Reviewerly/exHarmony huggingface dataset:
Natural Image Matting via Guided Contextual Attention

Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. Many of these methods can generate visually plausible alpha estimations, but typically yield blurry structures or textures in the semitransparent area. This is due to the local ambiguity of transparent objects. One possible solution is to leverage the far-surrounding information to estimate the local opacity. Traditional affinity-based methods often suffer from the high computational complexity, which are not suitable for high resolution alpha estimation. Inspired by affinity-based method and the successes of contextual attention in inpainting, we develop a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matting. Guided contextual attention module directly propagates high-level opacity information globally based on the learned low-level affinity. The proposed method can mimic information flow of affinity-based methods and utilize rich features learned by deep neural networks simultaneously. Experiment results on Composition-1k testing set and alphamatting.com benchmark dataset demonstrate that our method outperforms state-of-the-art approaches in natural image matting. Code and models are available at https://github.com/Yaoyi-Li/GCA-Matting.
Population-Guided Parallel Policy Search for Reinforcement Learning

In this paper, a new population-guided parallel learning scheme is proposed to enhance the performance of off-policy reinforcement learning (RL). In the proposed scheme, multiple identical learners with their own value-functions and policies share a common experience replay buffer, and search a good policy in collaboration with the guidance of the best policy information. The key point is that the information of the best policy is fused in a soft manner by constructing an augmented loss function for policy update to enlarge the overall search region by the multiple learners. The guidance by the previous best policy and the enlarged range enable faster and better policy search. Monotone improvement of the expected cumulative return by the proposed scheme is proved theoretically. Working algorithms are constructed by applying the proposed scheme to the twin delayed deep deterministic (TD3) policy gradient algorithm. Numerical results show that the constructed algorithm outperforms most of the current state-of-the-art RL algorithms, and the gain is significant in the case of sparse reward environment.
A novel rotation invariant and Manhattan metric–based pose refinement: Augmented reality–based oral and maxillofacial surgery

Abstract Background Augmented reality (AR) is gaining attention in medicine because of the convenience and innovation that it brings to operating rooms. Furthermore, oral and maxillofacial surgery (OMS), which is one of sensitive and narrow spatial surgery, requires high accuracy in image registration and low processing time of the system. However, the current systems are suffering from image registration problems while matching two different posture images. We thus aimed to increase that overlay accuracy and decrease the processing time. Methodology The proposed system consists of an Iterative Closest Point (ICP) algorithm, which is the combination of a rotation invariant and Manhattan error metric, to provide the best initial parameters and to decrease the computational cost by sorting high and low processing pixel images, respectively. Result The study on maxillary and mandibular jaw bone demonstrates that the proposed work overlay accuracy ranges from 0.22 to 0.30 mm, and processing time ranges from 10 to 14 frames per second as opposed to the 0.23‐ to 0.35‐mm overlay accuracy and the current 8 to 12 frames per second processing time. Conclusion This research aimed to improve the visualization and fast AR system for the OMS. Thus, the proposed system achieved an improvement in overlay accuracy and processing time by implementing the Rotation Invariant and Manhattan error metric ICP algorithm.
Single-Level Production Planning in Petrochemical Industries Using Novel Computational Intelligence Algorithms

Optimal production planning requires the solution of combinatorial optimization problems that need to be efficiently modelled so as to be solved with computational intelligence (CI) techniques. In this work, we report the computational performance of five recently proposed CI techniques, namely the sanitized-teaching-learning-based optimization (s-TLBO), moth flame optimization (MFO), flower pollination optimization, water cycle optimization, and adaptive wind driven optimization on the single level production planning problem which involves complex domain-hole constraints, nonlinearities in the production and investment costs, resource and unique process constraints. In this work, the domain-hole constraints are handled using a hard-penalty approach. The performance is evaluated on the case study of the Saudi Arabia petrochemical industry to determine optimal portfolio from 54 processes for producing 24 products with three production levels. Based on 2040 (8 Cases × 51 runs × 5 algorithms) unique instances of the problem, it was observed that s-TLBO and MFO are consistently able to determine efficient solutions and that s-TLBO was able to quickly discover feasible solutions and had relatively low variance.
Application of Augmented Reality Technology in Chemistry Experiment Teaching

