Reviewerly/exHarmony huggingface dataset:
Multiple Instance Learning Convolutional Neural Networks for Fine-Grained Aircraft Recognition

The key to fine-grained aircraft recognition is discovering the subtle traits that can distinguish different subcategories. Early approaches leverage part annotations of fine-grained objects to derive rich representations. However, manual labeling part information is cumbersome. In response to this issue, previous CNN-based methods reuse the backbone network to extract part-discrimination features, the inference process of which consumes much time. Therefore, we introduce generalized multiple instance learning (MIL) into fine-grained recognition. In generalized MIL, an aircraft is assumed to consist of multiple instances (such as head, tail, and body). Firstly, instance-level representations are obtained by the feature extractor and instance conversion component. Secondly, the obtained instance features are scored by an MIL classifier, which can yield high-level part semantics. Finally, a fine-grained object label is inferred by a MIL pooling function that aggregates multiple instance scores. The proposed approach is trained end-to-end without part annotations and complex location networks. Experimental evidence is conducted to prove the feasibility and effectiveness of our approach on combined aircraft images (CAIs).
Decentralised Content Creation in Digital Learning: A Blockchain Concept

In today's times, when the whole world is practising social distancing, the utility of in-person training is going through an existential dilemma. The learning ecosystem needs to rapidly evolve into fulfilling the global needs of learning and retooling to stay relevant and use new opportunities. The existing models of content creation and learning generally assume that a single faculty has to create all modules with topics and lessons both included. This article proposes a conceptual model for applying blockchain in decentralised content creation. Here, education is taken up as a developmental case. While much more content is now available online for learning than ever before, the quality of the content at topic and lesson levels is being observed to have a wide variation in quality resulting in inconsistent delivery of knowledge. The blocks are made of modular knowledge pieces consisting of lessons that can be appended to the initial module/topic creator (s) tightly integrated with a permissioned blockchain with nodes consisting of co-creators, reviewers, approvers and other interested parties.
Research and Development of Copyright Registration and Monitoring System Based on Digital Watermarking and Fingerprint Technology

According to the characteristics of the existing copyright protection system and market demand, a copyright registration and monitoring system based on digital watermarking and fingerprint technology was designed and implemented.Users can register audio, video, image and other types of digital content on our system, in which the only fingerprint is used to replace the original work itself for registration. After the approval, the unique watermark is issued for embedding in the digital content, so that when the infringement is identified, the watermark can be extracted from the suspected infringing works to prove the copyright, and this will break through the difficulty of obtaining evidence and safeguarding rights.The system also is real-time and accurate for copyright monitoring works. It can provide users with instant feedback, including suspected infringement works. Our system combines the advantages of digital watermarking and fingerprint technology in copyright protection, simplifies the copyright registration, makes the infringement identification effective, and makes the copyright monitoring accurate, so as to effectively prevent the occurrence of infringement and piracy, which is conducive to the promotion and popularization of digital works copyright protection.
Efficient video quality assessment with deeper spatiotemporal feature extraction and integration

The challenge of video quality assessment (VQA) modeling for user-generated content (UGC) (i.e., UGC-VQA) is how to accurately extract discriminative features and elaborately quantify interfeature interactions by following the behavior patterns of human eye-brain vision perception. To address this issue, we propose the Deeper Spatial–Temporal Scoring Network (DSTS-Net) to give a precise VQA. Concretely, we first deploy the multiscale feature extraction module to characterize content-aware features accounting for nonlinear reverse hierarchy theory in video perception process, which is not fully considered in the reported UGC-VQA models. Hierarchical handcraft and semantic features are simultaneously considered using content adaptive weighting. Second, we develop a feature integration structure, i.e., deeper gated recurrent unit (DGRU), to fully imitate the interfeature interactions in visionary perception, including feedforward and feedback processes. Third, the dual DGRU structure is employed to further account for interframe interactions of hierarchical features, imitating the nonlinearity of perception as much as possible. Finally, improved pooling is achieved in the local adaptive smoothing module accounting for the temporal hysteresis. Holistic validation of the proposed method on four public challenging UGC-VQA datasets presents a comparable performance over the state-of-the-art no-reference VQA methods, especially our method can give an accurate prediction of the low quality videos with weak temporal correlation. To promote reproducible research and public evaluation, an implementation of our method has been made available online: https://github.com/liu0527aa/DSTS-Net.
Shongket: A Comprehensive and Multipurpose Dataset for Bangla Sign Language Detection

