Reviewerly/exHarmony huggingface dataset:
N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

High-resolution images provided by synthetic aperture radar (SAR) play an increasingly important role in the field of ship detection. Numerous algorithms have been so far proposed and relative competitive results have been achieved in detecting different targets. However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). The N-YOLO includes a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. First, NLC derives and classifies the noise level of SAR images. Secondly, the STPAE module is composed by a CA-CFAR and expansion operation, which is used to extract the complete region of potential targets. Thirdly, the YOLOv5-based detection module combines the potential target area with the original image to get a new image. To evaluate the effectiveness of the N-YOLO, experiments are conducted using a reference GaoFen-3 dataset. The detection results show that competitive performance has been achieved by N-YOLO in comparison with several CNN-based algorithms.
A methodology for selection of a Blockchain platform to develop an enterprise system

Blockchain technology was introduced more than a decade ago, and it has already begun to disrupt traditional business processes. After the recognition gained by Blockchain 1.0 for cryptocurrency transactions and Blockchain 2.0 for financial applications, there is a high demand for Blockchain 3.0, which focuses on using blockchain for various applications in industries other than finance. There are a plethora of permissioned and permissionless blockchain platforms that are available in the industry, and some could be used to develop an enterprise system. As a result, selection of a suitable blockchain platform to develop an enterprise system has become extremely difficult. This paper introduces a methodology for selection of a suitable blockchain platform for resolving industry or enterprise issues where blockchain provides a plausible solution. The methodology comprised of four stages; identification, selection, evaluation and validation. Initially, the available blockchain platforms are identified, followed by selecting a suitable blockchain platform using a multi-criteria decision-making method such as Simple Multi Attribute Rating Technique (SMART). Subsequently, the selected system is evaluated in detail considering the system architecture, libraries, tools, domain-specific applications and capability analysis of the selected blockchain platform. The proposed methodology has been validated through developing a blockchain-based enterprise solution. The process protocol introduced in this study could be used by any stakeholder to select a suitable blockchain platform to develop a blockchain application despite its scale.
Hybrid path planning using positioning risk and artificial potential fields

Most conventional path generation algorithms search for an optimal path that avoids collisions with obstacles under the constraint of platforms' kino-dynamics. These conventional algorithms usually assume that a user position and obstacle locations are accurately known at any point in navigation environments. However, in a positioning network, the accuracy of a position estimate varies depending on, e.g., the ranging accuracy, network geometry, multipath error, and signal blockages which may lead to unexpected situations, including collision and low efficiency path planning. Therefore, positioning accuracy must be considered in path generation to ensure a reliable navigation capability and collision avoidance. To consider positioning accuracy in path planning, the proposed method in this paper uses a mixture of potential and positioning risk fields that generates a hybrid directional flow to guide an unmanned vehicle (UV) in a safe and efficient path. The results of simulations showed that the proposed method generated successful paths for around 90% percent of the tested routes, while using only the potential field method failed for around 50%. To demonstrate the effectiveness of the proposed local hybrid path planning method, we perform an experiment using a small-size quadcopter, and the results are analyzed and discussed.
Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss

Arbitrary-oriented objects exist widely in remote sensing images. The mainstream rotation detectors use oriented bounding boxes (OBBs) or quadrilateral bounding boxes (QBBs) to represent the rotating objects. However, these methods suffer from the representation ambiguity for oriented object definition, which leads to suboptimal regression optimization and the inconsistency between the loss metric and the localization accuracy of the predictions. In this letter, we propose a representation invariance loss (RIL) to optimize the bounding box regression for the rotating objects in the remote sensing images. RIL treats multiple representations of an oriented object as multiple equivalent local minima and hence transforms bounding box regression into an adaptive matching process with these local minima. Next, the Hungarian matching algorithm is adopted to obtain the optimal regression strategy. Besides, we propose a normalized rotation loss to alleviate the weak correlation between different variables and their unbalanced loss contribution in OBB representation. Extensive experiments on remote sensing datasets show that our method achieves consistent and substantial improvement. The code and models are available at <uri>https://github.com/ming71/RIDet</uri> to facilitate future research.
Efficient Color Image Segmentation Using Fastmap Algorithm

In this paper, a new approach to fast digital image processing is proposed. Digital image processing (DIP) is a rapidly developing technology for quality development for primary products, also referred to as computer or machine vision. In communication systems, digital images have become an information source. Image segmentation is a cue for the field of image analysis and image editing. Our understanding of this field depends on advances in image segmentation and image editing, yet it remains open and challenging. The proposed approach suffers from complexity, improper segmentation, enhanced computing time, etc. To overcome these drawbacks, three segmentation levels are used here. The first stage is the key component analysis that provides more edge knowledge. Quick mapping that maps similar objects in a faster way is the next step. Finally, multidimensional scaling in a picture with the same colours to classify and segment related areas. This technique has a very fast and robust benefit. It also offers improved picture quality.
Adoption of artificial intelligence-integrated CRM systems in agile organizations in India

