Reviewerly/exHarmony huggingface dataset:
Exploiting layer and spatial correlations to enhance SVC and tile based 360-degree video streaming

Streaming 360-degree videos over today's Internet is challenging due to its high bandwidth and low Motion-to-Photon latency requirements. Viewport adaptive streaming is a promising technique to cope with these challenges by allocating more bitrates to a user's constrained field of view and streaming appropriate representations based on the user's estimated throughput and fixation point. A typical dynamic adaptive streaming system uses a Media Presentation Description (MPD) file to describe the sustainable bitrates of a period for different quality representations. However, video bitrates vary significantly along the temporal dimension for Variable Bitrate (VBR) videos. In addition to the throughput overestimate/underestimate problem, video bitrate overestimate/underestimate also affects the efficiency of Adaptive Bitrate (ABR) algorithms. In this paper, we first discovered that for SVC and tile-based encoding, strong video chunk size correlation can be observed on both the layer and spatial dimensions for 360-degree videos. We then proposed to enhance the 360-degree video scheduling framework with a chunk size predictor that predicts the size of: (1) a future enhancement layer video chunk based on the corresponding base layer chunk of the same tiled segment; (2) future base layer video chunks based on the horizontally adjacent base layer video chunks. Experiments showed that ABR algorithms augmented by a chunk size predictor can significantly improve a user's perceived video playback quality.
Understanding context in computer-mediated communication

Abstract With unprecedented transformations taking place in the landscape of what to say and how we mean, interactions in the digital age take on various new forms of doing and being . To make sense of “what it is that is going on” requires an understanding of the context wherein the computer-mediated communications take place. Focusing on a burgeoning online video commenting discourse in mainland China called Danmaku (a media feature that projects viewer comments onto the video, like a ‘bullet curtain’), the present study applies the schematic construct of context of situation and its paradigmatic representation developed in Systemic Functional Linguistics to a functionally-driven discussion of Danmaku context. Drawing on a corpus of comments from 18 well-received videos on Bilibili.com (a major Danmaku site in mainland China), the study provides a fine-grained analysis that highlights emergent technological and semiotic variables in the Danmaku Mode, such as anonymity, invisibility, dynamicity, and pseudo-synchronicity. It then discusses how these variables mediate the properties of Field and Tenor and further impinge upon the experiential and interpersonal meanings made in Danmaku communication. The analysis also highlights the carnivalesque nature of Danmaku which makes it an increasingly popular social media platform in mainland China.
P2GT: Fine-Grained Genomic Data Access Control With Privacy-Preserving Testing in Cloud Computing

With the rapid development of bioinformatics and the availability of genetic sequencing technologies, genomic data has been used to facilitate personalized medicine. Cloud computing, features as low cost, rich storage and rapid processing can precisely respond to the challenges brought by the emergence of massive genomic data. Considering the security of cloud platform and the privacy of genomic data, we first introduce P2GT which utilizes key-policy attribute-based encryption to realize genomic data access control with unbounded attributes, and employs equality test algorithm to achieve personalized medicine test by matching digitized single nucleotide polymorphisms (SNPs) directly on the users' ciphertext without encrypting multiple times. We then propose an enhanced scheme P2GT+, which adopts identity-based encryption with equality test supporting flexible joint authorization to realize privacy-preserving paternity test, genetic compatibility test and disease susceptibility test over the encrypted SNPs with P2GT. We prove the security of proposed schemes and conduct extensive experiments with the 1,000 Genomes dataset. The results show that P2GT and P2GT+ are practical and scalable enough to meet the privacy-preserving and authorized genetic testing requirements in cloud computing.
Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines

We assess the variability of PRNU-based camera fingerprints with mismatched imaging pipelines (e.g., different camera ISP or digital darkroom software). We show that camera fingerprints exhibit non-negligible variations in this setup, which may lead to unexpected degradation of detection statistics in real-world use-cases. We tested 13 different pipelines, including standard digital darkroom software and recent neural-networks. We observed that correlation between fingerprints from mismatched pipelines drops on average to 0.38 and the PCE detection statistic drops by over 40%. The degradation in error rates is the strongest for small patches commonly used in photo manipulation detection, and when neural networks are used for photo development. At a fixed 0.5% FPR setting, the TPR drops by 17 ppt (percentage points) for 128 px and 256 px patches.
A Survey on Mobile User Perceptions of Sensitive Data and Authentication Methods

