Reviewerly/exHarmony huggingface dataset:
Efficient Strategies for Computing Euler Number of a 3D Binary Image

As an important topological property for a 3D binary image, the Euler number can be computed by finding specific a voxel block with 2 × 2 × 2 voxels, named the voxel pattern, in the image. In this paper, we introduce three strategies for enhancing the efficiency of a voxel-pattern-based Euler number computing algorithm used for 3D binary images. The first strategy is taking advantage of the voxel information acquired during computation to avoid accessing voxels repeatedly. This can reduce the average number of accessed voxels from 8 to 4 for processing a voxel pattern. Therefore, the efficiency of computation will be improved. The second strategy is scanning every two rows and processing two voxel patterns simultaneously in each scan. In this strategy, only three voxels need to be accessed when a voxel pattern is processed. The last strategy is determining the voxel accessing order in the processing voxel pattern and unifying the processing of the voxel patterns that have identical Euler number increments to one group in the computation. Although this strategy can theoretically reduce the average number of voxels accessed from 8 to 4.25 for processing a voxel pattern, it is more efficient than the above two strategies for moderate- and high-density 3D binary images. Experimental results demonstrated that the three algorithms with each of our proposed three strategies exhibit greater efficiency compared to the conventional Euler number computing algorithm based on finding specific voxel patterns in the image.
Video2Flink: real-time video partitioning in Apache Flink and the cloud

Abstract Video2Flink is a distributed highly scalable video processing system for bounded (i.e., stored) or unbounded (i.e., continuous) and real-time video streams with the same efficiency. It shows how complicated video processing tasks can be expressed and executed as pipelined data flows on Apache Flink, an open-source stream processing platform. Video2Flink uses Apache Kafka to facilitate the machine-to-machine (m2m) communication between the video production and the video processing system that runs on Apache Flink. Features that make the combination of Apache Kafka and Apache Flink a desirable solution to the problem of video processing are the ease of customization, portability, scalability, and fault tolerance. The application is deployed on a Flink cluster of worker machines that run on Kubernetes in the Google Cloud Platform. The experimental results support our claims of speed showing excellent speed-up results for all tested video resolutions. The highest (i.e., more than seven times) speed-up was observed with the videos of the highest resolutions and in real time.
Automated Segmentation to Make Hidden Trigger Backdoor Attacks Robust against Deep Neural Networks

The successful outcomes of deep learning (DL) algorithms in diverse fields have prompted researchers to consider backdoor attacks on DL models to defend them in practical applications. Adversarial examples could deceive a safety-critical system, which could lead to hazardous situations. To cope with this, we suggested a segmentation technique that makes hidden trigger backdoor attacks more robust. The tiny trigger patterns are conventionally established by a series of parameters encompassing their DNN size, location, color, shape, and other defining attributes. From the original triggers, alternate triggers are generated to control the backdoor patterns by a third party in addition to their original designer, which can produce a higher success rate than the original triggers. However, the significant downside of these approaches is the lack of automation in the scene segmentation phase, which results in the poor optimization of the threat model. We developed a novel technique that automatically generates alternate triggers to increase the effectiveness of triggers. Image denoising is performed for this purpose, followed by scene segmentation techniques to make the poisoned classifier more robust. The experimental results demonstrated that our proposed technique achieved 99% to 100% accuracy and helped reduce the vulnerabilities of DL models by exposing their loopholes.
Emoji, Text, and Sentiment Polarity Detection Using Natural Language Processing

Virtual users generate a gigantic volume of unbalanced sentiments over various online crowd-sourcing platforms which consist of text, emojis, or a combination of both. Its accurate analysis brings profits to various industries and their services. The state-of-art detects sentiment polarity using common sense with text only. The research work proposes an emoji-based framework for cognitive–conceptual–affective computing of sentiment polarity based on the linguistic patterns of text and emojis. The proposed emoji and text-based parser articulates sentiments with proposed linguistic features along with a combination of different emojis to generate the part of speech into n-gram patterns. In this paper, the sentiments of 650 world-famous personages consisting of 1,68,548 tweets have been downloaded from across the world. The results illustrate that the proposed natural language processing framework shows that the existence of emojis in sentiments many times seems to change the overall polarity of the sentiment. By extension, the CLDR name of the emoji is utilized to evaluate the accurate polarity of emoji patterns, and a dictionary of sentiments is adopted for evaluating the polarity of text. Eventually, the performances of three ML classifiers (SVM, DT, and Naïve Bayes) are evaluated for proposed distinctive linguistic features. The robust experiments indicate that the proposed approach outperforms the SVM classifier as compared to other ML classifiers. The proposed polarity detection generator has achieved an exceptional perspective of sentiments presented in the sentence by employing the flow of concept established, based on linguistic features, polarity inversion, coordination, and discourse patterns, surpassing the performance of extant state-of-the-art approaches.
Computer Aided Classifier of Colorectal Cancer on Histopatological Whole Slide Images Analyzing Deep Learning Architecture Parameters

