Reviewerly/exHarmony huggingface dataset:
Impact of PolSAR Pre-Processing and Balancing Methods on Complex-Valued Neural Networks Segmentation Tasks

In this paper, we investigated the semantic segmentation of (polSAR) using (CVNN). Although the coherency matrix is more widely used as the input of CVNN, the Pauli vector has recently been shown to be a valid alternative. We exhaustively compare both methods for six model architectures, three complex-valued, and their respective real-equivalent models. We are comparing, therefore, not only the input representation impact but also the complex- against the real-valued models. We then argue that the dataset splitting produces a high correlation between training and validation sets, saturating the task and thus achieving very high performance. We, therefore, use a different data pre-processing technique designed to reduce this effect and reproduce the results with the same configurations as before (input representation and model architectures). After seeing that the performance per class is highly different according to class occurrences, we propose two methods for reducing this gap and performing the results for all input representations, models, and dataset pre-processing.
Scaling the Inference of Digital Pathology Deep Learning Models Using CPU-Based High-Performance Computing

Digital pathology whole-slide images (WSIs) are large-size gigapixel images and image analysis based on deep learning artificial intelligence (AI) technology often involves pixel-wise testing of a trained deep learning neural network (DLNN) on hundreds of WSI images, which is time consuming. We take advantage of High-Performance Computing (HPC) facilities to parallelize this procedure into multiple independent (and hence delightfully parallel) tasks. However, traditional software parallelization techniques and regular file formats can have significant scaling problems on HPC clusters. In this work, a useful computational strategy is designed to localize and extract relevant patches in WSI files and group them in HDF5 files well suited for parallel I/O. HPC's array job facilities are adapted for hierarchical scaling and parallelization of WSI pre-processing and testing of trained algorithms. Applying these techniques to testing a trained DLNN on the CAMELYON datasets with 399 WSIs reduced the theoretical processing time of 18 years on a single CPU or 30 days on a single GPU to less than 45 hours on an HPC cluster of 4,000 CPU cores. The efficiency-accuracy trade-off we demonstrated on this dataset further reinforced the importance of efficient computation techniques, without which the accuracy may be sacrificed. The framework developed here for testing DLNNs does not rely on any specific neural network architecture and HPC cluster setup and can be utilized for any large-scale image processing and big-data analysis.
Behavioral Economics in IR

Following previous discussions on extending rational assumptions and formal user models, this chapter introduces the research progress on factors associated with human bounded rationality, especially cognitive and perceptual biases, in IR and other closely related fields, including information seeking and recommender systems. By explaining and synthesizing the findings and methods extracted from empirical research on bounded rational IR, we hope to (1) clarify the existing progress and achievements that the research community has already made toward developing and applying intelligent bias-aware search support and, more importantly, (2) identify existing gaps, open challenges, and unsolved problems that may require further investigations. In addition, the knowledge learned from previous studies that leverage the theories from behavioral economics in user modeling, re-ranking, and simulation-based evaluation of algorithm performances can inform the development of our behavioral economics research agenda on bias-aware user modeling, search system design, and evaluation.
Fusion-based backlit image enhancement and analysis of results using contrast measure and SSIM

Backlit images simultaneously contain both dark and bright intensity regions. This is obtained due to the intense reflection of light while capturing the image. In backlit images, the dark and low contrast objects are difficult to perceive for human vision or not visible. Such information related to these regions in backlit images is treated as a region of interest. Many fusion-based methods have been employed to enhance the backlit images and regions of interest, usually dark, making it difficult to visualize. This chapter describes a fusion-based approach to enhancing backlit images' contrast for better visualization. Fusion-based methods for enhancing backlit images involve multiple techniques, such as pyramid-based operations, global tone mapping functions (GTMF), filters, gradient, weight map, exposure maps, etc. Log transform and gamma correction are implemented to improve the visual quality. This chapter aims to discuss a methodology for fusion-based ROI enhancement of backlit images.
Video enhancement and super-resolution

This study expects to enhance visual effects or visual features by exploring a novel Image Enhancement (IE) mechanism for low-resolution and low-quality video images. Consequently, an Attention Mechanism (AM)-improved General Adversarial Network (GAN) is proposed based on Deep Learning (DL), namely, the Least Squares GAN (LSGAN) model, which can deal with image quality degradation and Motion Blur (MB) problems. Specifically, the high-order gated AM is adopted to automatically learn different attention levels to realize the super-resolution and motion deblurring of the synthetic image. The results prove that the proposed model can better retain low-frequency content and deal with large-scale motion based on the fusion of nonlocal features and the time-domain information. The test of MB data sets shows that the proposed model can restore multidegradation in most cases; meanwhile, the proposed image quality restoration method is better on the synthetic dataset DIVerse 2K (div2k) and the real dataset GoPro (PSNR = 27.8 db and SSIM = 0.85), and there is no significant difference. Therefore this study uses multiframe fusion and nonlocal modules to process complex motion images, and has gained a good video IE effect.
Medical image denoising using convolutional neural networks

