Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning

An increasing number of Internet application services are relying on encrypted traffic to offer adequate consumer privacy. Anomaly detection in encrypted traffic to circumvent and mitigate cyber security threats is, however, an open and ongoing research challenge due to the limitation of existing traffic classification techniques. Deep learning is emerging as a promising paradigm, allowing reduction in manual determination of feature set to increase classification accuracy. The present work develops a deep learning-based model for detection of anomalies in encrypted network traffic. Three different publicly available datasets including the NSL-KDD, UNSW-NB15, and CIC-IDS-2017 are used to comprehensively analyze encrypted attacks targeting popular protocols. Instead of relying on a single deep learning model, multiple schemes using convolutional (CNN), long short-term memory (LSTM), and recurrent neural networks (RNNs) are investigated. Our results report a hybrid combination of convolutional (CNN) and gated recurrent unit (GRU) models as outperforming others. The hybrid approach benefits from the low-latency feature derivation of the CNN, and an overall improved training dataset fitting. Additionally, the highly effective generalization offered by GRU results in optimal time-domain-related feature extraction, resulting in the CNN and GRU hybrid scheme presenting the best model.
Exemplar-Based 3D Portrait Stylization

Exemplar-based portrait stylization is widely attractive and highly desired. Despite recent successes, it remains challenging, especially when considering both texture and geometric styles. In this article, we present the first framework for one-shot 3D portrait style transfer, which can generate 3D face models with both the geometry exaggerated and the texture stylized while preserving the identity from the original content. It requires only one arbitrary style image instead of a large set of training examples for a particular style, provides geometry and texture outputs that are fully parameterized and disentangled, and enables further graphics applications with the 3D representations. The framework consists of two stages. In the first geometric style transfer stage, we use facial landmark translation to capture the coarse geometry style and guide the deformation of the dense 3D face geometry. In the second texture style transfer stage, we focus on performing style transfer on the canonical texture by adopting a differentiable renderer to optimize the texture in a multi-view framework. Experiments show that our method achieves robustly good results on different artistic styles and outperforms existing methods. We also demonstrate the advantages of our method via various 2D and 3D graphics applications.
Efficient and secure multiple digital signature to prevent forgery based on ECC

Adversary can be able to perform selective forgery where valid signature on message are created by someone else, with a significant probability and existential forgery, in which a pair of message and valid signature is created by him. For a signature scheme to be perfectly secured, it should be secured computationally. We present an ID based multiple signatures scheme secure against selective forgery attack based on ECC. The objective of the work carried out is to design and implement multi digital signature based on ECC against selective forgery attack.
Multi-Scale Fusion U-Net for the Segmentation of Breast Lesions

Breast lesion is a malignant tumor that occurs in the epithelial tissue of the breast. The early detection of breast lesions can make patients for treatment and improve survival rate. Thus, the accurate and automatic segmentation of breast lesions from ultrasound images is a fundamental task. However, the effectively segmentation of breast lesions is still faced up with two challenges. One is the characteristics of breast lesions’ multi-scale and the other one is blurred edges make segmentation difficult. To solve these problems, we propose a deep learning architecture, named Multi-scale Fusion U-Net (MF U-Net), which extracts the texture features and edge features of the image. It includes two novel modules and a new focal loss: 1) the Fusion Module (WFM) which segmenting irregular and fuzzy breast lesions, 2) the Multi-Scale Dilated Convolutions Module (MDCM) which overcoming the segmentation difficulties caused by large-scale changes in breast lesions, and 3) focal-DSC loss is proposed to solve the class imbalance problems in breast lesions segmentation. Moreover, there are some convolutional layers with different receptive fields in MDCM, which improves the network’s ability to extract multi-scale features. Comparative experiments reveal that the MF U-Net proposed in this paper outperforms other segmentation methods, and the proposed MF U-Net achieves state-of-the-art breast lesions segmentation result with 0.9421 Recall, 0.9345 Precision, 0.0694 FPs/image, 0.9535 DSC and 0.9112 IOU on Benchmark for Breast Ultrasound Image Segmentation (BUSIS) dataset.
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs

Recently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset.However, these models are trained and evaluated in a setting where hand-annotated question entities are supplied to the model, leaving the important and non-trivial task of entity resolution (ER) outside the scope of E2E learning.In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component.Our model only needs the question text and the answer entities to train, and delivers a stand-alone QA model that does not require an additional ER component to be supplied during runtime.Our approach is fully differentiable, thanks to its reliance on a recent method for building differentiable KGs (Cohen et al., 2020).We evaluate our E2E trained model on two public datasets and show that it comes close to baseline models that use handannotated entities.
Chapter 2. Australia : Media concentration and deteriorating conditions for investigative journalism

Chapter 2. Australia : Media concentration and deteriorating conditions for investigative journalism
Integration Strategy and Tool between Formal Ontology and Graph Database Technology

Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling.
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation

Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement learning provides a promising direction for learning to manipulate deformable objects with data driven methods. However, existing reinforcement learning benchmarks only cover tasks with direct state observability and simple low-dimensional dynamics or with relatively simple image-based environments, such as those with rigid objects. In this paper, we present SoftGym, a set of open-source simulated benchmarks for manipulating deformable objects, with a standard OpenAI Gym API and a Python interface for creating new environments. Our benchmark will enable reproducible research in this important area. Further, we evaluate a variety of algorithms on these tasks and highlight challenges for reinforcement learning algorithms, including dealing with a state representation that has a high intrinsic dimensionality and is partially observable. The experiments and analysis indicate the strengths and limitations of existing methods in the context of deformable object manipulation that can help point the way forward for future methods development. Code and videos of the learned policies can be found on our project website.
Dynamic Perception Framework for Fine-Grained Recognition

Fine-grained recognition poses the challenge of discriminating categories with only small subtle visual differences, which can be easily overwhelmed by diverse appearance within categories. Conventional approaches generally locate discriminative parts and then recognize the part-based features. However, we find that tuning the effective receptive field (ERF) of the network to the task plays the key role, which enables significant regions to contribute more to the output. Inspired by the receptive field stimulation mechanism of the visual cortex, we propose a Dynamic Perception framework as a solution. Our framework adapts the ERF by considering the image space and the kernel space simultaneously. In the image space, the Spatial Selective Sampling module is adopted to enlarge informative regions locally. In the kernel space, Spatial Selective Kernel convolution is introduced to adapt different kernel sizes for regions of interest and backgrounds by embedding spatial attention in the multi-path convolution. Extensive experiments on challenging benchmarks, including CUB-200-2011, FGVC-Aircraft, and Stanford Cars, demonstrate that our method yields a performance boost over the state-of-the-art methods.
Efficient static analysis and verification of featured transition systems

Abstract A Featured Transition System (FTS) models the behaviour of all products of a Software Product Line (SPL) in a single compact structure, by associating action-labelled transitions with features that condition their presence in product behaviour. It may however be the case that the resulting featured transitions of an FTS cannot be executed in any product (so called dead transitions ) or, on the contrary, can be executed in all products (so called false optional transitions ). Moreover, an FTS may contain states from which a transition can be executed only in some products (so called hidden deadlock states ). It is useful to detect such ambiguities and signal them to the modeller, because dead transitions indicate an anomaly in the FTS that must be corrected, false optional transitions indicate a redundancy that may be removed, and hidden deadlocks should be made explicit in the FTS to improve the understanding of the model and to enable efficient verification—if the deadlocks in the products should not be remedied in the first place. We provide an algorithm to analyse an FTS for ambiguities and a means to transform an ambiguous FTS into an unambiguous one. The scope is twofold: an ambiguous model is typically undesired as it gives an unclear idea of the SPL and, moreover, an unambiguous FTS can efficiently be model checked. We empirically show the suitability of the algorithm by applying it to a number of benchmark SPL examples from the literature, and we show how this facilitates a kind of family-based model checking of a wide range of properties on FTSs.
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration

High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm innovations, challenges remain over content association ambiguities caused by saturation, motion, and various artifacts introduced during multi-exposure fusion such as ghosting, noise, and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) HDR restoration model which aims to address these issues within one framework. An efficient two-stream structure is proposed which separately focuses on texture feature transfer over saturated regions and multi-exposure tonal and texture feature fusion. A neural feature transfer mechanism is proposed which establishes spatial correspondence between different exposures based on multi-scale VGG features in the masked saturated HDR domain for discriminative contextual clues over the ambiguous image areas. A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner. In addition, we introduce several novel attention mechanisms, i.e., the motion attention module detects and suppresses the content discrepancies among the reference images; the saturation attention module facilitates differentiating the misalignment caused by saturation from those caused by motion; and the scale attention module ensures texture blending consistency between different coder/decoder scales. We carry out comprehensive qualitative and quantitative evaluations and ablation studies, which validate that these novel modules work coherently under the same framework and outperform state-of-the-art methods.
Concept-cognitive computing system for dynamic classification

In the context of big data, organizations and individuals can often benefit from the data mining techniques, such as classification. However, decision-makers must quickly react to insights over time under dynamic environments. In this paper, we present a novel perspective, named concept-cognitive computing system (C3S or ConceptCS), to achieve dynamic classification learning over the partially labeled data and labeled data. More specifically, to store and consolidate knowledge, a concept falling space is first employed as a basic knowledge memory mechanism in C3S. Then, we design a new concept-cognitive process by means of simulating human learning processes, which can incorporate new information into the old knowledge. Finally, a strategy of constructing two different concept spaces is considered in our system when faced with the scenario of a partially labeled dynamic data. Although there exist significant differences between C3S and the conventional incremental learning methods in the learning paradigm, our proposed C3S still performs strong performance for dynamic classification in comparison with several state-of-the-art incremental learning approaches. In addition, the experiments on various datasets have demonstrated that our system can obtain a good performance on the partially labeled data and labeled data simultaneously in dynamic environments.
A blockchain bee colony double inhibition labor division algorithm for spatio-temporal coupling task with application to UAV swarm task allocation

It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering, after clustering tasks according to spatio-temporal attributes, the clustered groups are linked into task sub-chains according to similarity.Then, based on the correlation between clusters, the child chains are connected to form a task chain.Therefore, the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs, a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements, the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm, a task allocation model for the unmanned aerial vehicle (UAV) group is constructed, and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.
A cascaded nested network for 3T brain MR image segmentation guided by 7T labeling

Accurate segmentation of the brain into gray matter, white matter, and cerebrospinal fluid using magnetic resonance (MR) imaging is critical for visualization and quantification of brain anatomy. Compared to 3T MR images, 7T MR images exhibit higher tissue contrast that is contributive to accurate tissue delineation for training segmentation models. In this paper, we propose a cascaded nested network (CaNes-Net) for segmentation of 3T brain MR images, trained by tissue labels delineated from the corresponding 7T images. We first train a nested network (Nes-Net) for a rough segmentation. The second Nes-Net uses tissue-specific geodesic distance maps as contextual information to refine the segmentation. This process is iterated to build CaNes-Net with a cascade of Nes-Net modules to gradually refine the segmentation. To alleviate the misalignment between 3T and corresponding 7T MR images, we incorporate a correlation coefficient map to allow well-aligned voxels to play a more important role in supervising the training process. We compared CaNes-Net with SPM and FSL tools, as well as four deep learning models on 18 adult subjects and the ADNI dataset. Our results indicate that CaNes-Net reduces segmentation errors caused by the misalignment and improves segmentation accuracy substantially over the competing methods.
VisDrone-CC2021: The Vision Meets Drone Crowd Counting Challenge Results