Chemistry experiment is an important part of chemistry teaching.Using augmented reality technology to design virtual chemistry experiment is a new way to effectively solve the problem of traditional chemistry experiment teaching.The system takes mobile terminal as its platform, takes mobility and openness, three-dimensional simulation scene, natural interaction and instant feedback as its design principles, and takes Unity3D and augmented reality technology as its development means.Augmented reality virtual chemistry experiment system has three functional modules: learning knowledge, doing experiments and measuring learning effect.The system transforms the traditional chemical experiment teaching into a mobile natural interactive virtual experiment form, which will greatly enhance the students' sense of autonomy and participation, and bring new changes to the chemical experiment teaching.
Humanitarian Documentary: A Comparison Study between VR and Non-VR Productions

Virtual Reality is broadly recognized as an "empathy machine".This reputation is due to the feeling of 'presence' that it provides to users, which is the sensation of being bodily present in a space, even when that space is virtual.The possibility of complete immersion attracts many creators looking to induce empathy and awareness about the most diverse subjects.One of the first types of VR non-fiction productions to be released was in the morally sensitive humanitarian documentary genre.This research aims to explore how VR productions differ from non-VR productions with a focus on humanitarian communication.Rather than targeting mechanical aspects of VR technology, this article compares the visual and narrative storytelling characteristics in VR and non-VR media.First, humanitarian communication and its nuances are explained.Then, 360º video filming characteristics are analyzed, followed by a comparison table contrasting VR and non-VR non-fiction.After evaluating VR non-fiction empirical studies, a discussion is initiated over the betterment of VR non-fiction storytelling in a way that could help it generate more empathy, since many productions seem to purely rely on the technology as a production novelty, and end up lacking emotional depth and audience engagement through story.
Smart Transcription

The Intelligent Voice SmartTranscript is an interactive HTML5 document that contains the audio, a speech transcription and the key topics from an audio recording. It is designed to enable a quick and efficient review of audio communications by encapsulating the recording with the speech transcript and topics within a single HTML5 file. This paper outlines the rationale for the design of the SmartTranscript user experience. The paper discusses the difficulties of audio review, how there is large potential for misinterpretation associated with reviewing transcripts in isolation, and how additional diarization and topic tagging components augment the audio review process.
Lexicon-Grammar Tables Development for Arabic Psychological Verbs

The identification of psychological verbs is very important in corpora analyses in order to give the polarity of a given text and define the emotional component. The classification of those verbs represents a challenge for linguists since they classify them according to their needs and their understanding. The aim of this paper is the identification and classification of Arabic psychological verbs through lexicon-grammar tables that are well structured, easy to use for linguists and allow them to describe all the grammatical, syntactic and semantic characteristics of the lexicon. In this work we create lexicon-grammar tables of Arabic psychological verbs with about 400 verbs entries in three main classes and subclasses to use them in lexical, syntactic and semantic analyzers. Using NooJ as an automatic natural language processing platform, we can automatically recognize Arabic psychological verbs by transforming our lexicon-grammar tables into NooJ dictionaries and syntactic grammars enabling the detection of those verbs in texts and corpora.
On a Parallel Spark Workflow for Frequent Itemset Mining Based on Array Prefix-Tree

Frequent Itemset Mining (FIM) is a fundamental procedure in various data mining techniques such as association rule mining. Among many existing algorithms, FP-Growth is considered as a milestone achievement that discovers frequenti temsets without generating candidates. However, due to the high complexity of its mining process and the high cost of its memory usage, FP-Growth still suffers from a performance bottleneck when dealing with large datasets. In this paper, we design a new Array Prefix-Tree structure, and based on that, propose an Array Prefix-Tree Growth (APT-Growth) algorithm, which explicitly obviates the need of recursively constructing conditional FP-Tree as required by FP-Growth. To support big data analytics, we further design and implement a parallel version of APTGrowth, referred to as PAPT-Growth, as a Spark workflow. We conduct FIM workflow experiments on both real-life and synthetic datasets for performance evaluation, and extensive results show that PAPT-Growth outperforms other representative parallel FIM algorithms in terms of execution time, which sheds light on its potential applications to big data mining.
ENABLING DATA DEMOCRACY IN SUPPLY CHAIN USING BLOCKCHAIN AND IOT

