Reviewerly/exHarmony huggingface dataset:
Image Retrieval for Complex Queries Using Knowledge Embedding

With the increase in popularity of image-based applications, users are retrieving images using more sophisticated and complex queries. We present three types of complex queries, namely, long, ambiguous, and abstract. Each type of query has its own characteristics/complexities and thus leads to imprecise and incomplete image retrieval. Existing methods for image retrieval are unable to deal with the high complexity of such queries. Search engines need to integrate their image retrieval process with knowledge to obtain rich semantics for effective retrieval. We propose a framework, Image Retrieval using Knowledge Embedding (ImReKE), for embedding knowledge with images and queries, allowing retrieval approaches to understand the context of queries and images in a better way. ImReKE (IR_Approach, Knowledge_Base) takes two inputs, namely, an image retrieval approach and a knowledge base. It selects quality concepts (concepts that possess properties such as rarity, newness , etc.) from the knowledge base to provide rich semantic representations for queries and images to be leveraged by the image retrieval approach. For the first time, an effective knowledge base that exploits both the visual and textual information of concepts has been developed. Our extensive experiments demonstrate that the proposed framework improves image retrieval significantly for all types of complex queries. The improvement is remarkable in the case of abstract queries, which have not yet been dealt with explicitly in the existing literature. We also compare the quality of our knowledge base with the existing text-based knowledge bases, such as ConceptNet, ImageNet, and the like.
Densely Dilated Spatial Pooling Convolutional Network Using Benign Loss Functions for Imbalanced Volumetric Prostate Segmentation

Background: The high incidence rate of prostate disease poses a requirement of accurate early detection. Magnetic Resonance Imaging (MRI) is one of the main imaging methods used for prostate cancer detection so far, but it has problems of imbalance and variation in appearance, therefore, automated prostate segmentation is still challenging. Objective: Aiming to accurately segment the prostate from MRI, the focus was on designing a unique network with benign loss functions. Methods: A novel Densely Dilated Spatial Pooling Convolutional Network (DDSP ConNet) in an encoderdecoder structure, with a unique DDSP block was proposed. By densely combining dilated convolution and global pooling layers, the DDSP block supplies coarse segmentation results and preserves hierarchical contextual information. Meanwhile, the DSC and Jaccard loss were adopted to train the DDSP ConNet. And it was proved theoretically that they have benign properties, including symmetry, continuity, and differentiability on the parameters of the network. Results: Extensive experiments have been conducted to corroborate the effectiveness of the DDSP ConNet with DSC and Jaccard loss on the MICCAI PROMISE12 challenge dataset. In the test dataset, the DDSP ConNet achieved a score of 85.78. Conclusion: In the conducted experiments, DDSP network with DSC and Jaccard loss outperformed most of the other competitors on the PROMISE12 dataset. Therefore, it has a better ability to extract hierarchical features and solve the imbalanced medical image problem.
Secure extended wildcard pattern matching protocol from cut-and-choose oblivious transfer

Secure two-party pattern matching involves two parties, where a server owns the long text and a client has the pattern. The goal is for the client to learn the positions in which his pattern appears in the text, while leaking nothing to the server. In secure wildcard pattern matching (WPM), wildcards that can match any character arbitrarily are allowed in the pattern of the client. In this study, we first propose an extended variant of the standard WPM functionality, which we termed extended wildcard pattern matching (EWPM). The EWPM functionality allows the client to acquire the entire set of substrings that match his pattern, rather than the corresponding locations. We constructed a secure protocol of the EWPM functionality using cut-and-choose oblivious transfer (CCOT) in a semi-honest model. The efficiency of the proposed protocol approximates that of the state-of-the-art scheme. Furthermore, we implemented our protocol to demonstrate its actual practicality. The experimental result shows that when the pattern length is 210 and the text length is less than 220, the total execution time is less than 2 s.
Blind Quality Index of Depth Images Based on Structural Statistics for View Synthesis

The quality of depth images is crucial for virtual view synthesis. However, the quality assessment of depth images is still largely unexplored. This letter presents a blind quality metric of Depth image based on Structural Statistics (DSS). The design philosophy is inspired by the fact that structural distortion in the depth images usually leads to geometric distortion, which is the main cause for degraded quality of synthesized views. Specifically, the statistical features for shape and orientation are calculated based on discrete orthogonal moments and gradients, generating two groups of quality-aware features. Then, the quality model is built from the extracted statistical features using a regression module. The experimental results demonstrate the effectiveness of the proposed metric.
Blockchain-based Management of Video Surveillance Systems: A Survey