The diagnosis of different pathologies and stages of cancer using whole histopathology slide images (WSI) is the gold standard for determining the degree of tissue metastasis. The use of deep learning systems in the field of medical images, especially histopathology images, is becoming increasingly important. The training and optimization of deep neural network models involve fine-tuning parameters and hyperparameters such as learning rate, batch size (BS), and boost to improve the performance of the model in task-specific applications. Tuning hyperparameters is a major challenge in designing deep neural network models, having a large impact on the performance. This paper analyzes how the parameters and hyperparameters of a deep learning architecture affect the classification of colorectal cancer (CRC) histopathology images using the well-known VGG19 model. This paper also discusses the pre-processing of these images, such as the use of color normalization and stretching transformations on the data set. Among these hyperparameters, the most important neural network hyperparameter is the learning rate (LR). In this paper, different strategies for the optimization of LR are analyzed (both static and dynamic) and a new experiment based on the variation of LR is proposed (the relevance of dynamic strategies over fixed LR is highlighted), after each layer of the neural network together with decreasing variations according to the epochs. The results obtained are very remarkable, obtaining in the simulation an accurate system that achieves 96.4% accuracy on test images (for nine different tissue classes) using the triangular-cyclic learning rate.
Double Color Image Visual Encryption Based on Digital Chaos and Compressed Sensing

Image encryption is an effective way to protect images in secure transmission or storage. In this paper, we propose a novel double color image visual encryption algorithm based on the improved Chebyshev map (ICM) and compressed sensing. Firstly, a new nonlinear term is introduced into the classical one-dimensional Chebyshev map, and then the ICM is used to generate the secret code stream for the encryption algorithm. Next, the key-controlled sensing measurement matrices are constructed through the ICM, and they are used to compress the integer wavelet coefficients of two plain images. Subsequently, the compressed images are dislocated by dislocation matrices and diffused by an ICM-generated diffusion matrix, respectively. Finally, the encrypted images are embedded into the carrier image using the least significant bit embedding algorithm. Experimental results demonstrate that the proposed method has good visual safety, large key space, and high key sensitivity.
AI based Two Way Sign Language System

Most hearing persons do not understand sign language. But hearing-impaired people communicate through the sign language only. Hence, there is a challenge in the communication between hearing and the hearing-impaired people. The proposed system is to build a bridge between the hearing and hearing-impaired communities and to start two-way communication. The proposed system suggests a two-way sign language translator that translates speech in real time into sign language and vice versa. Using Python’s OpenCV Library, the proposed system processes the video frame by frame. Additionally, each frame’s background is removed using the Background/Foreground Segmentation Algorithm based on Gaussian Mixtures. The outlines of the processed image are classified into the comparable written language terms using a convolutional neural network (CNN). In order to accurately preserve the sign language’s grammar during conversion from sign language to voice, we use gTTS (Google Text-to-Speech) and fundamental NLP. Enrolment and reading rates among hearing-impaired youngsters are significantly lower than the general population average. This technology will therefore assist regular schools in better integrating the hearing challenged community, making education more accessible and affordable for them.
Prediction of Insufficient Accuracy for Human Activity Recognition with Limited Range of Age using K-Nearest Neighbor

The research study is to predict that humans are using electronic gadgets recognition to communicate with the other person. The framework to classify the Innovative K-Nearest Neighbour and Naive Bayes to perform all measures. This research study used Novel K-Nearest Neighbour and Naive Bayes to perform the operations to give the best exactness of human activity recognition in various age factor. Recent study results a 95% credibility range for the mean and standard deviation, a significance level of 0.05 percent, and 47 samples were obtained from a variety of internet sources for this study. Since the Innovative K-Nearest Neighbor algorithm and the local invariant approaches have achieved 93.05% accuracy in predicting the activity analysis, this research wants to discover greater accuracy for activity prediction using the Naive Bayes algorithm in machine learning. When applied to the analysis of human activity, the Innovative K-Nearest Neighbor algorithm achieved 90.12% accuracy, with a p-value of 0.045 (p0.05) and a 95% confidence range. According to the results of this investigation, the K-Nearest Neighbor technique executes better than the Naive Bayes approach after analyzing the effect of age on human performance.
Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations

In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user and item embeddings in existing knowledge graph-based recommendation methods, a knowledge-aware enhanced network, combining neighborhood information recommendation (KCNR), is proposed. Specifically, KCNR first encodes prior information about the user–item interaction, and obtains the user’s different knowledge neighbors by propagating them in the knowledge graph, and uses a knowledge-aware attention network to distinguish and aggregate the contributions of the different neighbors in the knowledge graph, as a way to enrich the user’s description. Similarly, KCNR samples multiple-hop neighbors of item entities in the knowledge graph, and has a bias to aggregate the neighborhood information, to enhance the item embedding representation. With the above processing, KCNR can automatically discover structural and associative semantic information in the knowledge graph, and capture users’ latent distant personalized preferences, by propagating them across the knowledge graph. In addition, considering the relevance of items to entities in the knowledge graph, KCNR has designed an information complementarity module, which automatically shares potential interaction characteristics of items and entities, and enables items and entities to complement the available information. We have verified that KCNR has excellent recommendation performance through extensive experiments in three real-life scenes: movies, books, and music.
Cost Efficiency Evaluation of an On-Chain, Decentralized Ride-Sharing Platform

Traditional taxi providers are slowly losing their clients to online taxi providers, which are becoming increasingly popular. These companies typically provide a commercial, centralized platform in order to provide these services. The concept of centralization gives those providers significant power over their users, such as demanding and changing fees and collecting data over provided services. We aim to discuss how a decentralized approach using blockchains can solve those problems. A ride-sharing platform with the most essential functionality is implemented using a decentralized application based on smart contracts, which are, in turn, deployed on different blockchains. Various effects with respect to implementing and providing a decentralized ride-sharing service are taken in to account, especially regarding the effective cost for users. The fees of the developed platform heavily depend on which blockchain it is deployed on. Blockchains may compete in term of cost with traditional ride-sharing platforms, but choosing a suitable blockchain for the application is essential. Furthermore, the prototype shows that the time it takes for a transaction to be manifested into a block and subsequently confirmed is multiple times longer than a traditional web service response. These new problems require a new approach to implementing those requests that do not negatively affect the user experience.
Restoration of Spatially Variant Blurred Images with Wide-Field Telescope Based on Deep Learning

The wide-field telescope is a research hotspot in the field of aerospace. Increasing the field of view of the telescope can expand the observation range and enhance the observation ability. However, a wide field will cause some spatially variant optical aberrations, which makes it difficult to obtain stellar information accurately from astronomical images. Therefore, we propose a network for restoring wide-field astronomical images by correcting optical aberrations, called ASANet. Based on the encoder-decoder structure, ASANet improves the original feature extraction module, adds skip connection, and adds a self-attention module. With these methods, we enhanced the capability to focus on the image globally and retain the shallow features in the original image to the maximum extent. At the same time, we created a new dataset of astronomical aberration images as the input of ASANet. Finally, we carried out some experiments to prove that the structure of ASANet is meaningful from two aspects of the image restoration effect and quality evaluation index. According to the experimental results, compared with other deblur networks, the PSNR and SSIM of ASANet are improved by about 0.5 and 0.02 db, respectively.
A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain; with proper path planning, it can also minimize the potential damage by setting constraints and optimizing the insertion path. Recently, reinforcement learning (RL)-based path planning algorithm has shown promising results in neurosurgery, but because of the trial and error mechanism, it can be computationally expensive and insecure with low training efficiency. In this paper, we propose a heuristically accelerated deep Q network (DQN) algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment. Furthermore, a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm. Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms. Tests showed promising results of our algorithm in saving over 50 training episodes, calculating path lengths of 0.35 after normalization, which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm, respectively. Moreover, the maximum curvature during planning is reduced to 0.046 from 0.139 mm-1 using the proposed algorithm compared to DQN.
Adoption of smart contracts in the construction industry: an institutional analysis of drivers and barriers