Dataset is considered to be the most important resource in machine learning fields or computer vision-based applications. Image classification problems are heavily influenced by the quality of a dataset. Bangla Sign Language is a communication medium in Bangla that is used for communication between normal people and speech and hearing disabled people. As a result, recognition of Bangla Sign Language is vital for these disabled people. Any complete Bangla Sign Language dataset is extremely rare and fewer research works have been done in this field. To address this issue, we have created a complete dataset of Bangla sign language that includes all letters and digits, which we intend to make public for use in future research. We have captured 150 images per class for the 10 digit classes and 120 images per class for the 36 Bangla letter classes. In total, we have captured 5820 images for both digits and letters from volunteers. We have included as many variation as possible in our dataset to improve our training accuracy. We have tested the performance of various well-known deep learning models on our dataset which produced the maximum accuracy of around 95%. This dataset will help researchers in future Bangla sign language related works.
Minimum Query Set for Decision Tree Construction

A new two-stage method for the construction of a decision tree is developed. The first stage is based on the definition of a minimum query set, which is the smallest set of attribute-value pairs for which any two objects can be distinguished. To obtain this set, an appropriate linear programming model is proposed. The queries from this set are building blocks of the second stage in which we try to find an optimal decision tree using a genetic algorithm. In a series of experiments, we show that for some databases, our approach should be considered as an alternative method to classical ones (CART, C4.5) and other heuristic approaches in terms of classification quality.
A Secured Healthcare Management and Service Retrieval for Society Over Apache Spark Hadoop Environment

Typically, the amount of health data increases by their size, computational complexity, and speed. A traditional healthcare system often requires the delivery of medical data to the large storage system, which is comprised of patient’s sensitive information and causes communication overhead. Basically, medical sharing is a serious threat and also a challenging issue. For these reasons, a range of big data analytical approaches such as Machine Learning and Text Mining are often required for organizing huge amounts of data. In this paper, we focus on two big data analytics strategies: Data Retrieval and Data Management over Hadoop Distributed File System (HDFS). The functions of this paper include Privacy Protection, Data Sharing, and Management. In both stages of data retrieval and management, we first authenticate data requestors and data owners who want to store data and share the stored data in the HDFS. Secondly, we present a new model to help the requestor to select trustable and similar partners who want to access the health data stored in the HDFS clusters and give them a proper and accurate set of partners. Thirdly, we cluster patients’ medical data stored in the HDFS and find out their health status (diseases or any serious threats) using a Deep learning algorithm over Spark environment. Finally, in order to prevent the patients from serious diseases, a report is generated and forwarded to stakeholders. Our experiments illustrate the effectiveness of the proposed scheme in terms of accuracy, precision, recall, and f-measure.
Reconstruction of Isolated Moving Objects by Motion-Induced Phase Shift Based on PSP

The reconstruction of moving objects based on phase shifting profilometry has attracted intensive interests. Most of the methods introduce the phase shift by projecting multiple fringe patterns, which is undesirable in moving object reconstruction as the errors caused by the motion will be intensified when the number of the fringe pattern is increased. This paper proposes the reconstruction of the isolated moving object by projecting two fringe patterns with different frequencies. The phase shift required by the phase shifting profilometry is generated by the object motion, and the model describing the motion-induced phase shift is presented. Then, the phase information in different frequencies is retrieved by analyzing the influence introduced by movement. Finally, the mismatch on the phase information between the two frequencies is compensated and the isolated moving object is reconstructed. Experiments are presented to verify the effectiveness of the proposed method.
Speaker Accent Recognition Using MFCC Feature Extraction and Machine Learning Algorithms

