Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Few-Shot Class-Incremental Learning

The capability of incrementally learning new classes and learning from a few examples is one of the hallmarks of human intelligence. It is crucial to endow a practical recognition system with such ability. Therefore, in this paper, we conduct pioneering work and focus on a challenging yet practical Semi-Supervised Few-Shot Class-Incremental Learning (SSFSCIL) problem, which requires CNN models incrementally learn new classes from very few labeled samples and a large number of unlabeled samples, without forgetting the previously learned ones. To address this problem, a simple and efficient solution for SSFSCIL is proposed to learn novel categories using a self-training strategy in a semi-supervised manner and avoid catastrophic forgetting by distillation-based methods. Our extensive experiments on CIFAR100, mini ImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.
Research on Basketball Sports Training Based on Virtual Reality Technology

Abstract Physical education is an important part of teaching content, and basketball is an important part of physical education. Taking the effective way carries on the basketball sports training, may improve the student’s physical quality effectively. This paper first explains the concept of virtual reality technology and the necessity of simulation technology, and at the same time the application of virtual reality basketball teaching. And the research of fixed-point shooting based on virtual reality technology is discussed for readers’ reference.
Cloud data security and various security algorithms

Abstract The term Cloud computing is a most trending and promisingtechnology. It is the most basic need for many organizations. Cloud technology has many advantages as user can access the files from anywhere over the internet. It has also a very known feature of using the resources according to their demand and user does need to pay extra charges for the unused resources. In Cloud Computing user do not need to carry all their documents with them as cloud access is global. But as Cloud computing means sending data over internet so sometimes there may be security issues and threat of intruder attack at the time of data transmission. As traditional banking industry is totally changed and now the total control in the hand of customer rather than the bank. In banking industry cloud computing provides various features viz; cost saving, business continuity, business agility and time saving. but as data in bank is the most crucial data so cloud security is the first priority for every bank before adopting cloud. security at cloud is the biggest obstacle for the banking industry to adopt this trending technology and its services. So there is need to provide security to the cloud. This paper aims on cloud basic methodology and analyze some security algorithms and their applicability in cloud banking environment.
A multi-stage chaotic encryption technique for medical image

Usually, medical image relates to the disease. Its pixel distortion may cause an erroneous diagnosis. The multiple chaos-based encryptions can protect it significantly due to high sensitive control parameters. This paper proposes a multi-stage chaotic encryption technique by consecutively adopting Logistic map and Lorenz attractor to enrich the security level. At first, the usage of the Logistic map converts the plain medical image into a confusing image. Now, a confusion key encrypts this blur image. Later on, the involvement of the Lorenz attractor generates the final cipher image. Lastly, different security and statistical analyses including the National Institute of Standards and Technology (NIST) randomness test and comparisons ensure the strength of the cipher.
Tumor saliency estimation for breast ultrasound images via breast anatomy modeling

Tumor saliency estimation aims to localize tumors by modeling the visual stimuli in medical images. However, it is a challenging task for breast ultrasound (BUS) image due to the complicated anatomic structure of the breast and poor image quality; and existing saliency estimation approaches only model the generic visual stimuli, e.g., local and global contrast, location, and feature correlation, and achieve poor performance for tumor saliency estimation. In this paper, we propose a novel optimization model to estimate tumor saliency by utilizing breast anatomy. First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues. The extensive experiments demonstrate that the proposed approach obtains more accurate foreground and background maps with breast anatomy; especially, for the images having large or small tumors. Meanwhile, the new objective function can handle the images without tumors. The newly proposed method achieves state-of-the-art performance comparing to eight tumor saliency estimation approaches using two BUS datasets.
Depth completion towards different sensor configurations via relative depth map estimation and scale recovery

Depth completion, which combines additional sparse depth information from the range sensors, substantially improves the accuracy of monocular depth estimation, especially using the deep-learning-based methods. However, these methods can hardly produce satisfactory depth results when the sensor configuration changes at test time, which is important for real-world applications. In this paper, the problem is tackled by our proposed novel two-stage mechanism, which decomposes depth completion into two subtasks, namely relative depth map estimation and scale recovery. The relative depth map is first estimated from a single color image with our designed scale-invariant loss function. Then the scale map is recovered with the additional sparse depth. Experiments on different densities and patterns of the sparse depth input show that our model always produces satisfactory depth results. Besides, our approach achieves state-of-the-art performance on the indoor NYUv2 dataset and performs competitively on the outdoor KITTI dataset, demonstrating the effectiveness of our method.
Craquelurenet: Matching The Crack Structure In Historical Paintings For Multi-Modal Image Registration

