salient regions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local aesthetic descriptors (graphlets in this work) into a low-dimensional semantic space. Thereafter, each graphlet can be described by multiple types of visual features, both at low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent graphlets in a photo. They are sequentially linked into a path that simulates the process of humans actively viewing. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos that are marked as aesthetically pleasing by multiple users. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data; and 2) our photo aesthetic measure outperforms many of its competitors.
Outcomes of high- and low-intensity rehabilitation programme for persons in chronic phase after Guillain-Barré syndrome: a randomized controlled trial.

To assess the effectiveness of a high- vs low--intensity multidisciplinary ambulatory rehabilitation programme over 12 months for persons in the chronic phase after Guillain-Barré syndrome (pwGBS) in an Australian community cohort.



A total of 79 pwGBS, recruited from a tertiary hospital, were randomized to a treatment group (n = 40) for an individualized high-intensity programme, or a control group (n = 39) for a lower intensity programme. The primary outcome the Functional Independence Measure (FIM) motor subscale assessed "activity limitation"; while secondary measures for "participation" included: World Health Organization Quality of Life; Depression, Anxiety Stress Scale; and Perceived Impact Problem Profile (PIPP) scales. All outcome measures were assessed at baseline and at 12 months.



Intention to treat analysis of data from 69 participants (treatment n = 35, control n = 34) showed reduced disability in the treatment group in post-treatment FIM domains (mobility, transfers, sphincter control and locomotion; all p < 0.005) and PIPP scores (relationships; p = 0.011), with moderate-to-small effect sizes (r = 0.36-0.23). The treatment group compared with control group showed significant improvement in function (FIM scores): 68% vs 32%.



Higher intensity rehabilitation compared with less intense intervention reduces disability in pwGBS in later stages of recovery. Further information on rehabilitation modalities and impact on quality of life is needed.
Attention, please! A Critical Review of Neural Attention Models in Natural Language Processing

Attention is an increasingly popular mechanism used in a wide range of neural architectures. Because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures for natural language processing, with a focus on architectures designed to work with vector representation of the textual data. We discuss the dimensions along which proposals differ, the possible uses of attention, and chart the major research activities and open challenges in the area.
Unraveling the Anxious Mind: Anxiety, Worry, and Frontal Engagement in Sustained Attention Versus Off-Task Processing

Much remains unknown regarding the relationship between anxiety, worry, sustained attention, and frontal function. Here, we addressed this using a sustained attention task adapted for functional magnetic resonance imaging. Participants responded to presentation of simple stimuli, withholding responses to an infrequent "No Go" stimulus. Dorsolateral prefrontal cortex (DLPFC) activity to "Go" trials, and dorsal anterior cingulate (dACC) activity to "No Go" trials were associated with faster error-free performance; consistent with DLPFC and dACC facilitating proactive and reactive control, respectively. Trait anxiety was linked to reduced recruitment of these regions, slower error-free performance, and decreased frontal-thalamo-striatal connectivity. This indicates an association between trait anxiety and impoverished frontal control of attention, even when external distractors are absent. In task blocks where commission errors were made, greater DLPFC-precuneus and DLPFC-posterior cingulate connectivity were associated with both trait anxiety and worry, indicative of increased off-task thought. Notably, unlike trait anxiety, worry was not linked to reduced frontal-striatal-thalamo connectivity, impoverished frontal recruitment, or slowed responding during blocks without commission errors, contrary to accounts proposing a direct causal link between worry and impoverished attentional control. This leads us to propose a new model of the relationship between anxiety, worry and frontal engagement in attentional control versus off-task thought.
A design of miniaturization LPDA antenna with ultra wideband

This paper designed a Log-periodic miniaturization antenna working in 8GHz to 18GHz. It satisfies the characteristic of high gain, good radiation pattern, and a large proportion of the size reduction which other antenna don't possess. The lengthwise is 80mm, it reduces more than 50% of the transverse dimension with the original specification. A “mountain” terminal loaded design is used to shrink size of antenna under the premise of guaranteeing directivity of antenna. By optimizing backward feeding structure and antenna array to improve antenna performance. The final result is superior to other same type antenna distinctly.
A wideband CMOS LNA design approach

