snu-aidas/HVMap-dataset huggingface dataset:
Socio-emotional development: from infancy to young adulthood.

Results from the Uppsala Longitudinal Study (ULS), which started in 1985, are reported in two sections. The first section gives a summary of longitudinal data from infancy to middle childhood (age 9 years; n = 96) concerning predictions of social functioning aspects from the theoretical perspectives of temperament, attachment, and health psychology (social factors). The second section presents the first results emanating from a follow-up when participants were 21 years old (n = 85). The developmental roots of social anxiety symptoms were studied from the same perspectives as above, although with a special focus on the predictive power of the temperament trait of shyness/inhibition. Results for middle childhood outcomes showed that temperament characteristics were relevant for most outcomes, whereas the contribution of attachment was most convincingly shown in relation to social competence and personality. Social factors were found to have moderating functions, but direct effects were also shown, the most interesting perhaps being positive effects of non-parental day care. Results from the 21-year data confirmed the expected predictive relation from shyness/inhibition to symptoms of social anxiety and further showed this relation to be specific; the relation to symptoms of depression did not survive control for social anxiety, although the opposite was true. The broad analysis of predictor associations with social anxiety, showing the relevance of other temperament factors as well as interactive effects, again attested to the need for multi-faceted models to analyze developmental trajectories.
Motion Detection and Face Recognition for CCTV Surveillance System

Closed Circuit Television (CCTV) is currently used in daily basis for a wide variety of purposes. The development of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognition in CCTV video is used as the basis of decision making to produce automatic, effective and efficient integrated system. This CCTV video process provides three outputs, motion detection information, face detection information and face identification information. Accumulative Differences Images (ADI) method is used for motion detection, and Haar Classifiers Cascade method is used for face detection. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). Then, these features are trained by Counter-Propagation Network (CPN). Offline tests are performed on 45 CCTV video. The result shows 92.655% success rate on motion detection,76% success rate on face detection, and 60% success rate on face detection. It shows that this faces detection and identification through CCTV video have not been able to obtain optimal results. The motion detection process is ideal to be applied in real-time conditions. Yet if it’s combined with face recognition process, it causes a significant time delay. Keywords— ADI, Haar Cascade Classifiers, SURF, PCA, CPN  ISSN (print): 1978-1520, ISSN (online): 2460-7258 IJCCS Vol. 12, No. 2, July 2018 : 107 – 118 108
Deep neural networks with Elastic Rectified Linear Units for object recognition

Rectified Linear Unit (ReLU) is crucial to the recent success of deep neural networks (DNNs). In this paper, we propose a novel Elastic Rectified Linear Unit (EReLU) that focuses on processing the positive part of input. Unlike previous variants of ReLU that typically adopt linear or piecewise linear functions to represent the positive part, EReLU is characterized by that each positive value scales within a moderate range like a spring during training stage. On test time, EReLU becomes standard ReLU. EReLU improves model fitting with no extra parameters and little overfitting risk. Furthermore, we propose Elastic Parametric Rectified Linear Unit (EPReLU) by taking advantage of EReLU and parametric ReLU (PReLU). EPReLU ∗Corresponding author Email addresses: jiangxiaoheng@tju.edu.cn (Xiaoheng Jiang), pyw@tju.edu.cn (Yanwei Pang ), xuelong_li@opt.ac.cn (Xuelong Li), jingpan23@gmail.com (Jing Pan), xieyinghong@163.com (Yinghong Xie) Preprint submitted to Neurocomputing September 3, 2017
Large-scale Artificial Neural Network: MapReduce-based Deep Learning

Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload. This project is mainly focused on the solution to the issues above, combining deep learning algorithm with cloud computing platform to deal with large-scale data. A MapReduce-based handwriting character recognizer will be designed in this project to verify the efficiency improvement this mechanism will achieve on training and practical large-scale data. Careful discussion and experiment will be developed to illustrate how deep learning algorithm works to train handwritten digits data, how MapReduce is implemented on deep learning neural network, and why this combination accelerates computation. Besides performance, the scalability and robustness will be mentioned in this report as well. Our system comes with two demonstration software that visually illustrates our handwritten digit recognition/encoding application. 1
Velocity and acceleration estimation for optical incremental encoders q

