nils-herrmann/clean_scirepeval huggingface dataset:
The impact of culture and education on non-verbal neuropsychological measurements: A critical review

Clinical neuropsychology has frequently considered visuospatial and non-verbal tests to be culturally and educationally fair or at least fairer than verbal tests. This paper reviews the cross-cultural differences in performance on visuoperceptual and visuoconstructional ability tasks and analyzes the impact of education and culture on non-verbal neuropsychological measurements.  the test performance among groups with the same educational level and different cultural backgrounds (intra-education inter-culture comparisons). Several studies have demonstrated a strong association between educational level and performance on common non-verbal neuropsychological tests. When neuropsychological test performance in different cultural groups is compared, significant differences are evident. Performance on non-verbal tests such as copying figures, drawing maps or listening to tones can be significantly influenced by the individual's culture. Arguments against the use of some current neuropsychological non-verbal instruments, procedures, and norms in the assessment of diverse educational and cultural groups are discussed and possible solutions to this problem are presented.
Flexpad: highly flexible bending interactions for projected handheld displays

Flexpad is an interactive system that combines a depth camera and a projector to transform sheets of plain paper or foam into flexible, highly deformable, and spatially aware handheld displays. We present a novel approach for tracking deformed surfaces from depth images in real time. It captures deformations in high detail, is very robust to occlusions created by the user's hands and fingers, and does not require any kind of markers or visible texture. As a result, the display is considerably more deformable than in previous work on flexible handheld displays, enabling novel applications that leverage the high expressiveness of detailed deformation. We illustrate these unique capabilities through three application examples: curved cross-cuts in volumetric images, deforming virtual paper characters, and slicing through time in videos. Results from two user studies show that our system is capable of detecting complex deformations and that users are able to perform them quickly and precisely.
Financial Time Series Forecasting Using Support Vector Machine

The traditional financial time series forecasting methods use accurate input data for prediction, and then make single-step or multi-step prediction based on the established regression model. So its prediction result is one or more specific values. But because of the complexity of financial markets, the traditional forecasting methods are less reliable. In this paper, we transform the financial time series into fuzzy grain particle sequences, and use support vector machine regression to regress the upper and lower bounds of the fuzzy particles, and then apply regression model single-step prediction on the upper and lower bounds, which will limit the predict results within a range. This is a new idea. The Shanghai Composite Index Week closed index for the experimental data, experimental results show the effectiveness of this approach.
The darklight rises: visible light communication in the dark: demo

Visible Light Communication (VLC) emerges as a new wireless communication technology with appealing benefits not present in radio communication. However, current VLC designs commonly require LED lights to emit perceptible light beams, which greatly limits the applicable scenarios of VLC (e.g., in a sunny day when indoor lighting is not needed), and brings high energy overhead and unpleasant visual experiences for mobile devices to transmit data using VLC. We design and develop DarkLight, a new VLC primitive that allows light-based communication to be sustained even when LEDs emit extremely-low luminance. The key idea is to encode data into ultra-short, imperceptible light pulses. We tackle challenges in circuit designs, data encoding/decoding schemes, and DarkLight networking, to efficiently generate and reliably detect ultra-short light pulses using off-the-shelf, low-cost LEDs and photodiodes. Our DarkLight prototype supports 1.3-m distance with 1.6-Kbps data rate. By loosening up VLC's reliance on visible light beams, DarkLight presents an unconventional direction of VLC and fundamentally broadens VLC's application scenarios.
Anxiety and sensory over-responsivity in toddlers with autism spectrum disorders: bidirectional effects across time.

