Reviewerly/exHarmony huggingface dataset:
Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Artificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis. The main contribution of this study is based on the Natural Language Processing method, which is one of the machine learning topics in the literature. Sentiment analysis classification is a solid example for machine learning tasks that belongs to human-machine interaction. It is essential to make the computer understand people emotional situation with classifiers. There are a limited number of Turkish language studies in the literature. Turkish language has different types of linguistic features from English. Since Turkish is an agglutinative language, it is challenging to make sentiment analysis with that language. This paper aims to perform sentiment analysis of several machine learning algorithms on Turkish language datasets that are collected from Twitter. In this research, besides using public dataset that belongs to Beyaz (2021) to get more general results, another dataset is created to understand the impact of the pandemic on people and to learn about public opinions. Therefore, a custom dataset, namely, SentimentSet (Balli 2021), was created, consisting of Turkish tweets that were filtered with words such as pandemic and corona by manually marking as positive, negative, or neutral. Besides, SentimentSet could be used in future researches as benchmark dataset. Results show classification accuracy of not only up to ∼87% with test data from datasets of both datasets and trained models, but also up to ∼84% with small "Sample Test Data" generated by the same methods as SentimentSet dataset. These research results contributed to indicating Turkish language specific sentiment analysis that is dependent on language specifications.
Comparative Study on Handwritten Digit Recognition Classifier Using CNN and Machine Learning Algorithms

Digit Recognition is essential for interpreting image processing and pattern recognition since a machine cannot classify handwritten digits. Many real-time applications include OCR (Optical Character Recognition), which recognizes characters and digitizes printed texts. Converting handwritten digits to digital characters has been a challenging problem since the past. The physical documents cannot be efficiently processed without converting them to digital copies and it requires a lot of time and efforts. To provide a solution to handwritten classification, several algorithms and techniques have been proposed over the years. The objective of this research is to use Convolutional Neural Networks (CNN), K-Nearest Neighbor, and Support Vector Machine to recognize isolated handwritten digits. After implementing and training the models on the same dataset and comparing the results obtained for three different models, the results show that CNN is the most optimal machine learning technique to classify handwritten digits with an accuracy of 99.59 percent.
Combination of research questions and methods: A new measurement of scientific novelty

As critical building blocks of scientific research, research questions and research methods are put forward to reveal the nature of a publication's scientific novelty. Although existing studies have examined scientific novelty from multiple combination-based views, the temporal and semantic complexity of research questions and methods remains to be fully addressed. To remedy this, we introduce a new approach to measuring the novelty of papers from the perspective of question-method combination. Specifically, we demonstrated a life-index novelty measurement based on the frequency and age of question terms and method terms. Furthermore, by using deep learning and representation learning techniques, we proposed a semantic novelty measurement algorithm based on the semantic similarity of terms. By using the dataset of papers collected from ACM Digital Library for evaluation, the effectiveness of our methods was evaluated by case studies and statistical analysis. Our work innovatively integrates the age, frequency, and semantics of research methods and research questions that characterizes novelty in scientific publications.
Denoising multi-view images by soft thresholding: A short-time DFT approach

Short-time discrete Fourier transform (ST-DFT) is known as a promising technique for image and video denoising. The seminal work by Saito and Komatsu hypothesized that natural video sequences can be represented by sparse ST-DFT coefficients and noisy video sequences can be denoised on the basis of statistical modeling and shrinkage of the ST-DFT coefficients. Motivated by their theory, we develop an application of ST-DFT for denoising multi-view images. We first show that multi-view images have sparse ST-DFT coefficients as well and then propose a new statistical model, which we call the multi-block Laplacian model, based on the block-wise sparsity of ST-DFT coefficients. We finally utilize this model to carry out denoising by solving a convex optimization problem, referred to as the least absolute shrinkage and selection operator. A closed-form solution can be computed by soft thresholding, and the optimal threshold value is derived by minimizing the error function in the ST-DFT domain. We demonstrate through experiments the effectiveness of our denoising method compared with several previous denoising techniques. Our method implemented in Python language is available from https://github.com/ctsutake/mviden.
Robust RGB-T Tracking via Graph Attention-Based Bilinear Pooling

