Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of RCNN and Faster R-CNN for Text Detection in a Poor Quality Topographic Map

Abstract Topographic Map (TM) highlights essential morphological features. Some notable features include contours, elevation values, etc. It may be either distinct or overlapped with other features. All these attributes are annotations written using distinct color codes. These features along with their associated attributes is used for various morphological analyses. The process of extracting morphological features along with attributes from map is referred as digitization. Digitization is done manually or with an abled automatic computational process. The manual process is tedious and time-consuming whereas automated process takes comparatively lesser time but generates quality results. With advanced learning techniques, quality and integrity of result have considerably increased. CNN adopts traditional MLP where convolution layers replaces intermediate hidden layers in the network. Convolutional layers build a versatile mechanism to enable the detector to identify the features. This paper presents comparison of RCNN and Faster RCNN model result to detect and locate text present in the TM.
Emerging Trends in Security, Cybercrime, and Digital Forensics in the Context of the Internet of Things

The era of the Internet of Things (IoT) has changed the living standards of individuals by providing them with interconnectivity anywhere and anytime. Devices and applications have become smart with the help of the IoT, such as smart homes, smart wearables, smart hospitals, smart farmers, smart parking, smart grid electricity, etc. Due to the nature of technologies integrated with the IoT, such as RFID, sensors, and cloud computing, it also upswings a new sphere and challenges for cybercrimes. Security and privacy have always been challenging and the most vulnerable segments of the IoT. The various devices configured in IoT technologies create traces, and also the IoT data can be a rich source of evidence, which can be helpful for investigation and forensic resolutions in case of any offense. IoT forensics has a vigorous part in cybercrime investigation. The forensic agencies use several tools and techniques to examine the digital evidence. Many researchers have contributed their work in positively identifying these risks and the solutions for the problem. Still, there are many underlying concerns that need to be explored in order to perform an effective IoT investigation. This chapter presents the areas and issues related to security and cybercrime in IoT devices. The chapter also explores the recent opportunities, challenges, and potential solutions suggested until now regarding security, cybercrime, and digital forensics in the IoT.
A Novel Deep Belief Neural Network Model for Abstractive Text Summarization

Everyday researchers present their research works in various formats such as research articles, reports, patents, and study chapters. All over the world, the number of submissions increases due to technology development that leads to innovations. Search results will provide numerous relevant results for a single word and it is quite difficult for beginners and also for experts to analyze the research work by reading the entire article. Text summarization has evolved to reduce this issue. It automatically examines the articles and extracts the important information from the research works. Due to specific technical aspects, conventional text summarization methods cannot be employed in the text summarization of research works. Researchers pay more attention to bring efficient scientific article text summarization and this research work aims to achieve the goal of text summarization using a deep belief neural network. Abstractive text summarization is proposed in the research work by addressing the importance of the research article summarization. This novel approach attains better performance than conventional text summarization models. The experiental results shows the superior performance of our approach.
Foundation of Cognitive Computing

This chapter focuses on the basics of cognitive computing methodology and explains how this acts as a platform to change the data to make the machine learn. The computer performs all the processes of evaluating and executing in a quicker manner. The cognitive system attempts to mimic the activity of the human with the support of the computer. Also, it generates a solution when more data has to be added using the machines’ learning capacity. Furthermore, it explains how artificial intelligence and computers are involved in cognitive computing and its applications. Moreover, it presents an outline of cognitive computing technology.
Implementation of Continuous-Time Quantum Walks on Quantum Computers

Quantum walk is a useful model to simulate complex quantum systems and to build quantum algorithms; in particular, to develop spatial search algorithms on graphs, which aim to find a marked vertex as quickly as possible. Quantum walks are interesting candidates to be implemented on quantum computers. In this work, we describe efficient circuits that implement the evolution operator of continuous-time quantum-walk-based search algorithms on three graph classes: complete graphs, complete bipartite graphs, and hypercubes. For the class of complete and complete bipartite graphs, the circuits implement the evolution operator exactly. For the class of hypercubes, the circuit implements an approximate evolution operator, which tends to the exact evolution operator when the number of vertices is large. Our Qiskit simulations show that the implementation is successful at finding the marked vertex even for low-dimensional hypercubes.
Open Domain Machine Reading Comprehension using InferSent

An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.
Immersive Journalism and Cinematic VR. The Perspective of the Experts

