Reviewerly/exHarmony huggingface dataset:
Artificial Semantic Memory with Autonomous Learning Applied to Social Robots

Semantic memory stores knowledge about the meanings of words and the relationships between these meanings. In recent years, Artificial Intelligence, in particular Deep Learning, has successfully resolved the identification of classes of elements in images, and even instances of a class, providing a basic form of semantic memory. Unfortunately, incorporating new instances of a class requires a complex and long process of labeling and offline training. We are convinced that the combination of convolutional networks and statistical classifiers allows us to create a long-term semantic memory that is capable of learning online. To validate this hypothesis, we have implemented a long-term semantic memory in a social robot. The robot initially only recognizes people, but, after interacting with different people, is able to distinguish them from each other. The advantage of our approach is that the process of long-term memorization is done autonomously without the need for offline processing.
Collaborative Filtering Model Based on Time Context for IPTV Live Recommendation

With the increase in the number of IPTV live channels, the users are faced with the problem of information overload and the degraded users’ quality of experience (QoE). This paper combines the time context information with the collaborative filtering recommendation algorithm to design the IPTV live recommendation system (RS). Finally, the fusion recommendation model is proposed and its accuracy rate has a great improvement.
Research on Semantic Information Retrieval Model of Bamboo Rattan Domain Based on Semantic Relevance

At present, the research of semantic information retrieval technology is mostly in the initial stage, and the research is mostly in exploratory theoretical research. Among them, the construction of domain semantic information retrieval model is still immature. In this paper, bamboo rattan field was selected as the research object, and the key problems in semantic information retrieval technology of bamboo rattan domain were analyzed. Based on the domain ontology of bamboo and rattan, the weight calculation method of bamboo rattan domain terms is defined. This paper introduces semantic relevance degree extension into the domain semantic information retrieval model of bamboo rattan, constructs a domain semantic information retrieval model suitable for bamboo rattan, and realizes the association retrieval and semantic query extension of text information in bamboo rattan domain.
Korean Dependency Parsing with Proper Noun Encodin

Dependency parsing is the process of analyzing the linguistic relationship of words that make up a sentence. In natural language processing using deep learning, previously registered words are represented in a continuous vector space. However, if proper noun exists in a sentence, it is difficult to represent it in a continuous vector space, and there may be a problem in the dependency parsing. To solve these problems, we propose a dependency parsing method including proper noun in Korean. Before representing words in a continuous vector space, we replaced the proper noun with a special token and learned the environmental features using the multilayer bidirectional LSTM. We distinguished neural networks according to the presence of proper noun, and compared the performance with the Malt parser using the same transition-based method for the entire sentence. We show that the proposed model is 1.7%p better in UAS and 2.1%p better in LAS than Malt parser.
Automatic Training Data Generation Method for Pixel-Level Road Lane Segmentation

Lane detection or road detectionPan, Xun is one of the key features of autonomous driving. By using deep convolutional neural network based semantic segmentation, we can build models with high accuracy and robustness. However, training a pixel-level semanticWu, Yutian segmentation needs very fine-labeled training data, which requires large amount of labor. In this paper, we propose an automatic training data generating method, which can significantly reduce the effort of the training phase. Experiments prove thatOgai, Harutoshi our method can generate high-quality training data for lane segmentation task.
SysOptic: A Fine-Grained Monitoring System for Virtual Machines Based on PMU

Modern cloud data centers indicate the frequent existence of complex failure manifestation. Failures have become the norm, and not the exception. This is a key challenge since assumptions that underpin designing reliable systems are monitoring systems status and detecting anomaly at runtime. Performance Monitoring Unit on CPU (PMU) can obtain fine-grained monitoring data by adopting interrupt sampling method based on hardware events. However, profilers in virtual machines fail to receive PMU relevant information directly due to the limited capacity of PMU virtualization. In this paper, we present a fine-grained monitoring system SysOptic based on PMU virtualization. First, we propose a method to expose PMU information PMU and ensure the visibility ofsuch information at virtual machine level. Second, to maximize the PMU reusability, SysOptic supports the PMU sharing and simultaneous monitoring among multiple virtual machines. Furthermore, we also describe how to synchronize interrupts on physical machines to virtual machines by using injecting interrupts. Experimental results show that with the aid of SysOptic, profiler tools in virtual machines manage to perceive the existence of PMU and collect the monitoring data. The additional overhead incurred by SysOptic is at most 9.8%.
An Improved Seam-Line Searching Algorithm Based on Object Detection

