Reviewerly/exHarmony huggingface dataset:
Object Detection with Audio Comments using YOLO v3

Vision loss is a serious factor that can affect one's course of life and quality of life, making them barred from independence, easy mobility, unnecessary tripping over objects, etc. As per World Health Organization's most recent studies, there are around 2.2 billion people on the planet who are visually impaired, and a major proportion of the population is over the age of 50 years. The primary cornerstone of this paper is about how a simple Object Detection model, done with the help of some python libraries and APIs, shall prove to be helpful for visually impaired people, and help with those subtle challenges. In this proposed methodology, to overcome the aforementioned drawbacks, a Machine Learning model whose key purpose is to detect the location of the object surrounding the person and provide voice feedback using Google Text-to-S peech API has been implemented. To train the model around various aspects of the object, the MS -COCO dataset is being used.
Incorporation of Block Chain Technology for Better Operational Management within Medical Industry

Block-chain technology involves a transparent, fraud resistant and decentralised mechanism that helps in enhancing transparency as well as efficiency of operations management in different sectors. In medical sector, adoption of this technology is increasing at a slow pace and bringing significant transformation in ways of providing services to clients or end customers. Block-chain technology is known to have a positive influence on enhancing quality of healthcare services through integration of several advanced protocols and algorithms for effective detection of quality issues in medicine or device supply-chain. Besides that, patient data-protection has always been a challenge that this industry has suffered from and incorporation of block-chain-technologies can help this sector to cope with this problem by enhancing encryption of confidential information of patients. During transmission or storage of patient information, there could be reciprocations or changes in this information that are done for satisfaction of malevolent needs of people. This often causes organisations in this sector to face legal obligations resulting from poor and insecure management of operations. GPOs sometimes face significant challenges in determining if providers of medicines or devices are complying with quality standards as per contracts. However, Block-chain technology has aided in ensuring security and safety of medicines and other medical equipment by allowing tracing at different levels of supply chain. Present study is aimed at analysing impact of incorporating block-chain-technologies in operations-management of medical industry. Use of secondary data-collection and thematic analysis has helped in finding positive relationship between implementation of block-chain technology and improvement in operations management in this industry. A few challenges has also been found for which proper recommendations have been provided to ensure best ways of implementing this technology in medical sector.
Methods, Tools and Techniques for Trustworthy Autonomous Systems (TAS) Design and Development

This workshop focuses on methods, tools, and techniques to design and develop Trustworthy Autonomous Systems (TAS). TAS is an emerging area of interactive systems that is expanding the scope and remit of engineering. At every scale, making autonomous systems trustworthy is a collective task that requires a multidisciplinary team to work together to understand trust design requirements and provide effective and creative solutions. TAS introduce unique challenges in the design and development of interactive systems because they may have the capacity to learn and evolve, they may need to make decisions or take actions independently with little or no human oversight, and they will be deployed in quite different cultural and regulatory environments. TAS engineers need robust design methods, tools, and techniques to meet diverse TAS requirements and objectives. Our prior research argued for TAS engineers to develop skills in three core areas: soft, strategic, and technical [1]. However, little has been done to flesh out the specific methods, tools, and techniques that TAS engineers should draw on. This workshop intends to invite interactive systems experts to contribute promising design methods, tools, and techniques – particularly in the area of user/actor and design requirements modelling. The workshop aims to present innovative modelling techniques, test these approaches through discussion, think about the main challenges, refine TAS required skills and steer the overarching strategy in this new field for the future.
Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain

Efficient data sharing schemes are one of the key technologies in the Internet of Vehicles (IoV). However, the insufficient willingness of vehicle users to provide data makes the traditional blockchain-based IoV network have low throughput. The income of IoV providers decreases when the vehicle density increases on the road. In this paper, we investigated a mobile vehicle data sharing scheme based on the consortium blockchain. In detail, the consortium blockchain was used to limit the degree of decentralization and openness, and the optimal revenue strategy approach between vehicles and data-demand devices was obtained through the Stackelberg game. The load test library based on Node.js was used to simulate and compare the data transmission performance of the proposed consortium blockchain with traditional blockchain schemes. Simulation results show that the proposed scheme had higher buyer’s revenue, and the block transmission performance was significantly higher than that of traditional blockchain schemes.
Style-Aware Sketch-to-Code Conversion for the Web

