Reviewerly/exHarmony huggingface dataset:
Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks

Computer-aided diagnosis/detection (CADx) systems have been used to help doctors in improving the quality of diagnosis and treatment processes in many serious diseases such as breast cancer, brain stroke, lung cancer, and bone fracture. However, the performance of such systems has not been completely accurate. The key factor in CADx systems is to localize positive disease lesions from the captured medical images. This step is important as it is used not only to localize lesions but also to reduce the effect of noise and normal regions on the overall CADx system. In this research, we proposed a method to enhance the segmentation performance of thyroid nodules in ultrasound images based on information fusion of suggestion and enhancement segmentation networks. Experimental results with two open databases of thyroid digital image databases and 3DThyroid databases showed that our method resulted in a higher performance compared to current up-to-date methods.
Understanding How Designers Find and Use Data Visualization Examples

Examples are useful for inspiring ideas and facilitating implementation in visualization design. However, there is little understanding of how visualization designers use examples, and how computational tools may support such activities. In this paper, we contribute an exploratory study of current practices in incorporating visualization examples. We conducted semi-structured interviews with 15 university students and 15 professional designers. Our analysis focus on two core design activities: searching for examples and utilizing examples. We characterize observed strategies and tools for performing these activities, as well as major challenges that hinder designers' current workflows. In addition, we identify themes that cut across these two activities: criteria for determining example usefulness, curation practices, and design fixation. Given our findings, we discuss the implications for visualization design and authoring tools and highlight critical areas for future research.
Decentralized Machine Learning for Intelligent Health-Care Systems on the Computing Continuum

any societies and cultures perceive that sexually transmitted diseases (STDs) only affect "others" who follow "sinful" lifestyles and practices.Therefore, discrimination and stigmatization are common outcomes of such distorted depictions of STDs, especially related to the acquired immunodeficiency syndrome/human immunodeficiency virus.On a global level, the fear of stigmatization prohibits effective disease identification, prevention, care, and treatment adherence, negatively influencing many communities' quality of life. 1 The introduction of electronic personal health record (EHR) systems is a first step toward addressing these issues, especially for illness-related stigmatization.The purpose of EHR systems is to provide information exchange and curation among different national health-care systems, and support diagnosis quality, safety monitoring, or medical research.Although EHR systems promise substantial benefits for improved care and reduced health-care costs, there are unforeseen difficulties related to privacy and limited diagnosis-related functionalities.The essential barriers that limit the usability and application of the EHR systems are
Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images

Prostate cancer can be diagnosed by prostate biopsy using transectal ultrasound guidance. The high number of pathology images from biopsy tissues is a burden on pathologists, and analysis is subjective and susceptible to inter-rater variability. The use of machine learning techniques could make prostate histopathology diagnostics more precise, consistent, and efficient overall. This paper presents a new classification fusion network model that was created by fusing eight advanced image features: seven hand-crafted features and one deep-learning feature. These features are the scale-invariant feature transform (SIFT), speeded up robust feature (SURF), oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB) of local features, shape and texture features of the cell nuclei, the histogram of oriented gradients (HOG) feature of the cavities, a color feature, and a convolution deep-learning feature. Matching, integrated, and fusion networks are the three essential components of the proposed deep-learning network. The integrated network consists of both a backbone and an additional network. When classifying 1100 prostate pathology images using this fusion network with different backbones (ResNet-18/50, VGG-11/16, and DenseNet-121/201), we discovered that the proposed model with the ResNet-18 backbone achieved the best performance in terms of the accuracy (95.54%), specificity (93.64%), and sensitivity (97.27%) as well as the area under the receiver operating characteristic curve (98.34%). However, each of the assessment criteria for these separate features had a value lower than 90%, which demonstrates that the suggested model combines differently derived characteristics in an effective manner. Moreover, a Grad-CAM++ heatmap was used to observe the differences between the proposed model and ResNet-18 in terms of the regions of interest. This map showed that the proposed model was better at focusing on cancerous cells than ResNet-18. Hence, the proposed classification fusion network, which combines hand-crafted features and a deep-learning feature, is useful for computer-aided diagnoses based on pathology images of prostate cancer. Because of the similarities in the feature engineering and deep learning for different types of pathology images, the proposed method could be used for other pathology images, such as those of breast, thyroid cancer.
Development of an American Sign Language Recognition System using Canny Edge and Histogram of Oriented Gradient