With the development of virtual reality technologies, the research on how human beings interact with this type of audio-video media and their effects has become more and more relevant. Scholarship in the field of 360-degree videos, cinematic VR, and immersive journalism gained momentum within the last year. However, an in-depth understanding of the impact of user engagement with 360-degree videos as well as how the new technologies are adopted is needed. The present research explores the perspective of experts on the present and future of immersive journalism and cinematic VR. In line with previous research, emphasized the key role of spatial and self-presence in the reception of immersive media. Furthermore, they highlighted the role of immersive media in enhancing emotions. Overall, the engagement with immersive journalistic and cinematic VR was described as a memorable experience. The perspective of experts was that memory lasts long after exposure to immersive media and there is a feeling of being in a mediated world that resembles the real world. Even though the relevance of immersive technologies was pointed out, experts highlighted that the penetration of these technologies is rather limited in the German market. However, the experts expressed an optimistic future perspective having in mind that the technology will become more affordable and easier to use.
Huntsman develops bio-based polyurethane for lower carbon automotive acoustic applications

On November 02, Huntsman announced the launch of ACOUSTIFLEX® VEF BIO system – a new bio-based viscoelastic foam technology for molded acoustic applications in the automotive industry that contains up to 20% bio-based content, derived from vegetable oils. This new solution is claimed to be able to lower the carbon footprint of automotive carpet back-foaming by up to 25% compared to existing Huntsman systems for this application, compared to existing ACOUSTIFLEX® polyurethane systems of Huntsman. The technology can also be used for dash and wheel arch insulation.
Expansion of the dynamic strain field in 3D-printed structures using a hybrid modeling approach

The 3D printing of machine components is becoming an established technique. To ensure their robustness and longevity, it is crucial to numerically predict the dynamic response of these components in a variety of operating conditions. Dynamic properties are conventionally obtained in the form of displacement or strain-based response models. However, numerical modeling of the dynamic properties of 3D-printed structures can be a tedious task, mainly due to the complex geometry of the infill pattern and the influence of the printing parameters on the material and geometric properties. Alternatively, the dynamic properties can also be represented in the form of an experimental response model. This reflects the real dynamic properties, but is usually subject to measurement errors and has a low spatial resolution. To integrate the benefits of numerical and experimental response models, we propose a hybrid modeling approach with the System Equivalent Mixing method. The method was extended to a form that could integrate dynamic response models with different physical quantities (displacement and strain). The approach was then analyzed on a 3D-printed beam with a complex infill pattern, where an accurate expansion of the strain response to a high spatial resolution was demonstrated.
Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)

Interpretable Machine Learning The semantic explainable AI (S-XAI) discovers what makes a cat to be recognized as a cat in the convolutional neural networks by extracting common traits and establishing semantic space from diversified samples of cats. The visualized common traits contain identifiable semantic concepts like eyes, noses and beards, which give a semantic interpretation for the convolutional neural networks. The S-XAI has a promising prospect in the aspect of trustworthiness assessment and semantic sample searching. More details can be found in the article number 2204723 by Hao Xu, Yuntian Chen, and Dongxiao Zhang.
A Probabilistic Framework for Lifelong Test-Time Adaptation

Test-time adaptation (TTA) is the problem of updating a pre-trained source model at inference time given test input(s) from a different target domain. Most existing TTA approaches assume the setting in which the target domain is stationary, i.e., all the test inputs come from a single target domain. However, in many practical settings, the test input distribution might exhibit a lifelong/continual shift over time. Moreover, existing TTA approaches also lack the ability to provide reliable uncertainty estimates, which is crucial when distribution shifts occur between the source and target domain. To address these issues, we present PETAL (Probabilistic lifElong Test-time Adaptation with seLf-training prior), which solves lifelong TTA using a probabilistic approach, and naturally results in (1) a student-teacher framework, where the teacher model is an exponential moving average of the student model, and (2) regularizing the model updates at inference time using the source model as a regularizer. To prevent model drift in the lifelong/continual TTA setting, we also propose a data-driven parameter restoration technique which contributes to reducing the error accumulation and maintaining the knowledge of recent domains by restoring only the irrelevant parameters. In terms of predictive error rate as well as uncertainty based metrics such as Brier score and negative log-likelihood, our method achieves better results than the current state-of-the-art for online lifelong test-time adaptation across various benchmarks, such as CIFAR-10C, CIFAR-100C, ImageNetC, and ImageNet3DCC datasets. The source code for our approach is accessible at https://github.com/dhanajitb/petal.
Application of Accounting Information System at Fahiluka Health Center

