Reviewerly/exHarmony huggingface dataset:
Understanding and improving ontology reasoning efficiency through learning and ranking

Ontologies are the fundamental building blocks of the Semantic Web and Linked Data. Reasoning is critical to ensure the logical consistency of ontologies, and to compute inferred knowledge from an ontology. It has been shown both theoretically and empirically that, despite decades of intensive work on optimising ontology reasoning algorithms, performing core reasoning tasks on large and expressive ontologies is time-consuming and resource-intensive. In this paper, we present the meta-reasoning framework R2O2* to tackle the important problems of understanding the source of TBox reasoning hardness and predicting and optimising TBox reasoning efficiency by exploiting machine learning techniques. R2O2* combines state-of-the-art OWL 2 DL reasoners as well as an efficient OWL 2 EL reasoner as components, and predicts the most efficient one by using an ensemble of robust learning algorithms including XGBoost and Random Forests. A comprehensive evaluation on a large and carefully curated ontology corpus shows that R2O2* outperforms all six component reasoners as well as AutoFolio, a robust and strong algorithm selection system.
Design for Long-Term Tracking: Insights from a Six-Month Field Study Exploring Users’ Experiences with Activity Trackers

This paper presents recommendations for designing activity trackers that can support long-term tracking behaviour. These recommendations are based on a six-month field study exploring activity trackers’ impact on physical activity (PA) performance and the aspects leading to abandonment. Twenty-six individuals, who were assigned to the activity tracker condition (i.e. Fitbit One) or the control condition, took part in the study. Semi-structured interviews, Global Physical Activity Questionnaire (GPAQ), and activity data derived from the tracker were used to understand participants’ expectations, experiences, and insights about tracking PA. Proposed recommendations are: (1) consider the diversity in users’ expectations when designing activity trackers; (2) allow users to experiment with different trackers; (3) design half-way products that can be customized based on users’ lifestyles; (4) prioritize reflection over quantification; and (5) design for successful abandonment.
Deep Learning Aided System Design Method for Intelligent Reimbursement Robot

Recently, the massive use of invoices has increased the burden on financial staff. In order to ensure high accuracy, conventional financial reimbursement is mainly manual, which wastes lots of human and material resources. This paper proposes an intelligent reimbursement system based on a deep learning method. The system mainly contains face recognition, invoice identification, and information storage. Face recognition ensures the security in the invoice reimbursement, and invoice identification has acceptable accuracy and operating speed. The experimental results indicate that the proposed system achieves both high accuracy and fast running speed.
Geometry of the Vocal Tract and Properties of Phonation near Threshold: Calculations and Measurements

In voice research, analytically-based models are efficient tools to investigate the basic physical mechanisms of phonation. Calculations based on lumped element models describe the effects of the air in the vocal tract upon threshold pressure (Pth) by its inertance. The latter depends on the geometrical boundary conditions prescribed by the vocal tract length (directly) and its cross-sectional area (inversely). Using Titze’s surface wave model (SWM) to account for the properties of the vocal folds, the influence of the vocal tract inertia is examined by two sets of calculations in combination with experiments that apply silicone-based vocal folds. In the first set, a vocal tract is constructed whose cross-sectional area is adjustable from 2.7 cm2 to 11.7 cm2. In the second set, the length of the vocal tract is varied from 4.0 cm to 59.0 cm. For both sets, the pressure and frequency data are collected and compared with calculations based on the SWM. In most cases, the measurements support the calculations; hence, the model is suited to describe and predict basic mechanisms of phonation and the inertial effects caused by a vocal tract.
Autocorrelation Modulation-Based Audio Blind Watermarking Robust Against High Efficiency Advanced Audio Coding

High Efficiency Advanced Audio Coding (HE-AAC) is a lossy compression method for digital audio data which supplies high-quality audio at a very low bit rate. In this paper, the audio blind watermarking algorithm, on the basis of autocorrelation modulation, is introduced to maximize the robustness against low-bit rate HE-AAC. The watermark is embedded by modulating the normalized correlation of the original signal as well as its delayed version. The signal-to-noise ratio of before and after HE-AAC compression decides the strength of the embedding watermark. The watermarking embedding strength is guaranteed by the feedback process. The effectiveness of the proposed method is proven using the Perceptual Evaluation of Audio Quality algorithm and bit error rate of recovered watermarks under HE-AAC compression on mono, stereo and 5.1 channel audio. Experimental results show that the proposed method provides good performance in terms of imperceptibility, robustness and data payload compared with some recent state-of-the-art watermarking methods under an MPEG-2 Audio Layer III (MP3) compression attack.
Classification and Detection in Mammograms With Weak Supervision Via Dual Branch Deep Neural Net