Seam-line searching algorithm is one of the widely used image fusion method to stitch images. The major methods of generating seam-line mainly take considering the correlation between the pixels, resulting in a so-called “seam-line crossing the object” phenomenon, which greatly deteriorates the visual experience of the final stitching result. To overcome the above problem, this paper proposes an improved seam-line searching algorithm based on object detection. After image registration of reference and target images with the as-projective-as-possible warp or the adaptive as-natural-as-possible warp, the Single-Shot Detector model is applied to detect objects in the overlapping regions of the registered images. Considering the smallest difference in color and structure, the seam-line is allowed to extend along the edge of these objects which are not supposed to be crossed as much as possible if this line crosses these objects. The experimental results show that our method can effectively avoid “seam-line crossing the object” phenomenon and make the mosaic images look more natural. At the same time, our method can also be combined with global warp and other more advanced local-warp-based alignment methods to obtain better stitching results.
A Possible World View and a Normal Form for the Constellation Semantics

After Dung’s founding work in Abstract Argumentation Frameworks there has been a growing interest in extending the Dung’s semantics in order to describe more complex or real life situations. Several of these approaches take the direction of weighted or probabilistic extensions. One of the most prominent probabilistic approaches is that of constellation Probabilistic Abstract Argumentation Frameworks. In this paper, we introduce the probabilistic attack normal form for the constellation semantics; and we prove that the probabilistic attack normal form is sufficient to represent any Probabilistic Abstract Argumentation Framework of the constellation semantics.
A novel rating style mining method to improve collaborative filtering algorithm

Collaborative filtering (CF) algorithm is widely used in recommendation systems, which makes recommendation based on the neighbors' interests. Therefore, how to discover the neighbors with similar interests to target user is the core of the CF algorithm. Most existing algorithms discover neighbors by using rating similarity measure, which ignore the differences of users' rating styles. In this paper, we propose a user rating style mining method and use it to eliminate the rating style differences before calculating a similarity measure. Comparing with the raw similarity measure and another rating style mining method with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) over amazon movie dataset, we conclude that (i) use our method to eliminate the rating styles differences can improve the prediction accuracy and (ii) our method outperforms other rating style mining method.
A framework for evaluation in learning from label proportions

In the subfield of weakly supervised learning, the problem of learning from label proportions has received growing attention in the machine learning community. Learning from the type of data characteristic of this problem requires specific learning methodologies. Nowadays, the state of the art includes many different techniques to learn the most typical types of classifiers from proportions of labels. However, for the sake of simplicity, all these contributions use synthetic data to validate the proposed learning techniques. Evaluation with real label proportions data has barely been explored. This paper proposes a whole framework for model validation and evaluation when only data labeled with label proportions are available. An approximation to the number of true positive examples is proposed based on a reasonable assumption, which enables the completion of a confusion matrix and thus, the calculation of all the standard evaluation metrics. Additionally, a discussion on the performance of different approaches to cross-validation in this specific problem is presented. Both large empirical studies have been carried out to support the discussion on the contributions of this work.
Enhancing Learning at Primary School Through Augmented Reality

Studies have shown that interaction with the subject content in class is important to allow students to enrich their learning. Augmented Reality (AR) provides this unique capability of blending real and virtual worlds to allow students to be engaged in practical experiences. According to studies, the application of AR in the education sector is minimal mainly because government is not giving support financially. In Mauritius, the government vision is to equipped schools with tablets thereby increasing ICT literacy among students. Hence an AR application based on android platform has been developed to help Mauritian primary students to better comprehend the history and Geography subject. The AR application has been tested on different mobile phones in order to know the minimum requirements of device that can support the application. Furthermore, the application has been deployed on mobile phones of 10 teachers and feedback received from teachers was encouraging.
Adaptive Plane Fitting-Based Stereo Matching with Image Guided Disparity Refinement

In this paper, we propose a stereo matching method based on adaptive plane-fitting. In the proposed method, we use the disparity map adjustment with adaptive weight filter to preserve the boundary region. To estimate the initial disparity map, we use two cost aggregation functions, and in addition to that, we combined color and gradient for cost computation. In addition, the error value of the initial disparity map is calibrated by applying plane-fitting using mean-shift segmentation. The disparity map is adjusted by using adaptive weights to improve the accuracy of the disparity map. Experimental results show that the proposed method generates a high-quality disparity map which has the distinct boundary between objects compared to other methods.
An Experimental Study on the Relationships Among Neural Codes and the Computational Properties of Neural Networks

