Reviewerly/exHarmony huggingface dataset:
The ICT Antecedents and Sole Proprietary Practicing Audit Firms: A Quantitative Study

The main objective of this study is to investigate the mediating effect of Information Communication Technology (ICT) training on the linkage between ICT confidence, ICT challenges as antecedents of Sole Proprietary Practicing Audit Firms (SPPAFs) where the firms having lower than 100 clients.The study adopts a quantitative approach where a set of questionnaires were designed using a Likert scale to collect 165 usable data from auditors of SPPAFs in India.The data was analyzed using Smart PLS and SPSS statistical tools.The findings confirmed that ICT training mediates the linkages between ICT antecedents (ICT confidence and ICT challenges) and SPPAF.The conceptual model of the study can be useful to understand ICT antecedents of SPPAFs to increase the effectiveness of auditing.Hence, practicing auditors could realize ICT confidence and ICT challenges to improve firm effectiveness.
Medical Imaging using Deep Learning Models

Deep learning has played a potential role in quality healthcare with fast automated and proper medical image analysis. In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's body. However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image analysis. It is necessary to state here that the deep learning approach is gaining attention in the medical imaging field in evaluating the presence or absence of disease in a patient. Mammography images, digital histopathology images, computerized tomography, etc. are some of the areas on which DL implementation focuses. One upon going through the paper will get to know the recent development that has occurred in this field and come up with a critical review on this aspect. The paper has demonstrated in detail modern deep learning models that are implemented in medical image analysis. There is no doubt about the promising future of the deep learning models and according to experts; the implementation of deep learning techniques has outperformed medical experts in numerous tasks. However, deep learning also has some drawbacks and challenges that are required to be addressed like limited datasets and many more. To mitigate such kinds of challenges, researchers are working on this aspect so that they can enhance healthcare by deploying AI.
A Secured Smart Healthcare Monitoring Systems Using Blockchain Technology

The healthcare system that depends on the Internet of Things (IoT) assists individuals and aids their vital everyday life activities. The affordability and user-friendliness of the usage of IoT start revolutionizing healthcare services. The IoT and its related technologies have emerged as the most preferred use cases in the healthcare system. Hence, the security and privacy issues associated with smart healthcare systems and their participated entities make its general acceptance still a dream. Also, as a remote patient monitoring system using IoT-based devices are the increase in privacy, popularity, accessibility, security concerns of logging and the transfer of medical data continue arising. Therefore, this chapter proposes IoT-based blockchain-protected medical data utilizing a smart contract for secure analysis and management of wearable sensors based on the Ethereum protocol. The integration of blockchain will counter the security and privacy issues that may arise using IoT-based wearable devices in the healthcare monitoring system, thus facilitating safe and secure storage of patients’ information in the utilization of IoT devices in the healthcare system. IoT-based healthcare can be employed to enhance the well-being of patients, make the healthcare system more effective, and help respond quickly to emergencies.
Autoencoding low-resolution MRI for semantically smooth interpolation of anisotropic MRI

High-resolution medical images are beneficial for analysis but their acquisition may not always be feasible. Alternatively, high-resolution images can be created from low-resolution acquisitions using conventional upsampling methods, but such methods cannot exploit high-level contextual information contained in the images. Recently, better performing deep-learning based super-resolution methods have been introduced. However, these methods are limited by their supervised character, i.e. they require high-resolution examples for training. Instead, we propose an unsupervised deep learning semantic interpolation approach that synthesizes new intermediate slices from encoded low-resolution examples. To achieve semantically smooth interpolation in through-plane direction, the method exploits the latent space generated by autoencoders. To generate new intermediate slices, latent space encodings of two spatially adjacent slices are combined using their convex combination. Subsequently, the combined encoding is decoded to an intermediate slice. To constrain the model, a notion of semantic similarity is defined for a given dataset. For this, a new loss is introduced that exploits the spatial relationship between slices of the same volume. During training, an existing in-between slice is generated using a convex combination of its neighboring slice encodings. The method was trained and evaluated using publicly available cardiac cine, neonatal brain and adult brain MRI scans. In all evaluations, the new method produces significantly better results in terms of Structural Similarity Index Measure and Peak Signal-to-Noise Ratio (p<0.001 using one-sided Wilcoxon signed-rank test) than a cubic B-spline interpolation approach. Given the unsupervised nature of the method, high-resolution training data is not required and hence, the method can be readily applied in clinical settings.
Lcapy: symbolic linear circuit analysis with Python