The high cost of generating expert annotations, poses a strong limitation for supervised machine learning methods in medical imaging. Weakly supervised methods may provide a solution to this tangle. In this study, we propose a novel deep learning architecture for multi-class classification of mammograms according to the severity of their containing anomalies, having only a global tag over the image. The suggested scheme further allows localization of the different types of findings in full resolution. The new scheme contains a dual branch network that combines region-level classification with region ranking. We evaluate our method on a large multi-center mammography dataset including ~3,000 mammograms with various anomalies and demonstrate the advantages of the proposed method over a previous weakly-supervised strategy.
Access Control Management for Plant Phenotyping Using Integrated Blockchain

The high demand for food makes it necessary to implement plant phenotyping processes into breeding programs to deal with global food security. Image-based plant phenotyping generates vast amounts of data. Traditionally, this data has been managed in a centralized manner requiring that an administrator grants access permissions. This approach has some limitations when sharing data; for instance, we cannot track whether data has been shared with the permission of the owner. This research proposes a blockchain-based system for data access control management in the field of image-based plant phenotyping. This system integrates Ethereum blockchain to allow users to be the administrators of their data. Thus, users can grant or deny permissions to access their data without relying on any central administrator. This system is part of the research work at the Plant Phenotyping and Imaging Research Centre (P2IRC) in Saskatoon, Canada.
AttentionNet: Learning Where to Focus via Attention Mechanism for Anatomical Segmentation of Whole Breast Ultrasound Images

The main challenges of the anatomical segmentation of automated whole breast ultrasound (AWBUS) image are shadow effect, blurred boundary, low contrast and large target. To tackle them, a novel and effective framework named AttentionNet is developed via self-attention mechanism during both feature extraction and up-sampling phase. Specifically, features are firstly extracted based on ResNeXt-50 to explore the information of intra-channels. With the goal of extracting features and utilizing channel information effectively, a module named spatial attention refinement (SAR) is devised using the basic ResNeXt-50 module (a.k.a., ResNeXt-SAR). Then, a weighted up-sampling block (WUB) module for precise pixel localization is designed by introducing high-level semantic concept during up-sampling phase, playing an important role in guiding the low-level features by the category information. The extensive experiments are conducted on AWBUS image for multi-class image segmentation. Our proposed AttentionNet achieves the superior results over the state-of-the-art approaches and may help to assist the calculation of breast density.
A memetic NSGA-II with EDA-based local search for fully automated multiobjective web service composition

Web service composition aims to provide added values by loosely coupling web services to accommodate users' complex requirements. Evolutionary computation techniques have been used to efficiently find near-optimal composite services to satisfy users' requirements reasonably well. Often, the quality of a composite service is measured by two important quality criteria that are related to the non-functional quality (i.e., Quality of service, QoS for short) and function quality (i.e., Quality of semantic matchmaking, QoSM for short). One recent work [2] proposed a Hybrid method that combines NSGA-II and MOEA/D with swap-based local search to enhance the performance of NSGA-II. This Hybrid method handles two quality criteria in QoS as two trade-off objectives. However, the local search of this method is randomly applied to a predefined large number of subproblems without focusing on the most suitable candidate solutions. In this paper, we propose a memetic NSGA-II with EDA-based local search. Particular, EDA performs the local improvements of a few well-selected composite services in different regions of the Pareto front. We also aim to handle two practical trade-off objectives with respect to QoS and QoSM. Our experiments have shown that our proposed method outperforms the recent state-of-the-art algorithms and the baseline NSGA-II method with respect to effectiveness and efficiency.
A Simulation Model for the Cognitive Function of Static Objects Recognition Based on Machine-Learning Multi-agent Architectures

The purpose of the research is the development of the theoretical foundations and algorithms for the unstructured data flows recognition based on multi-agent neurocognitive architectures in artificial intelligence systems. The task of the study is to develop a simulation model of the cognitive function of static objects recognition according to the video stream.The article developed a simulation model of the cognitive function of static objects recognition based on multi-agent architectures and a software system that demonstrates its work in geometric shapes recognition. The simulation model based on multi-agent neurocognitive architectures allows to create concepts and categories in the autonomous mode according to the data of multimodal input information (event occurred). Through interaction with the user, the system can expand these concepts and categories, and correct the links between them.The system presented in the paper is autonomous and self-learning. It can be used in autonomous artificial intelligence systems, such as Smart Systems, robotic complexes, etc., to recognize unstructured data streams.
Conceptualizing Fidelity for HCI in Applied Gaming

