Reviewerly/exHarmony huggingface dataset:
Classification of reading and not reading behavior based on eye movement analysis

Nowadays, many researchers analyze reading behavior with eye trackers. Various traits of reading like engagement, or text difficulty have been observed in laboratory settings. But, their automatic application for daily life is usually prevented by one question: when is somebody reading? We have developed a tool to classify short sequences of fixations from eye gaze data into reading and not reading. Our specific use case is the Vocabulometer, a website for learning English by reading texts. We used supervised learning on data from nonnative English speakers to train decision trees for the classification. With features based on vertical eye movement, we achieved 93.1% of correct classifications.
Robust Semi-Supervised Non-Negative Matrix Factorization With Structured Normalization

Non-negative matrix factorization (NMF) approximates a non-negative data matrix with the product of two low-rank non-negative matrices by minimizing the cost of such approximation. However, traditional NMF models cannot be generalized in the cases when the dataset contains outliers and limited knowledge from domain experts. In this paper, we propose a robust semi-supervised NMF model (RSS-NMF) to overcome the aforementioned deficiency. RSS-NMF utilizes the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm to encourage approximation and makes the model insensitive to outliers by prohibiting them from dominating the cost function. To incorporate the discriminative information, RSS-NMF utilizes the structured normalization method when learns a diagonal matrix to normalize the coefficients such that they get close to the label indicators of the given labeled examples. Although the multiplicative update rule (MUR) can be adopted to minimize RSS-NMF, it converges slowly. In this paper, we adopt a fast gradient descent algorithm (FGD) to optimize RSS-NMF and prove its convergence to a stationary point. FGD uses a Newton method to search the optimal step length and thus, FGD converges faster than MUR. The experimental results show the promise of RSS-NMF comparing with the representative clustering models on several face image datasets.
AudienceAR - Utilising Augmented Reality and Emotion Tracking to Address Fear of Speech

With Augmented Reality (AR) we can enhance the reality by computer-generated information about real entities projected in the user's field of view. Hence, the user's perception of a real environment is altered by adding (or subtracting) information by means of digital augmentations. In this demo paper we present an application where we utilise AR technology to show visual information about the audience's mood in a scenario where the user is giving a presentation. In everyday life we have to talk to and in front of people as a fundamental aspect of human communication. However, this situation poses a major challenge for many people and may even go so far as to lead to fear and and avoidance behaviour. Based on findings in previous work about fear of speech, a major cause of anxiety is that we do not know how the audience judges us. To eliminate this feeling of uncertainty, we created an AR solution to support the speaker while giving a speech by tracking the audience's current mood and displaying this information in real time to the speaker's view: AudienceAR. By doing so we hypothesise to reduce the speaker's tension before and during presentation. Furthermore, we implemented a small web interface to analyse the presentation based on the audience mood after the speech is given. Effects will be tested in future work.
Information Retrieval System for Searching JSON Files with Vector Space Model Method

The purpose of this research is to build a retrieval system for searching files or documents in JSON or JavaScript Notation format. The source of JSON files that searched comes from the generated journal files that have been extracted from the title and the keywords. This retrieval system is designed using the Vector Space Model method to obtain optimal searching results by looking for similarity of query words with the documents sought. This system is made with Java programming based object-oriented so that it can be implemented on many platforms and operating systems. This system reads all the words in the JSON file, then filters it for the stopword list to remove it. Then the stemming process is carried out to find the basic word. Stemming results are indexed to store the number of word frequencies in each document, the indexing results are stored in memory to speed up the searching. When a user searches, the system will search for indexing results based on query words, then weighting TF-IDF each word searched and calculated similarity with the Cosine Similarity formula with related documents. All related documents will be sorted descending based on the scoring value of the similarity. The word search process in this system more or less takes 150-300 milliseconds in JSON documents which amount to 140.
Visualization of in-vitro Blood Vessels in Contrast Images Based on Discrete Wavelet Transform Decomposition

Visualization of blood vessels in contrast images is an important task as it is used to analyze the dynamics of the blood flow and the health status of biological tissue. However, this task becomes difficult due to the noise in the image, mainly at high depths. This work proposes a methodology based on the Discrete Wavelet Transform to improve in-vitro blood vessels visualization. It also addresses the segmentation of the regions of interest through morphological operations in improved contrast images. Results show that it is possible to visualize the blood vessels even at 700μm below an epidermis phantom.
Immersive Augmented Reality (I Am Real) – Remote Clinical Consultation