This study aims to find out how to prepare financial statements at the Fahiluka health center. This type of research uses a qualitative descriptive method with a case study approach in the form of financial statement data at the Fahiluka health center. The data sources used in this study are data obtained from an interview with one of the staff of the Fahiluka health center and books, official archives or forms of records related to the financial statements of the Fahiluka health center. and using data collection techniques in interviews and documentation. The results of this study show that the lack of training regarding the process of preparing public sector financial statements in accordance with applicable government regulations or accounting standards has resulted in the Fahiluka health center not implementing an accounting information system and the resulting financial statements are not in accordance with government regulations or government accounting standards. The preparation of financial statements at the Fahiluka health center is not someone who has expertise in the field of accounting (bookkeeping) and the financial statements are not only prepared by one person in charge or treasurer but are prepared by each person in charge of the activity program. The results of this study mean that the Fahiluka health center has not implemented an accounting information system where in the process of preparing financial statements is still manually using Microsoft excel, the stages of preparing financial statements at the Fahiluka health center are only recorded in the form of journals, then reporting in the form of financial statements so that the financial statement information produced is only in the form of a general cash book
Vision-based Hand Gesture Interface for Real-time Computer Operation Control

Humans typically perform simple actions with hand gestures. If a computer interprets gestures, then human-computer interaction can be enhanced. This paper proposes hand gesture-based interface for controlling computer operations using deep learning and custom dataset.
Research on Phased Teacher Training System in Engineering Education

As an important aspect in the construction of "Emerging Engineering Education", the major accreditation of engineering education has received more and more attention from colleges and universities in recent years, and the construction of teacher training system is an important guarantee for the effective implementation of major accreditation.Based on the development of higher education at home and abroad in recent years, this paper discusses the problems existing in the modern teacher training system under the background of engineering education, and proposed a phased teacher training system design scheme.According to the different stages of teachers and different types of courses, this paper constructs a four in one teacher training system of university, school, major and course group, so that the achievement of curriculum objectives can be effectively guaranteed.
BEMD based adaptive Lee filter for despeckling of SAR images

Synthetic aperture radar (SAR) image processing finds application in gathering features to detect the ecological changes in earth observatory. But the speckle noise in the acquired image degrades the quality of detection. This paper explains a new method to minimize speckle noise in SAR images using bidimensional empirical mode decomposition (BEMD) based adaptive Lee filter. The main innovation of this proposed work is the use of the BEMD technique on SAR images. The decomposed levels are called bidimensional intrinsic mode functions (BIMF). By this decomposition algorithm, the high-frequency noise component gets separated in the first level of BIMF, which is further filtered by the proposed adaptive filter and reconstructed. In this paper, we are proposing a BEMD based adaptive Lee filter. The BEMD based Lee filter with different window sizes is used. The designed filter algorithm is validated by calculating the performance parameters to demonstrate its denoising performance.
Evaluating Template Generation Methods for Spatially Normalizing Down Syndrome Brain Magnetic Resonance Images

Abstract Background Magnetic resonance (MR) image registration and segmentation algorithms commonly used in studies of neurodegeneration have a higher failure rate for Down syndrome (DS) populations due to anatomical and morphological differences. For example, the Desikan‐Killiany atlas used by FreeSurfer was defined using MR images from cognitively normal adults and patients with Alzheimer’s disease (AD). The aim of this work was to evaluate three template generation methods (or group‐wise image registration) for spatially matching DS MR images and generating a DS cohort‐specific template. Method 138 scans of DS subjects from the NiAD consortium were employed. The three template generation methods evaluated were DARTEL, SHOOT, and SyGN. DARTEL and SHOOT are implemented in the SPM12 (v7771) software package and operate on tissue segmentation maps. SyGN is implemented in ANTs software and operates on MR images. The data processing pipeline is presented in Figure 1. Scan‐specific grey matter (GM) and white matter (WM) tissue probability maps were generated using SPM12’s Unified Segmentation method for DARTEL and SHOOT, and MR images were skull‐stripped for SyGN. To evaluate spatial matching, all MR images were warped to each template space using method‐specific deformation fields and an average MR image was generated specific to each template method. Structural similarity index measure (SSIM) and Normalized Mutual Information (NMI) metrics were calculated on a scan specific basis relative to the average MR image for each template method. Lower SSIM and NMI measures effectively reflect greater spatial variability across DS scans and therefore worse spatial matching performance. Result The DARTEL and SHOOT tissue map (GM and WM) templates and SyGN MR template are presented in Figure 2. Average MRs for each template method are presented in Figure 3. Consistent relationships were observed between the 3 template generation methods for the mean (SHOOT &gt; SyGN &gt; DARTEL) and standard deviation (SyGN &lt; DARTEL &lt; SHOOT) of SSIM and NMI (Figures 4 and 5, Table 1). Conclusion With relatively high means and the lowest standard deviation, SyGN produced robust spatial matching across all scans. Future Down syndrome MRI and PET image processing will make use of the SyGN template.
Three‐stage classification of Alzheimer's disease in MRI in clinical records of the Mass General Brigham Health Care System