Biological neural networks (BNNs) have inspired the creation of artificial neural networks (ANNs) [19]. One of the properties of BNNs is computational robustness, but this property is often overlooked in computer science because ANNs are usually virtualizations executed in a physical machine that lacks computational robustness. However, it was recently proposed that computational robustness could be a key feature that drives the selection of the computational model in the evolution of animals [20]. Until now, only energetic cost and processing time had been considered as the features that drove the evolution of the nervous system. The new standpoint leads us to consider whether computational robustness could have driven the evolution of not only the computational model but also other nervous system traits in animals through the process of natural selection. Because an important feature of an animal’s nervous system is its neural code, we tested the relationship among the computational properties of feed-forward neural networks and the neural codes through in silico experiments. We found two main results: There is a relationship between the number of epochs needed to train a feed-forward neural network using back-propagation and the neural code of the neural network, and a relationship exists between the computational robustness and the neural code of a feed-forward neural network. The first result is important to ANNs and the second to BNNs.
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

The confounding factors of unexpectedness and semantic integration difficulty naturally residing in anomalous sentences in language studies make it difficult to determine the underlying processing mechanism of ERP components. Unlike the traditional static approach of manipulating expectancy through corpus frequency or cloze probability, this protocol proposes a dynamic method to enhance participants' expectancy for rarely-met anomalous sentences by multiple repetitions while maintaining their semantic integration difficulties. To address the time cost increase resulting from multiple repetitions, this protocol proposes to repeat only the strictly simplified core structure extracted from the anomalous sentence before presenting the semantically enriched, much more informative complete anomalous sentence containing the anomalous core structure to reinitiate the semantic integration processing. The complete anomalous sentence elicited a P600 effect. It suggests that the participants did not give up processing the anomalous information after repetitions and the same semantic integration difficulty was successfully reinitiated. Importantly, the representative experimental results reveal that the greatly attenuated N400 effect caused by multiple repetitions was not recovered by the follow-up reinitiated semantic integration difficulty. It suggests that the attenuated N400 effect should be mainly attributed to the enhancement of expectancy for anomalous information by multiple repetitions. The experimental results show that this method can effectively enhance participants' expectancy for anomalous sentences while retaining the semantic integration difficulty.
Medical image region of interest encryption combined with discrete wavelet transform and hyperchaotic system

In order to better suit the continuous development of medical environment and protect the medical image security, a novel region of interest (ROI) of medical image encryption scheme based on two-dimension discrete wavelet transform (2-D DWT) and five-dimension hyperchaotic system is proposed. In this paper, the medical image is first separated into ROI and background area, and one low frequency and three high frequency components in the horizontal(H), vertical(V) and diagonal(D) directions of ROI are obtained via 2-D DWT. Next, only the low frequency component which approximate the original image is scrambled and spread by chaotic sequence. Then, the reconstruction ROI of image is generated by using encrypted low frequency and components of H, V and D. Fin1 ally, the confusion-diffusion process is used to encrypt the reconstruction ROI of image. Our scheme can realize the encryption of medical image not only in space domain but also in frequency domain. Experiments simulation results, such as key sensitivity tests, correlation analysis, information entropy tests, known/chosen plaintext attack analysis and comparison tests, show the advantages of our scheme.
Salient object detection with adversarial training

The generative adversarial network has been shown to produce state-of-the-art results of image generation. In this study, the authors propose a novel adversarial training method to train salient object detection (SOD) models. They train a convolutional SOD network along with a gated adversarial network that discriminates salient maps coming either from the ground truth or from the SOD network. The motivation for our approach is that the adversarial network can detect and correct pixel-wise errors between ground truth salient detection maps and the ones produced by the convolutional network. Our experiments show that the adversarial training approach leads to state-of-the-art performance on MSRA-B, extended complex scene saliency dataset, HKU-IS, DUT, and SOD dataset.
Correlation-filter enhanced meta-learning for classification of biomedical images

Recent deep learning methods have demonstrated remarkable impact on the classification of biomedical images. In this paper, we proposed a correlation-filter enhanced meta-learning approach for the classification of biomedical images. Firstly, in the training stage, we use the training samples to optimize the model parameters of meta-learning. Secondly, in the testing stage, we utilize the data samples of the new task to generalize the model parameters. Thirdly, the nearest neighbor image from one sample batch is searched for the new instance image, with the classifying score provided by the meta-learning model. Fourthly, the template of the circular cross-correlation filter is optimized in the Fourier domain, using the new instance image and its nearest neighbor image. Fifthly, the support weight of the sample batch is calculated for the classified label by the meta-learning model. Finally, we propose the multi-batch voting mechanism to decide the label of the new instance based on the correlation-filter template. Experiments on the classification of biomedical images demonstrated the effectiveness of our approach, compared with other state-of-the-art methods.
Application of Fixed Skipped Steps Discrete Wavelet Transform in JP3D Lossless Compression of Volumetric Medical Images

