Reviewerly/exHarmony huggingface dataset:
A Hazard Detection and Tracking System for People with Peripheral Vision Loss using Smart Glasses and Augmented Reality

Peripheral vision loss is the lack of ability to recognise objects and shapes in the outer area of the visual field. This condition can affect people’s daily activities and reduces their quality of life. In this work, a smart technology that implements computer vision algorithms in real-time to detect and track moving hazards around people with peripheral vision loss is presented. Using smart glasses, the system processes real-time captured video and produces warning notifications based on predefined hazard danger levels. Unlike other obstacle avoidance systems, this system can track moving objects in real-time and classify them based on their motion features (such as speed, direction, and size) to display early warning notification. A moving camera motion compensation method was used to overcome artificial motions caused by camera movement before an object detection phase. The detected moving objects were tracked to extract motion features which were used to check if the moving object is a hazard or not. A detection system for camera motion states was implemented and tested on real street videos as the first step before an object detection phase. This system shows promising results in motion detection, motion tracking, and camera motion detection phases. Initial tests have been carried out on Epson’s smart glasses to evaluate the real-time performance for this system. The proposed system will be implemented as an assistive technology that can be used in daily life.
A Short Introduction to Procedural Content Generation Algorithms for Videogames

One of the main costs of developing a videogame is content creation. Procedural Content Generation (PCG) can help alleviate that cost by algorithmically generating some of the content a human would normally produce. We first describe and classify the different types of content that can be automatically generated for a videogame. Then, we review the most prominent PCG algorithms, focusing on current research on search-based and machine learning based methods. Finally, we close with our take on the most important open problems and the potential impact solving them will have on the videogame industry.
Joint Detection and Location of

Yanyan Zou, Wei Lu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Towards an articulated avatar in VR: Improving body and hand tracking using only depth cameras

An increasing number of virtual reality applications now use full-body avatars to represent the user in virtual environments. To fully control these virtual avatars, movement-tracking technology is required. However, most full-body tracking solutions are expensive and often cumbersome and time consuming to setup and use. Affordable depth cameras, on the other hand, are easy to set up, but most lack the ability to fully track a user’s body and fingers and have only limited accuracy. In this paper, we present a solution for combining multiple depth cameras to allow accurate full body movement tracking, including accurate hand and finger tracking. This provides users with the possibility of using natural gestures to interact in the virtual environment. In particular, we improve on previous work in the following five aspects. We have, (1) extended the calibration procedure to eliminate the tracking offsets between the RGB and depth cameras, (2) optimized facing-direction detection to improve the stability of data fusion, (3) implemented two new weighting methods for the depth data fusion of multiple cameras, (4) added the ability to also fuse joint-rotation data, and (5) integrated a short-range depth camera for finger tracking. We evaluated the system empirically and show that our new methods improved previous work in terms of tracking accuracy and particularly reduced the coupled hand-lifting phenomenon.
A New Color Augmentation Method for Deep Learning Segmentation of Histological Images

This paper addresses the problem of labeled data insufficiency in neural network training for semantic segmentation of color-stained histological images acquired via Whole Slide Imaging. It proposes an efficient image augmentation method to alleviate the demand for a large amount of labeled data and improve the network's generalization capacity. Typical image augmentation in bioimaging involves geometric transformation. Here, we propose a new image augmentation technique by combining the structure of one image with the color appearance of another image to construct augmented images on-the-fly for each training iteration. We show that it improves performance in the segmentation of histological images of human skin, and also offers better results when combined with geometric transformation.
Edmodo-Based Blended Learning Media in Learning Mathematics

This research is motivated by the crucial development of the information technology era in changing learning paradigm from conventional to technology-based learning. The purpose of this study is to develop Edmodo-based blended learning media in learning mathematics for Elementary Teacher Education students. This research is a research and development (R&amp;D) that uses the ADDIE procedures. This study results a valid, effective and practical Edmodo-based blended learning media in learning mathematics for Elementary Teacher Education students. It is implied that Edmodo-based blended learning media can be applied by the lecturers to support learning for Elementary Teacher Education students.
Automatic scratch detector for optical surface

