Reviewerly/exHarmony huggingface dataset:
A multi-strategy fusion artificial bee colony algorithm with small population

Although artificial bee colony (ABC) algorithm is more and more popular in solving complex problems, slow convergence rate limits its wide application. ABC with small population can use the limited function evaluation times more efficiently since it can avoid unnecessary searches. However, ABC with small population cannot ensure population diversity, and when the algorithm is weak or unstable, it may fall into local optimum easily. So based on the latest research, we are motivated to propose a stabler and more efficient algorithm design to improve the search ability of ABC with small population by the fusion of multiple search strategies, which used together for the employed bees and the onlooker bees. Firstly we select and design multiple strategies with different search abilities of exploration and exploitation. Secondly, we propose an evolution ratio, which is an indicator to fully reflect the adaptability of the search strategy. Thirdly, we design different fusion methods according to the characteristics of the strategies, in which the search strategy with high exploration is maintained at a certain frequency throughout the whole search process of the employed bees, and the selections of the other two search strategies are adjusted according to evolution ratio adaptively in the employed bee phase and the onlooker bee phase. In the end, a novel algorithm called MFABC is proposed, which can realize efficiently multi-strategy cooperative search according to the requirements of different problems and different search stages. Experimental results on a set of benchmark functions have shown the accuracy, stability, efficiency and convergence rate of MFABC.
Multilevel thresholding by fuzzy type II sets using evolutionary algorithms

The image segmentation based on Multilevel thresholding has attracted more attention in recent years, they have been used in different applications. Therefore, several evolutionary computation methods have been proposed to determine the optimal threshold values. However, these approaches suffer from some limitations such as the stagnation point which leads to degradation the quality of the segmented image. In addition, most of them used either Otsu or Kapur as a fitness function, and the complexity of these methods is increased with increasing the threshold levels. Moreover, they don't provide accurate results in all the cases. To overcome such situations, in this paper is proposed the use of evolutionary computation algorithms combined with the Type II Fuzzy Entropy as the objective function. Such methods are the Backtracking Search Optimization Algorithm (BSA) and the Salp Swarm Algorithm (SSA). The BSA and SSA are able to avoid the limitation of similar techniques for image threshold because the objective function removes the ambiguities helping to find more accurate results. The BSA and SSA are used to find the best parameters of the Type II Fuzzy Entropy that extracts the optimal thresholds that properly segment the histogram of a digital image. To evaluate the performance of the proposed two methods, a set of experiments are performed using a set of benchmark images which have different characteristics. Moreover, the experiments are also performed over medical images from blood cells. The experimental results indicate that the proposed two methods have a good performance. However, they provide better performance than other algorithms in terms of quality and accuracy.
Si-GCN: Structure-induced Graph Convolution Network for Skeleton-based Action Recognition

In recent years, the graph-convolution networks have been used to solve the problem of skeleton-based action recognition. Previous works often adopted a structure-fixed graph to model the physical joints of human skeleton, but cannot well consider these interactions of different human parts (e.g., the right arm and the left leg) to some extent. To deal with this problem, we propose a novel structure-induced graph convolution network (Si-GCN) framework to boost the performance of the skeleton-based action recognition task. Given a video sequence of human skeletons, the Si-GCN can produce the sample-wise category in an end-to-end way. Specifically, according to the natural divisions of human body, we define a collection of intra-part graphs for each input human skeleton (i.e., each graph denotes a specific part/global of human skeleton), and then formulate an inter-graph to model the relationships of different intra-part graphs. The Si-GCN framework, which will then perform the spectral graph convolutions on these constructed intra/inter-part graphs, can not only capture the internal modalities of each human part/subgraph, but also consider the interactions/relationships between different human parts. A temporal convolution follows to model the temporal and spatial dynamics of the skeleton in combination with the characteristics of time and space. Comprehensive evaluations on two public datasets (including NTU RGB+D and HDM05) well demonstrate the superiority of our proposed Si-GCN when compared with existing skeleton-based action recognition approaches.
An Efficient Helmet Detection for MVD using Deep learning