Sign language is used by people who have hearing and speaking difficulties, but not understood by many without these difficulties. Therefore, sign language recognition systems are developed to aid communication between hearing impaired people and others. This paper developed a static American Sign Language Recognition (ASLR) system using canny-edge and histogram of oriented gradient (HOG) for feature extraction with K-Nearest Neighbour (K-NN) as classifier. The sign language image datasets used consist of English alphabets from both Massey University and Kaggle, and numbers (0-9) from Massey University. Median filter was used to remove noise after images were converted to grayscale. Otsu algorithm was used for segmentation while edges in the images were preserved using canny edge detection technique with HOG parameters tuning to obtain feature vectors. The extracted features were used by K-NN for classification. An average recognition accuracy and computational testing time of 97.6% and 0.39s respectively were obtained based on experiments with the Massey University dataset. Similarly, an average recognition accuracy and computational testing time of 99.0% and 0.43s respectively were obtained based on experiments with the Kaggle dataset. The developed system successfully recognized static English alphabets and numbers and outperformed some existing systems.
Security analysis on an interference-based optical image encryption scheme

In this paper, the security strength of the improved optical cryptosystem based on interference has been evaluated. Compared to the previous interference-based cryptosystems in which the plaintext is encoded into two phase-only masks (POMs), here the plaintext is encoded into a POM and an amplitude mask (AM). Since the information of the plaintext cannot be recovered directly when one of the masks is released in the decryption process of the improved cryptosystem, it seems that it is free from the silhouette problem. However, we found that the random phase mask (RPM) serving as the encryption key is not related to the plaintext. Thus, it is possible to recover the RPM first by using the known-plaintext attack (KPA). Moreover, the POM and the AM generated in the encryption path only contains the phase and amplitude information, respectively. Thus, these can be utilized as additional constraints in the proposed iterative process. Based on these findings, two kinds of hybrid attacks, including a KPA and the iterative processes with different constraints, are proposed to crack the improved cryptosystem. In the designed KPA with a pair of the known plaintext and its corresponding masks, the RPM is recovered first. With the aid of the recovered RPM, two iterative processes with different released masks are proposed to recover the information of the plaintext without any knowledge of another mask. To the best of our knowledge, this is the first time that the existence of the silhouette problem in the cryptosystem under study has been reported. Numerical simulation has been carried out to validate the feasibility and effectiveness of the proposed hybrid attacks.
A Machine Learning-Based Technique with Intelligent WordNet Lemmatize for Twitter Sentiment Analysis

Laterally with the birth of the Internet, the fast growth of mobile strategies has democratised content production owing to the widespread usage of social media, resulting in a detonation of short informal writings.Twitter is microblogging short text and social networking services, with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users' tweets in terms of ideas, interests, and views in a range of settings and fields.This type of study can be useful for a variation of academics and applications that need knowing people's perspectives on a given topic or event.Although sentiment examination of these texts is useful for a variety of reasons, it is typically seen as a difficult undertaking due to the fact that these messages are frequently short, informal, loud, and rich in linguistic ambiguities such as polysemy.Furthermore, most contemporary sentiment analysis algorithms are based on clean data.In this paper, we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and Inverse Document Frequency (TF-IDF) and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing noise.We also utilise the Random Forest network to detect the emotion of a tweet.To authenticate the proposed approach performance, we conduct extensive tests on publically accessible datasets, and the findings reveal that the suggested technique significantly outperforms sentiment classification in multi-class emotion text data.
A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games