Abstract Background Diagnostic deep learning has frequently been applied to diagnosis of Alzheimer's and Mild Cognitive Impairment in brain MRIs collected in a controlled research setting, but due to the presence of confounding factors, similar methods have rarely been applied post‐hoc to clinical MRI. Data matching, or the matching of datasets across different classes with respect to a number of confounds, is a method of finding subsets of an overall dataset, that can be used to mitigate the confounding factor problem. Method 467,464 multimodal clinical MRIs of the brain from 37,311 patients in the Mass General Brigham (MGB) Healthcare System were either excluded (based on presence of neoplasms or head trauma) or labeled as AD, MCI, or control, based on a previous prescription of rivastigmine, galantamine, or donepezil (MCI), or memantine (AD), or the lack of a history of central nervous system medications. A data matching algorithm was applied to 18 technical and demographic confounding factors to isolate an unconfounded matched dataset set to train an ensemble of five 3D ResNet‐50 deep learning models. Across the ensemble, the test set totaled 287,367 files. See Figure 1. Result In distinguishing controls from AD/MCI on a per‐patient basis, an AUROC of 0.82 was achieved, though with lower performance in distinguishing between AD and MCI. Variations in performance were seen with MRI modality, with MPRAGE having the highest performance overall (0.859 AUROC). See Figure 2. Conclusion When carefully accounting for confounds, the use of deep learning models is feasible in the analysis of highly‐confounded clinical MRI.
THE IMPLEMENTATION OF EVERYONE IS A TEACHER HERE (EITH) STRATEGY TO IMPROVE THE STUDENTS' SPEAKING SKILL IN DELIVERING OPINION

This research aimed to see the implementation of Everyone is a Teacher Here Strategy for students’ speaking skills in learning English, in terms of self-confidence. This research used a pre-experimental method. The data collection technique was taken through pre-test and post-test. The researcher was conduct at SMA Muhammadiyah 5 Makassar, with the population was the XII grade students of SMA Muhammadiyah 5 MakassarThe result of this research indicated that the average pre-test score of students before treated on self-confidence was 45 and the average score of post-test students after treatment on self-confidence was 62. The significance between pre-test and post-test was 38%. The result of data analysis showed that the improvement in students’ speaking skills after given treatments used the EITH strategy was increasing significantly. This was evidenced by the result of statistical analysis with a significant level was = 0.025 with a degree of freedom (df) = 8, t-test = 4,477 and t-table = 2,306. Based on the result of post-test data obtained as the final result, this research provided a significant improvement with the comparison of t-test &gt; t-table which is 2.171.Based on the result of the post-test data obtained as a final result, this research improved. Finally, it can be concluded that the implementation of Everyone is a Teacher Here strategy was capable of providing a good effect on students delivering opinions, especially in speaking skills.
Cross-Modal Knowledge Distillation with Dropout-Based Confidence

In cross-modal distillation, e.g., from text-based inference modules to spoken language understanding module, it is difficult to determine the teacher's influence due to the different nature of both modalities that bring the heterogeneity in the aspect of uncertainty. Though error rate or entropy-based schemes have been suggested to cope with the heuristics of time-based scheduling, the confidence of the teacher inference has not been necessarily taken into deciding the teacher's influence. In this paper, we propose a dropout-based confidence that decides the teacher's confidence and to-student influence of the loss. On the widely used spoken language understanding dataset, Fluent Speech Command, we show that our weight decision scheme enhances performance in combination with the conventional scheduling strategies, displaying a maximum 20% relative error reduction concerning the model with no distillation.