Crowding counting research evolves quickly by the lever-age of development in deep learning. Many researchers put their efforts into crowd counting tasks and have achieved many significant improvements. However, current datasets still barely satisfy this evolution and high quality evaluation data is urgent. Motivated by high quality and quantity study in crowding counting, we collect a drone-captured dataset formed by 5,468 images(images in RGB and thermal appear in pairs and 2,734 respectively). There are 1,807 pairs of images for training, and 927 pairs for testing. We manually annotate persons with points in each frame. Based on this dataset, we organized the Vision Meets Drone Crowd Counting Challenge(Visdrone-CC2021) in conjunction with the International Conference on Computer Vision (ICCV 2021). Our challenge attracts many researchers to join, which pave the road of speed up the milestone in crowding counting. To summarize the competition, we select the most remarkable algorithms from participants’ sub-missions and provide a detailed analysis of the evaluation results. More information can be found at the website: http://www.aiskyeye.com/.
Learning to Rewrite for Non-Autoregressive Neural Machine Translation

Non-autoregressive neural machine translation, which decomposes the dependence on previous target tokens from the inputs of the decoder, has achieved impressive inference speedup but at the cost of inferior accuracy.Previous works employ iterative decoding to improve the translation by applying multiple refinement iterations.However, a serious drawback is that these approaches expose the serious weakness in recognizing the erroneous translation pieces.In this paper, we propose an architecture named REWRITENAT to explicitly learn to rewrite the erroneous translation pieces.Specifically, REWRITENAT utilizes a locator module to locate the erroneous ones, which are then revised into the correct ones by a revisor module.Towards keeping the consistency of data distribution with iterative decoding, an iterative training strategy is employed to further improve the capacity of rewriting.Extensive experiments conducted on several widely-used benchmarks show that REWRITE-NAT can achieve better performance while significantly reducing decoding time, compared with previous iterative decoding strategies.In particular, REWRITENAT can obtain competitive results with autoregressive translation on WMT14 En↔De, En→Fr and WMT16 Ro→En translation benchmarks 1 .
Suppressing measurement uncertainty in an inhomogeneous spin star system

The uncertainty principle is known as a foundational element of quantum theory, providing a striking lower bound to quantify our prediction for the measured result of two incompatible observables. In this work, we study the thermal evolution of the entropic uncertainty bound in the presence of quantum memory for an inhomogeneous four-qubit spin-star system that is in the thermal regime. Intriguingly, our results show that the entropic uncertainty bound can be controlled and suppressed by adjusting the inhomogeneity parameter of the system.
Opportunities and Challenges of Artificial Intelligence in Banking: Systematic Literature Review

The primary aim of this systematic literature review (SLR) was to identify, assess and synthesize the extant evidence about the opportunities and challenges concerning the use of Artificial Intelligence (AI) in the banking sector. From the SLR, it is evident that AI has several opportunities for the sector. There are many fin-tech start-ups that offer banking AI solutions, and banking regulators are fostering AI adoption through legislation and collaboration. Other opportunities include the following: personalized services, smart wallets, decision-making and problem-solving, customer satisfaction and loyalty, process automation (especially targeting repetitive tasks), transactional security and cybersecurity improvements, and promotion of digital financial inclusion. Nevertheless, the key banking industry stakeholders have to formulate appropriate strategies aimed at overcoming existing and prospect AI challenges. Among the AI challenges that should be prioritized we include the following: job loss and user acceptance concerns, privacy breaches, creativity and adaptability loss, restrictive implementation and operational requirements, digital divide, availability of vast quality data, AI-business strategy alignment, and loss of emotional “human touch”. However, existing studies are largely descriptive and based on secondary sources of data. This necessitates empirical studies to expand the existing body of knowledge regarding AI opportunities and challenges in the banking industry.