nils-herrmann/clean_scirepeval huggingface dataset:
Translating Constructivism into Instructional Design: Potential and Limitations

Instructional designers are expected to be familiar with the epistemological underpinnings of several theories and their consequences on the process of instruction. Constructivism is the dominant theory of the last decade and supports construction of knowledge by the individual. This paper discusses the basic principles underlying constructivism, particularly active, collaborative and authentic learning. Application of these principles on the process analysis, development, evaluation of instructional design poses certain challenges with regards to issues such as pre-specification of knowledge, authentic evaluation and learner control. Most of the problems are attributed to the fact that constructivism is a learning theory and not an instructional-design theory. Therefore, instructional designers must attempt to translate constructivism into instructional design through a more pragmatic approach that focuses on the principles of moderate rather than extreme constructivism and makes use of emergent technology tools. This shift could facilitate the development of more situated, experiential, meaningful and cost-effective learning environments.
Driver Distraction Using Visual-Based Sensors and Algorithms

Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.
Predicting Users' Personality from Instagram Pictures: Using Visual and/or Content Features?

Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at personality prediction from Instagram picture features. We explore two different features that can be extracted from pictures: 1) visual features (e.g., hue, valence, saturation), and 2) content features (i.e., the content of the pictures). To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. With our results we show that visual and content features can be used to predict personality from and perform in general equally well. Combining the two however does not result in an increased predictive power. Seemingly, they are not adding more value than they already consist of independently.
Smartphone-based Home Robotics

Humanoid robotics is a promising field because the strong human preference to interact with anthropomorphic interfaces. Despite this, humanoid robots are far from reaching main stream adoption and the features available in such robots seem to lag that of the latest smartphones. A fragmented robot ecosystem and low incentives to developers do not help to foster the creation of Robot-Apps either. In contrast, smartphones enjoy high adoption rates and a vibrant app ecosystem (4M apps published). Given this, it seems logical to apply the mobile SW and HW development model to humanoid robots. One way is to use a smartphone to power the robot. Smartphones have been embedded in toys and drones before. However, they have never been used as the main compute unit in a humanoid embodiment. Here, we introduce a novel robot architecture based on smartphones that demonstrates x3 cost reduction and that is compatible with iOS/Android.
SOCIAL MEDIA USE , COLLABORATIVE LEARNING AND STUDENTS ' ACADEMIC PERFORMANCE : A SYSTEMATIC LITERATURE REVIEW OF THEORETICAL MODELS

This research provided a systematic literature review of theoretical models on interaction and collaborations regarding Information system (IS) and Information Technology (IT). This paper conducted an review of studies dedicated to (IS & IT) on the basis of certain dimensions namely, research theories, review of constructivist theories, definitions of constructivism, social constructivism, theoretical of constructivism, active collaborative learning theory, technology acceptance model (TAM), theory of reasoned action, technology acceptance model and Its extensions, and finally research models and frameworks. The discussion of this research obtained revealed that the interest on the topic has shown an increasing trend over recent years that it has ultimately become a well-known topic for academic research in the future via theories use. From review of theoretical models and related theories we recommend to use constructivism, active collaborative learning theory with (TAM) to measurement performance and satisfaction with social media use as the mediator. However, to boost and enhance the IT continuance intention, it is important that future studies apply considerable use of theoretical and methodological approaches like the qualitative methods to examine the IT continuance intention.
Convolutional Neural Network for Computer Vision and Natural Language Processing

Machine learning techniques are widely used in the domain of Natural Language Processing (NLP) and Computer Vision (CV), In order to capture complex and non-linear features deeper machine learning architectures become more and more popular. A lot of the state of art performance have been reported by employing deep learning techniques. Convolutional Neural Network (CNN) is one variant of deep learning architectures which has received intense attention in recent years. CNN is inspired from the domain of biology, which tries to mimic the way of how signal are processed in human brain. CNN is type of feed forward artificial neural network which are constructed by multiple layers. Signals are passed through these layers with non-linear activation functions. Within each layer, there are a lot of independent node to process the signal in different regions or aspects. CNN has achieved great success in sentence modeling, image recognition and feature detection. In this paper, we introduce the motivation, intuition, architectures and algorithm of CNN. In particular, we discuss several recent achievements of CNN in NLP and CV.
Neural-based quality measurement of fingerprint images in contactless biometric systems

Traditional fingerprint biometric systems capture the user fingerprint images by a contact-based sensor. Differently, contactless systems aim to capture the fingerprint images by an approach based on a vision system without the need of any contact of the user with the sensor. The user finger is placed in front of a special CCD-based system that captures the pattern of ridges and valleys of the fingertips. This approach is less constrained by the point of view of the user, but it requires much more capability of the system to deal with the focus of the moving target, the illumination problems and the complexity of the background in the captured image. During the acquisition procedure, the quality of each frame must be carefully evaluated in order to extract only the correct frames with valuable biometric information from the sequence. In this paper, we present a neural-based approach for the quality estimation of the contactless fingertips images. The application of the neural classification models allowed for a relevant reduction of the computational complexity permitting the application in real-time. Experimental results show that the proposed method has an adequate accuracy, and it can capture fingerprints at a distance up to 0.2 meters.
An effective quality evaluation protocol for speech enhancement algorithms

