nils-herrmann/clean_scirepeval huggingface dataset:
Dynamically Constructed (PO)MDPs for Adaptive Robot Planning

To operate in human-robot coexisting environments, intelligent robots need to simultaneously reason with commonsense knowledge and plan under uncertainty. Markov decision processes (MDPs) and partially observable MDPs (POMDPs), are good at planning under uncertainty toward maximizing long-term rewards; P-LOG, a declarative programming language under Answer Set semantics, is strong in commonsense reasoning. In this paper, we present a novel algorithm called iCORPP to dynamically reason about, and construct (PO)MDPs using P-LOG. iCORPP successfully shields exogenous domain attributes from (PO)MDPs, which limits computational complexity and enables (PO)MDPs to adapt to the value changes these attributes produce. We conduct a number of experimental trials using two example problems in simulation and demonstrate iCORPP on a real robot. Results show significant improvements compared to competitive baselines.
Automatic Rain Drop Detection for Improved Sensing in Automotive Computer Vision Applications

The presence of raindrop induced distortion can have a significant negative impact on computer vision applications. Here we address the problem of visual raindrop distortion in standard colour video imagery for use in non-static, automotive computer vision applications where the scene can be observed to be changing over subsequent consecutive frames. We utilise current state of the art research conducted into the investigation of salience mapping as means of initial detection of potential raindrop candidates. We further expand on this prior state of the art work to construct a combined feature rich descriptor of shape information (Hu moments), isolation of raindrops pixel information from context, and texture (saliency derived) within an improved visual bag of words verification framework. Support Vector Machine and Random Forest classification were utilised for verification of potential candidates, and the effects of increasing discrete cluster centre counts on detection rates were studied. This novel approach of utilising extended shape information, isolation of context, and texture, along with increasing cluster counts, achieves a notable 13% increase in precision (92%) and 10% increase in recall (86%) against prior state of the art. False positive rates were also observed to decrease with a minimal false positive rate of 14% observed. iv ACKNOWLEDGEMENTS I wish to thank Dr Toby Breckon for his time and commitment during my project, and for the help given in patiently explaining things for me. I also wish to thank Dr Mark Stillwell for his never tiring commitment to proofreading and getting up to speed with my project. Without them, this thesis would never have come together in the way it did. I wish to thank my partner, Dr Victoria Gortowski for allowing me to go back to university, supporting me and having faith that I could do it, without which, I do not think I would have. And last but not least, Lara and Mitsy. Thank you.
Melanoma Skin Cancer Detection by Segmentation and Feature Extraction using combination of OTSU and STOLZ Algorithm Technique

Skin cancer exists in different forms like Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most dangerous and unpredictable. In this paper, we implement an image processing technique for the detection of Melanoma Skin Cancer using the software MATLAB which is easy for implementation as well as detection of Melanoma skin cancer. The input to the system is the skin lesion image. This image proceeds with the image pre-processing methods such as conversion of RGB image to Grayscale image, noise removal and so on. Further Otsu thresholding is used to segment the images followed by feature extraction that includes parameters like Asymmetry, Border Irregularity, Color and Diameter (ABCD) and then Total Dermatoscopy Score (TDS) is calculated. The calculation of TDS determines the presence of Melanoma skin cancer by classifying it as benign, suspicious or highly suspicious skin lesion.
Medicinal cannabis: rational guidelines for dosing.

The medicinal value of cannabis (marijuana) is well documented in the medical literature. Cannabinoids, the active ingredients in cannabis, have many distinct pharmacological properties. These include analgesic, anti-emetic, anti-oxidative, neuroprotective and anti-inflammatory activity, as well as modulation of glial cells and tumor growth regulation. Concurrent with all these advances in the understanding of the physiological and pharmacological mechanisms of cannabis, there is a strong need for developing rational guidelines for dosing. This paper will review the known chemistry and pharmacology of cannabis and, on that basis, discuss rational guidelines for dosing.
1 A Model of Event Knowledge

We present a connectionist model of event knowledge that is trained on examples of sequences of activities that are not explicitly labeled as events. The model learns co-occurrence patterns among the components of activities as they occur in the moment (entities, actions, and contexts), and also learns to predict sequential patterns of activities. In so doing, the model displays behaviors that in humans have been characterized as exemplifying inferencing of unmentioned event components, the prediction of upcoming components (which may or may not ever happen or be mentioned), reconstructive memory, and the ability to flexibly accommodate novel variations from previously encountered experiences. All of these behaviors emerge from what the model learns.
A comparison of feed-forward and recurrent neural networks in time series forecasting

