Reviewerly/exHarmony huggingface dataset:
Augmented reality for construction revolution – analysis of critical success factors

As technology is becoming advanced, the way of visualizing simulations and information has got better which makes the real and virtual world look alike. This study evaluates the critical success factors that affect the implementation of augmented reality in the construction industry. A well-structured questionnaire was sent to survey construction professionals engaged in the built environment in the study area. The professionals engaged are project managers, architects, quantity surveyors, engineers, and builders. Convenience sampling techniques was adopted in choosing those professionals. Percentile, Frequency, Standard Deviation (SD), Mean item score (MIS) and One Sample t-test were adopted in analyzing the collected data. The result revealed there is full awareness of ICT for respondents while approximately 50% have used mobile AR computing. The five most critical success factors for Augmented Realities are the need for innovativeness in executing construction projects, advancement in computer design interface and hardware device, improvement of accuracy in AR technology, development in mobile computing, and AR collaboration with ICT software. It was revealed that there is significant importance attached by professionals to all the critical success factor (CSF) for augmented realities. The study concluded for augmented reality to gain ground in the construction industry it is important stakeholders are ready to accept innovation in the use of new trends of technology. It is therefore recommended there should be government policies that support, enforces, and implement the use of new trends of technology in construction projects for efficiency and productivity.
Minimum dose path planning based on three-degree vertex algorithm and FLUKA modeling: Radiation source discrimination and shielding considerations

Minimum dose path planning in radioactive environments has received significant attention because workers need to be protected from undue radiation risks. The conventional approaches use the innovative shortest path algorithms to search for the optimal path. However, the methods are limited in complex geometries, large navigation space, and when shielding is considered. Also, the traditional approaches lack source discrimination capability (multiple sources terms, type of radiation, and size of source). This study presents a novel application of the Three-Degree of Vertex (TDV) shortest path algorithm with FLUKA Monte Carlo modeling for optimal protection of workers in radioactive environments. TDV algorithm solves the shortest path problem with bidirectional routing in complex environments. The implementation considers multiple radiation sources with diverse geometry and characteristics such as different radiation types. The current work also presents the algorithm performance with shielding consideration and complex geometry. The merits of the TDV are also evaluated in real scenarios using accuracy, reliability, and speed as evaluation metrics. In addition, the performance of the TDV is also compared with the Dijkstra algorithm for all scenarios considered.
Open Set Domain Adaptation via Joint Alignment and Category Separation

Prevalent domain adaptation approaches are suitable for a close-set scenario where the source domain and the target domain are assumed to share the same data categories. However, this assumption is often violated in real-world conditions where the target domain usually contains samples of categories that are not presented in the source domain. This setting is termed as open set domain adaptation (OSDA). Most existing domain adaptation approaches do not work well in this situation. In this article, we propose an effective method, named joint alignment and category separation (JACS), for OSDA. Specifically, JACS learns a latent shared space, where the marginal and conditional divergence of feature distributions for commonly known classes across domains is alleviated (Joint Alignment), the distribution discrepancy between the known classes and the unknown class is enlarged, and the distance between different known classes is also maximized (Category Separation). These two aspects are unified into an objective to reinforce the optimization of each part simultaneously. The classifier is achieved based on the learned new feature representations by minimizing the structural risk in the reproducing kernel Hilbert space. Extensive experiment results verify that our method outperforms other state-of-the-art approaches on several benchmark datasets.
Pipeline of turbine blade defect detection based on local geometric pattern analysis

Blade defects such as scratch and deformation always cause performance degradation or even failure of gas turbine, and furtherly threat energy safety. Therefore, defect detection of individual blades is important in gas turbine maintenance. Currently, defect detection is mostly manually implemented, so it is necessary to design an automatic method to reduce labor costs. However, most available detection methods require CAD models or prior shapes of blades, which might not always be acquirable for gas turbine users. Besides, those methods are not suitable for practical use because of their high demands for detection equipment. In this paper, a simple but efficient 3D vision-based defect detection process using point clouds is proposed to detect scratches and deformation accurately. First, sliding sampling window is used to reduce the computation burden in each detection process. Second, a pipeline of detection algorithms based on underline surface analysis is proposed to extract local geometry features and predict potential defects. Last, a filtering algorithm is introduced to reduce false detections. The whole process is carried on with real defective blades, and its effects are both intuitively and quantitatively evaluated. The results show that a multi-step detection process based on FPFH is able to detect both scratch and deformation accurately, which will be suitable for practical application.
Towards Improving Container Security by Preventing Runtime Escapes

Container escapes enable the adversary to execute code on the host from inside an isolated container. These high severity escape vulnerabilities originate from three sources: (1) container profile misconfigurations, (2) Linux kernel bugs, and (3) container runtime vulnerabilities. While the first two cases have been studied in the literature, no works have investigated the impact of container runtime vulnerabilities. In this paper, to fill this gap, we study 59 CVEs for 11 different container runtimes. As a result of our study, we found that five of the 11 runtimes had nine publicly available PoC container escape exploits covering 13 CVEs. Our further analysis revealed all nine exploits are the result of a host component leaked into the container. We apply a user namespace container defense to prevent the adversary from leveraging leaked host components and demonstrate that the defense stops seven of the nine container escape exploits.
Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams

The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of "web of data". In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches.
Sub-AVG: Overestimation reduction for cooperative multi-agent reinforcement learning

Decomposing the centralized joint action value(JAV) into per-agent individual action value(IAV) is attractive in cooperative multi-agent reinforcement learning(MARL). In such tasks, IAVs based on local observation can perform decentralized policies, and the JAV is used for end-to-end training through traditional reinforcement learning methods, especially through the Q-learning algorithm. However, the Q-learning-based method suffers from overestimation, in which the overestimated action values may result in a suboptimal policy. In this paper, we show that such overestimation can occur in the above Q-learning-based decomposition method. Our solution is Sub-AVG, which utilizes a lower update target by discarding the larger of previously learned IAVs and averaging the retained ones, thus eliminating the excessive overestimation errors. Experiments in the StarCraft Multi-Agent Challenge(SMAC) environment show that Sub-AVG can lead to lower JAV estimations and better-performing policies.
Strategies for building robust prediction models using data unavailable at prediction time

Abstract Objective Hospital-acquired infections (HAIs) are associated with significant morbidity, mortality, and prolonged hospital length of stay. Risk prediction models based on pre- and intraoperative data have been proposed to assess the risk of HAIs at the end of the surgery, but the performance of these models lag behind HAI detection models based on postoperative data. Postoperative data are more predictive than pre- or interoperative data since it is closer to the outcomes in time, but it is unavailable when the risk models are applied (end of surgery). The objective is to study whether such data, which is temporally unavailable at prediction time (TUP) (and thus cannot directly enter the model), can be used to improve the performance of the risk model. Materials and Methods An extensive array of 12 methods based on logistic/linear regression and deep learning were used to incorporate the TUP data using a variety of intermediate representations of the data. Due to the hierarchical structure of different HAI outcomes, a comparison of single and multi-task learning frameworks is also presented. Results and Discussion The use of TUP data was always advantageous as baseline methods, which cannot utilize TUP data, never achieved the top performance. The relative performances of the different models vary across the different outcomes. Regarding the intermediate representation, we found that its complexity was key and that incorporating label information was helpful. Conclusions Using TUP data significantly helped predictive performance irrespective of the model complexity.
How Software Refactoring Impacts Execution Time

Refactoring aims at improving the maintainability of source code without modifying its external behavior. Previous works proposed approaches to recommend refactoring solutions to software developers. The generation of the recommended solutions is guided by metrics acting as proxy for maintainability (e.g., number of code smells removed by the recommended solution). These approaches ignore the impact of the recommended refactorings on other non-functional requirements, such as performance, energy consumption, and so forth. Little is known about the impact of refactoring operations on non-functional requirements other than maintainability. We aim to fill this gap by presenting the largest study to date to investigate the impact of refactoring on software performance, in terms of execution time. We mined the change history of 20 systems that defined performance benchmarks in their repositories, with the goal of identifying commits in which developers implemented refactoring operations impacting code components that are exercised by the performance benchmarks. Through a quantitative and qualitative analysis, we show that refactoring operations can significantly impact the execution time. Indeed, none of the investigated refactoring types can be considered “safe” in ensuring no performance regression. Refactoring types aimed at decomposing complex code entities (e.g., Extract Class/Interface, Extract Method) have higher chances of triggering performance degradation, suggesting their careful consideration when refactoring performance-critical code.
Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.
Analyzing the use of internet of things (IoT) in artificial intelligence and its impact on business environment

IoT is a network of devices that emerge with connectivity, software, and exchange data. Performing on the internet is constantly converting from the “Internet of computers (IoC)” to the “Internet of things (IoT)”. Especially in the business world, the adoption of IoT is becoming highly required in order to protect data and resources. In the case of coming and collecting multiple essential data makes the device difficult to store them, so in that scenario, IoT Device is considered as effective. As IoT is capable of sharing vital data through a wireless network, the data remains safe and protected. All the processes of business and its development underlying the procedure of data exchange along with gathering. In this scenario, IoT has the ability to handle the data and record the pattern of the business process. In addition, customer interaction with the devices also can be managed by the IoT network. The business process can be effectively preceded with the help of IoT technology. In the business process, better information with high quality, productivity can be balanced in order to draw the attention of the consumers with this advanced technology system. IoT devices have many benefits, which can improve the business value, and it is described in the discussion part of the research work. In this study, primary along with secondary data analysis have been conducted based on a thematic approach and in that regard, four themes have been prepared and discussed in accordance with different research objectives. The study concluded about the business development by using IoT devices and highlighted the future scope of the research. Here, in this research, the descriptive method was applied to analyze the primary and secondary data. Thematic analysis of the data has become beneficial in getting the best understanding of the topic. Moreover, this study will be helpful for business leaders to understand both the positive and negative approaches of this network system.
Role of machine learning in improving tourism and education sector