FOR SPEECH ENHANCEMENT ALGORITHMS John H.L. Hansen and Bryan L. Pellom Robust Speech Processing Laboratory Duke University, Box 90291, Durham, NC 27708-0291 http://www.ee.duke.edu/Research/Speech jhlh@ee.duke.edu bp@ee.duke.edu  Much progress has been made in speech enhancement algorithm formulation in recent years. However, while researchers in the speech coding and recognition communities have standard criteria for algorithm performance comparison, similar standards do not exist for researchers in speech enhancement. This paper discusses the necessary ingredients for an e ective speech enhancement evaluation. We propose that researchers use the evaluation core test set of TIMIT (192 sentences), with a set of noise les, and a combination of objective measures and subjective testing for broad and ne phone-level quality assessment. Evaluation results include overall objective speech quality measure scores, measure histograms, and phoneme class and individual phone scores. The reported results are meant to illustrate speci c ways of detailing quality assessment for an enhancement algorithm.
Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003

About ten years ago, soon after the Web’s birth, Web “search engines” were first by word of mouth. Soon, however, automated search engines became a world wide phenomenon, especially AltaVista at the beginning. I was pleasantly surprised by the amount and diversity of information made accessible by the Web search engines even in the mid 1990’s. The growth of the available Web pages is beyond most, if not all, people’s imagination. The search engines enabled people to find information, facts, and references among these Web pages.
Audio Data Mining Using Multi-perceptron Artificial Neural Network

-Data mining is the activity of analyzing a given set of data. It is the process of finding patterns from large relational databases. Data mining includes: extract, transform, and load transaction data onto the data warehouse system, store and manage the data in a multidimensional database system, provides data, analyze the data by application software and visual presentation. Audio data contains information of each audio file such as signal processing componentpower spectrum, cepstral values that is representative of particular audio file. The relationship among patterns provides information. It can be converted into knowledge about historical patterns and future trends. This work involves in implementing an artificial neural network (ANN) approach for audio data mining. Acquired audio is preprocessed to remove noise followed by feature extraction using cepstral method. The ANN is trained with the cepstral values to produce a set of final weights. During testing process (audio mining), these weights are used to mine the audio file. In this work, 50 audio files have been used as an initial attempt to train the ANN. The ANN is able to produce only about 90% accuracy of mining due to less correlation of audio data.
Training Deeper Models by GPU Memory Optimization on TensorFlow

With the advent of big data, easy-to-get GPGPU and progresses in neural network modeling techniques, training deep learning model on GPU becomes a popular choice. However, due to the inherent complexity of deep learning models and the limited memory resources on modern GPUs, training deep models is still a nontrivial task, especially when the model size is too big for a single GPU. In this paper, we propose a general dataflow-graph based GPU memory optimization strategy, i.e.,“swap-out/in”, to utilize host memory as a bigger memory pool to overcome the limitation of GPU memory. Meanwhile, to optimize the memory-consuming sequence-to-sequence (Seq2Seq) models, dedicated optimization strategies are also proposed. These strategies are integrated into TensorFlow seamlessly without accuracy loss. In the extensive experiments, significant memory usage reductions are observed. The max training batch size can be increased by 2 to 30 times given a fixed model and system configuration.
Oral health information systems--towards measuring progress in oral health promotion and disease prevention.

This article describes the essential components of oral health information systems for the analysis of trends in oral disease and the evaluation of oral health programmes at the country, regional and global levels. Standard methodology for the collection of epidemiological data on oral health has been designed by WHO and used by countries worldwide for the surveillance of oral disease and health. Global, regional and national oral health databanks have highlighted the changing patterns of oral disease which primarily reflect changing risk profiles and the implementation of oral health programmes oriented towards disease prevention and health promotion. The WHO Oral Health Country/Area Profile Programme (CAPP) provides data on oral health from countries, as well as programme experiences and ideas targeted to oral health professionals, policy-makers, health planners, researchers and the general public. WHO has developed global and regional oral health databanks for surveillance, and international projects have designed oral health indicators for use in oral health information systems for assessing the quality of oral health care and surveillance systems. Modern oral health information systems are being developed within the framework of the WHO STEPwise approach to surveillance of noncommunicable, chronic disease, and data stored in the WHO Global InfoBase may allow advanced health systems research. Sound knowledge about progress made in prevention of oral and chronic disease and in health promotion may assist countries to implement effective public health programmes to the benefit of the poor and disadvantaged population groups worldwide.
A Brief Survey of Deep Reinforcement Learning