The automatic detection of events in complex sports games like soccer and handball using positional or video data is of large interest in research and industry. One requirement is a fundamental understanding of underlying concepts, i.e., events that occur on the pitch. Previous work often deals only with so-called low-level events based on well-defined rules such as free kicks, free throws, or goals. High-level events, such as passes, are less frequently approached due to a lack of consistent definitions. This introduces a level of ambiguity that necessities careful validation when regarding event annotations. Yet, this validation step is usually neglected as the majority of studies adopt annotations from commercial providers on private datasets of unknown quality and focuses on soccer only. To address these issues, we present (1) a universal taxonomy that covers a wide range of low and high-level events for invasion games and is exemplarily refined to soccer and handball, and (2) release two multi-modal datasets comprising video and positional data with gold-standard annotations to foster research in fine-grained and ball-centered event spotting. Experiments on human performance demonstrate the robustness of the proposed taxonomy, and that disagreements and ambiguities in the annotation increase with the complexity of the event. An I3D model for video classification is adopted for event spotting and reveals the potential for benchmarking. Datasets are available at: https://github.com/mm4spa/eigd
Quantum algorithm for ground state energy estimation using circuit depth with exponentially improved dependence on precision

A milestone in the field of quantum computing will be solving problems in quantum chemistry and materials faster than state-of-the-art classical methods. The current understanding is that achieving quantum advantage in this area will require some degree of fault tolerance. While hardware is improving towards this milestone, optimizing quantum algorithms also brings it closer to the present. Existing methods for ground state energy estimation are costly in that they require a number of gates per circuit that grows exponentially with the desired number of bits in precision. We reduce this cost exponentially, by developing a ground state energy estimation algorithm for which this cost grows linearly in the number of bits of precision. Relative to recent resource estimates of ground state energy estimation for the industrially-relevant molecules of ethylene-carbonate and PF$_6^-$, the estimated gate count and circuit depth is reduced by a factor of 43 and 78, respectively. Furthermore, the algorithm can use additional circuit depth to reduce the total runtime. These features make our algorithm a promising candidate for realizing quantum advantage in the era of early fault-tolerant quantum computing.
Adaptive Gabor Filters for Interpretable Color Texture Classification

We introduce the use of trainable feature extractors, based on the Gabor function, into the interpretable machine learning domain.The use of adaptive Gabor filters allows for interpretable feature extraction to be learned automatically in a domain agnostic way, and comes with the benefit of a large reduction in trainable parameters.We implemented the filters into an image classification variant of learning vector quantization We extend and compare the image classification variant of learning vector quantization with adaptive Gabor filters and demonstrate the proposed technique on VisTex color texture images.The adaptive Gabor filters show promising results for interpretable and efficient color texture classification.
Partitionable High-Efficiency Multilayer Diffractive Optical Neural Network

A partitionable adaptive multilayer diffractive optical neural network is constructed to address setup issues in multilayer diffractive optical neural network systems and the difficulty of flexibly changing the number of layers and input data size. When the diffractive devices are partitioned properly, a multilayer diffractive optical neural network can be constructed quickly and flexibly without readjusting the optical path, and the number of optical devices, which increases linearly with the number of network layers, can be avoided while preventing the energy loss during propagation where the beam energy decays exponentially with the number of layers. This architecture can be extended to construct distinct optical neural networks for different diffraction devices in various spectral bands. The accuracy values of 89.1% and 81.0% are experimentally evaluated for MNIST database and MNIST fashion database and show that the classification performance of the proposed optical neural network reaches state-of-the-art levels.
Joints Trajectory Planning of Robot Based on Slime Mould Whale Optimization Algorithm

The joints running trajectory of a robot directly affects it’s working efficiency, stationarity and working quality. To solve the problems of slow convergence speed and weak global search ability in the current commonly used joint trajectory optimization algorithms, a joint trajectory planning method based on slime mould whale optimization algorithm (SMWOA) was researched, which could obtain the joint trajectory within a short time and with low energy consumption. On the basis of analyses of the whale optimization algorithm (WOA) and slime mould algorithm (SMA) in detail, the SMWOA was proposed by combining the two methods. By adjusting dynamic parameters and introducing dynamic weights, the proposed SMWOA increased the probability of obtaining the global optimal solution. The optimized results of 15 benchmark functions verified that the optimization accuracy of the SMWOA is clearly better than that of other classical algorithms. An experiment was carried out in which this algorithm was applied to joint trajectory optimization. Taking 6-DOF UR5 manipulator as an example, the results show that the optimized running time of the joints is reduced by 37.674% compared with that before optimization. The efficiency of robot joint motion was improved. This study provides a theoretical basis for the optimization of other engineering fields.
Determining the Type of Document Read Using Eye Movement Properties by Hybrid CNN Method

