nils-herrmann/clean_scirepeval huggingface dataset:
Colostrum avoidance, prelacteal feeding and late breast-feeding initiation in rural Northern Ethiopia.

To identify specific cultural and behavioural factors that might be influenced to increase colostrum feeding in a rural village in Northern Ethiopia to improve infant health.



Background interviews were conducted with six community health workers and two traditional birth attendants. A semi-structured tape-recorded interview was conducted with twenty mothers, most with children under the age of 5 years. Variables were: parental age and education; mother's ethnicity; number of live births and children's age; breast-feeding from birth through to weaning; availability and use of formula; and descriptions of colostrum v. other stages of breast milk. Participant interviews were conducted in Amharic and translated into English.



Kossoye, a rural Amhara village with high prevalence rates of stunting: inappropriate neonatal feeding is thought to be a factor.



Women (20-60 years of age) reporting at least one live birth (range: 1-8, mean: ∼4).



Colostrum (inger) and breast milk (yetut wotet) were seen as different substances. Colostrum was said to cause abdominal problems, but discarding a portion was sufficient to mitigate this effect. Almost all (nineteen of twenty) women breast-fed and twelve (63 %) reported ritual prelacteal feeding. A majority (fifteen of nineteen, 79 %) reported discarding colostrum and breast-feeding within 24 h of birth. Prelacteal feeding emerged as an additional factor to be targeted through educational intervention.



To maximize neonatal health and growth, we recommend culturally tailored education delivered by community health advocates and traditional health practitioners that promotes immediate colostrum feeding and discourages prelacteal feeding.
Design of a wideband dual-polarized stacked patch antenna with high isolation and low cross polarization for X-band applications

The design and realization of a low-profile wideband dual-polarized patch antenna with high isolation and low cross polarization has been presented. The aperture-coupled antenna element has four layers of substrates. Two microstrip lines terminated with circular patches are printed on the backside of the bottom substrate for feeding the radiating patches, while on the other side is a ground plane with two offset H-shaped slots. One port is excited for horizontally polarized wave and the other one is for vertical polarization. Two slots are arranged in a `T' configuration to achieve a high isolation of over 35 dB. A square radiating patch is printed on the top layer of a Duroid substrate which is placed above the ground plane. A piece of Rohacell foam with a thickness of 3mm and a permittivity of 1.06 is located above the Duroid substrate. A top radiating patch is printed on the bottom side of a top Duroid substrate. Thus, a wideband performance can be obtained due to the multilayer structure and the impedance matching can be improved by adjusting the dimensions of the circular patches and the slots. Further to reduce the cross polarization, two identical elements with one rotated by 180° are combined and each pair of single linearly polarized elements are fed out of phase. In addition, a four-element array with two mirrored two-element arrays has been investigated. The obtained results can confirm that, the proposed dual-polarized patch array can provide a wideband performance across 8.5-11 GHz with VSWR <; 2. The isolation between the two ports is over 35 dB and the cross polarization level is less than -35 dB over the operating frequency band. Therefore, the proposed design technique is promising for X-band SAR applications.
Channel-Max, Channel-Drop and Stochastic Max-pooling

We propose three regularization techniques to overcome drawbacks of local winner-take-all methods used in deep convolutional networks. Channel-Max inherits the max activation unit from Maxout networks, but otherwise adopts complementary subsets of input and filters with different kernel sizes as better companions to the max function. To balance the training on different pathways, Channel-Drop is employed to randomly discard half pathways before their inputs are convolved respectively. Stochastic Max-pooling is defined to reduce the overfitting caused by conventional max-pooling, in which half activations are randomly dropped in each pooling region during training and top largest activations are probabilistically averaged during testing. Using Channel-Max, Channel-Drop and Stochastic Max-pooling, we demonstrate state-of-the-art performance on four benchmark datasets: CIFAR-10, CIFAR-100, STL-10 and SVHN.
Dry electrodes for electrocardiography.

Patient biopotentials are usually measured with conventional disposable Ag/AgCl electrodes. These electrodes provide excellent signal quality but are irritating for long-term use. Skin preparation is usually required prior to the application of electrodes such as shaving and cleansing with alcohol. To overcome these difficulties, researchers and caregivers seek alternative electrodes that would be acceptable in clinical and research environments. Dry electrodes that operate without gel, adhesive or even skin preparation have been studied for many decades. They are used in research applications, but they have yet to achieve acceptance for medical use. So far, a complete comparison and evaluation of dry electrodes is not well described in the literature. This work compares dry electrodes for biomedical use and physiological research, and reviews some novel systems developed for cardiac monitoring. Lastly, the paper provides suggestions to develop a dry-electrode-based system for mobile and long-term cardiac monitoring applications.
Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations

Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations “in the wild” often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations.
Outlier Detection with Kernel Density Functions

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).
Detection of covert attacks on cyber-physical systems by extending the system dynamics with an auxiliary system

