Reviewerly/exHarmony huggingface dataset:
Game play in virtual reality driving simulation involving head-mounted display and comparison to desktop display

Previous studies have reported the effect of driving simulator games on simulator sickness and eye symptoms experienced by users; however, empirical results regarding the game experience using commercial virtual reality head-mounted displays (VR-HMDs) are lacking. We conducted an experiment where participants played a driving simulator game (Live for Speed) displayed through an Oculus Rift DK2 for up to 120 min. Game play duration was recorded. Game experience was surveyed using questionnaires about simulator sickness, eye symptoms, and game engagement. The results showed that the average game play duration for this specific driving simulation game was approximately 50 min. Simulator sickness was negatively correlated with affordable play duration using the VR-HMD. We also found that age was negatively correlated with game play duration. There were no differences between those who did and did not wear frame glasses. In addition, we compared the VR-HMD game play and traditional desktop LCD game play, in terms of simulator sickness, subjective eye symptoms, game engagement, and game performance. The results showed that VR-HMD game play in the driving simulation game was similar to the experience using the desktop LCD display, except for a moderately increased level of simulator sickness. These findings provide new data about VR-HMD’s impact on game play and will inform game designers, players, and researchers for their choices and decisions on proper game duration and the type of devices.
Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture's future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.
Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games

In this work we tackle the problem of automatic recognition of ancient coin types using a semisupervised learning method, namely Graph Transduction Games. Such problem is complex, mainly due to the low inter-class and large intra-class variations and the task becomes even more complex due to lack of labeled large datasets from certain ancient ages. In this paper we propose a new dataset which is chiefly the extension of a previous one both in terms of quantity and diversity. Moreover, we propose a game-theoretic model that exploits both sides of a coin to achieve higher classification accuracy. We experimentally demonstrate that proposed approach brings performance improvement in this complex task even when few number of labelled images are available.
SCGSA: A sine chaotic gravitational search algorithm for continuous optimization problems

Gravitational search algorithm (GSA), as one of the novel meta-heuristic optimization algorithms inspired by the law of gravity and mass interactions, is however prone to local optima stagnation due to heavier gravity. Hence, an enhanced version, chaotic gravitational constants for the gravitational search algorithm (CGSA), was proposed to improve the exploration ability through various chaotic maps. In this paper, with insightful utilization of sine cosine algorithm, we put forward sine chaotic gravitational search algorithm (SCGSA) as a further step of CGSA to escape from its local optima stagnation. The experiments show remarkable results in both the speed of convergence and the ability of finding global optima in 30 benchmark functions (CEC 2014), thus proving a better balance between exploration and exploitation in SCGSA compared with CGSA.
3D geological modelling and uncertainty analysis for 3D targeting in Shanggong gold deposit (China)

Three dimensional (3D) deposit-scale modelling was used to identify exploration targets at depth and evaluation of mineral resources including 3D geometry and attribute models. Constructing 3D deposit-scale model need to combine exploration boreholes, cross-sections, and geochemistry assays additional mining dataset. The Shanggong gold deposit is a magmatic hydrothermal deposit with an orogenic setting, and it is located in the south margin of North China Craton. On basis of the geological features of the Shanggong gold deposit, the 3D geology model and attribute modelling aims at potential targets identification and mineral resource estimation adopting different mining and exploration datasets and uncertainty analysis in 3D space. The methodology is summarized as follows: 1) the 3D geological bodies, such as stratum, fault, ore bodies model, constructed using GoCAD software, an initial range of 3D model is 15 × 13 × 3 km3; 2) Ordinary Kriging and discrete smooth interpolation (DSI) methods were used to analyze and identify the trend of the mineralization (Au) features in 3D space combined boreholes and the primary geochemical haloes dataset, and the Au/Pb ratio model is constructed using primary geochemical haloes data with DSI interpolation method; 3) the grade simulation modelling and assessing the uncertainty of the geometry and feature models using the geostatistical method. The results show as follows: 1) the high gold grade value zones are associated with intrusion and NE-trending and NNE-trending cross fault structures; 2) the high gold grade value blocks of Au/Pb ratio can be used to depict mineralization zones; 3) Ordinary Kriging and DSI methods can show the trend of the attribute model; 4) the simulation results can characterize the uncertainty of the 3D model through uncertainty analysis.
Improving named entity recognition in noisy user-generated text with local distance neighbor feature