Forecasting performances of feed-forward and recurrent neural networks (NN) trained with different learning algorithms are analyzed and compared using the Mackey-Glass nonlinear chaotic time series. This system is a known benchmark test whose elements are hard to predict. Multi-layer Perceptron NN was chosen as a feed-forward neural network because it is still the most commonly used network in financial forecasting models. It is compared with the modified version of the so-called Dynamic Multi-layer Perceptron NN characterized with a dynamic neuron model, i.e., Auto Regressive Moving Average filter built into the hidden layer neurons. Thus, every hidden layer neuron has the ability to process previous values of its own activity together with new input signals. The obtained results indicate satisfactory forecasting characteristics of both networks. However, recurrent NN was more accurate in practically all tests using less number of hidden layer neurons than the feed-forward NN. This study once again confirmed a great effectiveness and potential of dynamic neural networks in modeling and predicting highly nonlinear processes. Their application in the design of financial forecasting models is therefore most recommended.
Greater Cortical Thickness in Elderly Female Yoga Practitioners—A Cross-Sectional Study

Yoga, a mind-body activity that requires attentional engagement, has been associated with positive changes in brain structure and function, especially in areas related to awareness, attention, executive functions and memory. Normal aging, on the other hand, has also been associated with structural and functional brain changes, but these generally involve decreased cognitive functions. The aim of this cross-sectional study was to compare brain cortical thickness (CT) in elderly yoga practitioners and a group of age-matched healthy non-practitioners. We tested 21 older women who had practiced hatha yoga for at least 8 years and 21 women naive to yoga, meditation or any mind-body interventions who were matched to the first group in age, years of formal education and physical activity level. A T1-weighted MPRAGE sequence was acquired for each participant. Yoga practitioners showed significantly greater CT in a left prefrontal lobe cluster, which included portions of the lateral middle frontal gyrus, anterior superior frontal gyrus and dorsal superior frontal gyrus. We found greater CT in the left prefrontal cortex of healthy elderly women who trained yoga for a minimum of 8 years compared with women in the control group.
INDIVIDUAL AND SOCIAL FACTORS RELATED TO STUDENTS ’ ACADEMIC ACHIEVEMENT AND MOTIVATION FOR LEARNING

The main objectives of this study were to determine the most frequent and the most significant individual and social factors related to students’ academic achievement and motivation for learning. The study was conducted among 740 students from the Faculty of Education and the Faculty of Philosophy in Vojvodina. The participants completed questionnaires measuring students’ dominant individual and social motivational factors, the level of their motivation for learning, the level of their academic achievement and students’ socio-demographic characteristics. The results of this study showed that the students reported that both individual and social factors are related to their academic achievement and motivation for learning. Individual factors – the perceived interest in content and perceived content usefulness for personal development proved to be the most significant predictors of a high level of motivation for learning and academic success, but social motivational factors showed themselves to be the most frequent among students. The results are especially important for university teachers as guidelines for improving students’ motivation.
NB-IoT system for M2M communication

In 3GPP, a narrowband system based on Long Term Evolution (LTE) is being introduced to support the Internet of Things. This system, named Narrowband Internet of Things (NB-IoT), can be deployed in three different operation modes - (1) stand-alone as a dedicated carrier, (2) in-band within the occupied bandwidth of a wideband LTE carrier, and (3) within the guard-band of an existing LTE carrier. In stand-alone operation mode, NB-IoT can occupy one GSM channel (200 kHz) while for in-band and guard-band operation modes, it will use one physical resource block of LTE (180 kHz). The design targets of NB-IoT include low-cost devices, high coverage (20-dB improvement over GPRS), long device battery life (more than 10 years), and massive capacity. Latency is relaxed although a delay budget of 10 seconds is the target for exception reports. The specifications for NB-IoT are expected to be finalized in 2016. In this paper, we describe the targets for NB-IoT and present a preliminary system design. In addition, coverage, capacity, latency, and battery life analysis are also presented.
Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform

Deep neural networks (DNNs) are used by different applications that are executed on a range of computer architectures, from IoT devices to supercomputers. The footprint of these networks is huge as well as their computational and communication needs. In order to ease the pressure on resources, research indicates that in many cases a low precision representation (1-2 bit per parameter) of weights and other parameters can achieve similar accuracy while requiring less resources. Using quantized values enables the use of FPGAs to run NNs, since FPGAs are well fitted to these primitives; e.g., FPGAs provide efficient support for bitwise operations and can work with arbitrary-precision representation of numbers. This paper presents a new streaming architecture for running QNNs on FPGAs. The proposed architecture scales out better than alternatives, allowing us to take advantage of systems with multiple FPGAs. We also included support for skip connections, that are used in state-of-the art NNs, and shown that our architecture allows to add those connections almost for free. All this allowed us to implement an 18-layer ResNet for 224×224 images classification, achieving 57.5% top-1 accuracy. In addition, we implemented a full-sized quantized AlexNet. In contrast to previous works, we use 2-bit activations instead of 1-bit ones, which improves AlexNet's top-1 accuracy from 41.8% to 51.03% for the ImageNet classification. Both AlexNet and ResNet can handle 1000-class real-time classification on an FPGA. Our implementation of ResNet-18 consumes 5× less power and is 4× slower for ImageNet, when compared to the same NN on the latest Nvidia GPUs. Smaller NNs, that fit a single FPGA, are running faster then on GPUs on small (32×32) inputs, while consuming up to 20× less energy and power.
A reactive controller framework for quadrupedal locomotion on challenging terrain

We propose a reactive controller framework for robust quadrupedal locomotion, designed to cope with terrain irregularities, trajectory tracking errors and poor state estimation. The framework comprises two main modules: One related to the generation of elliptic trajectories for the feet and the other for control of the stability of the whole robot. We propose a task space CPG-based trajectory generation that can be modulated according to terrain irregularities and the posture of the robot trunk. To improve the robot's stability, we implemented a null space based attitude control for the trunk and a push recovery algorithm based on the concept of capture points. Simulations and experimental results on the hydraulically actuated quadruped robot HyQ will be presented to demonstrate the effectiveness of our framework.
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction

This paper describes our submission for the ScienceIE shared task (SemEval2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).
Some considerations on de Waal and Preston review

In the embryonic and adult brain, neural stem cells proliferate and give rise to neurons and glia through highly regulated processes. Epigenetic mechanisms — including DNA and histone modifications, as well as regulation by non-coding RNAs — have pivotal roles in different stages of neurogenesis. Aberrant epigenetic regulation also contributes to the pathogenesis of various brain disorders. Here, we review recent advances in our understanding of epigenetic regulation in neurogenesis and its dysregulation in brain disorders, including discussion of newly identified DNA cytosine modifications. We also briefly cover the emerging field of epitranscriptomics, which involves modifications of mRNAs and long non-coding RNAs.
Multi-agent systems - an introduction to distributed artificial intelligence

This edition is a translation of the book formerly published in French in 1995 (Les systèmes multi-agents: Vers une intelligence collective, Inter Editions, Paris.) Even now, it is still the main reference for the French research community in multi-agent systems (MAS). The book is intended to be both a state of the art text and an introduction for people who are interested in capturing the main ideas of MAS. It deals mainly with the theoretical background, antecedents and applications. I will present a summary of the main ideas and the points that the author highlights as novel features arising from the multi-agent approach to computer science. When some details are very well developed in the book, I will just refer to them, since they are mainly useful for people who actually want to apply the techniques.
A laser-vision based obstacle detection and distance estimation for smart wheelchair navigation

The aim of the research is to present an approach of obstacle distance estimation and navigation for smart wheelchair. The smart wheelchair is an electric wheelchair equipped with camera and line laser to navigate and avoid an obstacle. The camera was used to capture images from the environment to sense the pathway condition. The line laser was used in combination with camera to recognize an obstacle in the pathway based on the shape of line laser image in certain angle. A blob method detection was applied on the line laser image to recognize the pattern of the detected obstacles. The line laser projector and camera were mounted in fixed-certain position to make sure a fixed relation between blobs-gaps and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to represent this relation as the estimated obstacle distance. As a result, the average error between the estimation and actual distance was 1.25 cm from 7 data testing experiments. The experiments result indicates that the proposed method is able to estimate well the distance between wheelchair and obstacle. Later, the smart wheelchair needs to decide further action whether it should turn left, right or just walk straight when facing certain obstacle to avoid it.
Time-related decay or interference-based forgetting in working memory?