RGB-T tracker possesses strong capability of fusing two different yet complementary target observations, thus providing a promising solution to fulfill all-weather tracking in intelligent transportation systems. Existing convolutional neural network (CNN)-based RGB-T tracking methods often consider the multisource-oriented deep feature fusion from global viewpoint, but fail to yield satisfactory performance when the target pair only contains partially useful information. To solve this problem, we propose a four-stream oriented Siamese network (FS-Siamese) for RGB-T tracking. The key innovation of our network structure lies in that we formulate multidomain multilayer feature map fusion as a multiple graph learning problem, based on which we develop a graph attention-based bilinear pooling module to explore the partial feature interaction between the RGB and the thermal targets. This can effectively avoid uninformed image blocks disturbing feature embedding fusion. To enhance the efficiency of the proposed Siamese network structure, we propose to adopt meta-learning to incorporate category information in the updating of bilinear pooling results, which can online enforce the exemplar and current target appearance obtaining similar sematic representation. Extensive experiments on grayscale-thermal object tracking (GTOT) and RGBT234 datasets demonstrate that the proposed method outperforms the state-of-the-art methods for the task of RGB-T tracking.
An Effective Spam Message Detection Model using Feature Engineering and Bi-LSTM

In today's digital era, the unwanted messages are communicated with many people and attack their device through the messages. Spammers steal the secret data of the users that are stored in their mobile device and also creates huge loss to the end user. The various spam detection techniques are proposed by many researchers in the past. Even though, no technique achieved the required detection accuracy. For fulfilling the current user requirements, this paper proposes an effective classifier that combines the Vectorization based Feature Engineering process and Bidirectional Long Short-Term Memory (Bi-LSTM) for detecting the spam in short text message (SMS). The proposed method applies feature engineering process for identifying useful features and also perform effective data pre-processing using vectorization and also perform classification using Bi-LSTM. The proposed method is evaluated by conducting experiments and proved as better than other methods in terms of precision, recall, f1-measure and detection accuracy.
Knowledge-Enhanced Attributed Multi-Task Learning for Medicine Recommendation

Medicine recommendation systems target to recommend a set of medicines given a set of symptoms which play a crucial role in assisting doctors in their daily clinics. Existing approaches are either rule-based or supervised. However, the former heavily relies on expert labeling, which is time-consuming and costly to collect, and the latter suffers from the data sparse problem. To automate medicine recommendation on sparse data, we propose MedRec, which introduces two graphs in modeling: (1) a knowledge graph connecting diseases, medicines, symptoms, and examinations; (2) an attribute graph connecting medicines via shared attributes and molecular structures. These two graphs enhance the connectivity between symptoms and medicines, which thus alleviate the data sparse problem. By learning the interrelationship between diseases, medicines, symptoms and examinations and the inner relationship within medicine, we can acquire unified embedding representations of symptoms and medicines which can be used in medicine recommendation. The experimental results show that the proposed model outperforms state-of-the-art methods. In addition, we find that these two tasks: learning graph representation and medical recommendation can benefit each other.
Internet of Things Assisted Blockchain based Secured Cloud Data Maintenance Scheme

The term “Internet-of-Things (IoT)” has received a loT of interest nowadays. Blockchain has previously been employed in IoT as a reliable, robust and decentralized way. Furthermore, the present blockchain technology has a number of shortcomings that preclude it from serving as a general IoT infrastructure. The networks in the Internet of Things are severely resource constrained, particularly in terms of processing and communication capabilities. Regrettably, they are required by the Blockchain in order to achieve research objectives and disseminate blocks. We present a novel mining and cloud handling methodologies in this study to address the aforementioned challenge in the Blockchain assisted IoT, which is called as ‘Deep Precise Networking Model (DPNM)‘. To be more precise, miners function as data mining drivers for IoT terminals, outsource processing duties to deliver effective cloud enabled servers and flexibly allocate networking connections. Additionally, in addition to improve efficiency, the access control determination challenge, computational resource allocation algorithm and communication resource allocation issue are combined into a single optimization method. We next offer a competing back - propagation learning technique for resolving this issue and the subsequent section thoroughly justifies the efficacy of our proposed technique DPNM.
Towards Lean Automation: Fine-Grained sentiment analysis for customer value identification

That customer value should drive product development and production is a basic tenet of the Toyota Production System and Lean. Traditional means to extract what the customer wants often focus on customer surveys. But surveys are time consuming and costly. At the same time, there exists a large amount of customer comments in online reviews that is easily accessible, whilst the advances of data science, for example as part of Lean Automation, provide new means to extract information from this data. In this context, a new approach to fine-grained sentiment analysis of Chinese consumer data is developed. The new approach integrates pre-training language model, conditional random field model and linguistic knowledge model. The new approach is shown to outperform traditional approaches in a comparison experiment, while an ablation experiment shows that our new approach is parsimonious, i.e., all three constituting components are needed. Finally, a use case is presented that exemplifies how our new approach can support managers in identifying customer value (through positive evaluations), and most importantly guide Lean improvement, through detailed information on characteristics that are evaluated negatively, ranked according to customer importance. Findings have important implications for research and practice.
CSR-Net: Learning Adaptive Context Structure Representation for Robust Feature Correspondence

