nils-herrmann/clean_scirepeval huggingface dataset:
Responses of preschool children with and without ADHD to sensory events in daily life.

The purpose of this study was to compare parents' perceptions of the responses of their preschool children, with and without attention deficit hyperactivity disorder (ADHD), to sensory events in daily life in Israel. In addition, the relationship between levels of hyperactivity and sensory deficits was examined.



The Sensory Profile Questionnaire (SP) was completed by mothers of forty-eight 4- to 6-year-old children with ADHD, and mothers of 46 children without disabilities. A matched group comparison design was used to identify possible differences in sensory processing.



Based on the measure of mothers' perceptions, children with ADHD demonstrated statistically significant differences from children without ADHD in their sensory responsiveness as reflected in 6 out of 9 factor scores (p < .001-.05), and on their sensory processing, modulation, and behavioral and emotional responses, as reflected in 11 out of 14 section scores (p < .001-.05). Scores on the SP yielded statistically significant low to moderate correlations with scores on the hyperactive scale of the Preschool Behavior Questionnaire (r = .28-.66).



The findings of the present study suggest that young children with ADHD may be at increased risk of deficits in various sensory processing abilities, over and above the core symptoms of ADHD. Early identification and treatment of sensory processing deficits from a young age may extend our ability to support the successful performance of children with ADHD in meaningful and productive occupations.
Enhancing Mobile Cloud Computing Security Using Steganography

Cloud computing is an emerging and popular method of accessing shared and dynamically configurable resources via the computer network on demand. Cloud computing is excessively used by mobile applications to offload data over the network to the cloud. There are some security and privacy concerns using both mobile devices to offload data to the facilities provided by the cloud providers. One of the critical threats facing cloud users is the unauthorized access by the insiders (cloud administrators) or the justification of location where the cloud providers operating. Although, there exist variety of security mechanisms to prevent unauthorized access by unauthorized user by the cloud administration, but there is no security provision to prevent unauthorized access by the cloud administrators to the client data on the cloud computing. In this paper, we demonstrate how steganography, which is a secrecy method to hide information, can be used to enhance the security and privacy of data (images) maintained on the cloud by mobile applications. Our proposed model works with a key, which is embedded in the image along with the data, to provide an additional layer of security, namely, confidentiality of data. The practicality of the proposed method is represented via a simple case study.
Microcontroller based Constant Voltage Battery Charger with Soft switching Buck Converter for Solar Home Lighting System

This paper presents Design of a Microcontroller based Constant Voltage Battery Charger. The circuit is implemented using soft switching buck converter. Solar panels of 75Wp and 37WP are used in parallel for the experimentation and a lead acid battery of 75Ah is used for charging. Microcontroller Atmega16 is used for programming using Win AVR ISP software. It is observed that during 10AM to 2PM, on 1may2012 when there is enough solar radiation at Nagpur, charging current of the battery is almost 7 to 8A.Time taken for charging the battery is 8 to 10 hours depending upon the intensity of solar radiation. The merits of the proposed charger are, highly efficient, simple to design mostly due to not having a transformer, puts minimal stress on the switch, and requires a relatively small output filter for low output ripple.
Monolithic LED Microdisplay on Active Matrix Substrate Using Flip-Chip Technology

A monolithic high-resolution (individual pixel size 300times300 mum2) active matrix (AM) programmed 8times8 micro-LED array was fabricated using flip-chip technology. The display was composed of an AM panel and a LED microarray. The AM panel included driving circuits composed of p-type MOS transistors for each pixel. The n-electrodes of the LED pixels in the microarray were connected together, and the p-electrodes were connected to individual outputs of the driving circuits on the AM panel. Using flip-chip technology, the LED microarray was then flipped onto the AM panel to create a microdisplay.
Predicting Prevalence of Influenza-Like Illness From Geo-Tagged Tweets