While sketching a graphical user interface (GUI) is a necessary step towards the creation of a Web application, its transformation into a coded GUI, with the proper styles, is still a tedious and time-consuming task that a designer should perform. Recently, a set of Machine Learning techniques has been applied to automatically generate code from sketches to ease this part of the design process. These techniques effectively convert the sketches into a skeleton structure of the GUI but are not designed to consider the styles to be applied to the generated HTML page. Moreover, having the possibility to explore different styles, starting from a sketch, might further support the designer in their work. In this paper, we move the first steps to enable this opportunity by proposing a method that allows the designer to input the sketch of the Web-based GUI and select a reference style to be applied. Our method automatically injects the reference styles into the sketch components and then uses an automatic code generation technique to obtain the final code. Preliminary experiments carried out with the navigation bar component show the effectiveness of the proposed method.
Near-data Prediction Based Speculative Optimization in a Distribution Environment

Hadoop is an open source from Apache with a distributed file system and MapReduce distributed computing framework. The current Apache 2.0 license agreement supports on-demand payment by consumers for cloud platform services, helping users leverage their respective different hardware to provides cloud services. In cloud-based environment, there is a need to balance the resource requirements of workloads, optimize load performance, and the cloud compute costs to manage. When the processing power of clustered machines varies widely, such as when hardware is aging or overloaded, Hadoop offers a speculative execution (SE) optimization strategy, by monitoring task progress in real time, in the starting identical backup tasks on different nodes when multiple tasks under a job are not running at the same speed, providing the first to go. The completed calculations maintain the overall progress of the job. At present, the SE strategy's incorrect selection of backup nodes and resource constraints may result in poor Hadoop performance, and subsequent tasks cannot be completed execution and other problems. This paper proposes an SE optimization strategy based on near data prediction, which analyzes the prediction of real-time task execution information to predict the required running time, select backup nodes based on actual requirements and approximate data to make the SE strategy achieve the best performance. Experiments prove that in a heterogeneous Hadoop environment, the optimization strategy can effectively improve the effectiveness and accuracy of various tasks and enhance the performance of cloud computing. Platform performance can benefits consumers better than before.
Text-to-image synthesis: Starting composite from the foreground content

Recently, text-to-image synthesis has become a hot issue in computer vision and has been widely concerned. Many methods have achieved encouraging results in this field at present, but it is still a great challenge to improve the quality of the synthesized image further. In this paper, we propose a multi-stage synthesis method, which starts composite from the foreground content. The whole synthesis process is divided into three stages. The first stage generates the foreground results, and the third stage synthesizes the final image results. The second stage results include two situations: one is to continue to synthesize the foreground results; the other is to synthesize the image results with background information. Experiments demonstrate that the method of continuing to generate the foreground results in the second stage can achieve better results on the Caltech-UCSD Birds (CUB) and Oxford-102 datasets, while the way of synthesizing foreground results only in the first stage can obtain better performance on the Microsoft Common Objects in Context (MS COCO) dataset. Besides, our synthesized results on the three datasets are subjectively more realistic with better detail processing. It also outperforms most existing methods in quantitative comparison results, which demonstrates the effectiveness and superiority of our method.
Entropy-Based Technique for Denoising of Acoustic Emission Signals

The acoustic emission (AE) method has been successfully used in recent years to monitor the condition of industrial and civil infrastructures. In AE, time-of-arrival (ToA) estimation is considered a key parameter for the accurate localization of a growing defect. This paper describes an entropy-based filtering approach for the ToA estimation of noisy signals and compares its performance to that of the commonly adopted Akaike Information Criterion (AIC). The proposed method consists in coarsening the input data using the Crutchfield-Packard algorithm and calculating the local (instantaneous) entropy. In the present study, we demonstrate that the local entropy of the background noise component differs from the useful (informative) signal. As a result, the approach permits filtering the noise component by selecting a proper threshold value. The proposed method has been tested on experimental data aimed at localizing a source of AE in a square $$1 \times 1$$ m aluminum plate. The entropy approach allows an overreaching precision in the final localization of the targets compared to the classical AIC.
A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