Visual light photography, infrared reflectography, ultraviolet fluorescence photography and x-radiography reveal even hidden compositional layers in paintings. To investigate the connections between these images, a multi-modal registration is essential. Due to varying image resolutions, modality dependent image content and depiction styles, registration poses a challenge. Historical paintings usually show crack structures called craquelure in the paint. Since craquelure is visible by all modalities, we extract craquelure features for our multi-modal registration method using a convolutional neural network. We jointly train our keypoint detector and descriptor using multi-task learning. We created a multi-modal dataset of historical paintings with keypoint pair annotations and class labels for craquelure detection and matching. Our method demonstrates the best registration performance on the multi-modal dataset in comparison to competing methods.
A Transformer-Based Decoupled Attention Network for Text Recognition in Shopping Receipt Images

Optical character recognition (OCR) of shopping receipts plays an important role in smart business and personal financial management. Many challenging issues remain in current OCR systems for text recognition of shopping receipts captured by mobile phones. This research constructs a multi-task model by integrating saliency object detection as a branch task, which enables us to filter out irrelevant text instances by detecting the outline of a shopping receipt. Moreover, the developed model utilized a deformable convolution so as to learning visual information more effectively. On the other hand, to deal with attention drift of text recognition, we propose a transformer-based decoupled attention network, which is able to decouple the attention and prediction processes in attention mechanism. This mechanism can not only increase prediction accuracy, but also increase the inference speed. Extensive experimental results on a large-scale real-life dataset exhibit the effectiveness of our proposed method.
A Novel Affine Covariant Feature Mismatch Removal for Feature Matching

Feature matching is a fundamental technique in remote sensing image processing. This article proposes a new formulation of affine covariant feature matching for remote sensing images, where we suggest matching features by matching two sets of triplets. Compared with previous works, the formulation exploits the whole feature frame rather than the 2-D location to reject outliers. Besides, we also develop a new latent variable model to combine the feature frame and the SIFT ratio values, to enhance the convergence speed and success rate in challenging cases. We evaluate our model on three challenging datasets in terms of both qualitative and quantitative experiments. We also study the robustness to outliers since remote sensing images are typically affected by mismatches. The results demonstrate that the proposed method provides excellent matching performance with satisfying runtime and shows good robustness to outliers.
Improving the effectiveness of peer code review in identifying security defects

Prior studies found peer code review useful in identifying security defects. That is why most of the commercial and open-source software (OSS) projects embraced peer code review and mandated the use of it in the software development life cycle. However, despite conducting mandatory peer code review practices, many security-critical OSS projects such as Chromium, Mozilla, and Qt are reporting a high number of post-release vulnerabilities to the Common Vulnerabilities and Exposures (CVE) database. Practitioners may wonder if there is any missing piece in the puzzle that leads code reviews to miss those security defects. Therefore, the primary objective of this dissertation study is to improve the effectiveness of peer code review in identifying security defects.
Exploit those code reviews! bigger data for deeper learning

Modern code review (MCR) processes are prevalent in most organizations that develop software due to benefits in quality assurance and knowledge transfer. With the rise of collaborative software development platforms like GitHub and Bitbucket, today, millions of projects share not only their code but also their review data. Although researchers have tried to exploit this data for more than a decade, most of that knowledge remains a buried treasure. A crucial catalyst for many advances in deep learning, however, is the accessibility of large-scale standard datasets for different learning tasks. This paper presents the ETCR (Exploit Those Code Reviews!) infrastructure for mining MCR datasets from any GitHub project practicing pull-request-based development. We demonstrate its effectiveness with ETCR-Elasticsearch, a dataset of >231𝑘 review comments for >47𝑘 Java file revisions in >40𝑘 pull-requests from the Elasticsearch project. ETCR is designed with the challenge of deep learning in mind. Compared to previous datasets, ETCR datasets include all information for linking review comments to nodes in the respective program's Abstract Syntax Tree.
Fine-grained attribute weighted inverted specific-class distance measure for nominal attributes