The rapid evolution of mobile phone devices resulted in the conversion of a simple communication device to a complicated small portable computer offering a plethora of features and services. Sensitive data are now stored in devices that can be easily lost, stolen, or compromised. A mobile user survey was conducted to get a better understanding of what the users think about the data stored in their devices, the authentication methods they use, and their willingness to try a new one. This paper presents the results derived from 81 mobile users. The results reveal that the users still use text-based authentication methods, with the preferred method being the Pin-Code. The bio-metric authentication adopters have a better understanding of what sensitive data are and feel they are not fully protected. The analysis also revealed that the majority of users, regardless of their preferred authentication method are willing to try new authentication methods that promise to offer a higher level of security for their personal data.
Studying Physics, Getting to Know Python: <i>RC</i> Circuit, Simple Experiments, Coding, and Data Analysis With Raspberry Pi

Raspberry Pi (RPi) is a well-known single-board computer natively equipped with a Linux-based operating system, Raspbian, and a powerful programming language, Python. In this article, we propose an integrated project on physics and computer science exploiting RPi and Python: a set of lab activities, coding, and discussion related to the study of charging and discharging phases of a capacitor in an RC circuit. In our simple experiments, entirely computer-controlled, the RPi and Python scripts are used to: (i) apply a known constant voltage to the circuit at a desired time; (ii) measure the voltage on selected circuit elements as a function of time; (iii) evaluate and analyze experimental data. This approach is based on inexpensive hardware and open source software. It allows a hands-on experience with electric circuits and with dedicated examples of Python coding. The codes involve Python modules such as Numpy, Scipy, and Matplotlib that prove to be easy to use and efficient for our goals, supporting the choice of Python language for further study or research tasks.
Decentralized Exploration of a Structured Environment Based on Multi-agent Deep Reinforcement Learning

Multi-robot environment exploration is one of the widely discussed topics in the field of robotics. It is the foundation for many real-world robotic applications. Many decentralized methods (that is, without a centralized controller) have been proposed in the past decades. Most of them focus on improving collaboration efficiency by utilizing low-level heuristic information, such as distances to obstacles and robot positions. In contrast, although a human being can make decisions on a similar task, he/she exploits high-level knowledge, such as the building's common structure pattern. This paper proposes a novel distributed multi-robot exploration algorithm based on deep reinforcement learning (DME-DRL) for structured environments that enables robots to make decisions on the basis of this high-level knowledge. DME-DRL is a distributed algorithm that uses deep neural networks to extract the structural pattern of the environment, and it can work in scenarios with or without communication. The experimental results showed that this approach can decrease the travel distance by approximately 10.84% on average, compared with those of traditional heuristic methods and can significantly reduce the communication cost in the exploration process.
Person identification in low resolution CCTV footage using deep learning

The advancement in camera technology, high speed connectivity, and availability of huge information over media resulted into developing better algorithms for person identification. However, the recognition of a person in low resolution cameras like CCTV is still a challenging problem. This paper aims at identifying a person in low resolution image captured by webcam or in various frames of CCTV footage by using deep learning convolutional neural network (CNN). The proposed system uses facial image of a person for this task. The designed CNN pipeline has six convolution layers, one flattened layer and two fully connected layers. Total 6667 images of 62 subjects are used for training and validation of CNN framework with 500 epochs. The designed CNN attained 99.99% and 98.45% of training and validation accuracy, respectively. The network was tested on 1599 test images and was able to achieve a testing accuracy of 96.03%. The proposed CNN framework is also tested on low resolution face recognition benchmark dataset (TinyFace) for which it achieved 94.55% testing accuracy.
Learning adaptive spatial–temporal regularized correlation filters for visual tracking

Recently, there have been many visual tracking methods based on correlation filters. These methods mainly enhance the tracking performances by considering the information of background, space, or time in the appearance model. This paper proposes an effective tracking method, named adaptive spatial–temporal regularized correlation filter (ASTRCF) tracker, based on the popular adaptive spatially regularized correlation filter (ASRCF) tracker. That is, the continuity of object's motion in the process of tracking is considered by introducing a temporal-regularized term in the appearance model of ASRCF tracker. Furthermore, its solution is inferred by applying the alternating direction method of multipliers. The proposed appearance model contains a background-awareness term, a spatially regularized term, an adaptive-weight term, and a temporal-regularized term. Therefore, it can not only keep the good performances of ASRCF tracker, such as learning the background information and the spatial information adaptively to enhance the discriminating ability, but also take advantage of the relation of correlation filters in the last frame and the current frame for addressing the complex cases, such as occlusion, and fast motion. Extensive experimental results on various challenging databases show that the proposed ASTRCF tracker achieves better tracking performances than some state-of-the-art trackers.
A Geometric and Textural Model of the Colon as Ground Truth for Deep Learning-based 3D-reconstruction