Optical incremental encoders are extensively used for position measurements in motion systems. The position measurements suffer from quantization errors. Velocity and acceleration estimations obtained by numerical differentiation largely amplify the quantization errors. In this paper, the time stamping concept is used to obtain more accurate position, velocity and acceleration estimations. Time stamping makes use of stored events, consisting of the encoder counts and their time instants, captured at a high resolution clock. Encoder imperfections and the limited resolution of the capturing rate of the encoder events result in errors in the estimations. In this paper, we propose a method to extend the observation interval of the stored encoder events using a skip operation. Experiments on a motion system show that the velocity estimation is improved by 54% and the acceleration estimation by 92%. 2009 Elsevier Ltd. All rights reserved.
Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective

The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This paper reviews efforts in the modeling and simulation of lithium-ion batteries and their use in the design of better batteries. Likely future directions in battery modeling and design including promising research opportunities are outlined. © 2011 The Electrochemical Society. [DOI: 10.1149/2.018203jes] All rights reserved.
RTL Hardware IP Protection Using Key-Based Control and Data Flow Obfuscation

Recent trends of hardware intellectual property (IP) piracy and reverse engineering pose major business and security concerns to an IP-based system-on-chip (SoC) design flow. In this paper, we propose a Register Transfer Level (RTL) hardware IP protection technique based on low-overhead key-based obfuscation of control and data flow. The basic idea is to transform the RTL core into control and data flow graph (CDFG) and then integrate a well-obfuscated finite state machine (FSM) of special structure, referred as “Mode-Control FSM”, into the CDFG in a manner that normal functional behavior is enabled only after application of a specific input sequence. We provide formal analysis of the effectiveness of the proposed approach and present a simple metric to quantify the level of obfuscation. We also present an integrated design flow that implements the proposed obfuscation at low computational overhead. Simulation results for two open-source IP cores show that high levels of security is achievable at nominal area and power overheads under delay constraint.
Fingerprint matching by thin-plate spline modelling of elastic deformations

This paper presents a novel minutiae matching method that describes elastic distortions in 4ngerprints by means of a thin-plate spline model, which is estimated using a local and a global matching stage. After registration of the 4ngerprints according to the estimated model, the number of matching minutiae can be counted using very tight matching thresholds. For deformed 4ngerprints, the algorithm gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100 ms on a 1 GHz P-III machine. Furthermore, it is shown that the observed deformations are di6erent from those described by theoretical models proposed in the literature. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Design Thinking Methods and Tools for Innovation in Multidisciplinary Teams

Design Thinking (DT), a human-centered approach to innovation, is regarded as a system of overlapping spaces of viability, desirability and feasibility. Innovation increases when these three perspectives are addressed. This position paper proposes DT methods and tools to foster innovation in a multidisciplinary team by facilitating decision-making processes. We discuss how DT methods and tools reflect one or more DT perspectives, namely, the human, business and technology perspectives. We also discuss how these DT methods and tools can support decision-making processes, collaboration and engagement in a multidisciplinary team. Author
Decision Trees: Theory and Algorithms

4.
Clustering of periodic multichannel timeseries data with application to plasma fluctuations

A periodic datamining algorithm has been developed and used to extract distinct plasma fluctuations in multichannel oscillatory timeseries data. The technique uses the Expectation Maximisation algorithm to solve for the maximum likelihood estimates and cluster assignments of a mixture of multivariate independent von Mises distributions (EM-VMM). The performance of the algorithm shows significant benefits when compared to a periodic k-means algorithm and clustering using non-periodic techniques on several artificial datasets and real experimental data. Additionally, a new technique for identifying interesting features in multichannel oscillatory timeseries data is described (STFT-clustering). STFTclustering identifies the coincidence of spectral features overmost channels of amulti-channel array using the averaged short time Fourier transform of the signals. These features are filtered using clustering to remove noise. This method is particularly good at identifying weaker features and complements existing methods of feature extraction. Results from applying the STFT-clustering and EM-VMM algorithm to the extraction and clustering of plasma wave modes in the time series data from a helical magnetic probe array on the H-1NF heliac are presented. © 2014 Elsevier B.V. All rights reserved.
Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights

A two-layer neural network can be used to approximate any nonlinear function. T h e behavior of the hidden nodes tha t allows the network to do this is described. Networks w i th one input are analyzed first, and the analysis is then extended to networks w i t h mult iple inputs. T h e result of th is analysis is used to formulate a method for ini t ial izat ion o f the weights o f neural networks to reduce t ra in ing t ime. Training examples are given and the learning curve for these examples are shown to illustrate the decrease in necessary training t ime. Introduction Two-layer feed forward neural networks have been proven capable of approximating any arbitrary functions [l], given that they have sufficient numbers of nodes in their hidden layers. We offer a description of how this works, along with a method of speeding up the training process by choosing the networks’ initial weights. The relationship between the inputs and the output of a two-layer neural network may be described by Equation (1) H l y = wi . sigmoid(LqX + W b i ) (1) i=O where y is the network’s output, X is the input vector, H is the number of hidden nodes, Wi is the weight vector of the i th node of the hidden layer, Wbi is the bias weight of the ith hidden node, w i is the weight of the output layer which connects the i th hidden unit to the output. The behavior of hidden nodes in two-layer networks with one input To illustrate the behavior of the hidden nodes, a two-layer network with one input is trained to approximate a function of one variable d(z) . That is, the network is trained to produce d(z) given z as input using the back-propagation algorithm [2]. The output of the network is given as
Language Models with Pre-Trained (GloVe) Word Embeddings

In this work we present a step-by-step implementation of training a Language Model (LM) , using Recurrent Neural Network (RNN) and pre-trained GloVe word embeddings, introduced by Pennigton et al. in [1]. The implementation is following the general idea of training RNNs for LM tasks presented in [2] , but is rather using Gated Recurrent Unit (GRU) [3] for a memory cell, and not the more commonly used LSTM [4]. The implementation presented is based on using keras1 [5].
Cognitive architectures: Research issues and challenges

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. 2008 Published by Elsevier B.V.
Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients.

OBJECTIVE
To ascertain the current burden of adverse drug reactions (ADRs) through a prospective analysis of all admissions to hospital.


DESIGN
Prospective observational study.


SETTING
Two large general hospitals in Merseyside, England.


PARTICIPANTS
18 820 patients aged > 16 years admitted over six months and assessed for cause of admission.


MAIN OUTCOME MEASURES
Prevalence of admissions due to an ADR, length of stay, avoidability, and outcome.


RESULTS
There were 1225 admissions related to an ADR, giving a prevalence of 6.5%, with the ADR directly leading to the admission in 80% of cases. The median bed stay was eight days, accounting for 4% of the hospital bed capacity. The projected annual cost of such admissions to the NHS is 466m pounds sterling (706m Euros, 847m dollars). The overall fatality was 0.15%. Most reactions were either definitely or possibly avoidable. Drugs most commonly implicated in causing these admissions included low dose aspirin, diuretics, warfarin, and non-steroidal anti-inflammatory drugs other than aspirin, the most common reaction being gastrointestinal bleeding.


CONCLUSION
The burden of ADRs on the NHS is high, accounting for considerable morbidity, mortality, and extra costs. Although many of the implicated drugs have proved benefit, measures need to be put into place to reduce the burden of ADRs and thereby further improve the benefit:harm ratio of the drugs.
Personality Analysis Through Handwriting
Search Based Software Engineering

The use of evolutionary algorithms for solving multi-objective optimization problems has become increasingly popular, mainly within the last 15 years. From among the several research trends that have originated in recent years, one of the most promising is the use of hybrid approaches that allow to improve the performance of multi-objective evolutionary algorithms (MOEAs). In this talk, some of the most representative research on the use of hybrid approaches in evolutionary multi-objective optimization will be discussed. The topics discussed will include multi-objective memetic algorithms, hybridization of MOEAs with gradient-based methods and with direct search methods, as well as multi-objective hyperheuristics. Some applications of these approaches as well as some potential paths for future research in this area will also be briefly discussed. Towards Evolutionary Multitasking: A New Paradigm
Recurrent Network Models for Human Dynamics