Modeling disease spread and distribution using social media data has become an increasingly popular research area. While Twitter data has recently been investigated for estimating disease spread, the extent to which it is representative of disease spread and distribution in a macro perspective is still an open question. In this paper, we focus on macroscale modeling of influenza-like illnesses (ILI) using a large dataset containing 8,961,932 tweets from Australia collected in 2015. We first propose modifications of the state-of-theart ILI-related tweet detection approaches to acquire a more refined dataset. We normalize the number of detected ILIrelated tweets with Internet access and Twitter penetration rates in each state. Then, we establish a state-level linear regression model between the number of ILI-related tweets and the number of real influenza notifications. The Pearson correlation coefficient of the model is 0.93. Our results indicate that: 1) a strong positive linear correlation exists between the number of ILI-related tweets and the number of recorded influenza notifications at state scale; 2) Twitter data has promising ability in helping detect influenza outbreaks; 3) taking into account the population, Internet access and Twitter penetration rates in each state enhances the prevalence modeling analysis.
Chapter 2 Normal Glucose Homeostasis

Arterial plasma glucose values throughout a 24-h period average approximately 90 mg/dl, with a maximal concentration usually not exceeding 165 mg/dl such as after meal ingestion1 and remaining above 55 mg/dl such as after exercise2 or a moderate fast (60 h).3 This relative stability contrasts with the situation for other substrates such as glycerol, lactate, free fatty acids, and ketone bodies whose fluctuations are much wider (Table 2.1).4 This narrow range defining normoglycemia is maintained through an intricate regulatory and counterregulatory neuro-hormonal system: A decrement in plasma glucose as little as 20 mg/dl (from 90 to 70 mg/dl) will suppress the release of insulin and will decrease glucose uptake in certain areas in the brain (e.g., hypothalamus where glucose sensors are located); this will activate the sympathetic nervous system and trigger the release of counterregulatory hormones (glucagon, catecholamines, cortisol, and growth hormone).5 All these changes will increase glucose release into plasma and decrease its removal so as to restore normoglycemia. On the other hand, a 10 mg/dl increment in plasma glucose will stimulate insulin release and suppress glucagon secretion to prevent further increments and restore normoglycemia. Glucose in plasma either comes from dietary sources or is either the result of the breakdown of glycogen in liver (glycogenolysis) or the formation of glucose in liver and kidney from other carbons compounds (precursors) such as lactate, pyruvate, amino acids, and glycerol (gluconeogenesis). In humans, glucose removed from plasma may have different fates in different tissues and under different conditions (e.g., postabsorptive vs. postprandial), but the pathways for its disposal are relatively limited. It (1) may be immediately stored as glycogen or (2) may undergo glycolysis, which can be non-oxidative producing pyruvate (which can be reduced to lactate or transaminated to form alanine) or oxidative through conversion to acetyl CoA which is further oxidized through the tricarboxylic acid cycle to form carbon dioxide and water. Non-oxidative glycolysis carbons undergo gluconeogenesis and the newly formed glucose is either stored as glycogen or released back into plasma .
Applications of Machine Learning to Predict Yelp Ratings

In this project, we investigate potential factors that may affect business performance on Yelp. We use a mix of features already available in the Yelp dataset as well as generating our own features using location clustering and sentiment analysis of reviews for businesses in Phoenix, AZ. After preprocessing the data to handle missing values, we ran various featureselection techniques to evaluate which features might have the greatest importance. Multi-class classification (logistic regression, SVM, tree and random forest classifiers, Naive Bayes, GDA) was then run on these feature subsets with an accuracy of ∼ 45%, significantly higher than random chance (16.7% for 6-class classification). Regression models (linear regression, SVR) were also tested but achieved lower accuracy. In addition, the accuracy was approximately the same across different feature sets. We found that across feature selection techniques, the important features included positive/negative sentiment of reviews, number of reviews, location, whether a restaurant takes reservations, and cluster size (which represents the number of businesses in the surrounding neighborhood). However, sentiment seemed to have the largest predictive power. Thus, in order to improve business performance, a future step would be to conduct additional analysis of review text to determine new features that might help the business to achieve a higher number of positive reviews.
Classifying Lexical-semantic Relationships by Exploiting Sense / Concept Representations

This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming that a specific type of lexical-semantic relation between a pair of words would be signaled by a carefully designed set of relation-specific similarities between the words. These similarities are computed by exploiting “sense representations” (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.
Ultra-Fine Pitch Stencil Printing for a Low Cost and Low Temperature Flip-Chip Assembly Process