For endoscopic examinations of the large intestine, the limited field of vision related to the keyhole view of the endoscope can be a problem. A panoramic view of the video images acquired during a colonoscopy can potentially enlarge the field of view in real-time and may ensure that the performing physician has examined the entire organ. To train and test such a panorama-generation system, endoscopic video sequences with information about the geometry are necessary, but rarely exist. Therefore, we created a virtual phantom of the colon with a 3D-modelling software and propose different methods for realistic-looking textures. This allows us to perform a “virtual colonoscopy” and provide a well-defined test environment as well as supplement our training data for deep learning.
Motion Recognition Based on Sum of the Squared Errors Distribution

Human motion recognition is playing an increasingly important role in modern society. Direct recognition of complex motions has great limitations, so we usually study basic motions first. The key to the establishment of the basic motion set is how to determine the number of basic motions. Sum of the Squared Errors Distribution (SSED) is therefore proposed to determine the number of clustering classes of Self Organizing Maps (SOM). Secondly, the Weighted Tangent Segmentation (WTS) is also proposed to segment complex motions into simple ones, and the sequence with the time stamp is generated. Finally, Back Propagation with the time stamp (BPTS) is proposed to classify the simple motions according to the basic motion set, and the complex motion is recognized according to the time stamp. The motions of human upper limbs are used to verify the effectiveness of this method. SSED determines that the number of clusters of human upper limb motion is 10. The experimental results show that the correct segmentation rate (CSR) is 98.13%. The recognition rate of basic motions is 98.67% and 99.33% in user independent (UI) and user dependent (UD) experiments, respectively. The recognition rate of complex motions is 96.37%. Experiments on UI and UD recognitions verified the effectiveness of our algorithm. Compared with other recognition algorithms, the complex motion recognition method proposed in this article has better recognition performance.
Transformer Network for video to text translation

Recently generation of natural language descriptions for videos has created a lot of focus in computer vision and natural language processing research. Video understanding involves detecting scene's visual and temporal elements and reasoning it for description generation.Several real world implementations such as video indexing and retrieval,video to sign language translation etc, are based on this.Because of the complicated nature and diversified content, the captioning problem becomes more challenging.This is a machine translation problem which uses encoder decoder architecture of GRU or LSTM to deal with this kind of problem.But here the decoding starts with the final hidden state of encoder as input.This can't be a good summary of the input sequence,because all the intermediate states of encoder are ignored.This paper proposes a transformer network with deep attention based encoder and decoder to generate the natural language description for video sequence data. This network processes the sequences as a whole and learns relationship between each elements in the sequence by providing attention.
Ontologies for information entities: State of the art and open challenges

Information entities are used in ontologies to represent engineering technical specifications, health records, pictures or librarian data about, e.g., narrative fictions, among others. The literature in applied ontology lacks a comparison of the state of the art, and foundational questions on the nature of information entities remain open for research. The purpose of the paper is twofold. First, to compare existing ontologies with both each other and theories proposed in philosophy, semiotics, librarianship, and literary studies in order to understand how the ontologies conceive and model information entities. Second, to discuss some open research challenges that can lead to principled approaches for the treatment of information entities, possibly by getting into account the variety of information entity types found in the literature.
Data mining techniques with machine learning algorithm to predict patients of heart disease

Abstract Data mining is a way of searching for information from large amounts of data for the purposes of various applications. Several techniques in data mining can be used for association, classification, clustering, prediction, and sequential modeling. Machine learning is used in medical science to help medical teams find out the condition of patients with heart disease. A lot of machine learning still has limited predictive capabilities, and is incompatible. This study uses different machine learning techniques, namely PSO-based SVM, Neural Network, Decision Tree, Naïve Bayes and SVM to assist in building, understanding and interpreting different models of heart disease diagnosis. The use of the pso-based svm algorithm in the prediction of heart disease shows a 100% greatest accuracy than the Decision Tree, only 88.68% and Naïve Bayes of 82.15%, Neural Network with an accuracy of. 95.71%, SVM with an accuracy of 99.71%. The results of this study are expected to be beneficial for the world of health and for researchers who use machine learning techniques.
Analysis and Improvement of External Knowledge Usage in Machine Multi-Choice Reading Comprehension Tasks