Bike riders have been rapidly increasing amid time in various countries. Motorbikes are favoured by citizens belonging to different classes of the society due to many reasons such as its economic value. Wearing helmets is compulsory according to the standard however the vast majority avoid it. A principal goal of the helmet is to guarantee the safety of the riders. Our research intends to make an automated system to distinguish whether a biker is wearing a helmet or not and to impose fine to defaulters as a part of law enforcement. According to our research, such a system is not currently used by the police or by any other authority. Executing the proposed framework can convey more mindfulness and need to wear a helmet at any rate with the goal that they don't get captured on camera and avoid fine. This project intends to make deep learning based automated detection system for helmet identification using trained models and datasets that would be useful for the police department to enforce the law for the betterment of the society.
Video captioning using boosted and parallel Long Short-Term Memory networks

Video captioning and its integration with deep learning is one of the most challenging issues in the field of machine vision and artificial intelligence. In this paper, a new boosted and parallel architecture is proposed for video captioning using Long Short-Term Memory (LSTM) networks. The proposed architecture comprises two LSTM layers and a word selection module. The first LSTM layer has the responsibility of encoding frame features extracted by a pre-trained deep Convolutional Neural Network (CNN). In the second LSTM layer, a novel architecture is used for video captioning by leveraging several decoding LSTMs in a parallel and boosting architecture. This layer, which is called Boosted and Parallel LSTM (BP-LSTM) layer, is constructed by iteratively training LSTM networks using a special kind of AdaBoost algorithm during the training phase. During the testing phase, the outputs of BP-LSTMs are concurrently combined using the maximum probability criterion and word selection module. We tested the proposed algorithm with two well-known video captioning datasets and compared the results with state-of-the-art algorithms. The results show that the proposed architecture considerably improves the accuracy of the generated sentence.
SOSA

Resource management strategies for many-core systems dictate the sharing of resources among applications such as power, processing cores, and memory bandwidth in order to achieve system goals. System goals require consideration of both system constraints (e.g., power envelope) and user demands (e.g., response time, energy-efficiency). Existing approaches use heuristics, control theory, and machine learning for resource management. They all depend on static system models, requiring a priori knowledge of system dynamics, and are therefore too rigid to adapt to emerging workloads or changing system dynamics.
Blockchain Education

Blockchain is one of the hottest topics nowadays at many societal, industrial and academic conferences. The Blockchain is the core mechanism of digital currencies like Bitcoin. It has far reaching possibilities for applications in healthcare, public document management, and voting systems to name just a few. However, due to the many intrinsic technical aspects of blockchain technology, it is not easy for the general audience to understand the concepts well. This paper deals with the most natural methodology to educate Blockchain technology to young students and the general audience in a manner intended to those who have a very limited technical background.
Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure From Motion

Given dense image feature correspondences of a non-rigidly moving object across multiple frames, this paper proposes an algorithm to estimate its 3D shape for each frame. To solve this problem accurately, the recent state-of-the-art algorithm reduces this task to set of local linear subspace reconstruction and clustering problem using Grassmann manifold representation [34]. Unfortunately, their method missed on some of the critical issues associated with the modeling of surface deformations, for e.g., the dependence of a local surface deformation on its neighbors. Furthermore, their representation to group high dimensional data points inevitably introduce the drawbacks of categorizing samples on the high-dimensional Grassmann manifold [32, 31]. Hence, to deal with such limitations with [34], we propose an algorithm that jointly exploits the benefit of high-dimensional Grassmann manifold to perform reconstruction, and its equivalent lower-dimensional representation to infer suitable clusters. To accomplish this, we project each Grassmannians onto a lower-dimensional Grassmann manifold which preserves and respects the deformation of the structure w.r.t its neighbors. These Grassmann points in the lower-dimension then act as a representative for the selection of high-dimensional Grassmann samples to perform each local reconstruction. In practice, our algorithm provides a geometrically efficient way to solve dense NRSfM by switching between manifolds based on its benefit and usage. Experimental results show that the proposed algorithm is very effective in handling noise with reconstruction accuracy as good as or better than the competing methods.
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention

Lesion detection from computed tomography (CT) scans is challenging compared to natural object detection because of two major reasons: small lesion size and small inter-class variation. Firstly, the lesions usually only occupy a small region in the CT image. The feature of such small region may not be able to provide sufficient information due to its limited spatial feature resolution. Secondly, in CT scans, the lesions are often indistinguishable from the background since the lesion and non-lesion areas may have very similar appearances. To tackle both problems, we need to enrich the feature representation and improve the feature discriminativeness. Therefore, we introduce a dual-attention mechanism to the 3D contextual lesion detection framework, including the cross-slice contextual attention to selectively aggregate the information from different slices through a soft re-sampling process. Moreover, we propose intra-slice spatial attention to focus the feature learning in the most prominent regions. Our method can be easily trained end-to-end without adding heavy overhead on the base detection network. We use DeepLesion dataset and train a universal lesion detector to detect all kinds of lesions such as liver tumors, lung nodules, and so on. The results show that our model can significantly boost the results of the baseline lesion detector (with 3D contextual information) but using much fewer slices.
Is image-based CAPTCHA secure against attacks based on machine learning? An experimental study

The completely automated public Turing test to tell computers and humans apart (CAPTCHA) is among the most common methods of authentication used by websites and web services. It is intended to protect online services from automated scripts and malicious programs. Text-based and audio CAPTCHA are two of the earliest such methods, and have been shown to be inadequate at protecting systems and services. Image-based CAPTCHA has been introduced to address the limitations of previous CAPTCHA methods. It uses image recognition tasks to determine whether the user is a human or a malicious program. In light of the sensitivity of protected resources, challenges to their security arising from advances in machine learning algorithms are investigated here. This study examines the strength of image-based CAPTCHA by proposing an image-based CAPTCHA breaking system. The proposed system can automatically answer challenges posed by the recently proposed Google image reCAPTCHA with minimal human intervention. It employs deep learning technologies and machine learning algorithms, including random forest, classification and regression trees (CART), bagging with CART, and Naïve Bayes to automatically answer challenges. The proposed attack mechanism achieved an average accuracy of 85.32% while successfully solving 56.29% of reCAPTCHA challenges posed to it. The results show current image-based CAPTCHAs to deter automated scripts and malicious programs provide a false sense of security.
Accuracy Prediction for Distributed Decision Tree using Machine Learning approach

Machine Learning is one of the finest fields of Computer Science world which has given the innumerable and invaluable solutions to the mankind to solve its complex problems. Decision Tree is one such modern solution to the decision making problems by learning the data from the problem domain and building a model which can be used for prediction supported by the systematic analytics. In order to build a model on a huge dataset Decision Tree algorithm needs to be transformed to manifest itself into distributed environment so that higher performance of training the model is achieved in terms of time, without compromising the accuracy of the Decision Tree built. In this paper, we have proposed an enhanced version of distributed decision tree algorithm to perform better in terms of model building time without compromising the accuracy.
Vehicle Detection in the Aerial Infrared Images via an Improved Yolov3 Network

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more adopted to capture the aerial images. Therefore, how to fully utilize the aerial infrared images for the vehicle detection has attracted widespread attentions. However, due to the low-resolution, low contrast, and few texture features of the infrared image, it is really difficult to implement the vehicle detection in the aerial infrared images. In our work, an efficient and accurate vehicle detection algorithm in aerial infrared images is proposed via an improved yolov3 network. To increase the detection efficiency, we construct a new structure of the improved yolov3 network with only 16 layers. Besides, we expand the anchor boxes to four scales to improve the detection accuracy of the small vehicles. Meanwhile, for the limitation of the infrared vehicle samples, the transfer learning is introduced to train the improved yolov3 network. Finally, the proposed algorithm is evaluated on the VIVID and NPU data sets. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive vehicle detection results.
A Blockchain-Based Platform for Consent Management of Personal Data Processing in the IoT Ecosystem

In the Internet of Things (IoT) ecosystem, the volume of data generated by devices in the user’s environment is continually increasing and becoming of particular value. In such an environment the average user is bound to face considerable difficulties in understanding the size and scope of his/her collected data. However, the provisions of the European General Data Protection Regulation (GDPR) require data subjects to be able to control their personal data, be informed, and consent to its processing in an intelligible manner. This paper proposes ADVOCATE platform, a user-centric solution that allows data subjects to easily manage consents regarding access to their personal data in the IoT ecosystem. The proposed platform also assists data controllers to meet GDPR requirements, such as informing data subjects in a transparent and unambiguous manner about the data they will manage, the processing purposes, and periods. The integrity of personal data processing consents and the immutable versioning control of them are protected by a blockchain infrastructure. Finally, the paper provides a prototype implementation of the proposed platform that supports the main consents management functionality.
Face completion with Hybrid Dilated Convolution