The inverted specific-class distance measure (ISCDM) ranks first in the list of distance metrics that deal solely with nominal attributes, especially when values are missing and noise exists in non-class attributes. However, the attribute independence assumption still inevitably exists, which is almost untenable in many real-world applications with sophisticated attribute dependencies. Many improved versions based on different attribute weighting schemes have been proposed to relax this unrealistic assumption and circumvent its damage to measuring performance. However, existing attribute weighting schemes are limited to assigning a weight corresponding to each attribute; they ignore the more fine-grained dependence relationships between attributes and classes. Thus, in this study, we derive a novel fine-grained attribute weighting scheme, which first calculates the initial fine-grained attribute weights according to different attribute values and class labels and then uses random walk with restart to optimize them. We titled our improved measure fine-grained attribute-weighted ISCDM (FAWISCDM). Extensive experimental results on 66 datasets from a machine learning repository, collected by the University of California at Irvine illustrate that the FAWISCDM is notably superior to the original ISCDM and some other state-of-the-art competing methods, in terms of the negative conditional log likelihood and root relative squared error.
Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.
Vision And Text Transformer For Predicting Answerability On Visual Question Answering

Answerability on Visual Question Answering is a novel and attractive task to predict answerable scores between images and questions in multi-modal data. Existing works often utilize a binary mapping from visual question answering systems into Answerability. It does not reflect the essence of this problem. Together with our consideration of Answerability in a regression task, we propose VT-Transformer, which exploits visual and textual features through Transformer architecture. Experimental results on VizWiz 2020 dataset show the effectiveness and robustness of VT-Transformer for Answerability on Visual Question Answering when comparing with competitive baselines.
A Generic Back-End for Exploratory Programming

Exploratory programming is a form of incremental program development in which the programmer can try and compare definitions, receives immediate feedback and can simultaneously experiment with the language, the program and input data. Read-Eval-Print-Loop interpreters (REPLs) and computational notebooks are popular tools for exploratory programming. However, their usability, capabilities and user-friendliness are strongly dependent on the underlying interpreter and, in particular, on the ad hoc engineering required to ready the underlying interpreter for incremental program development. To break this dependency, this paper adopts a principled approach and implements a so-called exploring interpreter as a back-end to support various development environments for exploratory programming.This paper contributes by presenting a generic Haskell implementation of the exploring interpreter – applicable to a large class of software languages – and demonstrates its usage to develop a variety of interfaces with a shared back-end, including command-line REPLs, computational notebooks and servers with reactive APIs. The design of the back-end is evaluated by defining a variety of interfaces for existing languages, including eFLINT, a domain-specific language for normative reasoning, and Funcons-beta, the language developed by the PLanCompS project to enable component-based operational semantics.
A Systemic Approach for Natural Language Scenario Elicitation of Security Requirements

Security analysts rely on scenarios to assess vulnerabilities, project attacks, and decide on security requirements that mitigate the threat. However, eliciting natural language scenarios from stakeholders can be an ad-hoc process and subject to ambiguity and incompleteness. In this article, we examine systematic scenario elicitation by introducing a method based on user stories that uses a simplified process model of iterative scenario refinement. The process consists of three steps: 1) eliciting an interaction statement that describes a critical action performed by a user or system process; 2) eliciting one or more descriptive statements about a technology that enables the interaction; and 3) refinement of the technology into technical variants that correspond to design alternatives. We empirically evaluated our method by implementing our prototype in a user study that collects 30 security scenarios from participants. Based on our analysis, our proposed method is shown effective. Participants had a 100 percent task completion rate with 57 percent of participants achieving complete task-success, and the remaining 43 percent of participants achieving partial task-success. We also show the effect of security domain knowledge, and the benefit of using structure when collecting security requirements in natural language text. Finally, we present lessons learned and future research directions.
Effect of Hybrid Multi-Verse with Whale Optimization Algorithm on Optimal Inventory Management in Block Chain Technology with Cloud