Machine reading comprehension (MRC) and multi-choice task is an important branch of natural language processing. With the advent of pre-trained language models, such as Bert, Roberta, fine tuning model parameters according to different downstream tasks has become the mainstream of current research directions. By using pre-trained language models, sufficient and effective training samples are the key to ensure the high performance of the model to a certain degree. At the same time, compared with the thinking patterns of human beings, adding effective external knowledge to training data can also help machines to understand natural language better. In current research, such external knowledge has various ways to combine with the original data. In this paper, we believe that an effective way of external knowledge combination can help machines greatly improve their performance in MRC such as multi-choice and question-and-answering (QA) tasks. Therefore, we design some special experiments and compare various knowledge fusion methods' performance, analyze the effect of different methods and select the most effective way to put forward relevant opinions. The accuracy of the most effective way to use external knowledge is seven percentage points higher than our baseline.
A context-aware monitoring architecture for supporting system adaptation and reconfiguration

Modern services and applications need to react to changes in their context (e.g. location, memory consumption, number of users) to improve the user’s experience. To obtain this context, a monitoring infrastructure with adequate functionality and quality levels is required. But this monitoring infrastructure needs to react to the context as well, raising the need for context-aware monitoring tools. Provide a generic solution for context-aware monitoring able to effectively react to contextual changes. We have designed CAMA, a service-oriented Context-Aware Monitoring Architecture that can be easily configured, adapted and evolved according to contextual changes. CAMA implements a decoupled architecture and manages a context domain ontology for modelling the inputs, outputs and capabilities of monitoring tools. CAMA has been demonstrated in three real use cases. We have also conducted different evaluations, including an empirical study. The results of the evaluations show that (1) the overhead introduced by the architecture does not degrade the behavior of the system, except in extreme conditions; (2) the use of ontologies is not an impediment for practitioners, even when they have little knowledge about this concept; and (3) the reasoning capabilities of CAMA enable context-aware adaptations. CAMA is a solution useful for both researchers and practitioners. Researchers can use this architecture as a baseline for providing different extensions or implementing new approaches on top of CAMA that require context-aware monitoring. Practitioners may also use CAMA in their projects in order to manage contextual changes in an effective way.
A New Algorithm for Extracting Textual Maximal Frequent Itemsets from Arabic Documents

Abstract In this paper, a new technique has been suggested for extracting textual maximal frequent itemsets named Maximal Itemset Miner Algorithm (MIMA). This algorithm begins search process through generating the best initial border in search space depending on minimum support of items in the first level that achieves the general minimum support determined by the user. Our approach for counting itemsets support combines the idea of vertical representation of the data with a queue data structure to store the itemsets. To reduce search space, the algorithm adopted several pruning conditions for each itemsets in the initial border. Experiments performed on standard textual CNN Arabic dataset and proposed method registers less execution time comparing with the Apriori algorithm when applying it on three different size datasets.
Mask-GradCAM: Object Identification and Localization of Visual Presentation for Deep Convolutional Network

This paper presents the conceptually simple, flexible and more suitable framework to demonstrate object localization and object recognition by Mask RCNN along with Grad-CAM (Mask-GradCAM) method that is mainly used to build framework to provide the better visual identification. Because Mask RCNN based method provides a function that take array of pixel values for load the images and aspects of the prediction dictionary such that all class labels, scores, bounding boxes and will plot the image with all these annotations. However, applying this method along with Mask RCNN raises significant challenges such that image pixel issues and quality breaking while producing the heat map. Therefore, this entire system is mentioned as Mask-Grad CAM. This research work considers GradCAM++ and GradCAM methods as basic functionality derivation to implement the Mask-GradCAM. In this combination of MaskRCNN along with Grad-CAM provide the fine-grained display to generate higher resolution visual representation. Hence this method is presented as Mask-GradCAM.
Modeling Winner-Take-All Competition in Sparse Binary Projections

With both theoretical and practical significance, the study of sparse binary projection models has attracted considerable recent research attention. The models project dense input samples into a higher-dimensional space and output sparse binary data representations after the Winner-Take-All competition, subject to the constraint that the projection matrix is also sparse and binary. Following the work along this line, we developed a supervised-WTA model which works when training samples with both input and output representations are available, from which the optimal projection matrix can be obtained with a simple, efficient, yet effective algorithm after proper relaxation. We further extended the model and the algorithm to an unsupervised setting where only the input representation of the samples is available. In a series of empirical evaluation on similarity search tasks, the proposed models reported significantly improved results over state-of-the-art methods in both search accuracies and running speed. The successful results give us strong confidence that the work provides a useful tool for industrial applications.