Abstract In recent years, understanding human behavior has become one of the most important topics in the field of computer vision research. The reason for this growing attention is the wide range of applications that can benefit from the results of this field of research. The human behavior analysis (HBA) includes a wide range of research areas from the detection of human motion and action. Datasets created through actions and detection of human activities make it possible to compare different detection methods with the same input data. Data mining and big data approaches are very popular for analyzing data related to human behavior and can be used to address the challenges of fast processing. This article provides a systematic survey of data mining and big data in the HBA. We focus on current datasets and models related to the detection of human behavior patterns in the literature. The purpose of this survey is to assist researchers in select appropriate datasets and models to evaluate algorithms, as well as to identify research gaps for future work. To achieve this goal, articles published between 2010 and 2021 have been reviewed. These articles fall into five general categories in terms of dataset focus: object detection, motion, action, activity, and behavior. This article provides a summary of data mining and big data models in the HBA, as well as related datasets based on these categories, to highlight promising research avenues for future work.
On the interaction between Λ-type five-level atom and one-mode squeezed coherent field

This article investigates the effect of the detuning interaction parameters of a single-mode electromagnetic field with a five-level Λ-type. The differential system is solved in the presence of detuning parameter under the initial conditions when the atom and field are prepared in an excited and squeezed state, respectively. The non-classical properties of the field initially in the squeezed coherent states based on the evolution of the second-order correlation function. The study of entanglement between parts of a quantum system is done through the von Neumann entropy. We analyze the effect of the squeeze and detuning parameters on the dynamics of some field aspects and non-local correlation. The results obtained from this study show that the detuning parameter has an essential impact on the field properties.
Organizational Culture and its Impact on the BizDev Interface

Currently, organizations are looking for evolving and adapting software engineering methodologies targeting a wider and healthier collaboration among its functional areas. In this context, the interface between business and development includes all the interactions between the IT and the business sectors within an organization. The term BizDev is used to represent this link between business strategy and software development [1], complementing how DevOps represents the link between development and operations. Understanding how the BizDev interface works in different methodologies and contexts is relevant to have an in-depth characterization and, then, improving towards the continuous flow and healthier collaboration between these areas.
A Type 2 wavelet brain emotional learning network with double recurrent loops based controller for nonlinear systems

Conventional controllers for nonlinear systems often suffer from co-existences of non-linearity and uncertainty. This paper proposes a novel brain emotional neural network to address such challenges. The proposed network integrates a Type 2 wavelet neural network into a conventional brain emotional learning network which is further enhanced by the introduction of a recurrent structure. The proposed network, therefore, combines the advantages of the Type 2 wavelet function, recurrent mechanism, and brain emotional learning system, so as to obtain optimal performance under uncertain environments. The proposed network works with a compensator to mimic an ideal controller, and the parameters of both the network and compensator are updated based on laws derived from the Lyapunov stability analysis theory. The proposed system was applied to a z-axis microelectromechanical system gyroscope. The experimental results demonstrate that the proposed system outperformed other popular neural-network-based control systems, indicating the superiority of the proposed network-based controller.
(Semi-)Automatic Computation of User Interface Consistency

Many measures exist to (semi-)automatically compute the quality of a graphical user interface, such as aesthetic metrics, visual metrics, and performance metrics. These measures are mostly individual as they apply to a single graphical user interface at a time. Unlike these measures, consistency requires evaluating a number of screens within the same application (intra-application consistency) or across applications (inter-application consistency). This paper presents a formula, a method, and supporting software for computing this consistency and its counterpart, inconsistency, either completely automatically when the interface segmentation is performed by the software or semi-automatically when the interface segmentation is performed manually by the end-user.
Fault diagnosis of industrial robot gearbox using joint interclass and intraclass mappings

Abstract Due to complex structure and harsh operating conditions, it remains challenging to accurately diagnose industrial robot gearbox fault patterns. For this reason, a joint interclass and intraclass mappings (JIIM) strategy is presented in this paper to enhance the performance of industrial robot gearbox fault diagnosis. To this end, an echo state network (ESN) is first adopted to map the training data into their class centers for minimizing the interclass distance. Meanwhile, the maximization of interclass distance in the data space is achieved by equalizing the distance between class centers. For the mapped data, another ESN is proposed to classify the fault patterns. The proposed JIIM method is evaluated by fault-diagnosis experiments for industrial robot gearboxes. Improvements owing to minimizing the intraclass distance and equalizing the interclass distance are discussed in detail. Results show that the presented JIIM can efficiently improve the performance of industrial robot gearbox fault diagnosis.
Fractional salp swarm algorithm: An association rule based privacy-preserving strategy for data sanitization