In this survey, we go through all the papers that lead up to our idea of a smart video surveillance system and make a comparative study of available technologies. The increase in efficiency by using hyper ledger for blockchain is analyzed along with the study of modern data sharing techniques like CDN and IPFS.
Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and social influence, geographical influence has also been widely researched in location recommendation. Kernel density estimation (KDE) is a key method in modeling geographical influence. However, most current studies based on KDE do not consider the problems of influence range and outliers on users’ check-in behaviors. In this paper, we propose a method to exploit geographical and synthetic social influences (GeSSo) on location recommendation. GeSSo uses a kernel estimation approach with a quartic kernel function to model geographical influences, and two kinds of weighted distance are adopted to calculate bandwidth. Furthermore, we consider the social closeness and connections between friends, and a synthetic friend-based recommendation method is introduced to model social influences. Finally, we adopt a sum framework which combines user’s preferences on a location with geographical and social influences. Extensive experiments are conducted on three real-life datasets. The results show that our method achieves superior performance compared to other advanced geo-social recommendation techniques.
Learning to Recognize Visual Concepts for Visual Question Answering With Structural Label Space

Solving visual question answering (VQA) task requires recognizing many diverse visual concepts as the answer. These visual concepts contain rich structural semantic meanings, e.g., some concepts in VQA are highly related (e.g., red & blue), some of them are less relevant (e.g., red & standing). It is very natural for humans to efficiently learn concepts by utilizing their semantic meanings to concentrate on distinguishing relevant concepts and eliminate the disturbance of irrelevant concepts. However, previous works usually use a simple MLP to output visual concept as the answer in a flat label space that treats all labels equally, causing limitations in representing and using the semantic meanings of labels. To address this issue, we propose a novel visual recognition module named Dynamic Concept Recognizer (DCR), which is easy to be plugged in an attention-based VQA model, to utilize the semantics of the labels in answer prediction. Concretely, we introduce two key features in DCR: 1) a novel structural label space to depict the difference of semantics between concepts, where the labels in new label space are assigned to different groups according to their meanings. This type of semantic information helps decompose the visual recognizer in VQA into multiple specialized sub-recognizers to improve the capacity and efficiency of the recognizer. 2) A feature attention mechanism to capture the similarity between relevant groups of concepts, e.g., human-related group “chef, waiter” is more related to “swimming, running, etc.” than scene related group “sunny, rainy, etc.”. This type of semantic information helps sub-recognizers for relevant groups to adaptively share part of modules and to share the knowledge between relevant sub-recognizers to facilitate the learning procedure. Extensive experiments on several datasets have shown that the proposed structural label space and DCR module can efficiently learn the visual concept recognition and benefit the performance of the VQA model.
The Semantics of Anomalies in IoT Integrated BlockChain Network

The recent substantial increase of Internet connected devices can be attributed to the new paradigm known as Internet of Things (IoT). IoT refers to objects that are connected and able to smartly function together to achieve a goal without human intervention. IoT systems have many weaknesses concerning confidentiality, security, privacy, and data integrity. This is due to having a large number of devices on a network, leading to the creation of anomalous behavior and security problems. For this reason, researchers have recently integrated IoT with Blockchain. Blockchain technology, a security by design technology that aims to overcome IoT weaknesses. This is because Blockchain permits validation, non-repudiation, reliability, controls authorization and automation of networks. However, Blockchain-based systems/networks may still face malicious attacks that threaten their security. This paper proposes using a link-mining tool based on anomaly detection within Blockchain networks. Aiming at gathering Meta-data in the form of forks (i.e. divergent paths from the Blockchain protocol) in order to find the semantic interpretation of anomalous paths/activities, through mutual information based measure. This tool will aid in the prevention of malicious attacks and improve the security of the Blockchain network and IoT devices.
The Sentiment Analysis of Unstructured Social Network Data Using the Extended Ontology SentiWordNet

In this paper the features of semantic and sentiment analysis of textual data of social networks are presented, and an original model and algorithm for sentiment analysis of textual fragments of social networks using fuzzy linguistic ontology are proposed. This approach involves the use of various subgraphs of fuzzy ontology when considering texts of various subject areas with regard to contexts. In addition, the algorithm involves the assessment of the sentiment scores of individual syntagmatic structures into which the analyzed text fragments are divided. It also presents the results of experiments comparing the efficiency of the developed algorithm with a group of existing approaches in analyzing text fragments on the example of data from the social network VKontakte.
Unsupervised generation of polygonal approximations based on the convex hull