Artificial intelligence integrated with customer relationship management (CRM) systems has revolutionized organizations’ means of analyzing their huge volumes of customer data. To effectively respond to and manage the opportunities and challenges that arise from this, organizations are developing competencies and processes that evolve their agility, fine-tuning them to the artificial intelligence customer service system (AICS) and wider digitalization setting. In this context, this study identifies the factors impacting the adoption of an AI-integrated CRM system (AICS) in agile organizations as a part of their digitalization strategy. Methodologically, the research builds its theoretical foundation on extant works to develop hypotheses and a corresponding conceptual model. The model is quantitatively validated through a survey across the spectrum of Indian companies, following expert-based pretesting and pilot testing, and subsequently it is statistically tested using the partial least squares structural equation modeling (PLS-SEM) technique. The results, contextualized against the backdrop of organizational agility, identify and elucidate the relationship between stakeholders and perceived value and easiness of AICS, between employee trust and attitude, and the influence of attitude and behavioral intention as key mediators towards AICS adoption. The findings are conclusively transcribed into tangible implications for practice and explicit avenues for future research.
Asymmetric CNN for Image Superresolution

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to the nature of different applications, designing appropriate CNN architectures is developed. However, customized architectures gather different features via treating all pixel points as equal to improve the performance of given application, which ignores the effects of local power pixel points and results in low training efficiency. In this article, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB), and a high-frequency feature enhancement block (HFFEB) for image superresolution (SR). The AB utilizes one-dimensional (1-D) asymmetric convolutions to intensify the square convolution kernels in horizontal and vertical directions for promoting the influences of local salient features for single image SR (SISR). The MEB fuses all hierarchical low-frequency features from AB via a residual learning technique to resolve the long-term dependency problem and transforms obtained low-frequency features into high-frequency features. The HFFEB exploits low- and high-frequency features to obtain more robust SR features and address the excessive feature enhancement problem. Additionally, it also takes charge of reconstructing a high-resolution image. Extensive experiments show that our ACNet can effectively address SISR, blind SISR, and blind SISR of blind noise problems. The code of the ACNet is shown at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hellloxiaotian/ACNet</uri> .
An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach

Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naïve byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier.
Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence

This research analyzes how digital technologies contribute to improving the successive stages of the recruitment process: identifying, selecting, and retaining talented people. E-recruitment is an emerging and polymorphous phenomenon that starts with identification of candidates on social networks, continues through gamification of recruitment and job interviews with chatbots, and ends by matching a candidate and a job using artificial intelligence. These technologies are particularly useful for social businesses looking to recruit not only skilled people, but above all employees who have behaviors and values that match their mission. The methodology is based on grounded theory, participant observation, and qualitative data collection. A multiple case study is designed to analyze, compare, and combine several technologies dedicated to recruitment: (1) a social network with LinkedIn, (2) a MOOC with Udacity, (3) a serious game called Reveal from L'Oréal, (4) a chatbot called Ari from TextRecruit, and (5) a massive data analysis matching system with Randstad.tech. The discussion examines the respective performance and limits of these tools and their convergence via a progressive integration that leads to an uberization of recruitment. Managerial recommendations are formulated to support recruiters in their adoption of e-recruitment.
M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

We present a novel architecture for 3D object detection, M3DETR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids. M3DETR is the first approach that unifies multiple point cloud representations, feature scales, as well as models mutual relationships between point clouds simultaneously using transformers. We perform extensive ablation experiments that highlight the benefits of fusing representation and scale, and modeling the relationships. Our method achieves state-of-the-art performance on the KITTI 3D object detection dataset and Waymo Open Dataset. Results show that M3DETR improves the baseline significantly by 1.48% mAP for all classes on Waymo Open Dataset. In particular, our approach ranks 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on the well-known KITTI 3D Detection Benchmark for both car and cyclist classes, and ranks 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on Waymo Open Dataset with single frame point cloud input. Our code is available at: https://github.com/rayguan97/M3DETR.
Exploring Sybil and Double-Spending Risks in Blockchain Systems