Securing cyber-physical systems is vital for our modern society since they are widely used in critical infrastructure like power grid control and water distribution. One of the most sophisticated attacks on these systems is the covert attack, where an attacker changes the system inputs and disguises his influence on the system outputs by changing them accordingly. In this paper an approach to detect such an attack by extending the original system with a switched auxiliary system is proposed. Furthermore, a detection system using a switched Luenberger observer is presented. The effectiveness of the proposed method is illustrated by a simulation example.
Investigations on speaker adaptation of LSTM RNN models for speech recognition

Recently Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) acoustic models have demonstrated superior performance over deep neural networks (DNN) models in speech recognition and many other tasks. Although a lot of work have been reported on DNN model adaptation, very little has been done on LSTM model adaptation. In this paper we present our extensive studies of speaker adaptation of LSTM-RNN models for speech recognition. We investigated different adaptation methods combined with KL-divergence based regularization, where and which network component to adapt, supervised versus unsupervised adaptation and asymptotic analysis. We made a few distinct and important observations. In a large vocabulary speech recognition task, by adapting only 2.5% of the LSTM model parameters using 50 utterances per speaker, we obtained 12.6% WERR on the dev set and 9.1% WERR on the evaluation set over a strong LSTM baseline model.
Recognizing human activity in smart home using deep learning algorithm

There is a topic of interest for many researchers about human activities recognition in recent years. In this paper, we propose the research to recognize human activities by deep learning (DL) algorithm. We collected the data from participants performing activities in order to evaluate the human activities recognition results. After pre-training the deep network, the fine-tuning process begins. Compared with Hidden Markov Model (HMM) and naïve Bayes Classifier (NBC), the experiment results show that the proposed deep learning algorithm is an effective way for recognizing human activities in smart home.
Particle Belief Propagation

The popularity of particle filtering for inference in Markov chain models defined over random variables with very large or continuous domains makes it natural to consider sample–based versions of belief propagation (BP) for more general (tree–structured or loopy) graphs. Already, several such algorithms have been proposed in the literature. However, many questions remain open about the behavior of particle–based BP algorithms, and little theory has been developed to analyze their performance. In this paper, we describe a generic particle belief propagation (PBP) algorithm which is closely related to previously proposed methods. We prove that this algorithm is consistent, approaching the true BP messages as the number of samples grows large. We then use concentration bounds to analyze the finitesample behavior and give a convergence rate for the algorithm on tree–structured graphs. Our convergence rate is O(1/ √ n) where n is the number of samples, independent of the domain size of the variables.
Marketing segmentation using support vector clustering

Marketing segmentation is widely used for targeting a smaller market and is useful for decision makers to reach all customers effectively with one basic marketing mix. Although several clustering algorithms have been proposed to deal with marketing segmentation problems, a soundly method seems to be limited. In this paper, support vector clustering (SVC) is used for marketing segmentation. A case study of a drink company is used to demonstrate the proposed method and compared with the k-means and the self-organizing feature map (SOFM) methods. On the basis of the numerical results, we can conclude that SVC outperforms the other methods in marketing segmentation.
Android malware detection method based on naive Bayes and permission correlation algorithm

In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.
Novel blood pressure estimation method using single photoplethysmography feature

Continuous blood pressure (BP) monitoring has a significant meaning to the prevention and early diagnosis of cardiovascular disease. However, existing continuous BP monitoring approaches, especially cuff-less BP monitoring approaches, are all contraptions which complex and huge computation required. For example, for the most sophisticated cuff-less BP monitoring method using pulse transit time (PTT), the simultaneous record of photoplethysmography (PPG) signal and electrocardiography (ECG) are required, and various measurement of characteristic points are needed. These issues hindered widely application of cuff less BP measurement in the wearable devices. In this study, a novel BP estimation method using single PPG signal feature was proposed and its performance in BP estimation was also tested. The results showed that the new approach proposed in this study has a mean error −0.91 ± 3.84 mmHg for SBP estimation and −0.36 ± 3.36 mmHg for DBP estimation respectively. This approach performed better than the traditional PTT based BP estimation, which mean error for SBP estimation was −0.31 ± 4.78 mmHg, and for DBP estimation was −0.18 ± 4.32 mmHg. Further investigation revealed that this new BP estimation approach only required measurement of one characteristic point, reducing much computation when implementing. These results demonstrated that this new approach might be more suitable implemented in the wearable BP monitoring devices.
User-click modeling for understanding and predicting search-behavior

Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.
 Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.
The theory of bandpass sampling