Scratches on the surface of optical components have serious impacts on optical system such as imaging quality of lens and/or mirrors in optical imaging systems, light-collecting abilities of laser fusion and solar concentrator systems. The size of the scratches is a key issue for analyzing and assessing the impacts quantitatively. Most of the available testing methods for scratches depend on human visual inspection (HVI) with naked eyes by workers, which leads to low efficiency and accuracy. This paper presents an automatic detecting method for the scratches on optical surface with machine vision inspection (MVI) method. The microscopic dark-field scattering imaging system is used as the front end of the detection system. A dedicated algorithm is designed for non-closing scratch detection. The core merits of this algorithm lies in three folds: 1) automatic processing capabilities, which includes positioning, clustering, and precise estimation of the length of the scratches; 2) high efficiency, which is characterized by a short time interval, i.e., about 0.138 second per binary image with 2724 × 2724 pixels in our experiments; 3) high accuracy, where the error rate of the total length of the scratches detected is less than 5% when compared with the nominal visual measurement result obtained via HVI method. The proposed scratch detecting algorithm can be used for non-destructive testing (NDT) of the glass-like surfaces.
Designing an Effective Metric Learning Pipeline for Speaker Diarization

State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the appropriate feature extractor, ranging from pre-trained i–vectors to representations learned via different sequence modeling architectures (e.g. 1D-CNNs, LSTMs, attention models), while adopting off-the-shelf metric learning solutions. In this paper, we argue that, regardless of the feature extractor, it is crucial to carefully design a metric learning pipeline, namely the loss function, the sampling strategy and the discriminative margin parameter, for building robust diarization systems. Furthermore, we propose to adopt a fine-grained validation process to obtain a comprehensive evaluation of the generalization power of metric learning pipelines. To this end, we measure diarization performance across different language speakers, and variations in the number of speakers in a recording. Using empirical studies, we provide interesting insights into the effectiveness of different design choices and make recommendations.
Learnable Embedding Space for Efficient Neural Architecture Compression

We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search. Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with a kernel function defined over our proposed embedding space to select architectures for evaluation. We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and reinforcement learning (Ashok et al., 2018). The compressed architectures found by our method are also better than the state-of-the-art manually-designed compact architecture ShuffleNet (Zhang et al., 2018). We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training. Code is publicly available here: this https URL .
Modelling and analysis of temporal preference drifts using a component-based factorised latent approach

In recommender systems, human preferences are identified by a number of individual components with complicated interactions and properties. Recently, the dynamicity of preferences has been the focus of several studies. The changes in user preferences can originate from substantial reasons, like personality shift, or transient and circumstantial ones, like seasonal changes in item popularities. Disregarding these temporal drifts in modelling user preferences can result in unhelpful recommendations. Moreover, different temporal patterns can be associated with various preference domains, and preference components and their combinations. These components comprise preferences over features, preferences over feature values, conditional dependencies between features, socially-influenced preferences, and bias. For example, in the movies domain, the user can change his rating behaviour (bias shift), her preference for genre over language (feature preference shift), or start favouring drama over comedy (feature value preference shift). In this paper, we first propose a novel latent factor model to capture the domain-dependent component-specific temporal patterns in preferences. The component-based approach followed in modelling the aspects of preferences and their temporal effects enables us to arbitrarily switch components on and off. We evaluate the proposed method on three popular recommendation datasets and show that it significantly outperforms the most accurate state-of-the-art static models. The experiments also demonstrate the greater robustness and stability of the proposed dynamic model in comparison with the most successful models to date. We also analyse the temporal behaviour of different preference components and their combinations and show that the dynamic behaviour of preference components is highly dependent on the preference dataset and domain. Therefore, the results also highlight the importance of modelling temporal effects but also underline the advantages of a component-based architecture that is better suited to capture domain-specific balances in the contributions of the aspects.
Tracking Discrete and Continuous Entity State for Process Understanding