In this paper, we report preliminary results of applying a step skipping to the discrete wavelet transform (DWT) in lossless compression of volumetric medical images. In particular, we generalize the two-dimensional (2D) fixed variants of skipped steps DWT (SS-DWT), which earlier were found effective for certain 2D images, to a three-dimensional (3D) case and employ them in JP3D (JPEG 2000 standard extension for 3D data) compressor. For a set of medical volumetric images of modalities CT, MRI, and US, we find that, by adaptively selecting 3D fixed variants of SS-DWT, we may improve the JP3D bitrates in an extent competitive to much more complex modifications of DWT and JPEG 2000.
REEFocus: A VR/AR mixed reality digital game system for non-medication treatment of ADHD and remote monitoring with data privacy and security by design: the FocusLocus approach

ADHD (Attention Deficit Hyperactivity Disorder) affects 7% of the population. ADHD causes behavioral problems, learning limitations and social exclusion. Current ADHD Treatment has limitations in terms of cost, drug-base medication, and labor-intensive educational treatments. Focus Locus aims to provide an alternative treatment based on gamification through VR, AR and Mixed Reality modalities, telemetry and remote monitoring. In this paper we present the REEFocus game system that has been developed in the context of the Focus Locus project and present the first analysis results of a two-month clinical trial in a companion paper. The paper is meant to give an overview of the REEFocus game system design and implementation, the pilot description and some preliminary validation results. A detailed analysis and results of the data collected during the pilot is still an ongoing process and the quantitative analysis results keep on being published as they become available.
Using Open Channels to Trigger the Invited, Unintended Consequences of the Internet of Things

We present techniques for bridging the Internet of Things air gap using device speakers to communicate with voice assistants and propose methods to protect critical applications from such attacks. Approaches include two-factor authentication, contextual monitoring, and source metadata embedding.
Fuzzy neural network-based fetal health monitoring using cardiotocography data

Fuzzy neural networks have been widely applied in the medical field. In this paper, we apply cascade architectures of fuzzy neural networks to monitoring fetal health using Cardiotocography data. Cascade architectures of neural networks can select reduced size of input subspace by selecting useful inputs. For the optimization of the input subspace and the structure of the cascade architectures of fuzzy neural networks, genetic algorithms and gradient decent method are used. To verify the applicability of the proposed method, Cardiotocography data available on the Machine Learning Repository site at the University of California at Irvine is used.
Learning Ontologies with Epistemic Reasoning: The $$\mathcal{E\!L}$$ Case

We investigate the problem of learning description logic ontologies from entailments via queries, using epistemic reasoning. We introduce a new learning model consisting of epistemic membership and example queries and show that polynomial learnability in this model coincides with polynomial learnability in Angluin’s exact learning model with membership and equivalence queries. We then instantiate our learning framework to $$\mathcal{E\!L}$$ and show some complexity results for an epistemic extension of $$\mathcal{E\!L}$$ where epistemic operators can be applied over the axioms. Finally, we transfer known results for $$\mathcal{E\!L}$$ ontologies and its fragments to our learning model based on epistemic reasoning.
Towards Usability Guidelines for the Design of Effective Arabic Websites

The Arabic websites constitute 1% of the web content with more than 225 million viewers and 41% Internet penetration.However, there is a lack of design guidelines related to the selection and use of appropriate font type and size and images in Arabic websites.Both text and images are vital multimedia components of websites and thereby were selected for investigation in this study.The herein paper performed an indepth inspection of font and image design practices within 73 most visited Arabic websites in Saudi Arabia according to Alexa Internet ranking in the first quarter of 2019.Our exhaustive analysis showed discrepancies between the international design recommendations and the actual design of Arabic websites.There was a considerable variation and inconsistency in using font types and sizes between and within the Arabic websites.Arabic Droid Kufi was used mostly for styling page titles and navigation menus, whilst Tahoma was used for styling paragraphs.The font size of the Arabic text ranged from 12 to 16 pixels, which may lead to poor readability.Images were used heavily in the Arabic websites causing prolonged site loading times.Moreover, the images strongly reflected the dimensions of the Saudi culture, especially collectivism and masculinity.Current Arabic web design practices are compared against the findings from past studies about international designs and lessons aiming at ameliorating the Arabic web design are inferred.