Speech and speaker recognition systems aim to analyze parametric information contained in the human voice and recognize it at the highest possible rate. One of the most important features in the audio signal for the speaker to be recognized successfully by the system is the speaker's accent. Speaker accent recognition systems are based on the analysis of patterns such as the way the speaker speaks and the word choice he uses while speaking. In this study, the data obtained by the MFCC feature extraction technique from voice signals of 367 speakers with 7 different accents were used. The data of 330 speakers in the data set were taken from the "Speaker Accent Recognition" data set in the UC Irvine Machine Learning (ML) open data source. The data of the other 37 speakers were obtained by converting the voice recordings in the "Speaker Accent Archive" data set created by George Mason University into data using the MFCC feature extraction technique. 9 ML classification algorithms were used for the designed speaker accent recognition system. Also, the k-fold cross-validation technique was used to test the data set independently. In this way, the performance of ML algorithms is shown when the data set is divided into a k number of parts. Information about the classification algorithms used in the designed system and the hyperparameter optimizations made in these algorithms are also given. The success performances of the classification algorithms are shown with performance metrics.
Hybrid Filtering Recommendation System in an Educational Context

In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, performance and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as e-commerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. This article suggests that a hybridization of recommendation systems filtering methods can improve the quality of recommendations. An experiment was conducted to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.
Argumentation and Defeasible Reasoning in the Law

Different formalisms for defeasible reasoning have been used to represent knowledge and reason in the legal field. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: defeasible logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches under three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software resources). On top of that, two real examples in the legal domain are designed and implemented in ASPIC+ to showcase the benefit of an argumentation approach in real-world domains. The CrossJustice and Interlex projects are taken as a testbed, and experiments are conducted with the Arg2P technology.
Deformable medical image registration based on unsupervised generative adversarial network integrating dual attention mechanisms

Registration is a basic subject of medical image research, and has been a research hotspot for decades. In the process of optimizing each pair of images, the traditional registration requires a lot of calculation, which is very time-consuming for a large amount of data. In recent years, the existing deep learning network framework, especially the model based on U-Net structure, has not only improved the computing speed, but also greatly improved the registration performance. However, the feature loss occurs in the UpSampling process of this structure. Hence, We propose a generative adversarial network using a dual attention mechanisms without any supervised information. In UpSampling process of the registration network, the dual attention mechanism is introduced to improve feature recovery ability. The dual attention mechanism consists of channel attention mechanism and location attention mechanism. For the registration network, local crosscorrelation loss functions are proposed to improve image similarity. Experiments show that our method has achieved perfect registration effect, especially in the edge region.
Machine Learning Theory and Methods

Machine learning alludes to the changes in frameworks that perform correlate functions connected with computing. This chapter presents introduction varieties and the application of machine learning. This chapter additionally presents the essential structures of Machine learning, the process and basic ideas associated with the varied machine learning techniques like Naive Thomas Bayes classifier, support vector machine, k-nearest-neighbor, decision trees and regression analysis algorithms. At the top a number of the necessary applications square measure bestowed.
Recognition of Isolated Handwritten Gurumukhi Numerals Using Hopfield Neural Network

Despite the design challenges, automatic character recognition has been the subject of intensive research. However, when it comes to handwritten character recognition, the challenges are not only daunting but also achieving acceptable accuracy, which is also a big concern. Many machine learning techniques have been applied by various researchers for different scripts and characters. However, in case of Gurumukhi characters (an Indian language), the research is not as advanced and effective as per the latest reviews. In this work, the Hopfield Neural Network (HNN) model is applied for the recognition of the isolated handwritten Gurumukhi digits. A total of 1500 different input patterns have been tested over the network sequentially, and a recognition accuracy of 95.4% has been recorded. All preprocessing activities and implementation of Hopfield model have done using MATLAB.
Weakly Supervised Deep Nuclei Segmentation with Sparsely Annotated Bounding Boxes for DNA Image Cytometry

Nuclei segmentation is an essential step in DNA ploidy analysis by image-based cytometry (DNA-ICM) which is widely used in cytopathology and allows an objective measurement of DNA content (ploidy). The routine fully supervised learning-based method requires often tedious and expensive pixel-wise labels. In this paper, we propose a novel weakly supervised nuclei segmentation framework which exploits only sparsely annotated bounding boxes, without any segmentation labels. The key is to integrate the traditional image segmentation and self-training into fully supervised instance segmentation. We first leverage the traditional segmentation to generate coarse masks for each box-annotated nucleus to supervise the training of a teacher model, which is then responsible for both the refinement of these coarse masks and pseudo labels generation of unlabeled nuclei. These pseudo labels and refined masks along with the original manually annotated bounding boxes jointly supervise the training of student model. Both teacher and student share the same architecture and especially the student is initialized by the teacher. We have extensively evaluated our method with both our DNA-ICM dataset and public cytopathological dataset. Without bells and whistles, our method outperforms all existing weakly supervised entries on both datasets. Code and our DNA-ICM dataset are publicly available at https://github.com/CVIU-CSU/Weakly-Supervised-Nuclei-Segmentation.
Web API Security Vulnerabilities and Mitigation Mechanisms: A Systematic Mapping Study