One of the important works of supply chain management is the optimal inventory control. The optimal inventory control techniques plan to minimize the supply chain cost by efficiently managing the inventory. This paper tactics to analyze the influence of hybrid Multi-Verse Optimization (MVO) and Whale Optimization Algorithm (WOA) termed as Whale-based Multi-Verse Optimization Algorithm (W-MVO) on optimal inventory management in block chain under cloud sector. The costs like transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, back-ordering cost and quality improvement cost are considered for deriving the multi-objective model. The effectiveness of the proposed hybrid algorithm is analyzed by varying Travelling Distance Rate (TDR) from 0.2 to 1.2, and the model is evaluated with the assistance of block chain under the cloud sector.
Adversarial Examples Against a BERT ABSA Model – Fooling Bert With L33T, Misspellign, and Punctuation,

The BERT model is de facto state-of-the-art for aspect-based sentiment analysis (ABSA), an important task in natural language processing. Similar to every other model based on deep learning, BERT is vulnerable to so-called adversarial examples: strategically modified inputs that cause a change in the model’s prediction of the underlying input. In this paper we propose three new methods to create character-level adversarial examples against BERT and evaluate their effectiveness on the ABSA task. Specifically, our attack methods mimic human behavior and use leetspeak, common misspellings, or misplaced commas. By concentrating these changes on important words, we are able to maximize misclassification rates with minimal changes. To the best of our knowledge, we are the first to look into adversarial examples for the ABSA task and the first to propose these attacks.
Low Bitwidth CNN Accelerator on FPGA Using Winograd and Block Floating Point Arithmetic

Convolutional neural networks (CNNs) have achieved near or exceeding human performance in computer vision, yet their large computational and memory requirements made them difficult to deploy in both large data centers and embedded systems. CNNs are typically trained with floating point for accommodating large dynamic ranges of values during training. However, the large range of values comes with increased resource usage, power consumption, and latency. Fixed-point quantization is promising towards reducing the resource requirements of CNNs, yet low bitwidth implementations require fine-tuning to recover accuracy. In this work, we propose a CNN accelerator that utilizes block floating point (BFP) for reducing bitwidth to 10-bit and supporting Winograd filtering algorithm. We compare our design with a baseline FP16 design and show that our block floating point quantization reduces 50.1% LUTs, 48.3% Register, 27.3% BRAM and 43.8% DSP as well as achieves 32.1% higher frequency. Finally, we perform case studies with different CNNs and show that the accuracy drop is within 1% of the FP32 network.
Zoomable Intra Prediction for Multi-Focus Plenoptic 2.0 Video Coding

Plenoptic 2.0 videos that record time-varying light fields by focused plenoptic cameras are promising to immersive visual applications because of capturing dense sampled light fields with high spatial resolution in the rendered sub-apertures. In this paper, an intra prediction method is proposed for compressing multi-focus plenoptic 2.0 videos efficiently. Based on the imaging principle analysis of multi-focus plenoptic cameras, zooming relationships among the microimages are discovered and exploited by the proposed method. Positions of the prediction candidates and the zooming factors are derived, after which block zooming and tailoring are proposed to generate novel prediction candidates for weighted prediction. Experimental results demonstrated the superior performance of the proposed method relative to HEVC and state-of-the-art methods.
PVO-based reversible data hiding using adaptive multiple histogram generation and modification

A recently proposed pixel-value-ordering (PVO)–based reversible data hiding (RDH) method takes a sharp histogram derived from two skewed histograms, which realizes good performance through reducing the number of shifted pixels. However, in this recent work, since pixels with different local complexity are processed with the same modification manner based on a single histogram without considering their different local properties, the embedding performance is far from optimal. In this paper, to better exploit the pixel complexity and enhance the reversible embedding performance, a novel PVO-based RDH method using adaptive multiple histogram generation and modification is proposed. First, pixels with different local complexity are divided into several classes utilizing multiple thresholds. Then, a PVO-based predictor is used for prediction and multiple prediction-error histograms corresponding to the different pixel classes are obtained. Next, the generated histograms are modified to embed data according to their statistical characteristics. Here, based on the established capacity–distortion model, the histogram generation and modification are processed in an adaptive way to optimize the embedding performance. Moreover, by extending the proposed method to multiple two-dimensional prediction-error histograms, the embedding performance can be further improved. Experimental results verify that the proposed method outperforms certain state-of-the-art techniques with good marked-image quality.