We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture. The ERD model is a recurrent neural network that incorporates nonlinear encoder and decoder networks before and after recurrent layers. We test instantiations of ERD architectures in the tasks of motion capture (mocap) generation, body pose labeling and body pose forecasting in videos. Our model handles mocap training data across multiple subjects and activity domains, and synthesizes novel motions while avoiding drifting for long periods of time. For human pose labeling, ERD outperforms a per frame body part detector by resolving left-right body part confusions. For video pose forecasting, ERD predicts body joint displacements across a temporal horizon of 400ms and outperforms a first order motion model based on optical flow. ERDs extend previous Long Short Term Memory (LSTM) models in the literature to jointly learn representations and their dynamics. Our experiments show such representation learning is crucial for both labeling and prediction in space-time. We find this is a distinguishing feature between the spatio-temporal visual domain in comparison to 1D text, speech or handwriting, where straightforward hard coded representations have shown excellent results when directly combined with recurrent units [31].
Modeling Human Motion Using Binary Latent Variables

We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. Website: http://www.cs.toronto.edu/ ∼gwtaylor/publications/nips2006mhmublv/
Notification and awareness: synchronizing task-oriented collaborative activity

People working collaboratively must establish and maintain awareness of one another’s intentions, actions and results. Notification systems typically support awareness of the presence, tasks and actions of collaborators, but they do not adequately support awareness of persistent and complex activities. We analysed awareness breakdowns in use of our Virtual School system—stemming from problems related to the collaborative situation, group, task and tool support—to motivate the concept of activity awareness. Activity awareness builds on prior conceptions of social and action awareness, but emphasizes the importance of activity context factors like planning and coordination. This work suggests design strategies for notification systems to better support collaborative activity. r 2003 Elsevier Science Ltd. All rights reserved.
SYSTEM DYNAMICS : SYSTEMIC FEEDBACK MODELING FOR POLICY ANALYSIS
CAB: Connectionist Analogy Builder

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance can be related to human performance (e.g., solution trajectory, error patterns, time-on-task). Furthermore, CAB’s bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB’s strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature. © 2003 Cognitive Science Society, Inc. All rights reserved.
A systematic review and meta-analysis of dressings used for wound healing: the efficiency of honey compared to silver on burns.

BACKGROUND
Honey has the antibacterial effect of silver without the toxic effect of silver on the skin. Even so, silver is the dominant antibacterial dressing used in wound healing.


OBJECTIVES
To evaluate the healing effects of honey dressings compared to silver dressings for acute or chronic wounds.


DESIGN
A systematic review with meta-analysis.


METHOD
The search, conducted in seven databases, resulted in six randomised controlled trial studies from South Asia focusing on antibacterial properties and healing times of honey and silver.


RESULT
Honey was more efficacious for wound healing than silver, as measured in the number of days needed for wounds to heal (pooled risk difference -20, 95% CI -0.29 to -0.11, p < .001). Honey turned out to have more antibacterial qualities than silver.


CONCLUSION
All the included studies based on burns showed the unequivocal result that honey had an even more positive effect than silver on wound healing.
Block Chain based Searchable Symmetric Encryption

The mechanism for traditional Searchable Symmetric Encryption is pay-then-use. That is to say, if a user wants to search some documents that contain special keywords, he needs to pay to the server firstly, then he can enjoy search service. Under this situation, these kinds of things will happen: After the user paying the service fees, the server may either disappear because of the poor management or returning nothing. As a result, the money that the user paid cannot be brought back quickly. Another case is that the server may return incorrect document sets to the user in order to save his own cost. Once such events happen, it needs the arbitration institution to mediate which will cost a long time. Besides, to settle the disputes the user has to pay to the arbitration institution. Ideally, we deeply hope that when the user realizes the server has a tendency to cheat in the task of searching, he can immediately and automatically withdraw his money to safeguard his right. However, the existing SSE protocols cannot satisfy this demand. To solve this dilemma, we find a compromised method by introducing the block chain into SSE. Our scheme achieves three goals stated below. Firstly, when the server does not return any thing to user after he gets the search token, the user can get some compensation from the server, because the server can infer some important information from the Index and this token. Besides, the user also doesn’t pay the service charge. Secondly, if the documents that the server returns are false, the server cannot receive service fees, meanwhile, he will be punished. Lastly, when the user receives some bitcoin from server at the beginning, he may terminate the protocol. Under this situation, the server is a victim. In order to prevent such thing from happening, the server will broadcast a transaction to redeem his pledge after an appointed time. ∗Corresponding author Email addresses: tianhb@mail.sysu.edu.cn (Haibo Tian), isszhfg@mail.sysu.edu.cn (Fangguo Zhang ) Preprint submitted to Elsevier April 26, 2017