The growth of the web over the last couple of decades opened the door for the creation of an increasing number of web-based software systems. This change brought the need for new software solutions to establish communication between distributed software entities. One of the adopted solutions was web APIs; however, their appearance brought with itself new challenges that need to be solved. Among these new challenges, we find the necessity to protect the API at a design level from attacks by malicious users, in other words, making the API secure by design. This task is not trivial, and to be able to perform it effectively, it is necessary to know the vulnerabilities which APIs are commonly exposed to, alongside the mechanisms which exist to defend against them. The objective of this systematic mapping study is to gather the existing scientific knowledge about security threats that a web API faces, alongside design-level mechanisms for detecting, resisting, reacting, and recovering from attacks. Our results discovered 66 threats described in the literature. We observed that the most reported threats are those related to Spoofing and Tampering, both mostly related to the network traffic the API interacts with. In contrast, the least reported threats are those related to repudiation. We identified 21 techniques, 11 patterns and 34 methods that can be employed at a design level to detect, resist, react to or recover from these threats.
Coherence Trapping in Open Two-Qubit Dynamics

In this manuscript, we examine the dynamical behavior of the coherence in open quantum systems using the l1 norm. We consider a two-qubit system that evolves in the framework of Kossakowski-type quantum dynamical semigroups (KTQDSs) of completely positive maps (CPMs). We find that the quantum coherence can be asymptotically maintained with respect to the values of the system parameters. Moreover, we show that the quantum coherence can resist the effect of the environment and preserve even in the regime of long times. The obtained results also show that the initially separable states can provide a finite value of the coherence during the time evolution. Because of such properties, several states in this type of environments are good candidates for incorporating quantum information and optics (QIO) schemes. Finally, we compare the dynamical behavior of the coherence with the entire quantum correlation.
A QR Code-Based Approach to Differentiating the Display of Augmented Reality Content

The augmented reality (AR) system requires markers to recognize and locate virtual objects on the screens of mobile devices. However, both markers and objects must be registered via the online platform in advance. In addition, an AR marker can only pair with a fixed set of virtual objects, limiting the flexibility and immediacy of changing and updating these data. This paper incorporates the quick response barcode (QR code) into the AR system to address these issues. We propose an algorithm with two vital goals, including (1) generating differentiated virtual objects for different target users by using only one QR code as the marker and (2) concealing different private authentication in QR modules by applying the error correction capability. We then demonstrate the proposed approach via a simulation of two practical scenarios, the electronic catalogs for business applications, and differentiated instructional materials for digital learning. This paper contributes to AR and QR code research and practices.
Comparison of Discrete Memetic Evolutionary Metaheuristics for TSP

In our paper we compare discrete memetic evolutionary metaheuristics (and other algorithms) which are applicable (also) for the widely studied and industrially applied (symmetric, Euclidean) NP-hard combinatorial optimization problem called Traveling Salesman Problem (TSP) such as DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm), DMTLBO (Discrete Memetic Teaching–Learning Based Optimization) not to mention DMSSA (Discrete Memetic Squirrel Search Algorithm) algorithms. The comparisons occurred under the same fixed conditions.
Template Aging in Multi-Modal Social Behavioral Biometrics

The uniqueness of social interactions on online social networks draws attention to cybersecurity research.Social Behavioral Biometric (SBB) systems extract unique patterns from online communication traits trails and generate digital fingerprints for user identification.However, with time those behavioral patterns change.These affect the authentication ability of a SBB system.In this paper, we have combined for the first time textual, contextual and interpersonal communicative information of users in online social networks to develop a biometric system.The SBB traits are combined using the weighted sum rule score level fusion algorithm with the genetic algorithm employed to choose the feature weights.The effects of template aging on the individual SBB traits and overall system have been analyzed for the first time.The proposed system achieves the recognition accuracy of 99.25% and outperforms all prior research on SBB.The experimental results on permanence evaluation demonstrate that the developed system can perform remarkably well despite the template aging effect.