The time-based resource-sharing model of working memory assumes that memory traces suffer from a time-related decay when attention is occupied by concurrent activities. Using complex continuous span tasks in which temporal parameters are carefully controlled, P. Barrouillet, S. Bernardin, S. Portrat, E. Vergauwe, & V. Camos (2007) recently provided evidence that any increase in time of the processing component of these tasks results in lower recall performance. However, K. Oberauer and R. Kliegl (2006) pointed out that, in this paradigm, increased processing times are accompanied by a corollary decrease of the remaining time during which attention is available to refresh memory traces. As a consequence, the main determinant of recall performance in complex span tasks would not be the duration of attentional capture inducing time-related decay, as Barrouillet et al. (2007) claimed, but the time available to repair memory traces, and thus would be compatible with an interference account of forgetting. The authors demonstrate here that even when the time available to refresh memory traces is kept constant, increasing the processing time still results in poorer recall, confirming that time-related decay is the source of forgetting within working memory.
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems

In this paper, we describe our approach of enabling an interactive dialogue system to recognize user emotion and sentiment in realtime. These modules allow otherwise conventional dialogue systems to have “empathy” and answer to the user while being aware of their emotion and intent. Emotion recognition from speech previously consists of feature engineering and machine learning where the first stage causes delay in decoding time. We describe a CNN model to extract emotion from raw speech input without feature engineering. This approach even achieves an impressive average of 65.7% accuracy on six emotion categories, a 4.5% improvement when compared to the conventional feature based SVM classification. A separate, CNN-based sentiment analysis module recognizes sentiments from speech recognition results, with 82.5 Fmeasure on human-machine dialogues when trained with out-of-domain data.
Factors influencing online repurchase intention: The case of clothing customers in Thailand

The purpose of this study is to explore Thai online customers' repurchase intention towards clothing. This study integrated Delone and Mclean's e-commerce success model to predict customers' repurchase intention to purchase clothing on the Internet. The data was collected using convenience sampling method with a survey of the customers in Thailand who had experienced purchasing clothing online. The findings indicate that repurchase intention is mostly influenced by both online shopping satisfaction and online shopping trust. The relationships between Internet shopping value and online shopping satisfaction and online shopping trust are found to be significant as well. Components of website quality have differing effect on utilitarian and hedonic value. System quality and service quickness influences utilitarian value as well as the hedonic value. System accessibility and information timely positively influence utilitarian value while information variety and service receptiveness have a positive effect hedonic value.
Flipping the classroom to improve student performance and satisfaction.

This study aimed to determine the effects of a flipped classroom (i.e., reversal of time allotment for lecture and homework) and innovative learning activities on academic success and the satisfaction of nursing students. A quasi-experimental design was used to compare three approaches to learning: traditional lecture only (LO), lecture and lecture capture back-up (LLC), and the flipped classroom approach of lecture capture with innovative classroom activities (LCI). Examination scores were higher for the flipped classroom LCI group (M = 81.89, SD = 5.02) than for both the LLC group (M = 80.70, SD = 4.25), p = 0.003, and the LO group (M = 79.79, SD = 4.51), p < 0.001. Students were less satisfied with the flipped classroom method than with either of the other methods (p < 0.001). Blending new teaching technologies with interactive classroom activities can result in improved learning but not necessarily improved student satisfaction.
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of 4.53 comparable to a MOS of 4.58 for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the conditioning input to WaveNet instead of linguistic, duration, and $F_{0}$ features. We further show that using this compact acoustic intermediate representation allows for a significant reduction in the size of the WaveNet architecture.
Principal Component Analysis

Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis (CA) in order to handle qualitative variables and as multiple factor analysis (MFA) in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semidefinite matrices and upon the singular value decomposition (SVD) of rectangular matrices.
Case-Based Reasoning for Invoice Analysis and Recognition

This paper introduces the approach CBRDIA (Case-based Reasoning for Document Invoice Analysis) which uses the principles of case-based reasoning to analyze, recognize and interpret invoices. Two CBR cycles are performed sequentially in CBRDIA. The first one consists in checking whether a similar document has already been processed, which makes the interpretation of the current one easy. The second cycle works if the first one fails. It processes the document by analyzing and interpreting its structuring elements (adresses, amounts, tables, etc) one by one. The CBR cycles allow processing documents from both knonwn or unknown classes. Applied on 923 invoices, CBRDIA reaches a recognition rate of 85,22% for documents of known classes and 74,90% for documents of unknown classes.