Recent advances in Augmented Reality (AR) have shown great opportunities to enhance patients and clinicians' experience in telehealth. However most existing AR technologies focus on medical procedural training and thus have low compliance to patient uptake due to low portability and high cost. In this paper, using the emerging AR technologies on mobile devices, we developed an AR clinical consultation system through an iPad and a Kinect sensor. This low-cost and highly portable AR consultation system can be easily deployed in patient home and clinician office with minimum burden on their normal daily activities. This will not only provides a great and immersive telehealth consultation experience for patients, but also enable clinicians to easily explain complex medical conditions to patients through visualization and simulation.
Presumptions, and How They Relate to Arguments from Ignorance

Abstract By explaining the argument from ignorance in terms of the presumption of innocence , many textbooks in argumentation theory suggest that some arguments from ignorance might share essential features with some types of presumptive reasoning. The stronger version of this view, suggesting that arguments from ignorance and presumptive reasoning are almost indistinguishable, is occasionally proposed by Douglas Walton. This paper explores the nature and limits of the stronger proposal and argues that initial presumptions and arguments from ignorance are not closely connected. There are three main reasons. First, the argument from ignorance, unlike typical presumptive reasoning, is a negative kind of inference. Second, the typical initial presumption is sensitive to a broader set of defeaters and thus assumes a higher (negative) standard of acceptability. Third, in dialectical terms, initial presumption and argument from ignorance bring different attacking rights and obligations. I conclude that Waltonian intuition is unsupported or, at best, is limited only to practical presumptions and practical arguments from ignorance.
English as a linguistic resource

With the intensification of globalization and advancement of digital communication, interactions online have gained increasing vitality in our life. Online communication now influences the spread of various languages (e.g., English) and semiotic signs (e.g., emoticons) to many countries, especially for those characterized by Kachru (1986) as belonging to the Expanding Circle (e.g., Japan, China — countries where English is usually practiced as a foreign language). Recent inquiries of digital discourse analysis have researched how English and its features are adopted in countries of the Expanding Circle (e.g., Crystal, 2008; Zhang, 2012). This paper continues to explore the linguistic landscape of English practices online, and specifically focuses on how micro-blogging users in mainland China adopt English as a resource for meaningful multilingual communication.
On the Robustness of Domain-Independent Planning Engines

Recent advances in automated planning are leading towards the use of planning engines in a wide range of real-world applications. As the exploitation of planning techniques in applications increases, it becomes imperative to assess the robustness of planning engines with regards to poorly-engineered (or maliciously modified) knowledge models provided as input for the reasoning process. In this work, to understand the impact of poorly-engineered knowledge on planning engines, we consider the perspective of a hypothetical attacker that is interested in subtly manipulating such knowledge to introduce unnecessary overheads that consequently slow down the planning process. This narrative ploy allows us to describe different types of knowledge engineering issues that cannot be detected via validation of the models, and to measure their impact on the performance of a range of planning engines exploiting very different approaches for steps like pre-processing and search.
Envisioning Future Challenges and Possibilities for Shape-Changing Interfaces through Speculative Scenarios

In this paper, we present three speculative scenarios that explore the future of living with shape-changing interfaces. Each scenario is concerned with one area of this potential future. Three areas, which originated in a workshop with 25 researchers, are considered: 1) shape as a service, 2) security and trust, and 3) personalization and materiality. Each scenario is critically reflected. We use speculative scenarios as an evocative way of illustrating future possibilities, beyond the limitations of current technology. We point to future challenges that the research community needs to address, so that shape-changing interfaces can successfully venture out, into people's homes and lives.
Data-driven unmanned surface vessel path following control method based on reinforcement learning

Aiming at many difficult problems of path-following(PF) control of Unmanned surface vessel (USV), such as disturbance of wind, wave and current, nonlinearity of control effect, etc. This paper proposes a data-driven USV PF control method based on SARSA. Using the behavior-reward scoring mechanism of reinforcement learning to learn a set of behavioral rules. These behavioral rules are used for USV to make the best control behavior based on the state of the ternary array consisting of heading deviation, wind and current. The experiment uses the simulation environment to learn the algorithm. Through the analysis of the experimental results and the comparison with the traditional PID algorithm, the feasibility and advantages of the algorithm are verified.
Enhancing Augmented VR Interaction via Egocentric Scene Analysis