This report focuses on the emergence of and bidirectional effects between anxiety and sensory over-responsivity (SOR) in toddlers with autism spectrum disorders (ASD). Participants were 149 toddlers with ASD and their mothers, assessed at 2 annual time points. A cross-lag analysis showed that anxiety symptoms increased over time while SOR remained relatively stable. SOR positively predicted changes in anxiety over and above child age, autism symptom severity, NVDQ, and maternal anxiety, but anxiety did not predict changes in SOR. Results suggest that SOR emerges earlier than anxiety, and predicts later development of anxiety.
Sparse Coding for Classification via Discrimination Ensemble

Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes. Many existing approaches consider only a simple single linear classifier whose discriminative power is rather weak. In this paper, we proposed a discriminative sparse coding method which jointly learns a dictionary for sparse coding and an ensemble classifier for discrimination. The ensemble classifier is composed of a set of linear predictors and constructed via both subsampling on data and subspace projection on sparse codes. The advantages of the proposed method over the existing ones are multi-fold: better discriminability of sparse codes, weaker dependence on peculiarities of training data, and more expressibility of classifier for classification. These advantages are also justified in the experiments, as our method outperformed several recent methods in several recognition tasks.
Classifying Dialogue Acts in One-on-One Live Chats

We explore the task of automatically classifying dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service. In particular, we investigate the effectiveness of various features and machine learners for this task. While a simple bag-of-words approach provides a solid baseline, we find that adding information from dialogue structure and inter-utterance dependency provides some increase in performance; learners that account for sequential dependencies (CRFs) show the best performance. We report our results from testing using a corpus of chat dialogues derived from online shopping customer-feedback data.
Scale-Space and Edge Detection Using Anisotropic Diffusion

Abstracf-The scale-space technique introduced by Witkin involves generating coarser resolution images by convolving the original image with a Gaussian kernel. This approach has a major drawback: it is difficult to obtain accurately the locations of the “semantically meaningful” edges at coarse scales. In this paper we suggest a new definition of scale-space, and introduce a class of algorithms that realize it using a diffusion process. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing in preference to interregion smoothing. It is shown that the “no new maxima should be generated at coarse scales” property of conventional scale space is preserved. As the region boundaries in our approach remain sharp, we obtain a high quality edge detector which successfully exploits global information. Experimental results are shown on a number of images. The algorithm involves elementary, local operations replicated over the image making parallel hardware implementations feasible.
Partial Least Square Regression PLS-Regression

PLS regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. Its goal is to predict or analyze a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by extracting from the predictors a set of orthogonal factors called latent variables which have the best predictive power. PLS regression is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent variables (i.e., predictors). It originated in the social sciences (specifically economy, Herman Wold 1966) but became popular first in chemometrics (i.e., computational chemistry) due in part to Herman’s son Svante, (Wold, 2001) and in sensory evaluation (Martens & Naes, 1989). But PLS regression is also becoming a tool of choice in the social sciences as a multivariate technique for nonexperimental and experimental data alike (e.g., neuroimaging, see Mcintosh & Lobaugh, 2004; Worsley, 1997). It was first presented
A stochastic data discrimination based autoencoder approach for network anomaly detection

Machine learning based network anomaly detection methods, which are already effective defense mechanisms against known network intrusion attacks, have also proven themselves to be more successful on the detection of zero-day attacks compared to other types of detection methods. Therefore, research on network anomaly detection using deep learning is getting more attention constantly. In this study we created an anomaly detection model based on a deterministic autoencoder, which discriminates normal and abnormal data by using our proposed stochastic threshold determination approach. We tested our proposed anomaly detection model on the NSL-KDD's test dataset KDDTest+ and obtained an accuracy of 88.28%. The experimental results show that our proposed anomaly detection model can perform almost the same as the most successful and up-to-date machine learning based anomaly detection models in the literature.
Unique Signatures of Histograms for Local Surface Description

This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.
Object Detector on Coastal Surveillance Radar Using Two-Dimensional Order-Statistic Constant-False Alarm Rate Algoritm