Lcapy is an open-source Python package for solving linear circuits symbolically. It uses a superposition of DC analysis, AC (phasor) analysis, transient (Laplace) analysis, and noise analysis. Expressions are evaluated using the computer algebra system SymPy. Lcapy can model circuits comprised of combinations of one-port and two-port networks or circuits specified using a netlist with a Spice-like notation. Lcapy can present the system of equations produced from nodal analysis, modified nodal analysis, loop analysis, and state-space analysis. Expressions can be formatted into many representations, parameterized, and transformed to other domains. Dimensional analysis is performed to reduce user errors and to present results with units. Both continuous and discrete signals are supported. Lcapy produces high-quality output. Textbook quality schematics in a number of different formats can be generated from netlists and customized for different conventions. Expressions can be formatted into LaTeX format for inclusion into a document or numerically evaluated and plotted. An overview of the features and capabilities of Lcapy is presented, along with implementation details and performance considerations.
Robotic Sponge and Watercolor Painting Based on Image-Processing and Contour-Filling Algorithms

In this paper, the implementation of a robotic painting system using a sponge and the watercolor painting technique is presented. A collection of tools for calibration and sponge support operations was designed and built. A contour-filling algorithm was developed, which defines the sponge positions and orientations in order to color the contour of a generic image. Finally, the proposed robotic system was employed to realize a painting combining etching and watercolor techniques. To the best of our knowledge, this is the first example of robotic painting that uses the watercolor technique and a sponge as the painting media.
Winograd Schemas in Portuguese

The Winograd Schema Challenge has become a common benchmark for question answering and natural language processing. The original set of Winograd Schemas was created in English; in order to stimulate the development of Natural Language Processing in Portuguese, we have developed a set of Winograd Schemas in Portuguese. We have also adapted solutions proposed for the English-based version of the challenge so as to have an initial baseline for its Portuguese-based version; to do so, we created a language model for Portuguese based on a set of Wikipedia documents.
Indonesian language teaching in elementary school

In learning activities, there is often a learning saturation that arises from students. This can be triggered by various things. Therefore, as a researcher, he took the initiative to apply modeling in learning, especially in language learning. Thus, the purpose of this research is the application of an explicit type of cooperative learning model instructions chronological technique of events in writing narratives from interview texts. This research is applied or piloted to students consisting of several schools in Indonesia as well as the object of research. The research instrument is the researcher himself as the key/main instrument. In addition, the researcher was also assisted by several other teams of research students and teachers from local schools. Based on the results of research through testing the model, it is categorized that there is a significant increase in learning. It can be seen that the average score of students reaches 85% of the maximum threshold of 90%. Thus, it can be concluded that the application of the model is considered successful and can be applied in various schools in the world.
Digital approaches to translation history

Digital translation history is defined here as a methodological approach that uses digital technologies to produce, enhance or disseminate research on translation history. This can help translation historians pose fresh questions and answer new and old ones. It entails mastering technical competencies in varying degrees while remaining grounded in the fundamentals of the historian’s craft. This paper outlines the main affordances of digital approaches as applied to the study of translation history (how these can help translation historians do things better and/or differently in some respects), as well as the limitations. It introduces relevant techniques of text analysis (such as distant reading, topic modelling and stylometrics) and data visualization, which can help tease out patterns and relationships (e.g. textual, conceptual, geographic and personal networks) in dynamic ways that potentially create new knowledge and facilitate public engagement with scholarship.
PICA: Point-wise Instance and Centroid Alignment Based Few-shot Domain Adaptive Object Detection with Loose Annotations