Procedural text, which describes entities and their interactions as they undergo some process, depicts entities in a uniquely nuanced way. First, each entity may have some observable discrete attributes, such as its state or location; modeling these involves imposing global structure and enforcing consistency. Second, an entity may have properties which are not made explicit but can be effectively induced and tracked by neural networks. In this paper, we propose a structured neural architecture that reflects this dual nature of entity evolution. The model tracks each entity recurrently, updating its hidden continuous representation at each step to contain relevant state information. The global discrete state structure is explicitly modelled with a neural CRF over the changing hidden representation of the entity. This CRF can explicitly capture constraints on entity states over time, enforcing that, for example, an entity cannot move to a location after it is destroyed. We evaluate the performance of our proposed model on QA tasks over process paragraphs in the ProPara dataset and find that our model achieves state-of-the-art results.
Daily solar radiation from NASA-POWER product: assessing its accuracy considering atmospheric transparency

Satellite remote sensing in estimating solar energy budget components at the top of the atmosphere (TOA) level and at the terrestrial level plays a very important role in various types of applications. Solar radiation data are especially problematic because of a quite generalized lack of sufficient data in quantity and quality. Satellite images allow solving the problem of continuity or lack of solar radiation data. The objective of this work was to fit daily solar radiation from NASA-POWER (National Aeronautics and Space Administration – Prediction Of Worldwide Energy Resources), considering different intervals of atmospheric transparency index. The accuracy was assessed from the analysis of voluminous data-sets registered by meteorological ground stations, 31 in number, located in whole Spain, during the period from 2000 to 2017. Clearness index (KT) was calculated to define nine classes of cloud cover conditions. The study reveals that the degree of correlation between the satellite data and observatory data depends upon atmospheric conditions and the correlation accuracy improves for higher values of KT. The coefficients of determination (R2), considering all KT values, were between 0.85 and 0.96; particularly for clear days R2 = 0.96 and root-mean-square error equal to 1.78 MJ m−2 d−1 were obtained. Geographically, the better statistic values were located in the central region of the country. NASA-POWER shows potential to estimate solar radiation and that it is an important information resource for different applications.
Simultaneous measurement method of the physical thickness and group refractive index free from a non-measurable range

An optical method to resolve the non-measurable thickness problem caused by the overlap of optical path differences within a specific thickness range when measuring the physical thickness of a sample using a spectral-domain interferometer is proposed and realized. Optical path differences can be discerned by inserting a correction glass piece into the measurement path, thus increasing the measurement optical path length. To verify the proposed method, 0.2-mm-thick N-BK7 glass was used as a sample, with physical thickness and group refractive index measurements conducted according to three different correction glass elements with corresponding nominal thicknesses of 3.0 mm, 3.5 mm, and 4.0 mm. Through uncertainty evaluations according to the correction glass used, the physical thicknesses of the sample were found to be in good agreement within measurement uncertainties of less than 100 nm, results comparable to those of previous works which did not use any correction glass.
Asymptotics for Sketching in Least Squares Regression

We consider a least squares regression problem where the data has been generated from a linear model, and we are interested to learn the unknown regression parameters. We consider sketch-and-solve methods that randomly project the data first, and do regression after. Previous works have analyzed the statistical and computational performance of such methods. However, the existing analysis is not fine-grained enough to show the fundamental differences between various methods, such as the Subsampled Randomized Hadamard Transform (SRHT) and Gaussian projections. In this paper, we make progress on this problem, working in an asymptotic framework where the number of datapoints and dimension of features goes to infinity. We find the limits of the accuracy loss (for estimation and test error) incurred by popular sketching methods. We show separation between different methods, so that SRHT is better than Gaussian projections. Our theoretical results are verified on both real and synthetic data. The analysis of SRHT relies on novel methods from random matrix theory that may be of independent interest.
Memory Efficient Adaptive Optimization

Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling. However, these methods maintain second-order statistics for each parameter, thus introducing significant memory overheads that restrict the size of the model being used as well as the number of examples in a mini-batch. We describe an effective and flexible adaptive optimization method with greatly reduced memory overhead. Our method retains the benefits of per-parameter adaptivity while allowing significantly larger models and batch sizes. We give convergence guarantees for our method, and demonstrate its effectiveness in training very large translation and language models with up to 2-fold speedups compared to the state-of-the-art.
Camera planning for area surveillance: A new method for coverage inference and optimization using Location-based Service data