The present paper proposes a new non-optimal but unsupervised algorithm, called ICT-RDP, for generation of polygonal approximations based on the convex hull. Firstly, the new algorithm takes into account the convex hull of the 2D closed curves or contours to select a set of initial points; secondly, the significance levels of the contour points are computed using a symmetric version of the well-known Ramer, Douglas-Peucker algorithm; and, finally, a thresholding process is applied to obtain the vertices or dominant points of the polygonal approximation. Since the convex hull can select many initial points in rounded parts of the contour, an additional deletion process is required to remove quasi-collinear dominant points. Furthermore, an additional improvement process is applied to shift the dominant points in order to increase the quality of the polygonal approximation. Experiments performed on a public available dataset show that the new proposal outperforms other unsupervised algorithms for generation of polygonal approximations.
On the construction of one-dimensional discrete chaos theory based on the improved version of Marotto’s theorem

The purpose of this paper is to design one-dimensional chaotic maps with a non-zero intercept in the Y-axis and research the basic dynamic characteristics. First, by studying the common conditions that make one-dimensional discrete dynamical systems chaotic under the modulo operation, the general theory of the chaotic behavior of one-dimensional nonlinear functions with a non-zero origin is presented. It is proved that the discrete dynamic system derived from the proposed theory exhibits chaos phenomena in the sense of Li-Yorke, utilizing the improved version of Marotto’s theorem. Furthermore, the part of the discrete dynamical functions satisfying the proposed chaos theory is given. The function track, bifurcation diagram and Lyapunov exponential spectrum are also analyzed. Numerical simulations agree with the analytical framework for the suggested chaos theory. Finally, given the application of chaos in cryptographic communication, two significant corollaries of the original functions as general polynomials are put forward according to the theory of chaos structure.
Deep 3D Facial Landmark Localization on position maps

3D facial landmark detection is a crucial step for many computer vision applications, such as 3D facial expression analysis, 3D face recognition, and 3D cephalometry. Pose variations, expression changes and self-occlusion yet make 3D facial landmark detection a very challenging task. In this paper, we propose a novel 3D Facial Landmark Localization Network (3DLLN), which is robust to the above challenges. Different from existing methods, the proposed 3DLLN utilizes the position maps as an intermediate representation, from which 3DLLN detects 3D landmark coordinates. Further, we demonstrate the usage of a deep regression architecture to improve the accuracy and robustness of a large number of landmarks. The proposed scheme is evaluated on two public datasets FRGCv2 subset and BU-3DFE, and is shown to achieve state-of-the-art results.
An Assistive Hand Glove for Hearing and Speech Impaired Persons

Communication is more than spoken language; it is a way of expressing ones thought, ideas and messages. Communication for them has become a great challenge especially the need to communicate with normal people in their daily routine. Hearing/Speech Impaired persons used hand sign language as their language of communication; however, it will only be effective if the person they are interacting with knew the hand sign language. This study developed an assistive device for persons with hearing/speech impairment. The system was designed to interpret their signs or gesture into a corresponding message which were preloaded in the device. The device produced a voice-based message through the audio module and displays the message in LCD. This study was basically a glove based system and a microcontroller based system. Flex sensors, Gyroscope and Accelerometer were incorporated in a glove that can identify the sign language and an Arduino Nano microcontroller unit converted these gestures into voice and text output. The result of the Assistive device during its test-experiments showed that the system can recognized the hand gestures with male and female voice output on each hand gesture with an overall accuracy of recognition of 83.58%.
Benchmarking Deep Learning Models for Classification of Book Covers