Tourism is viewed as a major economy booster. Although tourism has a beneficial influence on the economy in terms of national income generation, job creation, tax revenue generation and foreign exchange generation, it is a multi-segment sector. As a primary goal of higher education institutions, improving student performance is a top priority. Before a performance improvement program can be designed, it is necessary to map the students' current situation. Traditional computer techniques in the IT industry differ from machine learning. To describe or solve a problem, computers utilize a set of well-written instructions. Data inputs for factual research can be prepared using master learning approaches by computers. We'll look at how machine learning can be used in the tourist and education sectors.
Topic Modeling for Customer Service Chats

Chatbot, as a virtual assistant employed by companies, can provide benefits for its users. Users can communicate directly to the chatbot via a short message, after which the chatbot system spontaneously identifies the intent of the message and responds with relevant actions. Unfortunately, the scope of the chatbot knowledge is limited in handling messages by an increasingly varied group of users. The main consequence of such variation is a change in the composition of the intent labels. This paper focuses on topic modeling in dealing with two tasks: first, to find new intents from user messages that are not yet included in any previous intents; and second, to reorganize existing intents by analyzing the topic model generated. In the analysis, two possible changes in intent compositions are intent merging and splitting. The topic modeling approaches used in this research are LDA as a baseline, as well as state-of-the-art Top2Vec and BERTopic. The labeled datasets for evaluation are taken from one of the major e-commerce companies in Indonesia and four public datasets. We evaluate the topic models by using the metrics of topic coherence, diversity, and quality. Our results show that the BERTopic and Top2Vec topic models produced better evaluation scores than the LDA topic model. Additionally, we perform proportion threshold analysis for reorganizing the intents of the datasets.
Adaptive region-aware feature enhancement for object detection

Increasing object detectors reveal the importance of feature representation in improving detection performance. Currently, feature enhancement mainly focuses on Feature Pyramid Network (FPN) as well as Region-of-Interest (RoI) feature fusion in two-stage object detectors. Based on this, we propose Adaptive Region-aware Feature Enhancement method including Adaptive Region-aware FPN (AR-FPN) and Adaptive Region-aware RoI Feature Fusion (AR-RFF) modules. Specifically, AR-FPN aims to capture position-sensitive map for each level to enhance the pixel-wise interest degree and make the differences among levels more distinctive. AR-RFF focuses on obtaining distinguishable RoI features by introducing adaptive region information and eliminating scale inconsistency between the refined and original features. Extensive experiments show that our method acquires 1.7% AP higher at least and strong generalization capability compared to others.
An Impact of Radon Transforms and Filtering Techniques for Text Localization in Natural Scene Text Images

Detection and localization of text in natural scene images have become a greater in need of image-based indexing, searching of images, and in many application areas. Scene text content conveys vital information. The effectiveness of the text localization depends on the efficiency in locating the text regions. In this paper, a detailed exploration of various filter techniques such as radon transform, conservative filter, Gabor filter, bilateral filter, and context-aware filters with maximally stable extremal regions as have been carried out. Maximally stable extremal regions (MSER) give many false positive rates to localize the text regions in the images, hence we have conducted experiments with radon transform and rule-based approach to remove the false positive rate and improve the precision, recall, and F-measure. Each filter has its own application-specific uses. The efficiency of the filtering techniques is examined on the basis of the results obtained for the text localization of scene images. Radon transform has been shown to achieve the best performance among the selected filtering techniques.
Systematic realization of a fully connected deep and convolutional neural network architecture on a field programmable gate array

A detailed methodology for implementing a fully connected (FC) deep neural network (DNN) and convolutional neural network (CNN) inference system on a field programming gate array (FPGA) is presented. Minimal computational units are used for the DNN. For the CNN, systolic array (SA) architecture endowed with parallel processing potential is utilized. Algorithmic analysis determines the optimum memory requirement for the fixed point trained parameters. The size of the trained parameters and the available memory on the target FPGA device govern the choice of on-chip memory to utilize. Experimental results indicate that the choice of block over distributed memory saves ≈62% look-up-tables (LUTs) for the DNN ([784-512-512-10]), and the choice of distributed over block memory saves ≈30% block random access memory (BRAM) for the LeNet-5 CNN unit. This study provides insights for developing FPGA-based digital systems for applications requiring DNN and CNN.
Deep Stereo Matching With Hysteresis Attention and Supervised Cost Volume Construction

Stereo matching disparity prediction for rectified image pairs is of great importance to many vision tasks such as depth sensing and autonomous driving. Previous work on the end-to-end unary trained networks follows the pipeline of feature extraction, cost volume construction, matching cost aggregation, and disparity regression. In this paper, we propose a deep neural network architecture for stereo matching aiming at improving the first and second stages of the matching pipeline. Specifically, we show a network design inspired by hysteresis comparator in the circuit as our attention mechanism. Our attention module is multiple-block and generates an attentive feature directly from the input. The cost volume is constructed in a supervised way. We try to use data-driven to find a good balance between informativeness and compactness of extracted feature maps. The proposed approach is evaluated on several benchmark datasets. Experimental results demonstrate that our method outperforms previous methods on SceneFlow, KITTI 2012, and KITTI 2015 datasets.