In this work, we focus on supervised domain adaptation for object detection in few-shot loose annotation setting, where the source images are sufficient and fully labeled but the target images are few-shot and loosely annotated. As annotated objects exist in the target domain, instance level alignment can be utilized to improve the performance. Traditional methods conduct the instance level alignment by semantically aligning the distributions of paired object features with domain adversarial training. Although it is demonstrated that point-wise surrogates of distribution alignment provide a more effective solution in few-shot classification tasks across domains, this point-wise alignment approach has not yet been extended to object detection. In this work, we propose a method that extends the point-wise alignment from classification to object detection. Moreover, in the few-shot loose annotation setting, the background ROIs of target domain suffer from severe label noise problem, which may make the point-wise alignment fail. To this end, we exploit moving average centroids to mitigate the label noise problem of background ROIs. Meanwhile, we exploit point-wise alignment over instances and centroids to tackle the problem of scarcity of labeled target instances. Hence this method is not only robust against label noises of background ROIs but also robust against the scarcity of labeled target objects. Experimental results show that the proposed instance level alignment method brings significant improvement compared with the baseline and is superior to state-of-the-art methods.
Applying Deep Learning Models to Analyze Users’ Aspects, Sentiment, and Semantic Features for Product Recommendation

As there is a huge amount of information on the Internet, people have difficulty in sorting through it to find the required information; thus, the information overload problem becomes a significant issue for users and online businesses. To resolve this problem, many researchers and applications have proposed recommender systems, which apply user-based collaborative filtering, meaning it only considers the users’ rating history to analyze their preferences. However, users’ text data may contain users’ preferences or sentiment information, and such information can be used to analyze users’ preferences more precisely. This work proposes a method called the aspect-based deep learning rating prediction method (ADLRP), which can extract the aspects, sentiment, and semantic features from users’ and items’ reviews. Then, the deep learning method is used to generate users’ and items’ latent factors. According to these three features, the matrix factorization method is applied to make rating predictions for items. The experimental results show that the proposed method performs better than the traditional rating prediction methods and conventional artificial neural networks. The proposed method can precisely and efficiently extract the sentiments and semantics of each aspect from review texts and enhance the prediction performance of rating predictions.
Information Entropy Augmented High Density Crowd Counting Network

The research proposes an innovated structure of the density map-based crowd counting network augmented by information entropy. The network comprises of a front-end network to extract features and a back-end network to generate density maps. In order to validate the assumption that the entropy can boost the accuracy of density map generation, a multi-scale entropy map extraction process is imported into the front-end network along with a fine-tuned convolutional feature extraction process, In the back-end network, extracted features are decoded into the density map with a multi-column dilated convolution network. Finally, the decoded density map can be mapped as the estimated counting number. Experimental results indicate that the devised network is capable of accurately estimating the count in extremely high crowd density. Compared to similar structured networks which don’t adapt entropy feature, the proposed network exhibits higher performance. This result proves the feature of information entropy is capable of enhancing the efficiency of density map-based crowd counting approaches.
Digital Forensic Investigation for Non-Volatile Memory Architecture by Hybrid Evaluation Based on ISO/IEC 27037:2012 and NIST SP800-86 Framework

In the implementation of Digital Forensics, one of the derivatives of practice is the handling of Digital Evidence. Handling Digital Evidence requires important steps and procedures. Digital evidence is a source of artifacts in handling a digital-based crime case, one of which comes from digital storage. In this research, the author will design a framework for Digital Forensic investigations by simulating digital evidence in the form of a non-volatile architecture. The reference commonly used by researchers in previous articles is the National Institute of Justice (NIST). The framework is a reference and steps in the practice of acquiring digital evidence. The purpose of designing this framework is as a legal procedure that is specifically implemented in the practice of acquiring non-volatile digital evidence. In the design, the author conducted a literature study on the NIST SP 800-86 and ISO 27037:2012 standards and then combined them in a hybrid terminology. The output of this research is to combine the two standards to become framework as reference for handling and investigating Digital Forensic science.
Leadership Styles and Competitive Advantage: Does Employee Creativity Mediate The Relationship