Data Analytics in today's Demand Driven Supply chain Management (SCM) involving trusted as well as untrusted fulfilment partners will see exponential improvements with large volumes of reliable, trusted data generated automatically through IoT and stored in immutable format in the Blockchain.This paper examines the challenges which Supply Chain Partners in a Highly Digitized ecosystem have in getting access to good quality reliable data.It examines Use Cases and benefits of current IoT and Blockchain integrated frameworks for SCM.It also explores limitations of the traditional IoT and Blockchain integration patterns and presents an architectural framework for a permissioned Blockchain that can be used to overcome these limitations.
Real-time Motion-planning of Curvature-constrained AUVs under Steady Ocean Currents

In this paper, we propose a novel approach to obtain a real-time solution for the minimum-time motion-planning problem for curvature-constrained AUVs in the presence of ocean currents. The existing solution to this problem is obtained by solving the six Dubins path types (i.e., LSL, RSR, LSR, RSL, LRL and RLR) in the presence of currents, four of which involve a root-finding problem consisting of transcendental functions. Thus, the computational complexity of the exiting solution makes it infeasible for real-time applications, such as rapidly changing oceanic environments. The proposed approach utilizes only the LSL and RSR path types from the Dubins set, which provide analytical solutions as needed for real-time applications. It is shown that by extending the feasible range of circular arcs in these path types from 2π to 4π, full reachability is guarantee; i.e., a solution can be obtained for every pair of start and goal poses. Furthermore, it is shown that the proposed solution provides better time-costs with lower travel times as compared to the existing Dubins LSL and RSR paths. The proposed approach is validated by numerical studies through various examples to highlight its benefits for real-time applications.
Experimental Study on Dynamic Map Information Layout Based on Eye Tracking

In the GIS system interface, the map module occupies more than 90% of the interface size, which is the focus of the GIS interface design. There are a large number of targets, target signs, target trajectories and other information on the map. The layout of various information is crowded and overlapped, making it difficult for users to search and identify information. The purpose of this paper is to explore the impact of different presentation forms of target signs on user search efficiency. This paper used 8 kinds of overlap and 4 kinds of target sign layout display to design the experiment. This paper analyzed the search performance of subjects, and gaze plot based on eye tracking data.
Correlation Self-Expression Shrunk for Subspace Clustering

In subspace clustering, low-rank representation (LRR) is a nice recipe for learning a good affinity matrix of a set of unlabeled data by representing themselves under the low-rank constraint in order to subsequently couple with clustering methods to divide data into several groups. However, the underlying geometric structure within data is still insufficiently explored in LRR. For this reason, we propose a correlation self-expression shrunk (CSS) to refresh LRR with two novel insights: 1) the compound schatten p-norm instead of the nuclear norm which is used to induce adaptive affinity matrix by addressing the issues of both sparse and dense representation; 2) to model compact group structure and relieve side effect of the outliers, CSS designs a robust self-expression shrunk regularization to reduce the deviation of similar samples. Thus, CSS can reap the benefits of both insights to boost clustering performance. In addition, we optimize CSS in the frame of alternating direction multiplier method (ADMM) where each sub-problem has a closed-form solution. Experiments of image clustering on four datasets verify the efficacy of CSS against several baseline variants of LRR.
Enabling Primary School Teachers to Deliver STEM Programmes with the Internet of Things

The Internet of Things (IoT) offers opportunities for data collection, processing and visualisation driving fruitful mechanisms for the demonstration and execution of STEM activities. Nevertheless its application in a primary school setting is not straightforward. There are issues ranging from the capability to setup an IoT system in the class, sound knowledge of technical terminology, relevancy to curriculum and finally the provision of a positive experience to students. In this paper we discuss our experiences including lessons learnt towards providing an IoT platform for STEM promotion in primary schools, the challenges identified and the solutions formed to tackle them. The outcome, is a set of practices for making IoT practically applicable for STEM activities.
Families of Optimal Low-Hit-Zone Frequency-Hopping Sequence Sets Under the Periodic Partial Hamming Correlation Properties