Recognizing infrequent or emerging named entities in a user-generated text is a challenging task, especially when informal or slang text is used. Some recent works propose to use a gazetteer to solve this problem, but this solution is not general because the gazetteer is task-specific and its maintenance is costly. In this paper, we overcome this drawback by presenting Local Distance Neighbor (LDN), a novel feature that substitutes the gazetteer and makes that the model obtains state-of-the-art results. LDN captures an initial guess for each input token based on the categories of its neighboring tokens within an embedding space. We evaluated the proposed network on the W-NUT-2017 dataset, and we obtained the state-of-the-art F1 score for the Group, Person, and Product categories. We employed our new feature together with the model proposed by Aguilar et al. to recognize named entities in the Tor Darknet related to suspicious activities associated with weapons and drug selling. After increasing the samples of the W-NUT-2017 dataset with 851 manually annotated entries, we repeated our evaluation in this extended version of the dataset, achieving entity and surface F1 scores of 52.96% and 50.57%, respectively. Furthermore, we demonstrate that our proposal can be useful for Law Enforcement Agencies in mining the textual information in the Tor hidden services, being especially adequate for the Group, Person, and Product categories.
Distributing Any Elliptic Curve Based Protocol

We show how to perform a full-threshold n-party actively secure MPC protocol over a subgroup of order p of an elliptic curve group E(K). This is done by utilizing a full-threshold n-party actively secure MPC protocol over $$\mathbb {F}_p$$ in the pre-processing model (such as SPDZ), and then locally mapping the Beaver triples from this protocol into equivalent triples for the elliptic curve. This allows us to transform essentially any (algebraic) one-party protocol over an elliptic curve, into an n-party one. As an example we show how to transform a general $$\varSigma $$-protocol over elliptic curves and the shuffle protocol of Abe into an n-party protocol. This latter application requires us to also give an MPC protocol to derive the switches in a Waksman network from a generic permutation, which may be of independent interest.
Hierarchical Two-Stream Growing Self-Organizing Maps With Transience for Human Activity Recognition

The rapid growth in autonomous industrial environments has increased the need for intelligent video surveillance. As a predominant element of video surveillance, recognition of complex human movements is important in a wide range of surveillance applications. However, the current state-of-the-art video surveillance techniques use supervised deep learning pipelines for human activity recognition (HAR). A key shortcoming of such techniques is the inability to learn from unlabeled video streams. To operate effectively in natural environments, video surveillance techniques have to be able to handle huge volumes of unlabeled video data, monitor and generate alerts and insights derived from multiple characteristics such as spatial structure, motion flow, color distribution, etc. Furthermore, most conventional learning systems lack memory persistence capability which can reduce the influence of outdated information in memory-guided decision-making resulting in limiting plasticity and overfitting based on specific past events. In this article, we propose a new adaptation of the Growing Self-Organizing Map (GSOM) to address these shortcomings by 1) adopting two proven concepts of traditional deep learning, hierarchical, and multistream learning, applied into GSOM self-structuring architecture to accommodate learning from unlabeled video data and their diverse characteristics, 2) address overfitting and the influence of outdated information on neural architecture by implementing a transience property in the algorithm. We demonstrate the proposed model using three benchmark video datasets and the results confirm its validity and usability for HAR.
Web Service Interaction Modeling with Colored Petri Nets