Competitive advantage is considered as an element which is critical to achieve. This study argued that employee creativity is important for gaining competitive advantage. Likewise. the study also argued that leadership behavior play a significant role in developing creativity among employees. For the purpose of colleting the data three leadership behaviors which are supported by the literature in incorporating employee creativity for gaining competitive advantage have been chosen. The data for transformational leadership, transactional leadership, and autocratic leadership has been collected from the prior studies. The reliability and validity of the instrument has also been ensured and the data has been collected from department of Government housing programs in Abu Dhabi, Dubai, and Sharjah – UAE. The results of partial least square, structural equation modeling revealed that transformational leadership, transactional leadership, and autocratic leadership has a direct and significant positive impact over employee creativity as well as competitive advantage. The findings further ensured that employee creativity mediates the relationship between transformational leadership, transactional leadership, and autocratic leadership and competitive advantage. The findings have practical as well as theoretical significance. At the end some limitations and future recommendations are also mentioned.
Domain-Aware Neural Architecture Search for Classifying Animals in Camera Trap Images

Camera traps provide a feasible way for ecological researchers to observe wildlife, and they often produce millions of images of diverse species requiring classification. This classification can be automated via edge devices installed with convolutional neural networks, but networks may need to be customized per device because edge devices are highly heterogeneous and resource-limited. This can be addressed by a neural architecture search capable of automatically designing networks. However, search methods are usually developed based on benchmark datasets differing widely from camera trap images in many aspects including data distributions and aspect ratios. Therefore, we designed a novel search method conducted directly on camera trap images with lowered resolutions and maintained aspect ratios; the search is guided by a loss function whose hyper parameter is theoretically derived for finding lightweight networks. The search was applied to two datasets and led to lightweight networks tested on an edge device named NVIDIA Jetson X2. The resulting accuracies were competitive in comparison. Conclusively, researchers without knowledge of designing networks can obtain networks optimized for edge devices and thus establish or expand surveillance areas in a cost-effective way.
Safe and adaptive autonomous navigation under uncertainty based on sequential waypoints and reachability analysis

This paper presents a new approach for a safe autonomous navigation based on reliable state space reachability analysis. This latter improves an already proposed flexible Navigation Strategy based on Sequential Waypoint Reaching (NSbSWR) framework (Vilca et al., 2015), while considering explicitly different uncertainties in modeling and/or perception. Indeed, NSbSWR is an emergent concept that exploits its flexibility and genericity to avoid frequent complex trajectories’ planning/re-planning. The paper’s main contribution is to introduce a reachability analysis scheme as a reliable risk assessment and management policy ensuring safe autonomous navigation between the successive assigned waypoints. For this aim, interval analysis is employed to propagate uncertainties influencing the vehicle’s dynamics into the navigation system states. By solving an ordinary differential equation with uncertain variables and parameters via an interval Taylor series expansion method, all the vehicle potential reachable state-space is revealed. According to the obtained bounds of the reachable sets, a decision about the navigation safety is made. Once a collision risk is captured, the risk management layer acts to update the control parameters to master the critical situation and guarantee a proper reaching of waypoint, while avoiding any risky state. Several simulation results prove the safety, efficiency and robustness of the overall navigation under uncertainties.
TunRoBERTa: A Tunisian Robustly Optimized BERT Approach Model for Sentiment Analysis

Sentiment Analysis has grown in importance and popularity due to the proliferation of microblogging sites and the increase in posted comments, tweets, and posts, as it allows for the prediction of people's feelings, thoughts, impressions, and opinions.Sentiment analysis is regarded as one of the most active research areas in NLP.As a result, this tool has piqued the interest of marketing and business firms, government organizations, and society as a whole.Based on that, we propose a Tunisian model in this paper.A robustly optimized BERT approach was used to establish sentiment classification from the Tunisian corpus.
Application of artificial intelligence algorithm in mathematical modelling and solving

Abstract To help people solve unsolved problems in everyday life and solve the complexity of life, this paper proposes an artificial intelligence algorithm and establishes a mathematical model for simulation experiments. The results show that the artificial intelligence algorithm can search the optimal solution in a short time, with a success rate of 95.87%. Under the same conditions, relative to other algorithms, it can not only improve the success rate of the optimal solution, but also the search solution has very significant advantages.