For the sake of meeting the variable requirements of different quasi-synchronous (QS) frequency-hopping multiple-access (FHMA) communication systems, low-hit-zone (LHZ) frequency-hopping sequence (FHS) sets with flexible parameters and optimal Hamming correlation properties are desired. In this paper, we have a generalized method of constructing LHZ-FHS sets through interleaving technique and analyze the maximum periodic partial Hamming correlation (PPHC) of our constructed LHZ-FHS sets, based on which we are capable of getting four new families of LHZ-FHS sets. It turns out that our new LHZ-FHS sets can be optimal under the PPHC properties and have parameters not covered in the related literature, and thus can be applied in QS-FHMA communication systems to minimize or reduce the mutual-interference.
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning

Techniques used for spatio-temporal anomaly detection in an unsupervised settings has attracted great attention in recent years. It has extensive use in a wide variety of applications such as: medical diagnosis, sensor events analysis, earth science, fraud detection systems, etc. Most of the real world time series datasets have spatial dimension as additional context such as geographic location. Although many temporal data are spatio-temporal in nature, existing techniques are limited to handle both contextual (spatial and temporal) attributes during anomaly detection process. Taking into account of spatial context in addition to temporal context would help uncovering complex anomaly types and unexpected and interesting knowledge about problem domain. In this paper, a new approach to the problem of unsupervised anomaly detection in a multivariate spatio-temporal dataset is proposed using a hybrid deep learning framework. The proposed approach is composed of a Long Short Term Memory (LSTM) Encoder and Deep Neural Network (DNN) based classifier to extract spatial and temporal contexts. Although the approach has been employed on crime dataset from San Francisco Police Department to detect spatio-temporal anomalies, it can be applied to any spatio-temporal datasets.
Analytic Expression of Quantum Discords in Werner States under LQCC

In this paper, quantum discords in a special kind of states, i.e., Werner states by local quantum operations and classical communication (LQCC) protocols (WLQCC states), are studied. Nineteen parameters to quantify the quantum discords are reduced to four parameters in terms of properties of Werner states and quantum discord. In the case of orthogonal projective measures, analytic expression of quantum discords in WLQCC states is analytically worked out. Some properties of the quantum discord in the WLQCC states are obtained, especially the variation relations between the quantum discords and the parameters characterizing the WLQCC states. By virtue of numerical computations, quantum discords in a Werner state before and after LQCC protocols are compared. It is found that quantum discord in any WLQCC state cannot exceed that in the original Werner state.
A gravitational density-based mass sharing method for imbalanced data classification

Classification is one of the most popular branches of supervised learning algorithms. In the real-world problems, there are some situations in which distributions of the two classes are not the same. This situation is known as a class imbalanced problem. In the past years, several investigations have been done to find a way of handling imbalanced data, which most of them stay in one of two groups, including internal techniques and external techniques. The proposed gravitational density-based mass sharing method (GDMS) is an internal method that is designed based on k-nearest neighbor and fixed radius nearest neighbor (FRNN) rules. GDMS is a new technique that assigns masses to instances based on their local density while considering the global information too. In the labeling phase, GDMS decides based on the sum of gravitational forces coming from the candidates set, which are defined by FRNN rule. The GDMS does not need any parameters to be set in the whole procedure of classification, which is an advantage in comparison with the previous methods. To demonstrate the effectiveness of our proposed method, we use 40 standard datasets from the KEEL repository. The results show the effectiveness and superiority of GDMS compared to the competing methods.
Deceptive AI Explanations: Creation and Detection

Artificial intelligence (AI) comes with great opportunities but also great risks. Automatically generated explanations for decisions are deemed helpful to better understand AI, increasing transparency and fostering trust. However, given e.g. economic incentives to create dishonest AI, can we trust its explanations? To address this issue, our paper investigates to what extent models of AI, i.e. deep learning, and existing instruments to increase transparency regarding AI decisions can be used to create and detect deceptive explanations. For empirical evaluation, we focus on text classification and alter explanations originating from GradCAM, a well-established technique for creating explanations in neural networks. We then evaluate the effect of deceptive explanations on users in an experiment with 200 participants. Our findings confirm that deceptive explanations can indeed fool humans while machine learning methods can detect seemingly minor attempts of deception with accuracy that exceeds 80\% given sufficient domain knowledge in the form of training data. Without domain knowledge, one can still infer inconsistencies in the explanations in an unsupervised manner given basic knowledge on the allegedly deceptive model.
Machine Learning Classification of Head Impact Sensor Data

Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.