Image completion is a challenging task which aims to fill the missing or masked regions in images with plausibly synthesized contents. In this paper, we focus on face image inpainting tasks, aiming at reconstructing missing or damaged regions of an incomplete face image given the context information. We specially design the U-Net architecture to tackle the problem. The proposed U-Net based method combines Hybrid Dilated Convolution (HDC) and spectral normalization to fill in missing regions of any shape with sharp structures and fine-detailed textures. We perform both qualitative and quantitative evaluation on two challenging face datasets. Experimental results demonstrate that our method outperforms previous learning-based inpainting methods. The proposed method can generate realistic and semantically plausible images.
Integrated Cloud Financial Accounting Cycle

In the business world the accounting cycle is characterized by numerous interconnected processes [1]. However, despite the invention of new technologies useful in each step of the process, their integration still appears limited and their potential is still little exploited. Here, the authors link all the available technologies to every step of the financial accounting cycle, envisaging the use of a cloud platform on which to build a solid integrated system. Although integration is far from easy (it is no coincidence that it has not happened yet) this model takes into account all the practical needs of the stakeholders involved in the process: accountants, chartered accountants, auditors, governments, public and fiscal authorities, shareholders, creditors, data analysts. This novel approach can seriously offer a breakthrough to accounting and finance big data management [2][3]. This represents an effective method to solve various challenges such as tax evasion, tax avoidance, money laundering, accounting errors, high data storage costs, high audit risks, difficult and unreliable reconciliations, data collection and data analysis, lack of comparability between financial statement of companies based in different countries [4][5].
Efficiently embedding dynamic knowledge graphs

Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems. In real world, knowledge graphs (KGs) are dynamic and evolve over time with addition or deletion of triples. However, most existing models focus on embedding static KGs while neglecting dynamics. To adapt to the changes in a KG, these models need to be retrained on the whole KG with a high time cost. In this paper, to tackle the aforementioned problem, we propose a new context-aware Dynamic Knowledge Graph Embedding (DKGE) method which supports the embedding learning in an online fashion. DKGE introduces two different representations (i.e., knowledge embedding and contextual element embedding) for each entity and each relation, in the joint modeling of entities and relations as well as their contexts, by employing two attentive graph convolutional networks, a gate strategy, and translation operations. This effectively helps limit the impacts of a KG update in certain regions, not in the entire graph, so that DKGE can rapidly acquire the updated KG embedding by a proposed online learning algorithm. Furthermore, DKGE can also learn KG embedding from scratch. Experiments on the tasks of link prediction and question answering in a dynamic environment demonstrate the effectiveness and efficiency of DKGE.
Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks

Image analysis tools for cancer, such as automatic nuclei segmentation, are impacted by the inherent variation contained in pathology image data. Convolutional neural networks (CNN), demonstrate success in generalizing to variable data, illustrating great potential as a solution to the problem of data variability. In some CNN-based segmentation works for digital pathology, authors apply color normalization (CN) to reduce color variability of data as a preprocessing step prior to prediction, while others do not. Both approaches achieve reasonable performance and yet, the reasoning for utilizing this step has not been justified. It is therefore important to evaluate the necessity and impact of CN for deep learning frameworks, and its effect on downstream processes. In this paper, we evaluate the effect of popular CN methods on CNN-based nuclei segmentation frameworks.
Self-Supervised Deep Depth Denoising

Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligible noise. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned non-uniform noise, while preserving geometric details. Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth. In this paper, we propose a fully convolutional deep autoencoder that learns to denoise depth maps, surpassing the lack of ground truth data. Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference. To enforce self-supervision, we leverage a differentiable rendering technique to exploit photometric supervision, which is further regularized using geometric and surface priors. As the proposed approach relies on raw data acquisition, a large RGB-D corpus is collected using Intel RealSense sensors. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed self-supervised denoising approach on established 3D reconstruction applications. Code is avalable at https://github.com/VCL3D/DeepDepthDenoising.
Deep learning-based image super-resolution considering quantitative and perceptual quality

Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively. In this paper, we propose a novel super-resolution method that can improve the perceptual quality of the upscaled images while preserving the conventional quantitative performance. The proposed method employs a deep network for multi-pass upscaling in company with a discriminator network and two qualitative score predictor networks. Experimental results demonstrate that the proposed method achieves a good balance of the quantitative and perceptual quality, showing more satisfactory results than existing methods.