A methodology for designing wideband CMOS LNAs is proposed. This is an extension to the existing narrowband power-constrained simultaneous noise-input matching (PCSNIM) LNA design technique. To demonstrate the application of the proposed method, two wideband single-ended cascode LNAs are designed in a 0.18 /spl mu/m CMOS technology to operate in the frequency bands of 1.5-2.5 GHz and 3.2-4.8 GHz, respectively. Based on simulation results, these LNAs achieve: maximum forward gain of 15.4 dB (17.9 dB) with 3 dB bandwidth of 1.6 GHz (2.7 GHz); maximum in-band noise figure of 0.9 dB (1.6 dB); worst case 1dB compression point of -13 dBm (-15 dBm); IIP3 of better than -2.5 dBm (-4.5 dBm) over the entire bandwidth. The LNAs draw 7.5 mA (8.8 mA) from a 1.5 V supply.
Scene text recognition and tracking to identify athletes in sport videos

We present an athlete identification module forming part of a system for the personalization of sport video broadcasts. The aim of this module is the localization of athletes in the scene, their identification through the reading of names or numbers printed on their uniforms, and the labelling of frames where athletes are visible. Building upon a previously published algorithm we extract text from individual frames and read these candidates by means of an optical character recognizer (OCR). The OCR-ed text is then compared to a known list of athletes’ names (or numbers), to provide a presence score for each athlete. Text regions are tracked in subsequent frames using a template matching technique. In this way blurred or distorted text, normally unreadable by the OCR, is exploited to provide a denser labelling of the video sequences. Extensive experiments show that the method proposed is fast, robust and reliable, out-performing results of other systems in the literature.
Can You Spot the Fakes?: On the Limitations of User Feedback in Online Social Networks

Online social networks (OSNs) are appealing platforms for spammers and fraudsters, who typically use fake or compromised accounts to connect with and defraud real users. To combat such abuse, OSNs allow users to report fraudulent profiles or activity. The OSN can then use reporting data to review and/or limit activity of reported accounts. Previous authors have suggested that an OSN can augment its takedown algorithms by identifying a “trusted set” of users whose reports are weighted more heavily in the disposition of flagged accounts. Such identification would allow the OSN to improve both speed and accuracy of fake account detection and thus reduce the impact of spam on users. In this work we provide the first public, data-driven assessment of whether the above assumption is true: are some users better at reporting than others? Specifically, is reporting skill both measurable, i.e., possible to distinguish from random guessing; and repeatable, i.e., persistent over repeated sampling? Our main contributions are to develop a statistical framework that describes these properties and to apply this framework to data from LinkedIn, the professional social network. Our data includes member reports of fake profiles as well as the more voluminous, albeit weaker, signal of member responses to connection requests. We find that members demonstrating measurable, repeatable skill in identifying fake profiles do exist but are rare: at most 2.4% of those reporting fakes and at most 1.3% of those rejecting connection requests. We conclude that any reliable “trusted set” of members will be too small to have noticeable impact on spam metrics.
Estimating Ad Effectiveness using Geo Experiments in a Time-Based Regression Framework

Two previously published papers (Vaver and Koehler, 2011, 2012) describe a model for analyzing geo experiments. This model was designed to measure advertising effectiveness using the rigor of a randomized experiment with replication across geographic units providing confidence interval estimates. While effective, this geo-based regression (GBR) approach is less applicable, or not applicable at all, for situations in which few geographic units are available for testing (e.g. smaller countries, or subregions of larger countries) These situations also include the socalled matched market tests, which may compare the behavior of users in a single control region with the behavior of users in a single test region. To fill this gap, we have developed an analogous time-based regression (TBR) approach for analyzing geo experiments. This methodology predicts the time series of the counterfactual market response, allowing for direct estimation of the cumulative causal effect at the end of the experiment. In this paper we describe this model and evaluate its performance using simulation.
Foundations of Transparency in Tactile Information Design