Feature matching, which refers to identifying and then corresponding the same or similar visual pattern from two or more images, is a key technique in any image processing task that requires establishing good correspondences between images. Given potential correspondences (matches) in two scenes, a novel whole-part deep learning framework, termed as Context Structure Representation Network (CSR-Net), is designed to infer the probabilities of arbitrary correspondences being inliers. Traditional approaches commonly build the local relation between correspondences by manually engineered criteria. Different from existing attempts, the main idea of our work is to learn explicitly neighborhood structure of each correspondence, allowing us to formulate the matching problem into a dynamic local structure consensus evaluation in an end-to-end fashion. For this purpose, we propose a permutation-invariant STructure Representation (STR) learning module, which can easily merge different types of networks into a unified architecture to deal with sparse matches directly. By the collaborative use of STR, we introduce a Context-Aware Attention (CAA) mechanism to adaptively re-calibrate structure features via a rotation-invariant context aware encoding and simple feature gating, thus arising the ability of fine-grained patterns recognition. Moreover, to further weaken the cost of establishing reliable correspondences, the CSR-Net is formulated as whole-part consensus learning, where the aim of whole level is compensating rigid transformations. In order to demonstrate our CSR-Net can effectively boost the baselines, we intensively experiment on image matching and other visual tasks. The results of the experiment confirm that the matching performances of CSR-Net have significantly improved over nine state-of-the-art competitors.
MH-Net: Model-data-driven hybrid-fusion network for medical image segmentation

Image segmentation is an essential step in medical image analysis, as its results directly affect the quality of the follow-up analysis. Because of the high calculation speed and the good performance of the segmentation algorithms based on neural networks (NNs), image segmentation has been extensively researched and developed in clinical analysis and applications. However, the existing NN-based image segmentation methods only consider the pixel information of the image that needs to be segmented, and they cannot guarantee the continuity and smoothness of the segmented edge. Consequently, we propose the model and data-driven hybrid approach, namely, the model-data-driven hybrid-fusion network for medical image segmentation. We consider the attention mechanism and the high-low features, and embed the traditional curvature regularisation segmentation model into the NN in the form of a loss function. Moreover, our model considers not only the semantic information of the segmented image but also the boundary length information of the ROI and the target region information. These can ensure the smoothness of the edge of the medical image segmentation results. We conduct intensive experiments on several benchmark datasets to evaluate the effectiveness of our method in dealing with complex backgrounds and noise. Experimental results demonstrate that the proposed model outperforms other state-of-the-art segmentation methods.
Accuracy of artificial intelligence software for the detection of confirmed pleural effusion in thoracic radiographs in dogs

Abstract The use of artificial intelligence (AI) algorithms in diagnostic radiology is a developing area in veterinary medicine and may provide substantial benefit in many clinical settings. These range from timely image interpretation in the emergency setting when no boarded radiologist is available to allowing boarded radiologists to focus on more challenging cases that require complex medical decision making. Testing the performance of artificial intelligence (AI) software in veterinary medicine is at its early stages, and only a scant number of reports of validation of AI software have been published. The purpose of this study was to investigate the performance of an AI algorithm (Vetology AI ® ) in the detection of pleural effusion in thoracic radiographs of dogs. In this retrospective, diagnostic case–controlled study, 62 canine patients were recruited. A control group of 21 dogs with normal thoracic radiographs and a sample group of 41 dogs with confirmed pleural effusion were selected from the electronic medical records at the Cummings School of Veterinary Medicine. The images were cropped to include only the area of interest (i.e., thorax). The software then classified images into those with pleural effusion and those without. The AI algorithm was able to determine the presence of pleural effusion with 88.7% accuracy ( P &lt; 0.05). The sensitivity and specificity were 90.2% and 81.8%, respectively (positive predictive value, 92.5%; negative predictive value, 81.8%). The application of this technology in the diagnostic interpretation of thoracic radiographs in veterinary medicine appears to be of value and warrants further investigation and testing.
Two‐view attention‐guided convolutional neural network for mammographic image classification