Along with the rapidly growing volume of public security events, efficient camera planning and configuration methods have been one of the most crucial challenges in the video surveillance field. How to allocate different types of surveillance cameras in an area is one of the fundamental problems; however, limited methods have been available for generating the deployment parameters of cameras. The purpose of the paper is to explore camera planning based on multi-source Location-based Service data. The main idea is to infer the camera coverage by the building footprints, Point of Interests (POI) and social network record (WeChat) data, and to optimize the camera placement using the Maximal Coverage Location Problem-Complementary Coverage (MCLP-CC) model. Based on the probability of cell monitored with the calculation of viewshed analysis, the candidate location with max probability is selected. The essential spots in the surveillance area are uncovered by the combination of the kernel density estimation of POIs and WeChat data. The inference algorithm of the location, the field of view angle, orientation yaw, and visible distance parameters are proposed using the candidate location and critical spots in the viewshed polygon. The MCLP-CC is modeled and implemented by Python scripts and Gurobi software. The experiment shows that the proposed method can generate the detailed camera parameters including location, the field of view angle, orientation yaw, and visible distance with the lower occlusion and overlapping ratio for camera coverage. We believe that the integration of the coverage inference and optimization methods into the existing GIS platform will promote a variety of innovative applications in the camera planning area.
Disfluencies and Human Speech Transcription Errors

This paper explores contexts associated with errors in transcrip-tion of spontaneous speech, shedding light on human perceptionof disfluencies and other conversational speech phenomena. Anew version of the Switchboard corpus is provided with disfluency annotations for careful speech transcripts, together with results showing the impact of transcription errors on evaluation of automatic disfluency detection.
Blockchain as a solution to the problem of illegal timber trade between Russia and China: SWOT analysis

Prolific illegal timber trade between Russia and China cannot be stemmed by present certification schemes and Russian governmental strategies. Blockchain could present a new technological opportunity, simultaneously addressing illegal logging, regulating timber trade and meeting the needs of environmentally-sensitive markets. SWOT analysis of blockchain applicability in Russian-Chinese timber trade is presented. Decentralisation based on smart contracts could ensure timber supply chain transparency through each log cryptographic encryption. However, it is considered complicated as cryptocurrency (on which trade depends) is beyond the legislative sphere of both countries and energy demands are enormous. Beyond cryptocurrency prohibition, huge businesses and governments cannot accept blockchain for international trade because of transparency and cost risks. Digitalisation through blockchain implementation could provide a highly transparent, non-editable system to handle decentralised big data characteristic of the forestry economy, realise Industry 4.0 and develop the "China-Mongolia-Russia" economic corridor. Fundamentally, a workable Russian-Chinese Government-Science-Business model must be cultivated for effective blockchain implementation.
Lattice-Based IBE with Equality Test in Standard Model

Public key encryption with equality test (PKEET) allows the testing of equality of underlying messages of two ciphertexts. PKEET is a potential candidate for many practical applications like efficient data management on encrypted databases. Identity-based encryption scheme with equality test (IBEET), which was introduced by Ma (Information Science 2016), can simplify the certificate management of PKEET. Potential applicability of IBEET leads to intensive research from its first instantiation. Ma's IBEET and most of the constructions are proven secure in the random oracle model based on number-theoretic hardness assumptions which are vulnerable in the post-quantum era. Recently, Lee et al. (ePrint 2016) proposed a generic construction of IBEET schemes in the standard model and hence it is possible to yield the first instantiation of IBEET schemes based on lattices. Their method is to use a 3-level hierarchical identity-based encryption (HIBE) scheme together with a one-time signature scheme. In this paper, we propose, for the first time, a concrete construction of an IBEET scheme based on the hardness assumption of lattices in the standard model and compare the data sizes with the instantiation from Lee et al. (ePrint 2016). Further, we have modified our proposed IBEET to make it secure against insider attack.
Evolving the Hearthstone Meta

Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.