Augmented virtual reality (AVR) takes portions of the physical world into the VR world to enable VR users to access physical objects. State-of-the-art solutions mainly focus on extracting and showing physical objects in the VR world. In this work, we go beyond previous solutions and propose a novel approach to realize AVR. We first analyze the physical environment in the user's egocentric view through depth sensing and deep learning, then acquire the layout and geometry of the surrounding objects, and further explore their affordances. Based on the above information, we create visual guidance (hollowed guiding path) and hybrid user interfaces (augmented physical notepad, LR finger slider, and LRRL finger slider) to augment the AVR interaction. Empirical evaluations showed that the participants responded positively to our AVR techniques.
Discovering eating routines in context with a smartphone app

In everyday life, eating follows patterns and occurs in context. We present an approach to discover daily eating routines of a population following a multidimensional representation of eating episodes, using data collected with the Bites'n'Bits smartphone app. Our approach integrates multiple contextual cues provided in-situ (food type, time, location, social context, concurrent activities, and motivations) with probabilistic topic models, which discover representative patterns across these contextual dimensions. We show that this approach, when applied on eating episode data for over 120 people and 1200 days, allows describing the main eating routines of the population in meaningful ways. This approach, resulting from a collaboration between ubiquitous computing and nutrition science, can support interdisciplinary work on contextual analytics for promotion of healthy eating.
Towards a taxonomy of interactive continual and multimodal learning for the internet of things

With advances in Internet of Things many opportunities arise if the challenges of continual learning in a multimodal setting can be tackled. One common issue in Online Learning is to obtain labelled data, as this generally is costly. Active Learning is a popular approach to collect labelled data efficiently, but in general includes unrealistic assumptions.
Synthetic minority oversampling for function approximation problems

Imbalanced data sets are a common occurrence in important machine learning problems. Research in improving learning under imbalanced conditions has largely focused on classification problems (ie, problems with a categorical dependent variable). However, imbalanced data also occur in function approximation, and far less attention has been paid to this case. We present a novel stratification approach for imbalanced function approximation problems. Our solution extends the SMOTE oversampling preprocessing technique to continuous-valued dependent variables by identifying regions of the feature space with a low density of examples and high variance in the dependent variable. Synthetic examples are then generated between nearest neighbors in these regions. In an empirical validation, our approach reduces the normalized mean-squared prediction error in 18 out of 21 benchmark data sets, and compares favorably with state-of-the-art approaches.
Automatic Estimation of Dog Age: The DogAge Dataset and Challenge

Automatic age estimation is a challenging problem attracting attention of the computer vision and pattern recognition communities due to its many practical applications. Artificial neural networks, such as CNNs are a popular tool for tackling this problem, and several datasets which can be used for training models are available. Despite the fact that dogs are the most well studied species in animal science, and that ageing processes in dogs are in many aspects similar to those of humans, the problem of age estimation for dogs has so far been overlooked. In this paper we present the DogAge dataset and an associated challenge, hoping to spark the interest of the scientific community in the yet unexplored problem of automatic dog age estimation.
A Deep Learning Assisted Method for Measuring Uncertainty in Activity Recognition with Wearable Sensors

For human activity recognition with wearable sensors, understanding the uncertainty in the classifier decision is necessary to predict sensor failures and design active learning paradigms. Although deep learning models have shown promising results in recognizing human activities from sensor data, it is still challenging to estimate their uncertainty in producing decisions. In this paper, we propose a Bayesian deep convolutional neural network with stochastic latent variables that allows us to estimate both aleatoric (data dependent) and epistemic (model dependent) uncertainties in recognition task. We put a distribution over the latent variables of the model, which are the features that are automatically extracted by the convolutional layers, and show how the inference can be approximated by combining a variational autoencoder with a typical deep neural network classifier. We also leverage Dropout Bayesian neural network to approximate the model uncertainty. The experimental results on a publicly available dataset of human activity recognition with wearable sensors show how each uncertainty (i.e., aleatoric and epistemic) measure is sensitive against different sources of uncertainty namely noisy as well as novel data. Moreover, the uncertainty for the samples that are misclassified by the model is significantly higher on average than the samples that are correctly classified.
On the Fast Algebraic Immunity of Majority Functions