Abstract Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature extraction. However, general deep learning models cannot achieve very satisfactory classification results on mammographic images because these models are not specifically designed for mammographic images and do not take the specific traits of these images into account. To exploit the essential discriminant information of mammographic images, we propose a novel classification method based on a convolutional neural network. Specifically, the proposed method designs two branches to extract the discriminative features from mammographic images from the mediolateral oblique and craniocaudal (CC) mammographic views. The features extracted from the two‐view mammographic images contain complementary information that enables breast cancer to be more easily distinguished. Moreover, the attention block is introduced to capture the channel‐wise information by adjusting the weight of each feature map, which is beneficial to emphasising the important features of mammographic images. Furthermore, we add a penalty term based on the fuzzy cluster algorithm to the cross‐entropy function, which improves the generalisation ability of the classification model by maximising the interclass distance and minimising the intraclass distance of the samples. The experimental results on The Digital database for Screening Mammography INbreast and MIAS mammography databases illustrate that the proposed method achieves the best classification performance and is more robust than the compared state‐of‐the‐art classification methods.
Distributed Renewable Energy Management: A Gap Analysis and Proposed Blockchain-Based Architecture

The heterogeneous and decentralized nature of renewable energy sources is too much to handle for traditional and centralized IT grid infrastructure. Blockchain technology can address many of the associated challenges. This paper provides an overview of the state-of-the-art technology layers of grid system infrastructure, a proposed future state using blockchain technology, and gap analysis. The paper also contributes a set of architectural requirements for a blockchain-enabled future state and a proposed hybrid architecture using blockchain technology, verifiable credentials, and smart contracts. This architecture can uniquely support the technology layers critical to renewable energies, including system architecture, registries, grid management, billing, privacy, and interoperability.
Using machine learning Meta-Classifiers to detect financial frauds

We develop Meta-Classifiers to detect financial frauds by combining several accurate and diverse stand-alone classifiers. Our results suggest that the Meta-Classifiers developed in our study can outperform the best stand-alone classifiers to detect fraudulent firms. We believe developing Meta-Classifiers can be a helpful technique to improve the predictive performance of models. Moreover, the methodology used to develop effective Meta-Classifiers in this study can also be replicated in other prediction related studies.
Task-aware adaptive attention learning for few-shot semantic segmentation

Few-shot semantic segmentation is a newly developing and challenging computer vision task which aims to predict pixel-wise segmentation on the novel categories where only a few annotated samples are supplied. Because of the scarcity of the annotated novel class samples, the main obstacle of this issue is the diversity of objects in the support set and query set. This paper proposes a novel network aiming to bridge the gap by exploring the correlation between the support feature and the query feature. Specifically, a task-aware adaptive attention module(TAAM) is introduced to extract the task-specific information from the current input and integrates it into the feature representations both in channel dimension and spatial dimension for adaptive reinforcement. Besides, an additional prediction refinement module(RPM) is attached to further optimize the predictions to present more details of objects. Furthermore, through a non-parameter aggregation operation, the proposed network is easy to generalize to k-shot segmentation without developing specific architectures. Extensive experiments on three benchmarks demonstrate that our method exceeds previous state-of-the-arts with a sizable margin, verifying the effectiveness of the proposed method.
Blockchain-based secure and trusted data sharing scheme for autonomous vehicle underlying 5G

This paper proposes a blockchain-based secure and reliable data sharing scheme for autonomous vehicles (AVs). It aims to secure data sharing among AVs. We integrate the fifth-generation (5G) communication network with the proposed blockchain-based scheme to enable ultra-low latency, high reliability, and availability. We have incorporated the edge intelligence nodes (EINs) with blockchain for efficient data processing. We have used a random forest classifier model (RFCM) on the road accidents dataset to predict the accident severity, i.e., fatal, serious, and slight. It is based on road type, light condition, and vehicle movement to be shared among other AVs. We have considered various scenarios and performed the predictive analysis that needs to be secured by using blockchain technology. Results of the study demonstrate the accuracy of RFCM as 93%, the precision of 98%, 97%, and 87% for fatal, serious, and slight categories of accident severity, respectively. The results show that the proposed scheme with RFCM yields better accuracy of 93% than other existing classification algorithms such as Decision tree, Naive Bayes, and AdaBoost. The blockchain-based proposed system has been compared with the traditional approach based on parameters such as network latency, scalability, computation time, delay comparison for node validation, data storage latency, and data storage cost. Obtained results show that the proposed scheme enables the data sharing reliable, secure, and efficient for AVs and outperforms the blockchain and non-blockchain systems in terms of security and efficiency.