Data mining is the process of extracting hidden patterns from huge repositories. Privacy preservation at the time of data shared for mining is a demanding dilemma. Conventional techniques, such as access control and authentication have been used to handle data privacy. Various data sanitization techniques like perturbation, generalization, and sampling are utilized to preserve confidential information from disclosure. This paper develops a Fractional-Salp swarm algorithm (Fractional-SSA) for data sanitization using privacy preserved data. The Fractional-SSA is developed by integrating Fractional calculus (FC) and the Salp swarm algorithm (SSA). The proposed Fractional-SSA hides sensitive rules considering a large transactional database and recovers the original database against malicious attacks. Here, the random key generation of the sanitization process is performed using the proposed Fractional-SSA algorithm by arbitrarily initializing various keys. In addition, the sanitized database is obtained from sanitization that derives association rules considering certain factors that involve privacy success, information preservation, hiding success, database difference, privacy loss, information loss, hiding failure, and database difference. Finally, the key value gets updated to estimate the best solution. The fitness is obtained using success and failure scenarios. The proposed Fractional-SSA offered enhanced efficiency providing the highest privacy success of 0.933, information preservation of 0.487, minimal hiding failure of 0.054, and database difference of 0.008.
Artificial Intelligence Enterprise Management Using Deep Learning

In this paper, we explore the application status of deep learning (DL) in enterprise management, with China Merchants Bank as an example, and the role of DL in bank enterprise management. We analysed the application status of AI in marketing, risk control, investment, and other fields of CMB and identified five types of problems encountered in the current practical application of AI. We proposed five countermeasures: strengthening the AI organisation system's construction, enhancing the financial data guarantee mechanism, concentrating on customer-oriented, tightly managing the danger of AI technology, and building a full AI talent system. Recent data are used to assess the impact of DL in marketing, risk management, and investment consulting. According to the data, by the end of 2019, the number of clients of CMB's two APP platforms had reached 114 million and 91.2643 million, respectively. In 2019, CMB's personal savings balance climbed by roughly 53% compared to 2016, and its personal loan amount increased by approximately 61%. These findings indicate that the use of AI improves consumer happiness and trust in businesses.
Relational tree ensembles and feature rankings

As the complexity of data increases, so does the importance of powerful representations, such as relational and logical representations, as well as the need for machine learning methods that can learn predictive models in such representations. A characteristic of these representations is that they give rise to a huge number of features to be considered, thus drastically increasing the difficulty of learning in terms of computational complexity and the curse of dimensionality. Despite this, methods for ranking features in this context, i.e., estimating their importance are practically non-existent. Among the most well-known methods for feature ranking are those based on ensembles, and in particular tree ensembles. To develop methods for feature ranking in a relational context, we adopt the relational tree ensemble approach. We thus first develop methods for learning ensembles of relational trees, extending a wide spectrum of tree-based ensemble methods from the propositional to the relational context, resulting in methods for bagging and random forests of relational trees, as well as gradient boosted ensembles thereof. Complex relational features are considered in our ensembles: by using complex aggregates, we extend the standard collection of features that correspond to existential queries, such as 'Does this person have any children?', to more complex features that correspond to aggregation queries, such as 'What is the average age of this person's children?'. We also calculate feature importance scores and rankings from the different kinds of relational tree ensembles learned, with different kinds of relational features. The rankings provide insight into and explain the ensemble models, which would be otherwise difficult to understand. We compare the methods for learning single trees and different tree ensembles, using only existential qualifiers and using the whole set of relational features, against 10 state-of-the-art methods on a collection of benchmark relational datasets, deriving also the corresponding feature rankings. Overall, the bagging ensembles perform the best, with gradient boosted ensembles following closely. The use of aggregates is beneficial and in some datasets drastically improves performance: In these cases, aggregate-based features clearly stand out in the feature rankings derived from the ensembles.