This paper describes the development of radar object detection using two dimensional constant false alarm rate (2D-CFAR). Objective of this development is to minimize noise detection if compared with the previous algorithm that uses one dimensional constant false alarm rate (1D-CFAR) algorithm such as order-statistic (OS) CFAR, cell-averaging (CA) CFAR, AND logic (AND) CFAR and variability index (VI) CFAR where has been implemented on coastal surveillance radar. The optimum detection result in coastal surveillance radar testing when Pfa set to 1e-2, K set to 3/4*Nwindow and Guard Cell set to 0. Principle of 2D-CFAR algorithm is combining of two CFAR algorithms for each array data of azimuth and range. Order statistic (OS) CFAR algoritm is implemented on this 2D-CFAR by fusion rule of AND logic.The algorithm of 2D-CFAR is developed using Microsoft Visual C++ 2008 and the output of 2D-CFAR is plotted on PPI scope radar using GDI+ library. The result of 2D-CFAR development shows that 2D-CFAR can minimize noise detected if compared with 1D-CFAR with the same parameter of CFAR. Best performance of 2DCFAR in object detection when Nwindow set to 128. The time of software processing of 2D-CFAR is about two times longer than the 1D-CFAR.
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class labels. To make segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. In this architecture, the model generates spatial highlights of each category presented in images using an attention model, and subsequently performs binary segmentation for each highlighted region using decoder. Combining attention model, the decoder trained with segmentation annotations in different categories boosts accuracy of weakly-supervised semantic segmentation. The proposed algorithm demonstrates substantially improved performance compared to the state-of-theart weakly-supervised techniques in PASCAL VOC 2012 dataset when our model is trained with the annotations in 60 exclusive categories in Microsoft COCO dataset.
Causally motivated attribution for online advertising

In many online advertising campaigns, multiple vendors, publishers or search engines (herein called channels) are contracted to serve advertisements to internet users on behalf of a client seeking specific types of conversion. In such campaigns, individual users are often served advertisements by more than one channel. The process of assigning conversion credit to the various channels is called "attribution," and is a subject of intense interest in the industry. This paper presents a causally motivated methodology for conversion attribution in online advertising campaigns. We discuss the need for the standardization of attribution measurement and offer three guiding principles to contribute to this standardization. Stemming from these principles, we position attribution as a causal estimation problem and then propose two approximation methods as alternatives for when the full causal estimation can not be done. These approximate methods derive from our causal approach and incorporate prior attribution work in cooperative game theory. We argue that in cases where causal assumptions are violated, these approximate methods can be interpreted as variable importance measures. Finally, we show examples of attribution measurement on several online advertising campaign data sets.
Classify Sentence from Multiple Perspectives with Category Expert Attention Network

Attention mechanisms achieve promising performance in text classification. When classifying the sentence into a certain category among multiple candidates, the existing attention utilizes a unified attention weight vector to determine contribution of each word. We observe that it is not accurate enough for a single vector to capture diverse category features of a sentence. In this paper, we propose a Category Expert Attention matrix to extract sentence features from different category perspectives. Each row of the attention matrix represents the unique feature of each category. We evaluate our model on four large scale datasets. Empirical results show that our model outperforms previous baseline methods by a significant margin. Especially on the NLPCC dataset, we achieve new state-of-the-art results. We also conduct visualization experiments to interpret how category expert information analyzes sentences.
Upper lateral cartilage fold-in flap: a combined spreader and/or splay graft effect without cartilage grafts.

Dorsal hump reduction almost always breaks the internal nasal valve and nasal obstruction is likely to occur postoperatively, unless reconstructed. One hundred eighty patients were operated using both open and closed rhinoplasty approaches. Upper lateral cartilages were meticulously separated from their junction with septum. Following bony and septal cartilaginous hump removal, upper lateral cartilages were folded inward. Either transcartilaginous horizontal mattress/simple sutures or perichondrial sutures were used depending of the desired width of the middle vault and the necessity for a splay-graft effect. In 7 patients unilateral, and in 1 patient bilateral, nasal synechia occurred and they were all treated under local anesthesia. All patients but 9 stated significantly improved nasal breathing. There was no inverted-V deformity or middle-vault narrowing observed. This technique is simple and physiologic, might be applicable for almost all primary rhinoplasty patients. Although it is possible with closed rhinoplasty approaches, it is easier with an open approach.
Transfer Learning Based Visual Tracking with Gaussian Processes Regression