Medical image analysis involves correct interpretation of image data for the proper diagnosis to decide further course of treatment. There are different medical imaging modalities, such as magnetic resonance imaging, computed tomography, ultrasound, positron emission tomography, etc. Each imaging modality is used for the diagnosis of a particular disease according to its features. The image denoising is an important step of the medical image pre-processing part for accurate interpretation of image data. There are different image denoising techniques, such as transform domain-based, fuzzy logic-based, spatial domain-based, optimization-based, and machine learning-based methods. Out of these techniques, machine learning-based convolutional neural networks (CNNs) are widely being used for image denoising applications. The auto extraction of image features by the convolutional operation is the core functionality of CNNs. Moreover, CNN's design is appropriate for image data, and the concept of weight sharing reduces computational complexity as well. This chapter gives an overview of various medical image modalities and corresponding denoising CNN architectures. It has been observed that denoising CNN (DnCNN) Gaussian denoiser is the benchmark model for the design of medical imaging denoisers. The basic design unit is convolution, batch normalization, and rectified linear units. The 3D-parallel RicianNet for Rician Noise removal in MRI, two-stage residual CNN, and complex CNN medical image denoiser for CT images, mixed attention-based residual U-Net for speckle removal in ultrasound, and unsupervised learning with image priors for PET images have outperformed other state-of-the-art methods.
Applications of Data Security and Blockchain in Smart City Identity Management

Applications of blockchain might be related to healthcare and commercial sector. Need of such application is growing in smart cities. It has been observed that its implication are quite complex. The goal of the blockchain technology is to make it possible to store and distribute digital data without the risk of it being altered in transit. A blockchain provides the basis for immutable ledgers or transaction records that cannot be edited, erased, or destroyed. The term “blockchain” is often used to refer to DLT. Hashing is a sophisticated procedure that cannot be modified or reversed. When it comes to data security, it's all about safeguarding your information against unwanted access and corruption at every stage of its lifespan. Encryption, hashing, tokenization, and key management methods are all part of the data security process. The term “identity and access management” refers to all of the methods and technologies used inside a business to identify, authenticate, and authorize users to access services and systems within that business or other associated businesses.
Lifelong Text-Audio Sentiment Analysis learning

Sentiment analysis refers to the mining of textual context, which is conducted with the aim of identifying and extracting subjective opinions in textual materials. However, most existing methods neglect other important modalities, e.g., the audio modality, which can provide intrinsic complementary knowledge for sentiment analysis. Furthermore, much work on sentiment analysis cannot continuously learn new sentiment analysis tasks or discover potential correlations among distinct modalities. To address these concerns, we propose a novel Lifelong Text-Audio Sentiment Analysis (LTASA) model to continuously learn text-audio sentiment analysis tasks, which effectively explores intrinsic semantic relationships from both intra-modality and inter-modality perspectives. More specifically, a modality-specific knowledge dictionary is developed for each modality to obtain shared intra-modality representations among various text-audio sentiment analysis tasks. Additionally, based on information dependence between text and audio knowledge dictionaries, a complementarity-aware subspace is developed to capture the latent nonlinear inter-modality complementary knowledge. To sequentially learn text-audio sentiment analysis tasks, a new online multi-task optimization pipeline is designed. Finally, we verify our model on three common datasets to show its superiority. Compared with some baseline representative methods, the capability of the LTASA model is significantly boosted in terms of five measurement indicators.
Learning to Generate Tips from Song Reviews