In different contexts such as filtered LFSR, Goldreich's PRG, and FLIP stream ciphers, the security of a cryptographic primitive mostly depends on the algebraic properties of one Boolean function. Since the Seventies, more and more efficient attacks have been exhibited in this context, related to more and more general algebraic properties, such as the degree, the algebraic immunity, and finally, the fast algebraic immunity. Once the properties to estimate the attack complexities are identified, it remains to determine the exact parameters of interesting family of functions with these properties. Then, these functions can be combined in secondary constructions to guarantee the good algebraic properties of a main function. In particular, the family of symmetric functions, and more precisely the subclass of majority functions, has been intensively studied in the area of cryptography, because of their practical advantages and good properties. The degree of all these functions is known, and they have been proven to reach the optimal algebraic immunity, but still very few is known relatively to its fast algebraic immunity. For a function in $$n=2^m+j$$ variables, an upper bound is known for all m and j, proving that these functions do not reach the optimal fast algebraic immunity. However, the exact fast algebraic immunity is known only for very few families indexed by j, where the parameter is exhibited for all members of the family since m is big enough. Recent works gave exact values for $$j=0$$ and $$j=1$$ (in the first case), and for $$j=2$$ and $$j=3$$ with $$m\ge 2$$ (in the second case). In this work, we determine the exact fast algebraic immunity for all possible values of j, for all member of the family assuming $$m\ge 1+\log _2(j+1)$$ .
PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles.
Augmented-Reality-Enhanced Product Comparison in Physical Retailing

Augmented reality technology has experienced great improvement in recent years and it has been successfully applied to industry and entertainment settings. However, its application in everyday contexts such as shopping is still very limited. One of the requirements to seamlessly incorporate augmented reality into everyday tasks is to find intuitive, natural methods to make use of it. Due to the inherent capabilities of augmented reality to work as a visual aid to explore and extend the knowledge a user has of the surroundings, this paper proposes the combination of AR technology and product advisors in a novel approach for product comparison. The user's awareness of the differences between multiple physically present objects is enhanced through virtual augmentations, supporting an intuitive way of comparing two or more products while shopping. To assess the validity of the concept, a prototype for an AR-based shopping assistant for comparing vacuum cleaners has been implemented and evaluated in a user study, testing different methods of visual comparison and interaction.
Administering elections in a digital age: Online voting in Ontario municipalities

Abstract To date, online voting studies have focused on impacts on political behaviour and voting security. Analyses of impacts on administrators and electoral governance remain sparse internationally and are relegated to overviews in technical reports in Canada (Pammett and Goodman 2013; Goodman and Pyman 2016). Drawing on an original survey of local administrators in Ontario following the 2014 municipal elections, this article explores how online voting impacts election administration. Findings suggest strong satisfaction with online voting, citizen‐centered rationales for adoption, and key differences in challenges and cost based on municipal size. We conclude with implications for the local governance of elections as technology plays a greater role in voting processes.
Gesture Recognition Based on Deep Learning in Complex Scenes

Aiming at the problems of low recognition rate and robustness of the gesture recognition in complex scenes such as light changes and background interference, a gesture recognition algorithm based on Faster R-CNN is proposed, which is envoled from CNN. Applied the Faster R-CNN framework in objects recognition and classification algorithm in deep learning, to recognize and classify the gesture images collected in complex scenes, we adjust the key parameters of the Faster R-CNN framework to achieve the purpose of simultaneously detecting and recognizing gestures. Compared with the traditional gesture recognition method, the target detection and classification algorithm based on deep learning method in the complex scene has a significant improvement in the recognition accuracy, and the average recognition rate reaches 98.19%, the proposed algorithm avoids the over-fitting problem of the training model and has higher recognition accuracy and stronger robustness than the traditional algorithm.
LiftSmart

In this paper, we demonstrate LiftSmart, a novel smart wearable to detect, track and analyse weight training activities. LiftSmart is the first wearable for weight training that is based on unsupervised machine learning techniques to eliminate the use of labelled data, which is expensive to collect, computationally intensive, and requires the tuning of multiple key parameters.
The Classification of the Movie Genre based on Synopsis of the Indonesian Film

The text classification system aims to classify a document content into the correct label. Synopsis text data in Indonesian films can be used as a feature to determine the appropriate genre of film by utilizing machine learning algorithms. This preliminary study aims to explore some feature extraction methods and machine learning model to classify the movie genre. Our model shows that the best results are the SVM+TF-IDF Classifier using the unigram with the f1 score of 45%.