Fidelity of games as a concept describes the level of representation of, or accordance with reality. The level of fidelity has influence on the interaction between player and game. Our study discusses the outcomes of a literature study and three cases with the goal to propose a comprehensive framework of game fidelity. This framework could help game designers and researchers to adopt the ‘right’ or sufficient level of fidelity to achieve the intended objectives related to applied games. Our results show that functional and psychological fidelity have a higher impact on the experience and effects of applied games than their physical fidelity. Social and ethical fidelity are proposed as new dimensions of game fidelity that still have to be explored. In literature, both low and high levels of fidelity are described as effective in applied games, while a medium level seems not to be beneficial for the player.
Neural Network-Based Vehicle and Pedestrian Detection for Video Analysis System

In our research we compare various neural network architectures that are used for object detection and recognition. In this work vehicles and pedestrians are considered objects of interest. Modern artificial neural networks are able to detect and localize objects of known classes. This allows them to be used in various technical vision systems and video analysis systems. In this paper we compare three architectures (YOLO, Faster R-CNN, SSD) by the following criteria: processing speed, mAP, precision and recall.
Vibrational Covert Channels using Low-Frequency Acoustic Signals

In this paper, we examine how acoustic signals in sub-bass and infrasonic range can be used to establish a vibrational covert channel between speaker-equipped computers and mobile devices. We show that typical consumer speakers are capable of producing low-frequency sounds, which are not perceivable by humans. At the same time, we show that producing such sounds by the speaker's woofer inevitably generates slight vibrations of the speaker and the surface where it is located. Being unnoticeable to people, such vibrations can be captured by the accelerometer sensor of a mobile device located on the same surface. Therefore, information can be encoded into low-frequency sounds played by a speaker and received on a mobile device by analyzing the produced vibrations. Note that access to the accelerometer on modern mobile devices does not require any user permissions, making the transmission completely unnoticeable. We evaluate the presented covert channel for different speakers, apply it to several application scenarios, and give an overview of possible countermeasures.
ChatPy: Conversational agent for SMEs

In the present research, we study the components and principles for chatbot built, perform an analysis and comparison of the different current technological platforms for Chatbots is done. The majority of SMEs in Ecuador feel the lack of support tools for customer service in their IT platforms, that is why they have seen in the development of this technology a solution to increase their sales and reach their customers. We propose ChatPy as an appropriate conversational agent for use in SMEs. An implementation of ChatPy is carried out in a wholesaler automotive spare parts, as a case of study. The ChatPy implementation uses the PSP framework. The results show improvement in sales, customer service and customer satisfaction.
Efficient privacy‐preserving scheme supporting disjunctive multi‐keyword search with ranking

Summary Information storage and retrieval from the cloud is growing continuously unabated. The cost‐efficient solutions offered by the cloud providers to the end‐users have motivated them to outsource their confidential data to the cloud. Outsourcing confidential data leads to enhanced privacy risks due to disclosure of sensitive information to adversaries. To handle this disclosure of information, encryption is preferred, but it hinders the efficient searching on the documents. The existing searchable encryption schemes either focused on optimization of search time or improvement of search efficiency. To solve this trade‐off between search time and search efficiency, we propose an efficient disjunctive search scheme using non‐positional inverted index. To the best of our knowledge, there is no searchable encryption scheme based on the non‐positional inverted index in the literature. Thus, we first propose a basic scheme based on a non‐positional inverted index to search the desired keywords and highlight its inefficiency in terms of high search time required. To perform efficient searching, an extended search scheme is proposed using keyword binning, which reduces comparisons required and improves the search time. The extended search scheme has recall of 100% and precision 99.75%. The experimental analysis of the proposed scheme on real datasets proves that the proposed scheme is privacy‐preserving and efficient.
A hybrid recommender system for the mining of consumer preferences from their reviews

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.
Some New Classes of Entanglement-Assisted Quantum MDS Codes Derived From Constacyclic Codes