Book covers usually provide a good depiction of a book's content and its central idea. The classification of books in their respective genre usually involves subjectivity and contextuality. Book retrieval systems would utterly benefit from an automated framework that is able to classify a book's genre based on an image, specifically for archival documents where digitization of the complete book for the purpose of indexing is an expensive task. While various modalities are available (e.g., cover, title, author, abstract), benchmarking the image-based classification systems based on minimal information is a particularly exciting field due to the recent advancements in the domain of image-based deep learning and its applicability. For that purpose, a natural question arises regarding the plausibility of solving the problem of book classification by only utilizing an image of its cover along with the current state-of-the-art deep learning models. To answer this question, this paper makes a three-fold contribution. First, the publicly available book cover dataset comprising of 57k book covers belonging to 30 different categories is thoroughly analyzed and corrected. Second, it benchmarks the performance on a battery of state-of-the-art image classification models for the task of book cover classification. Third, it uses explicit attention mechanisms to identify the regions that the network focused on in order to make the prediction. All of our evaluations were performed on a subset of the mentioned public book cover dataset. Analysis of the results revealed the inefficacy of the most powerful models for solving the classification task. With the obtained results, it is evident that significant efforts need to be devoted in order to solve this image-based classification task to a satisfactory level.
Quantum Machine Learning: Recent Advances and Outlook

Quantum computing is currently at the nexus of physics and engineering. Although current generation quantum processors are small and noisy, advancements are happening at an astounding rate. In addition, machine learning has played a crucial role in many recent advances. The combination of these two fields, Quantum Machine Learning, is a small but extremely promising new field with the possibility of unlimited abilities. This work seeks to provide an introduction to this emerging field, along with a discussion of recent advances as well as problems that are yet to be solved.
Complementing content of English courses for enhancing communication of IT-professionals for sustainable development

The paper deals with the issue of English language training for IT-specialists at Ukrainian universities. Understanding the importance of studying foreign languages has been confirmed by a number of normative documents. Peculiarities of professional training of IT-specialists at higher education institutions with the focus on foreign language training are considered. Pedagogical conditions for formation of communicative competence of IT-specialists are analysed. The content analysis of existing English language course books and textbooks for IT-specialists has been conducted to find out the content of foreign language training. It is stated that English language teaching aids in information technology, computer engineering, computing and software engineering can be used in the learning process, however, their use requires thorough refinement and modification. The series of guides and manuals for teaching English for professional purposes are presented.
Convergence of Blockchain and Artificial Intelligence to Decentralize Healthcare Systems

Owing to enlarged digital data obtainability and artificial intelligence (AI) progressions, there are quite a few occasions that can be reconnoitered in healthcare. Deep learning (DL) and inductive transfer practices are turning healthcare data- such as phantasmagorias and videotapes-into powerful data sources for predictive analytics. At the present time, patients fail to have entree to his/her individual medicinal records and hang around ignorant of data importance or prominence. This paper directs to offer a gestalt of AI and blockchain and exhibit a roadmap for a blockchain-assisted decentralized bionetwork of private healthcare data to expediate new methodologies to drug discovery and precautionary healthcare. A protected and crystal-clear disseminated marketplace of personal data by means of blockchain and DL technology will circumvent challenges faced by authorities of a given healthcare system and restore custody all across private records that includes medicinal documents back to humans. It also proposes a novel type of utility cryptotoken named LifeCoin, which can be produced through the stationing of data on the blockchain-assisted open market to streamline transactions and expedite inventive reward schemes.
Multi-hop Hierarchical Graph Neural Networks

Graph representation learning has been widely applied to graph tasks such as node classification, link prediction, graph-level classification and so on. Graph representation learning takes full advantage of spatial or spectral approaches to embed nodes into low-dimension space correctly and effectively. Especially, Graph Neural Networks (GNNs), which is one of representation learning, attract increasing interests due to their powerful ability to integrate local information and outstanding generalization in graph tasks. Currently, GNN models are also able to capture further nodes information with more stacked convolution layers. But when the number of layers reaches a certain level, these existing methods perform worse as the depth of networks increases continuously. Thus, most GNNs employ shallow architectures, leading to the failure of capturing further information. In this paper, we present Multi-hop Hierarchical Graph Neural Networks (MHGNNs), a new graph neural network framework, to address the shortcomings of lacking further node information and obtain broad receptive field. Distinct from prior works, one layer of MHGNNs can concatenate hop-level features in a hierarchical way, where features in the same hop are aggregated with each other. Another advantage is that MHGNNs have a flexible structure, where the number of used hops can be different in each layer. Besides, MHGNNs also use attention mechanism during the integrated step, which mines latent relationships among hops and adaptively selectes important hop-level features. Finally, our MHGNN model was evaluated on citation and protein-protein interaction graph benchmarks to conduct node classification, and has advanced or matched the outcomes of state-of-the-art methods in node classification tasks.