This paper places contemporary literature on the topic of unimodal, single-site display of information using complex tactile signals in the context of progress towards the achievement of transparent communication - placing minimal load on the user's attentional resources. We discuss recent evidence that more is possible with purely haptic display than is commonly believed, as well as procedural developments that support systematic design of transparent tactile information display; and we frame the advances required to realize significant benefits with the technology we have now. Examples used and objectives thus identified focus on establishing effective information representations, and outlining efficient tools and processes for perceptually guiding icon design. Our discussion is inspired by Weiser's vision of calm technology based on locatedness and seamless movement between center and periphery, and it is organized along the lines of potential utility, form and learning.
A signal conditioner for high-frequency inductive position sensors

The use of integrated circuit techniques in position sensing applications can offer significant advantages over discrete component solutions, such as miniaturization, reduced cost, increased reliability and enhanced sensitivity. We realized a signal conditioner that includes an application specific integrated circuit (ASIC) and an external microcontroller for readout and control of non-contact high-frequency inductive position sensors. Both the system architecture and details on the key blocks are described. The ASIC was designed in a 0.6-¿m CMOS process technology and preliminary measured results are presented. The signal conditioner architecture is universal and can be used for other sensor types such as LVDTs.
Debugging Machine Learning Tasks

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl’s theory of causation, and uses Pearl’s PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.
Impact Of Rapid Incident Detection On Freeway Accident Fatalities

Incident detection systems have the potential of reducing the time between the occurrence of accidents and the notification of emergency medical services, called the accident notification time. Reductions in this time, in turn, may affect the numbers of fatalities. A statistical analysis is conducted to determine the quantitative relationship between fatalities and the accident notification time on urban freeways. Using this relationship, the impact of freeway incident detection systems on fatalities is estimated. are also derived. The economic benefits of fatality reduction
Do cheerfulness , exhilaration , and humor production moderate pain tolerance ? A FACS study

Prior studies have shown that watching a funny film leads to an increase in pain tolerance. The present study aimed at separating three factors considered potentially essential (mood, behavior, and cognition related to humor) and examined whether they are responsible for this effect. Furthermore, the study examined whether trait cheerfulness and trait seriousness, as measured by the State-TraitCheerfulness-Inventory (STCI; Ruch et al. 1996), moderate changes in pain tolerance. Fifty-sixty female subjects were assigned randomly to three groups, each having a different task to pursue while watching a funny film: (1) get into a cheerful mood without smiling or laughing (”Cheerfulness”); (2) smile and laugh extensively (”Exhilaration”); and (3) produce a humorous commentary to the film (”Humor production”). Pain tolerance was measured using the cold pressor test before, immediately after, and twenty minutes after the film. Results indicated that pain tolerance increased for participants from before to after watching the funny film and remained high for the twenty minutes. This effect was moderated by facial but not verbal indicators of enjoyment of humor. Participants low in trait seriousness had an overall higher pain tolerance. Subjects with a high score in trait cheerfulness showed an increase in pain tolerance after producing humor while watching the film whereas subjects low in trait cheerfulness showed a similar increase after smiling and laughter during the film.  https://doi.org/10.1515/humr.2004.009 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-77579 Originally published at: Zweyer, K; Velker, B; Ruch, Willibald (2004). Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study.  https://doi.org/10.1515/humr.2004.009 Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study KAREN ZWEYER, BARBARA VELKER, and WILLIBALD RUCH
RISC I: A Reduced Instruction Set VLSI Computer

The Reduced Instruction Set Computer (RISC) Project investigates an alternatrve to the general trend toward computers wrth increasingly complex instruction sets: With a proper set of instructions and a corresponding architectural design, a machine wrth a high effective throughput can be achieved. The simplicity of the instruction set and addressing modes allows most Instructions to execute in a single machine cycle, and the srmplicity of each instruction guarantees a short cycle time. In addition, such a machine should have a much shorter design trme. This paper presents the architecture of RISC I and its novel hardware support scheme for procedure call/return. Overlapprng sets of regrster banks that can pass parameters directly to subrouttnes are largely responsible for the excellent performance of RISC I. Static and dynamtc comparisons between this new architecture and more traditional machines are given. Although instructions are simpler, the average length of programs was found not to exceed programs for DEC VAX 11 by more than a factor of 2. Preliminary benchmarks demonstrate the performance advantages of RISC. It appears possible to build a single chip computer faster than VAX 11/780.
The source code control system