The investigation focuses on the use of Petri Nets for web services interaction modeling. It is discovered that Hierarchical Time Petri Nets is the most effective way to create web service interaction models. That is due to the fact that they can be used to build hierarchical models of complex systems, and investigate their dynamics. Moreover, the research describes a custom algebra-algorithmic system. It comprises the basis for further web services semantics and description. The models are developed with the custom algebra-algorithmic system operations. Service models are created with Petri colored nets. Models and imitational modeling are performed in the CPN-tools environment.
A reversible privacy-preserving clustering technique based on k-means algorithm

Data mining techniques can efficiently extract valuable knowledge from the data. However, data publishing and mining may result in a potential threat: privacy invasion. Privacy-Preserving Data Mining (PPDM) aims at effectively protecting the privacy while retaining the knowledge contained in the original data, which has received much attention in recent years. PPDM techniques often use methods such as swap, modification, and deletion to protect the original data so that no correlation exists between the original data and the resultant protected data. As a result, it cannot recover the original data from the protected data. However, in certain applications, a reliance on the original data to perform precision analysis is necessary, and consequently the storage of the original data is of great importance. In this paper, we use the concept of the k-means algorithm and propose a Reversible Privacy-Preserving k-means Clustering (kRPP) algorithm for protecting the clustering knowledge of a dataset. The experimental results show that adding noises to a cluster with a minimum total distance in the kRPP algorithm, there is no significant effect on the movement of centroids, Precision, Recall, and F1 when the noise ratio or noise offset ratio increases.
Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features

Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.
Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification

The following topics are dealt with: feature extraction; learning (artificial intelligence); virtual reality; solid modelling; user interfaces; neurophysiology; electroencephalography; human computer interaction; augmented reality; medical signal processing.
Towards the first Maithili part of speech tagger: Resource creation and system development

Part of speech (POS) tagging for the Indian language Maithili is not an explored territory. There have been substantial efforts at developing POS taggers in several Indian languages including Hindi, Bengali, Tamil, Telugu, Kannada, Punjabi and Marathi; but we did not find any openly available POS tagger and tagged corpus in Maithili. However, Maithili is one of the official languages of India with around 50 million native speakers. Development of Maithili natural language processing (NLP) tools and resources is extremely important as the language is currently being used in education and official contexts in certain states in India. In this paper, we present our effort on the development of a Maithili POS tagger. As we did not find any open training data, we started the development by annotation of a POS tagged corpus. We defined a POS tagset and manually annotated a Maithili corpus containing 52,190 words. We used the corpus to train a conditional random fields (CRF) classifier. We ran experiments using various feature sets and achieved an accuracy of 82.67%. Then we collected large raw corpora containing Wikipedia dump and other Maithili web resources to train neural word embedding. The word2vec CBOW model was trained and the generated word vectors were utilized during CRF training. With this inclusion, the accuracy of the system increased to 85.88%.
Passive vision measurement for robust reconstruction of molten pool in wire and arc additive manufacturing

Robust measurement of molten pool in wire and arc additive manufacturing can help better understand the geometrical size of fabricated parts and even offer feedback to control this process. Most of existing detection methods are not sufficient to express full geometric features of molten pool. In this paper, a novel passive vision sensing system using a single camera is designed based on the refraction principle of a biprism. Intrinsic and external parameters of left-right virtual cameras and their relative position are solved through a planar pattern, and an image pair is rectified by transmission projection. A global-based iterative matching algorithm is optimized for calculating the left-right parallax images of the image pair, and coordinates of space points are calculated via the triangle measuring method. The proposed sensing system and algorithms are validated by a standard cylinder. The results show that the width error of the calculated cylinder appearance is approximately 3.02% and the maximum height error is 4.15%. Finally, molten pool geometry in gas metal arc (GMA) additive manufacturing is restructured, proving the effectiveness of the virtual binocular vision system and the accuracy of the reconstruction algorithms.
EHAUSM: An efficient algorithm for high average utility sequence mining