This paper presents the results of a packaging process based on the stencil printing of isotropic conductive adhesives (ICAs) that form the interconnections of flip-chip bonded electronic packages. Ultra-fine pitch (sub-100-mum), low temperature (100degC), and low cost flip-chip assembly is demonstrated. The article details recent advances in electroformed stencil manufacturing that use microengineering techniques to enable stencil fabrication at apertures sizes down to 20mum and pitches as small as 30mum. The current state of the art for stencil printing of ICAs and solder paste is limited between 150-mum and 200-mum pitch. The ICAs-based interconnects considered in this article have been stencil printed successfully down to 50-mum pitch with consistent printing demonstrated at 90-mum pitch size. The structural integrity or the stencil after framing and printing is also investigated through experimentation and computational modeling. The assembly of a flip-chip package based on copper column bumped die and ICA deposits stencil printed at sub-100-mum pitch is described. Computational fluid dynamics modeling of the print performance provides an indicator on the optimum print parameters. Finally, an organic light emitting diode display chip is packaged using this assembly process
The music of speech: music training facilitates pitch processing in both music and language.

The main aim of the present experiment was to determine whether extensive musical training facilitates pitch contour processing not only in music but also in language. We used a parametric manipulation of final notes' or words' fundamental frequency (F0), and we recorded behavioral and electrophysiological data to examine the precise time course of pitch processing. We compared professional musicians and nonmusicians. Results revealed that within both domains, musicians detected weak F0 manipulations better than nonmusicians. Moreover, F0 manipulations within both music and language elicited similar variations in brain electrical potentials, with overall shorter onset latency for musicians than for nonmusicians. Finally, the scalp distribution of an early negativity in the linguistic task varied with musical expertise, being largest over temporal sites bilaterally for musicians and largest centrally and over left temporal sites for nonmusicians. These results are taken as evidence that extensive musical training influences the perception of pitch contour in spoken language.
Goods recommendation based on retail knowledge in a Neo4j graph database combined with an inference mechanism implemented in jess

Along with the extensive use of ontologies as a well-established means for knowledge representation, there is a pressing need for methods that can transform ontology information into knowledge stored in a Graph Database (GDB) which is considered human-like thinking in terms of objects and their relations. In this paper, we describe a two-layer knowledge graph database: a concept layer and an instance layer. The concept layer is the resulting graph representation transferred from an ontology representation. The instance layer is the instance data associated with concept nodes. In this research, we apply the two-layer approach to a retail business transaction data for business information query and reasoning. The two-layer structure is implemented in Neo4j GDB platform and information query and recommendation is implemented with a Jess reasoning engine. The query and recommendation results are represented and visualized in knowledge graph structures. The performance of the system is evaluated in terms of the time efficiency of answering queries of retail data using the GDB and the novelty of recommendations.
A synthesis strategy for nonlinear model predictive controller on FPGA

This paper describes an implementation strategy of nonlinear model predictive controller for FPGA systems. A high-level synthesis of a real-time MPC algorithm by means of the MATLAB HDL Coder as well as the Vivado HLS tool is discussed. In order to exploit the parallel processing of FPGAs, the included integration schemes are parallelized using a fixed-point iteration approach. The synthesis results are demonstrated for two different example systems.
Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video

Models optimized for accuracy on single images are often prohibitively slow to run on each frame in a video. Recent work exploits the use of optical flow to warp image features forward from select keyframes, as a means to conserve computation on video. This approach, however, achieves only limited speedup, even when optimized, due to the accuracy degradation introduced by repeated forward warping, and the inference cost of optical flow estimation. To address these problems, we propose a new scheme that propagates features using the block motion vectors (BMV) present in compressed video (e.g. H.264 codecs), instead of optical flow, and bi-directionally warps and fuses features from enclosing keyframes to capture scene context on each video frame. Our technique, interpolation-BMV, enables us to accurately estimate the features of intermediate frames, while keeping inference costs low. We evaluate our system on the CamVid and Cityscapes datasets, comparing to both a strong single-frame baseline and related work. We find that we are able to substantially accelerate segmentation on video, achieving near real-time frame rates (20+ frames per second) on large images (e.g. 960×720 pixels), while maintaining competitive accuracy. This represents an improvement of almost 6× over the single-frame baseline and 2.5× over the fastest prior work.
Microcontroller based intelligent DC/DC converter to track Maximum Power Point for solar photovoltaic module