The Source Code Control System (SCCS) is a software tool designed to help programming projects control changes to source code. It provides facilities for storing, updating, and retrieving all versions of modules, for controlling updating privileges for identifying load modules by version number, and for recording who made each software change, when and where it was made, and why. This paper discusses the SCCS approach to source code control, shows how it is used and explains how it is implemented.
Time series forecasting using a deep belief network with restricted Boltzmann machines

Multi-layer perceptron (MLP) and other artificial neural networks (ANNs) have been widely applied to time series forecasting since 1980s. However, for some problems such as initialization and local optima existing in applications, the improvement of ANNs is, and still will be the most interesting study for not only time series forecasting but also other intelligent computing fields. In this study, we propose a method for time series prediction using Hinton & Salakhutdinov’s deep belief nets (DBN) which are probabilistic generative neural network composed by multiple layers of restricted Boltzmann machine (RBM). We use a 3-layer deep network of RBMs to capture the feature of input space of time series data, and after pretraining of RBMs using their energy functions, gradient descent training, i.e., back-propagation learning algorithm is used for fine-tuning connection weights between “visible layers” and “hidden layers” of RBMs. To decide the sizes of neural networks and the learning rates, Kennedy & Eberhart’s particle swarm optimization (PSO) is adopted during the training processes. Furthermore, “trend removal”, a preprocessing to the original data, is also approached in the forecasting experiment using CATS benchmark data. Additionally, approximating and short-term prediction of chaotic time series such as Lorenz chaos and logistic map were also applied by the proposed method.
Organization and maintenance of large ordered indexes

Organization and maintenance of an index for a dynamic random access file is considered. It is assumed that the index must be kept on some pseudo random access backup store like a disc or a drum. The index organization described allows retrieval, insertion, and deletion of keys in time proportional to logk I where I is the size of the index and k is a device dependent natural number such that the performance of the scheme becomes near optimal. Storage utilization is at least 50% but generally much higher. The pages of the index are organized in a special datastructure, so-called B-trees. The scheme is analyzed, performance bounds are obtained, and a near optimal k is computed. Experiments have been performed with indexes up to 100000 keys. An index of size 15000 (100000) can be maintained with an average of 9 (at least 4) transactions per second on an IBM 360/44 with a 2311 disc.
UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields

We describe our entry to SemEval 2016  Detecting Minimal Semantic Units and their Meanings. Our approach uses a discriminative first-order sequence model similar to Schneider and Smith (2015). The chief novelty in our approach is a factorization of the labels into multiword expression and supersense labels, and restricting first-order dependencies within these two parts. Our submitted models achieved first place in the closed competition (CRF) and second place in the open competition (2-CRF).
Investigating ICT using problem-based learning in face-to-face and online learning environments

This article reports on the design, implementation and evaluation of a module in the MEd (Business) in the Faculty of Education at the University of Hong Kong in which an explicit problem-based learning (PBL) approach was used to investigate the challenges associated with the adoption and use of information and communication technologies (ICT) in Hong Kong secondary school classrooms. PBL influenced both the way the curriculum was developed and the process by which students (n = 18) investigated topics related to the integration of ICT in business studies classrooms. The evaluation was based on five evaluative questions dealing with the implementation of PBL, the extent to which PBL facilitated academic discourse, the extent of new knowledge about ICT that had been created, the role of the tutor, and the online learning environment provided. The evaluation revealed that PBL provided a practical approach to investigating ICT in both face-to-face and online learning environments, leading to new knowledge about challenges associated with the adoption and use of new technologies in various educational settings.
Pedestrian detection in crowded scenes

In this paper, we address the problem of detecting pedestrians in crowded real-world scenes with severe overlaps. Our basic premise is that this problem is too difficult for any type of model or feature alone. Instead, we present an algorithm that integrates evidence in multiple iterations and from different sources. The core part of our method is the combination of local and global cues via probabilistic top-down segmentation. Altogether, this approach allows examining and comparing object hypotheses with high precision down to the pixel level. Qualitative and quantitative results on a large data set confirm that our method is able to reliably detect pedestrians in crowded scenes, even when they overlap and partially occlude each other. In addition, the flexible nature of our approach allows it to operate on very small training sets.