The first step to realise the true potential of blockchain systems is to explain the associated security risks and vulnerabilities. These risks and vulnerabilities, exploited by the threat agent to affect the valuable assets and services. In this work, we use a security risk management (SRM) domain model and develop a framework to explore two security risks - Sybil and Double-spending - that are observed and considered most concerning security risks within blockchain systems. The framework illustrates the protected assets or assets to secure, the classification of threats that the attacker can trigger using Sybil attack, the identification of threats that cause Double-spending, the vulnerabilities of identified threats, and their countermeasures. We evaluated a newly built framework by exploring Sybil and Double-spending risks in Ethereum-based healthcare applications. We also recognise the various other security and implementation challenges of blockchain that hinder the acceptance of blockchain-enabled solutions. Furthermore, we discuss the permissioned blockchain systems making an appearance in industry-level enterprises and how permissioned blockchain systems control these challenges. We conclude the paper and outline the future work that aims to build an ontology-based blockchain security reference model. The results of this work could help blockchain developers, practitioners, and other associated stakeholders to communicate about Sybil and Double-spending risks, what security countermeasures should be introduced, and what security and implementation challenges are emerging in blockchain systems.
Intermediate Loss Regularization for CTC-Based Speech Recognition

We present a simple and efficient auxiliary loss function for automatic speech recognition (ASR) based on the connectionist temporal classification (CTC) objective. The proposed objective, an inter-mediate CTC loss, is attached to an intermediate layer in the CTC encoder network. This intermediate CTC loss well regularizes CTC training and improves the performance requiring only small modification of the code and small and no overhead during training and inference, respectively. In addition, we propose to combine this intermediate CTC loss with stochastic depth training, and apply this combination to a recently proposed Conformer network. We evaluate the proposed method on various corpora, reaching word error rate (WER) 9.9% on the WSJ corpus and character error rate (CER) 5.2% on the AISHELL-1 corpus respectively, based on CTC greedy search without a language model. Especially, the AISHELL-1 task is comparable to other state-of-the-art ASR systems based on auto-regressive decoder with beam search.
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis

Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static analysis tools are known to generate an excess of false positives. The recent ability of Machine Learning models to understand programming languages opens new possibilities when applied to static analysis. However, existing datasets to train models for vulnerability identification suffer from multiple limitations such as limited bug context, limited size, and synthetic and unrealistic source code. We propose D2A, a differential analysis based approach to label issues reported by static analysis tools. The D2A dataset is built by analyzing version pairs from multiple open source projects. From each project, we select bug fixing commits and we run static analysis on the versions before and after such commits. If some issues detected in a before-commit version disappear in the corresponding after-commit version, they are very likely to be real bugs that got fixed by the commit. We use D2A to generate a large labeled dataset to train models for vulnerability identification. We show that the dataset can be used to build a classifier to identify possible false alarms among the issues reported by static analysis, hence helping developers prioritize and investigate potential true positives first.
Total relighting

We propose a novel system for portrait relighting and background replacement, which maintains high-frequency boundary details and accurately synthesizes the subject's appearance as lit by novel illumination, thereby producing realistic composite images for any desired scene. Our technique includes foreground estimation via alpha matting, relighting, and compositing. We demonstrate that each of these stages can be tackled in a sequential pipeline without the use of priors (e.g. known background or known illumination) and with no specialized acquisition techniques, using only a single RGB portrait image and a novel, target HDR lighting environment as inputs. We train our model using relit portraits of subjects captured in a light stage computational illumination system, which records multiple lighting conditions, high quality geometry, and accurate alpha mattes. To perform realistic relighting for compositing, we introduce a novel per-pixel lighting representation in a deep learning framework, which explicitly models the diffuse and the specular components of appearance, producing relit portraits with convincingly rendered non-Lambertian effects like specular highlights. Multiple experiments and comparisons show the effectiveness of the proposed approach when applied to in-the-wild images.
Mask Guided Matting via Progressive Refinement Network

We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask perturbation operations are also introduced in the training to further enhance its robustness to external guidance. We show that PRN can generalize to unseen types of guidance masks such as trimap and low-quality alpha matte, making it suitable for various application pipelines. In addition, we revisit the foreground color prediction problem for matting and propose a surprisingly simple improvement to address the dataset issue. Evaluation on real and synthetic benchmarks shows that MG Matting achieves state-of-the-art performance using various types of guidance inputs. Code and models are available at https://github.com/yucornetto/MGMatting.
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation

Existing NAS methods for dense image prediction tasks usually compromise on restricted search space or search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between realistic and proxy setting, we propose a novel Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset without proxy. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module and mixture layer to reduce the memory consumption of ample search space, hence favoring the proxyless searching. Compared with contemporary works, experiments reveal that the proxyless searching scheme is capable of bridging the gap between searching and training environments. Further, DCNAS achieves new state-of-the-art performances on public semantic image segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC 2012. We also retain leading performances when evaluating the architecture on the more challenging ADE20K and PASCAL-Context dataset.