Abstruct-The sampling of bandpass signals is discussed with respect to band position, noise considerations, and parameter sensitivity. For first-order sampling, the acceptable and unacceptable sample rates are presented, with specific discussion of the practical rates which are nonminimum. The minimum sampling rate is pathological in that any imperfection in the implementation will cause aliasing. The susceptibility of the acceptable sampling rates to aliasing decreases with increasing guardband size from the minimum sampling rate. In applying bandpass sampling to relocate signals to a baseband position, the signal-to-noise ratio is not preserved owing to the out-of-band noise being aliased. The degradation in signal-to-noise ratio is quantified in terms of the position of the bandpass signal. For the construction of a bandpass signal from second-order samples, the cost of implementing the interpolant (dynamic range and length) depends on Kohlenberg's sampling factor k, the relative delay between the uniform sampling streams. An elaboration on the disallowed discrete values of k shows that some allowed values are better than others for implementation. Optimum values of k correspond to the quadrature sampling values with the band being either integer or half-integer positioned. 0 f s f l f c fu
Game Theory and Distributed Control

Game theory has been employed traditionally as a modeling tool for describing and influencing behavior in societal systems. Recently, game theory has emerged as a valuable tool for controlling or prescribing behavior in distributed engineered systems. The rationale for this new perspective stems from the parallels between the underlying decision making architectures in both societal systems and distributed engineered systems. In particular, both settings involve an interconnection of decision making elements whose collective behavior depends on a compilation of local decisions that are based on partial information about each other and the state of the world. Accordingly, there is extensive work in game theory that is relevant to the engineering agenda. Similarities notwithstanding, there remain important differences between the constraints and objectives in societal and engineered systems that require looking at game theoretic methods from a new perspective. This chapter provides an overview of selected recent developments of game theoretic methods in this role as a framework for distributed control in engineered systems.
Human Action Recognition in Videos Using Hybrid Motion Features

In this paper, we present hybrid motion features to promote action recognition in videos. The features are composed of two complementary components from different views of motion information. On one hand, the period feature is extracted to capture global motion in timedomain. On the other hand, the enhanced histograms of motion words (EHOM) are proposed to describe local motion information. Each word is represented by optical flow of a frame and the correlations between words are encoded into the transition matrix of a Markov process, and then its stationary distribution is extracted as the final EHOM. Compared to traditional Bags of Words representation, EHOM preserves not only relationships between words but also temporary information in videos to some extent. We show that by integrating local and global features, we get improved recognition rates on a variety of standard datasets.
Personalized Security Messaging : Nudges for Compliance with Browser Warnings

Decades of psychology and decision-making research show that everyone makes decisions differently; yet security messaging is still one-size-fits-all. This suggests that we can improve outcomes by delivering information relevant to how each individual makes decisions. We tested this hypothesis by designing messaging customized for stable personality traits— specifically, the five dimensions of the General Decision-Making Style (GDMS) instrument. We applied this messaging to browser warnings, security messaging encountered by millions of web users on a regular basis. To test the efficacy of our nudges, we conducted experiments with 1,276 participants, who encountered a warning about broken HTTPS due to an invalid certificate under realistic circumstances. While the effects of some nudges correlated with certain traits in a statistically significant manner, we could not reject the null hypothesis—that the intervention did not affect the subjects’ behavior—for most of our nudges, especially after accounting for participants who did not pay close attention to the message. In this paper, we present the detailed results of our experiments, discuss potential reasons for why the outcome contradicts the decision-making research, and identify lessons for researchers based on our experience.
Lower bounds by probabilistic arguments

The purpose of this paper is to resolve several open problems in the current literature on Boolean circuits, communication complexity, and hashing functions. These lower bound results share the common feature that their proofs utilize probabilistic arguments in an essential way. Specifically, we prove that, to compute the majority function of n Boolean variables, the size of any depth-3 monotone circuit must be greater than 2nε, and the size of any width-2 branching program must have super-polynomial growth. We also show that, for the problem of deciding whether i ≤ j for two n-bit integers i and j, the probabilistic ε-error one-way communication complexity is of order θ(n), while the two-way ε-error complexity is O((log n)2). We will also prove that, to compute i ¿ j mod p for an n-bit prime p, the probabilistic ε-error two-way communication complexity is of order θ(n). Finally, we prove a conjecture of Ullman that uniform hashing is asymptotically optimal in its expected retrieval cost among open address hashing schemes.
Online Robust Low-Rank Tensor Learning

The rapid increase of multidimensional data (a.k.a. tensor) like videos brings new challenges for low-rank data modeling approaches such as dynamic data size, complex high-order relations, and multiplicity of low-rank structures. Resolving these challenges require a new tensor analysis method that can perform tensor data analysis online, which however is still absent. In this paper, we propose an Online Robust Lowrank Tensor Modeling (ORLTM) approach to address these challenges. ORLTM dynamically explores the high-order correlations across all tensor modes for low-rank structure modeling. To analyze mixture data from multiple subspaces, ORLTM introduces a new dictionary learning component. ORLTM processes data streamingly and thus requires quite low memory cost that is independent of data size. This makes ORLTM quite suitable for processing large-scale tensor data. Empirical studies have validated the effectiveness of the proposed method on both synthetic data and one practical task, i.e., video background subtraction. In addition, we provide theoretical analysis regarding computational complexity and memory cost, demonstrating the efficiency of ORLTM rigorously.