Although quantum maximal-distance-separable (MDS) codes that satisfy the quantum singleton bound have become an important research topic in the quantum coding theory, it is not an easy task to search for quantum MDS codes with the minimum distance that is larger than (q/2) + 1. The pre-shared entanglement between the sender and the receiver can improve the minimum distance of quantum MDS codes such that the minimum distance of some constructed codes achieves (q/2) + 1 or exceeds (q/2) + 1. Meanwhile, how to determine the required number of maximally entangled states to make the minimum distance of quantum MDS codes larger than (q/2) + 1 is an interesting problem in the quantum coding theory. In this paper, we utilize the decomposition of the defining set and q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -cyclotomic cosets of constacyclic codes with the form q = αm + t or q = αm + α - t and n = (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + 1/α) to construct some new families of entanglement-assisted quantum MDS codes that satisfy the entanglement-assisted quantum singleton bound, where q is an odd prime power and m is a positive integer, while both α and t are positive integers such that α = t <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + 1. The parameters of these codes constructed in this paper are more general compared with the ones in the literature. Moreover, the minimum distance of some codes in this paper is larger than (q/2) + 1 or q + 1.
Effpi

We present Effpi: an experimental toolkit for strongly-typed concurrent and distributed programming in Dotty, with verification capabilities based on type-level model checking.
Phoneme aware speech recognition through evolutionary optimisation

Phoneme awareness provides the path to high resolution speech recognition to overcome the difficulties of classical word recognition. Here we present the results of a preliminary study on Artificial Neural Network (ANN) and Hidden Markov Model (HMM) methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English Phonetic Alphabet, with a specific focus on evolutionary optimisation of bio-inspired classification methods. A set of audio clips are recorded by subjects from the United Kingdom and Mexico. For each recording, the data were pre-processed, using Mel-Frequency Cepstral Coefficients (MFCC) at a sliding window of 200ms per data object, as well as a further MFCC timeseries format for forecast-based models, to produce the dataset. We found that an evolutionary optimised deep neural network achieves 90.77% phoneme classification accuracy as opposed to the best HMM of 150 hidden units achieving 86.23% accuracy. Many of the evolutionary solutions take substantially longer to train than the HMM, however one solution scoring 87.5% (+1.27%) requires fewer resources than the HMM.
Target-Aware Correlation Filter Tracking in RGBD Videos

Recently, the surge of depth sensors makes RGBD data available and facilitates the development of RGBD tracking. Inspired by the popularity of discriminative correlation filter (DCF)-based trackers in RGB tracking, we propose a target-aware framework for RGBD tracking based on the existing DCF-based trackers in this paper. The framework comprehensively utilizes the prior, depth, and color information to compute a fine foreground-background segmentation map. This map is masked on the extracted features for adaptive reweighting and adjusts the DCF-based trackers to focus on the area with higher target possibility. Meanwhile, we propose an occlusion detection and handling mechanism based on this segmentation map, which helps to detect an occlusion early and avoids contaminations of the target model. Our proposed framework overcomes the limitations of the cosine window generally adopted in the existing DCF-based trackers and could be incorporated into these trackers. Experiments on the princeton tracking benchmark (PTB) demonstrate that DCF-based trackers outperform their baselines after integrated with our framework. Among these enhanced trackers, our proposed tracker ECO_TA (target-aware) based on efficient convolution operators (ECOs) for tracking achieves the best performance on the PTB and another RGBD benchmarks.
Generating Real Time Cyber Situational Awareness Information Through Social Media Data Mining

With the rise of the internet many new data sources have emerged that can be used to help us gain insights into the cyber threat landscape and can allow us to better prepare for cyber attacks before they happen. With this in mind, we present an end to end real time cyber situational awareness system which aims to efficiently retrieve security relevant information from the social networking site Twitter.com. This system classifies and aggregates the data retrieved and provides real time cyber situational awareness information based on sentiment analysis and data analytics techniques. This research will assist security analysts to evaluate the level of cyber risk in their organization and proactively take actions to plan and prepare for potential attacks before they happen as well as contribute to the field through a cybersecurity tweet dataset.
3D Reconstruction of Plants Under Outdoor Conditions Using Image-Based Computer Vision

3D reconstruction of plants under outdoor conditions is a challenging task, for applications such as plant phenotyping which needs non-invasive methods. With the availability of new sensors and reconstructions techniques, 3D reconstruction is improving rapidly. However, sensors are still expensive for researchers. In this paper, we propose a cost-effective image-based 3D reconstruction approach which can be achieved by off-the-shelf cameras. This approach is based on the structure-from-motion method. We implemented this approach in MATLAB and Meshlab is used for further processing to achieve an exact 3D model. We also investigated the effect of different adverse outdoor scenarios which affect quality of 3D model such as movement of plants because of strong wind, drastic change in light condition while capturing the images. We have decreased the appropriate number of images needed to get precise 3D model. This method gives accurate results and it is a fast platform for non-invasive plant phenotyping.