Modeling the target appearance is critical in many modern visual tracking algorithms. Many tracking-by-detection algorithms formulate the probability of target appearance as exponentially related to the confidence of a classifier output. By contrast, in this paper we directly analyze this probability using Gaussian Processes Regression (GPR), and introduce a latent variable to assist the tracking decision. Our observation model for regression is learnt in a semi-supervised fashion by using both labeled samples from previous frames and the unlabeled samples that are tracking candidates extracted from the current frame. We further divide the labeled samples into two categories: auxiliary samples collected from the very early frames and target samples from most recent frames. The auxiliary samples are dynamically re-weighted by the regression, and the final tracking result is determined by fusing decisions from two individual trackers, one derived from the auxiliary samples and the other from the target samples. All these ingredients together enable our tracker, denoted as TGPR, to alleviate the drifting issue from various aspects. The effectiveness of TGPR is clearly demonstrated by its excellent performances on three recently proposed public benchmarks, involving 161 sequences in total, in comparison with state-of-the-arts.
EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems

Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems.
 EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results.
 EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.
How do software engineers understand code changes?: an exploratory study in industry

Software evolves with continuous source-code changes. These code changes usually need to be understood by software engineers when performing their daily development and maintenance tasks. However, despite its high importance, such change-understanding practice has not been systematically studied. Such lack of empirical knowledge hinders attempts to evaluate this fundamental practice and improve the corresponding tool support.
 To address this issue, in this paper, we present a large-scale quantitative and qualitative study at Microsoft. The study investigates the role of understanding code changes during software-development process, explores engineers' information needs for understanding changes and their requirements for the corresponding tool support. The study results reinforce our beliefs that understanding code changes is an indispensable task performed by engineers in software-development process. A number of insufficiencies in the current practice also emerge from the study results. For example, it is difficult to acquire important information needs such as a change's completeness, consistency, and especially the risk imposed by it on other software components. In addition, for understanding a composite change, it is valuable to decompose it into sub-changes that are aligned with individual development issues; however, currently such decomposition lacks tool support.
On the Philosophy of Bitcoin/Blockchain Technology: Is it a Chaotic, Complex System?

The Philosophy of Blockchain Technology is concerned, among other things, with its ontology, how might it be characterised, how is it being created, implemented, and adopted, how does it operate in the world, and how does it evolve over time. Here, we concentrate on whether Bitcoin/blockchain can be considered a complex system and, if so, whether a chaotic one. Beyond mere academic curiosity, a positive response would raise concerns about the likelihood of Bitcoin/blockchain entering a 2010-FlashCrash-type of chaotic regime, with catastrophic consequences for financial systems based on it. This paper starts by enhancing the relevant details of Bitcoin/blockchain ecosystem formed by the blockchain itself, bitcoin end-users (payers and payees), capital gain seekers, miners, full nodes maintainers, and developers, and their interactions. Secondly, the Information Theory of Complex Systems is briefly discussed for later use. Finally, blockchain is investigated with the help of Crutchfield‟s Statistical Complexity measure. The low non-null statistical complexity value obtained suggests that blockchain may be considered algorithmically complicated, but hardly a complex system and unlikely to enter a chaotic regime.
Proceso de Valoración para la Mejora de Procesos Software en Pequeñas Organizaciones

1 Grupo IDIS Facultad de Ingeniería Electrónica y Telecomunicaciones Universidad del Cauca Calle 5 No. 4 – 70. Popayán, Cauca, Colombia. fjpino@unicauca.edu.co.  http://www.unicauca.edu.co/idis/ 2 Grupo Alarcos Escuela Superior de Informática Universidad Castilla-La Mancha Paseo de la Universidad 4, Ciudad Real, España.
Word Alignment Modeling with Context Dependent Deep Neural Network

In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNNHMM (Dahl et al., 2012) method introduced in speech recognition to the HMMbased word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale EnglishChinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.