By examining the change of eye movements during reading, it is possible to determine the type of document read.Returning to the previous position (negative saccade), blink, fixation, and position are important indicators in determining the type of document being read.In this paper, a hybrid deep learning model is proposed to determine the type of document read.The MPIIDPEye dataset, which includes eye movement data of 10-minute comic, newspaper and text document readings from 20 participants, was used.First, the eye movements obtained over time were augmented by the non-linear interpolation technique.In order to process the data of each class with convolutional neural network, spectrogram images of the signals were created.Spectrogram images were given as input to Resnet architectures and the features in the Fc1000 layer were combined.Concatenated feature vectors were given as an input to feature selection algorithms.The most effective features in classification accuracy were determined and classified using the SVM algorithm.The classification was carried out for 3 different cases, and the highest accuracy of 98.41% was obtained for case-2, where Fixation, Position, and Blink properties were used.
Lithostructural Mapping Using Landsat OLI images and Field Investigations in the Oumjrane–Boukerzia Mining District, Eastern Anti-Atlas, Morocco

The application of remote sensing is considered to be highly efficient in the field of geology, particularly for mapping and discrimination between lithological units, as well as the identification of different surface minerals which enable the acquisition of all the optical and radar data needed to obtain more reliable and particular information, especially in inaccessible areas. Landsat 8 (Operational Land Imager) image bands, which include color compositions 7, 4, and 1, band ratios 7/5, 4/2, 3/1, and 6/3, 6/1, 5/2, Principal Component Analyses PC1, PC4, and PC3, and directional filtering at 0° (NS), 45° (NE–SW), and 90° (EW), were used to analyze the spectral characteristics of the lithological units of the study area and delineate the alteration zones which may contain significant concentrations of base metals. Combined with geological field observations, these data were integrated and analyzed in a geographic information system to establish a multi-criteria characterization of mineralized zones. Hence, the use of remote sensing contributed to the improvement and updating of available geological maps of the Oumjrane–Boukerzia area. Lineament extraction allowed for performed analyses of the structural elements that play a crucial role in the mineral distribution in the study area. Several of the targeted areas were examined in the field by selective sampling. Consequently, the results helped identify new mineralized zones with important mining potential which could be evaluated in the future through detailed geophysical and geochemical work.
Ensemble Learning with Supervised Machine Learning Models to Predict Credit Card Fraud Transactions

In recent years, the highly boosting development in e-commerce technologies made it possible for people to select the most desirable items from shops and stores worldwide while being at home. Credit card frauds transactions are common nowadays because of online payments. Online transactions are the root cause of fraudulent credit card activity, bringing enormous financial losses. Financial institutions must install an automatic deterrent mechanism to check these fraudulent actions. The fraudulent transactions do not follow a specific pattern and continuously change their shape and behavior. This paper aims to use ensemble learning with supervised Machine Learning (ML) models to predict the occurrence of fraud transactions. The experimental study has been evaluated on the open-source Kaggle credit card fraud detection dataset. The performance of the proposed model is measured in terms of accuracy score, confusion matrix, and classification report. The results were state-of-the-art using the voting ensemble learning technique shows that it can be get the best results using PCA with 100.0% accuracy, 97.3% precision, 73.5% recall, and 83.7% f1-score against other ML classifiers.
Scoring Graphical Responses in TIMSS 2019 Using Artificial Neural Networks

Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our results show that convolutional neural networks (CNNs) outperform feed-forward neural networks in both loss and accuracy. The CNN models classified up to 97.53% of the image responses into the appropriate scoring category, which is comparable to, if not more accurate, than typical human raters. These findings were further strengthened by the observation that the most accurate CNN models correctly classified some image responses that had been incorrectly scored by the human raters. As an additional innovation, we outline a method to select human-rated responses for the training sample based on an application of the expected response function derived from item response theory. This paper argues that CNN-based automated scoring of image responses is a highly accurate procedure that could potentially replace the workload and cost of second human raters for international large-scale assessments (ILSAs), while improving the validity and comparability of scoring complex constructed-response items.