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policybased methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.
Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality

Topic models based on latent Dirichlet allocation and related methods are used in a range of user-focused tasks including document navigation and trend analysis, but evaluation of the intrinsic quality of the topic model and topics remains an open research area. In this work, we explore the two tasks of automatic evaluation of single topics and automatic evaluation of whole topic models, and provide recommendations on the best strategy for performing the two tasks, in addition to providing an open-source toolkit for topic and topic model evaluation.
Collective Entity Resolution with Multi-Focal Attention

Entity resolution is the task of linking each mention of an entity in text to the corresponding record in a knowledge base (KB). Coherence models for entity resolution encourage all referring expressions in a document to resolve to entities that are related in the KB. We explore attentionlike mechanisms for coherence, where the evidence for each candidate is based on a small set of strong relations, rather than relations to all other entities in the document. The rationale is that documentwide support may simply not exist for non-salient entities, or entities not densely connected in the KB. Our proposed system outperforms state-of-the-art systems on the CoNLL 2003, TAC KBP 2010, 2011 and 2012 tasks.
A Quality Framework for Agile Requirements: A Practitioner's Perspective

Verification activities are necessary to ensure that the requirements are specified in a correct way. However, until now requirements verification research has focused on traditional up-front requirements. Agile or just-in-time requirements are by definition incomplete, not specific and might be ambiguous when initially specified, indicating a different notion of ‘correctness’. We analyze how verification of agile requirements quality should be performed, based on literature of traditional and agile requirements. This leads to an agile quality framework, instantiated for the specific requirement types of feature requests in open source projects and user stories in agile projects. We have performed an initial qualitative validation of our framework for feature requests with eight practitioners from the Dutch agile community, receiving overall positive feedback.
Real-time American Sign Language recognition from video using hidden Markov models

Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. We describe two experiments that. demonstrate a real-time HMM-based system for recognizing sentence level American Sign Language (ASL) without explicitly modeling the fingers. The first experiment tracks hands wearing colored gloves and attains a word accuracy of 99%. The second experiment tracks hands without gloves and attains a word accuracy of 92%. Both experiments have a 40 word lexicon.
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an wordlevel to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.
FAKE IT TILL YOU MAKE IT : AN EXPLORATION OF GENERATIVE ADVERSARIAL NETWORKS

Generative Adversarial Networks (GANs) are an incredibly exciting approach for efficiently training computers to learn many features in data, as well as to generate realistic novel samples. Thanks to a number of their unique characteristics, some experts believe they may reinvent machine learning. In this thesis I explore the state of the GAN, focusing on the mechanisms by which they work, the fundamental challenges and strategies associated with training them, a selection of their various extensions, and what they may have to offer to the the greater machine learning community. I also consider the broader idea of building machine learning systems comprised of multiple neural networks, as opposed to using a single network. Using the state of the art progressive growing of GANs approach, I conducted experiments where I generated painting-like images that I believe to be the most authentic GAN-generated portrait paintings. I also generated highly realistic chest X-ray images, using a progressively grown GAN trained without labels on the NIH’s ChestX-ray14 dataset, which contains 112,000 chest X-ray images with 14 different disease diagnoses represented; it still remains to be seen whether the GAN-generated X-ray images contain clear identifying features of the various diseases. My generated results further demonstrate the relatively stable training of the progressive growing approach as well as the GAN’s compelling capacity for learning features in a variety of forms of image data.
Ankle-Dorsiflexion Range of Motion After Ankle Self-Stretching Using a Strap.

A variety of ankle self-stretching exercises have been recommended to improve ankle-dorsiflexion range of motion (DFROM) in individuals with limited ankle dorsiflexion. A strap can be applied to stabilize the talus and facilitate anterior glide of the distal tibia at the talocrural joint during ankle self-stretching exercises. Novel ankle self-stretching using a strap (SSS) may be a useful method of improving ankle DFROM.



To compare the effects of 2 ankle-stretching techniques (static stretching versus SSS) on ankle DFROM.



Randomized controlled clinical trial.



University research laboratory.



Thirty-two participants with limited active dorsiflexion (<20°) while sitting (14 women and 18 men) were recruited.



The participants performed 2 ankle self-stretching techniques (static stretching and SSS) for 3 weeks. Active DFROM (ADFROM), passive DFROM (PDFROM), and the lunge angle were measured. An independent t test was used to compare the improvements in these values before and after the 2 stretching interventions. The level of statistical significance was set at α = .05.



Active DFROM and PDFROM were greater in both stretching groups after the 3-week interventions. However, ADFROM, PDFROM, and the lunge angle were greater in the SSS group than in the static-stretching group (P < .05).



Ankle SSS is recommended to improve ADFROM, PDFROM, and the lunge angle in individuals with limited DFROM.