Maximum Power Point Tracking (MPPT) is widely used control technique to extract maximum power available from the solar cell of photovoltaic (PV) module. Since the solar cells have non-linear i–v characteristics. The efficiency of PV module is very low and power output depends on solar insolation level and ambient temperature, so maximization of power output with greater efficiency is of special interest. Moreover there is great loss of power due to mismatch of source and load. So, to extract maximum power from solar panel a MPPT needs to be designed. The objective of the paper is to present a novel cost effective and efficient microcontroller based MPPT system for solar photovoltaic system to ensure fast maximum power point operation at all fast changing environmental conditions. The proposed controller scheme utilizes PWM techniques to regulate the output power of boost DC/DC converter at its maximum possible value and simultaneously controls the charging process of battery. Incremental Conductance algorithm is implemented to track maximum power point. For the feasibility study, parameter extraction, model evaluation and analysis of converter system design a MATLAB/Simulink model is demonstrated and simulated for a typical 40W solar panel from Kyocera KC-40 for hardware implementation and verification. Finally, a hardware model is designed and tested in lab at different operating conditions. Further, MPPT system has been tested with Solar Panel at different solar insolation level and temperature. The resulting system has high-efficiency, lower-cost, very fast tracking speed and can be easily modified for additional control function for future use.
Disentangling the independently controllable factors of variation by interacting with the world

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors, and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.
Nonnative and second-language speech perception : Commonalities and complementarities 1

Language experience systematically constrains perception of speech contrasts that deviate phonologically and/or phonetically from those of the listener’s native language. These effects are most dramatic in adults, but begin to emerge in infancy and undergo further development through at least early childhood. The central question addressed here is: How do nonnative speech perception findings bear on phonological and phonetic aspects of second language (L2) perceptual learning? A frequent assumption has been that nonnative speech perception can also account for the relative difficulties that late learners have with specific L2 segments and contrasts. However, evaluation of this assumption must take into account the fact that models of nonnative speech perception such as the Perceptual Assimilation Model (PAM) have focused primarily on naïve listeners, whereas models of L2 speech acquisition such as the Speech Learning Model (SLM) have focused on experienced listeners. This chapter probes the assumption that L2 perceptual learning is determined by nonnative speech perception principles, by considering the commonalities and complementarities between inexperienced listeners and those learning an L2, as viewed from PAM and SLM. Among the issues examined are how language learning may affect perception of phonetic vs. phonological information, how monolingual vs. multiple language experience may impact perception, and what these may imply for attunement of speech perception to changes in the listener’s language environment. Commonalities and complementarities 3
Learning to Decompose and Disentangle Representations for Video Prediction

Our goal is to predict future video frames given a sequence of input frames. Despite large amounts of video data, this remains a challenging task because of the highdimensionality of video frames. We address this challenge by proposing the Decompositional Disentangled Predictive Auto-Encoder (DDPAE), a framework that combines structured probabilistic models and deep networks to automatically (i) decompose the high-dimensional video that we aim to predict into components, and (ii) disentangle each component to have low-dimensional temporal dynamics that are easier to predict. Crucially, with an appropriately specified generative model of video frames, our DDPAE is able to learn both the latent decomposition and disentanglement without explicit supervision. For the Moving MNIST dataset, we show that DDPAE is able to recover the underlying components (individual digits) and disentanglement (appearance and location) as we intuitively would do. We further demonstrate that DDPAE can be applied to the Bouncing Balls dataset involving complex interactions between multiple objects to predict the video frame directly from the pixels and recover physical states without explicit supervision.
An Improved Image Steganography algorithm based on PVD

Received Nov 12, 2017 Revised Jan 20, 2018  In this paper, a modification of PVD (Pixel Value Differencing) algorithm is used for Image Steganography in spatial domain. It is normalizing secret data value by encoding method to make the new pixel edge difference less among three neighbors (horizontal, vertical and diagonal) and embedding data only to less intensity pixel difference areas or regions. The proposed algorithm shows a good improvement for both color and gray-scale images compared to other algorithms. Color images performances are better than gray images. However, in this work the focus is mainly on gray images. The strenght of this scheme is that any random hidden/secret data do not make any shuttle differences to Steg-image compared to original image. The bit plane slicing is used to analyze the maximum payload that has been embeded into the cover image securely. The simulation results show that the proposed algorithm is performing better and showing great consistent results for PSNR, MSE values of any images, also against Steganalysis attack.