Identifying high utility sequences in a quantitative sequence database is an important data mining task. However, a key problem of current approaches is that extensions of a high utility sequence often have a high utility. Hence, traditional techniques are often biased toward finding long patterns. To circumvent this problem, this paper proposes techniques for the problem of high average-utility sequence (HAUS) mining (HAUSM). HAUSs are more meaningful than high utility sequences because the former are found using the average-utility measure, which consider the length of patterns in utility calculations. HAUSM is more general than high average-utility itemset mining but it is also a more difficult problem because the downward-closure property used for search space reduction does not hold for the average-utility. To overcome that challenge, this paper introduces two upper bounds and a weak upper bound on the average-utility measure, and four width pruning, depth pruning, reducing, and tightening strategies. These strategies are designed to eliminate candidate HAUSs to speed up HAUS mining. Based on these theoretical results, a novel algorithm named EHAUSM is proposed for HAUSM. Experiments on both real-life and synthetic quantitative sequence databases have confirmed its efficiency in terms of memory consumption and runtime.
Task definition, annotated dataset, and supervised natural language processing models for symptom extraction from unstructured clinical notes

Machine learning (ML) and natural language processing have great potential to improve information extraction (IE) within electronic medical records (EMRs) for a wide variety of clinical search and summarization tools. Despite ML advancements, clinical adoption of real time IE tools for patient care remains low. Clinically motivated IE task definitions, publicly available annotated clinical datasets, and inclusion of subtasks such as coreference resolution and named entity normalization are critical for the development of useful clinical tools. We provide a task definition and comprehensive annotation requirements for a clinically motivated symptom extraction task. Four annotators labeled symptom mentions within 1108 discharge summaries from two public clinical note datasets for the tasks of named entity recognition, coreference resolution, and named entity normalization; these annotations will be released to the public. Baseline human performance was assessed and two ML models were evaluated on the symptom extraction task. 16,922 symptom mentions were identified within the discharge summaries, with 11,944 symptom instances after coreference resolution and 1255 unique normalized answer forms. Human annotator performance averaged 92.2% F1. Recurrent network model performance was 85.6% F1 (recall 85.8%, precision 85.4%), and Transformer-based model performance was 86.3% F1 (recall 86.6%, precision 86.1%). Our models extracted vague symptoms, acronyms, typographical errors, and grouping statements. The models generalized effectively to a separate clinical note corpus and can run in real time. To our knowledge, this dataset will be the largest and most comprehensive publicly released, annotated dataset for clinically motivated symptom extraction, as it includes annotations for named entity recognition, coreference, and normalization for more than 1000 clinical documents. Our neural network models extracted symptoms from unstructured clinical free text at near human performance in real time. In this paper, we present a clinically motivated task definition, dataset, and simple supervised natural language processing models to demonstrate the feasibility of building clinically applicable information extraction tools.
Space Situational Awareness Sensor Tasking: Comparison of Machine Learning with Classical Optimization Methods

The object population in the space around the Earth is subject to increase. With the advancements in sensor capabilities, it can be expected that, at the same time, more of those objects will be detected. Although this allows for significant advances in understanding of the detectable objects and the expansion of object catalogs, it also leads to significant stress on the sensor systems and makes efficient sensor tasking a prime challenge. To solve sensor tasking as an optimization problem of observing objects when a priori information is available, various methods exist. Classical methods rely on the problem being formulated in a convex representation. Computationally intensive methods based on artificial intelligence, such as machine learning, have recently gained a lot of attention and are suitable for problems even when no convex formulation can be found. In this paper, performances of a simple traditional greedy algorithm and the more complex Weapon-Target Assignment algorithm are compared with the performance of two machine learning algorithms: ant colony and distributed Q-learning. Ant colony optimization is a swarm optimization path finding methodology based on probabilistic principles; distributed Q-learning aims to find an optimal policy by maximizing the expected reward received. As an application case the observation of known objects in the geosynchronous region with a ground-based sensor is used, and performance is evaluated in terms of the number of objects successfully tracked, the computational efficiency of running the algorithms, and the difficulty of tuning the algorithms. The ant colony solutions track the most objects, whereas the greedy algorithm is the most efficient; additionally, the ant colony and distributed Q-learning require significant tuning of the algorithms before employment.