Purpose The construction industry has arrived at a crossroads of rapid technological progress. While it is foreseen that the advent of new construction technologies will disrupt the construction industry’s future, such disruptions often create the ideal environment for innovation. As poor payment practices continue to plague the construction industry, the advent of smart contracts has created an opportunity to rectify the inherent flaws in the mitigation of payment problems in traditional construction contracts. Given the intrinsic resistance of construction firms to such revolutionary changes, this study aims to understand the various factors influencing the adoption of smart contracts in the Singapore construction industry. Design/methodology/approach A mixed method was adopted involving quantifying respondents’ perceptions of the factors influencing smart contract adoption, and validation from a group of interviewees on the matter. Out of 461 registered quantity surveyor members contacted via the Singapore institute of surveyors and valuers website, 55 respondents took part in the survey. This is followed by semi-structured interviews to validate the survey results. Findings The findings indicate that construction firms have neither a significant knowledge of nor willingness to adopt smart contracts. A total of 29 institutional factors were also identified that significantly influence the adoption of smart contracts. The quantitative findings were further reinforced by qualitative interviews with five industry experts. Originality/value With recognition of and the successful formulation of the significant institutional drivers and barriers, the key findings of this study will be integral in driving the commercial adoption of smart contracts within the construction industry.
Artificial intelligence service agents: a silver lining in rural India

Purpose The study aims to examine the impact of an artificial intelligent service agent (AISA) on customer services to the rural population provided by KAYA, Kotak Life's AI-enabled insurance chatbot avatar that offers quality insurance services. Design/methodology/approach Multi-stage cluster sampling method was adopted to collect the responses from the 707 customers across the rural population of southern states of India. SPSS V.2 and Smart PLS 4 were used to apply simple percentage analysis, multiple linear regression analysis, and structural equation modeling (SEM) to validate the hypothesis. The dependent variables are economic performance and market performance based on the independent variables: efficiency, security, availability, enjoyment and contact. Findings The study revealed that efficiency and security are the highest predictors and the most influencing variables in predicting the economic and market performance of the insurance companies in determining the quality of service when rendered through AISA among the customers. Efficiency, security, availability, contact and enjoyment are the critical dimensions of AISA. It has a more significant impact on quality service (claim processing) to the rural population. It improves the economic and market performance among the insurance companies and the rural population. Originality/value Customers need convenience when making claims. Even little challenges might lead to stress and unhappiness, depending on the situation. Restrictions on where customers can file claims may not be the most outstanding service insurance firms can offer, given rising travel and commuting costs and widening geographical borders. Customers value proactive communication from service providers about the status of their insurance claims.
Depth‐aware lightweight network for RGB‐D salient object detection

Abstract RGB‐D salient object detection (SOD) is to detect salient objects from one RGB image and its depth data. Although related networks have achieved appreciable performance, they are not ideal for mobile devices since they are cumbersome and time‐consuming. The existing lightweight networks for RGB‐D SOD use depth information as additional input, and integrate depth information with colour image, which achieve impressive performance. However, the quality of depth information is uneven and the acquisition cost is high. To solve this issue, depth‐aware strategy is first combined to propose a lightweight SOD model, Depth‐Aware Lightweight network (DAL), using only RGB maps as input, which is applied to mobile devices. The DAL's framework is composed of multi‐level feature extraction branch, specially designed channel fusion module (CF) to perceive the depth information, and multi‐modal fusion module (MMF) to fuse the information of multi‐modal feature maps. The proposed DAL is evaluated on five datasets and it is compared with 14 models. Experimental results demonstrate that the proposed DAL outperforms the state‐of‐the‐art lightweight networks. The proposed DAL has only 5.6 M parameters and inference speed of 39 ms. Compared with the best‐performing lightweight method, the proposed DAL has fewer parameters, faster inference speed, and higher accuracy.
DCIFPN: Deformable cross‐scale interaction feature pyramid network for object detection

Abstract Exploiting multi‐scale features is one of the most effective methods to recognize objects of different scales in object detection. Since image pyramid is time‐consuming, Feature Pyramid Network (FPN) becomes the most popular component used for obtaining pyramidal features. Despite its effectiveness, there still exist some intrinsic defects. In this work, it is attributed to insufficient information flow and a Deformable Cross‐scale Interaction Feature Pyramid Network (DCIFPN) is proposed, which aims to promote the information transfer process with content‐aware sampling and dynamic aggregation weights. More specifically, Deformable Semantic Enhancement Module (DSEM) is designed that can construct accurate information flow with dynamic aggregation weights. In addition, Deformable Spatial Refinement Module (DSRM) is proposed to enhance high‐level features with low‐level location details. When DCIFPN is deployed on RetinaNet and FCOS with ResNet‐50, the performance is improved by 1.6 AP and 1.1 AP, respectively, on the challenging MS COCO benchmark. Apart from one‐stage detectors, DCIFPN is also applicable to two‐stage methods such as Faster R‐CNN and Mask R‐CNN. Further experiments on Pascal VOC and CrowdHuman datasets can verify the effectiveness and generalization of the method.