Reviews of songs play an important role in online music service platforms. Prior research shows that users can make quicker and more informed decisions when presented with meaningful song reviews. However, reviews of songs are generally long in length and most of them are non-informative for users. It is difficult for users to efficiently grasp meaningful messages for making decisions. To solve this problem, one practical strategy is to provide tips, i.e., short, concise, empathetic, and self-contained descriptions about songs. Tips are produced from song reviews and should express non-trivial insights about the songs. To the best of our knowledge, no prior studies have explored the tip generation task in music domain. In this paper, we create a dataset named MTips for the task and propose a learning-to-generate framework named GenTMS for automatically generating tips from song reviews. The dataset involves 8,003 Chinese tips/non-tips from 128 songs which are distributed in five different song genres. Experimental results show that GenTMS achieves top-10 precision at 85.56%, outperforming the baseline models by at least 3.34%. Besides, to simulate the practical usage of our proposed framework, we also experiment with previously-unseen songs, during which GenTMS also achieves the best performance with top-10 precision at 78.89% on average. The results demonstrate the effectiveness of the proposed framework in tip generation of the music domain.
Multivariate analyses in paleobiogeography of Devonian rugose corals for revealing the multiple accretionary processes of the southwestern Central Asian Orogenic Belt

Biogeographic analysis has been applied on the globe- or orogen-scale tectonic evolution. The scarcity of paleobiogeographic study cases caused a debate as to the Late Paleozoic detailed accretion and convergence process of collages in the Central Asian Orogenic Belt. This contribution presents contrasting biota characteristics of the Early and Middle Devonian rugose corals in the Dananhu-Harlik Arc. By the multivariate analyses of the Early and Middle Devonian rugose coral faunas from series of the tectonic units in the Central Asian Orogenic Belt and neighboring areas, we reveal that two distinct paleobiogeographic units, namely, the Boreal and Tethyan Realms, were separated by the North Tianshan Ocean and Kalamaili Ocean in the Early and Middle Devonian, respectively. It means that the Dananhu-Harlik Arc experienced a paleobiogeographic change from the Boreal Realm in the Early Devonian to the Tethyan Realm in the Middle Devonian. Comprehensively considering the coral paleobiogeography, paleomagnetism, magmatism, provenance and distribution of ophiolites in the southwestern Central Asian Orogenic Belt, we speculate that the basal controlling factors of the Devonian coral paleobiogeographic pattern in the CAOB are the geographical isolation caused by the presence of oceanic basins, and the Mongolia and Kazakhstan Collages were separated by the North Tianshan Ocean sandwiched between the Dananhu-Harlik Arc and Central Tianshan Block during the Silurian to Early Devonian. In the Middle Devonian, the bidirectional and roll-back subduction of the North Tianshan Ocean caused the Dananhu-Harlik Arc separated from the Mongolia Collage by the Kalamaili Ocean and further moved towards the eastern Kazakhstan Collage. It caused the change of the rugose coral paleobiogeographic pattern in the southwestern Central Asian Orogenic Belt. The terminal convergence of Dananhu-Harlik Arc and Kazakhstan Collage occurred before the Latest Devonian and subsequently led to the formation of a new collage sandwiched by the Junggar-Kalamaili and South Tianshan Oceans.
Classification of Kidney Diseases Using Transfer Learning

A urologist confirms high risks of kidney stones just because of diabetic mellitus; however, other factors also exist, but a major cause is type 2 diabetes. Renal cyst and diabetes clinical features show 58% of affected subjects as the same. Research findings prove the high risk of renal cancer among diabetes patients. All these patients underwent abdominal MRI or CT scan to extract kidney high-definition 3D images. The dataset was gathered from two hospitals: the first is the SRMS IMS, and the second is the Bareilly MRI &amp; CT Scan Centre, both located in the city of Bareilly in the state of Uttar Pradesh of India. Research has been analyzed to note the classification among four classes using seven transfer learning methods. Results have been compared with seven transfer learning methods. The methods are EfficientNetB0, Xception, VGG16, ResNet50, MobileNet, InceptionV3, DenseNet121. Out of these deep learning-based algorithms, EfficientNetB0 shows the best accuracy of 96.02%.
A Self-adaptive neuroevolution approach to constructing Deep Neural Network architectures across different types

Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture design and its applications, while there is a lack of methods available across different DNN types concerning both their scale and performance. In this study, we propose a self-adaptive neuroevolution (SANE) approach to automatically construct various lightweight DNN architectures for different tasks. One of the key settings in SANE is the search space defined by cells and organs self-adapted to different DNN types. Based on this search space, a constructive evolution strategy with uniform evolution settings and operations is designed to grow DNN architectures gradually. SANE is able to self-adaptively adjust evolution exploration and exploitation to improve search efficiency. Moreover, a speciation scheme is developed to protect evolution from early convergence by restricting selection competition within species. To evaluate SANE, we carry out neuroevolution experiments to generate different DNN architectures including convolutional neural network, generative adversarial network and long short-term memory. The results illustrate that the obtained DNN architectures could have smaller scale with similar performance compared to existing DNN architectures. Our proposed SANE provides an efficient approach to self-adaptively search DNN architectures across different types. Our Code is available at https://doi.org/10.24433/CO.2989985.v1.
HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistance

As the number of users and the types of videos viewed increase, seamless video streaming services are becoming more important. Adaptive streaming aims to achieve high Quality of Experience (QoE) in time-varying network conditions. However, the existing schemes lack considerations for quality adaptation to improve QoE under dynamic network environments and multi-client competition. In this paper, we propose an HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistance. The proposed scheme uses reinforcement learning to generate robust adaptation policy for changes in the network conditions. Edge computing plays a role of managing multiple clients based on information about the network and client. The proposed scheme considers subjective quality, multiple videos, and learning variances to advance the adaptation policy. By balancing between individual QoE and QoE fairness, the proposed scheme enables multiple clients to utilize the bandwidth as much as required. Experimental results show that the proposed scheme has better performance for individual QoE and QoE fairness than the existing schemes.
Synthesis of 3D on-air signatures with the Sigma–Lognormal model

Signature synthesis is a computation technique that generates artificial specimens which can support decision making in automatic signature verification. A lot of work has been dedicated to this subject, which centres on synthesizing dynamic and static two-dimensional handwriting on canvas. This paper proposes a framework to generate synthetic 3D on-air signatures exploiting the lognormality principle, which mimics the complex neuromotor control processes at play as the fingertip moves. Addressing the usual cases involving the development of artificial individuals and duplicated samples, this paper contributes to the synthesis of: (1) the trajectory and velocity of entirely 3D new signatures; (2) kinematic information when only the 3D trajectory of the signature is known, and (3) duplicate samples of 3D real signatures. Validation was conducted by generating synthetic 3D signature databases mimicking real ones and showing that automatic signature verifications of genuine and skilled forgeries report performances similar to those of real and synthetic databases. We also observed that training 3D automatic signature verifiers with duplicates can reduce errors. We further demonstrated that our proposal is also valid for synthesizing 3D air writing and gestures. Finally, a perception test confirmed the human likeness of the generated specimens. The databases generated are publicly available, only for research purposes, at .
Importance filtered soft label-based deep adaptation network

Deep adaptation networks exploit domain-invariant features of the source and target domains with some label-induced losses that need ground-truth labels as inputs. As target ground-truth labels are unavailable, existing approaches often utilize target-predicted labels that could be expressed in different forms, to formulate the label-induced losses. Specifically, the form of a hard label classifies a sample into one definite category, but it is overconfident when the quality of predicted labels is poor. In contrast, the form of a soft label gives probability values to each category for a sample, which could mitigate the overconfident problem. However, the soft label would introduce too many small and noisy probability values. Therefore, both label forms may mislead the optimizing process of label-induced losses and produce a negative adaptation. To deal with this challenge, this paper maximizes the nuclear norm of a soft label matrix to filter noisy probability values out while only preserving those important ones, to produce a more reliable form of importance filtered soft label. Then, we utilize the proposed label form to reformulate several prevalent label-induced losses in a probability-weighted manner, so that they could be optimized more correctly during the training process and alleviate the negative adaptation accordingly. Besides, we conduct the proposed scheme in a deep neural network dubbed as importance filtered soft label-based deep adaptation network (IFSL-DAN). Finally, we evaluate the proposed IFSL-DAN with extensive experiments on three cross-domain datasets to demonstrate its effectiveness compared with some state-of-art domain adaptation methods.
The Effects of a Technology-Enriched Classroom on Student Learning

Smart classrooms are created by using smart environments equipped with various software and hardware devices and applications. The smart classroom is the integration of advanced teaching technologies into the classroom environment beyond the traditional classroom environment in order to contribute to the development of the student's learning ability and participation in the lesson. In general, the concept of the smart classroom will be discussed in this chapter from two aspects: (1) physical and (2) instructional. These two aspects are important in terms of showing how learning processes are affected in terms of educational technologies. This chapter also includes the outputs and experiences of the ongoing technology-enriched smart classroom project supported by a scientific research fund of Ege University.
Enabling scalable and unlinkable payment channel hubs with oblivious puzzle transfer

Payment channel networks (PCNs) are effective techniques for extending the scalability of cryptocurrencies. It achieves this by establishing a direct off-line channel from the sender to the receiver, going through one intermediary (aka. the hubs). In such scenarios, the hubs know the origin and destination of each transaction flowing through them, which jeopardizes the privacy of the underlying systems. Unfortunately, former efforts in ensuring transaction unlinkability either rely on trusted mixing services, are inefficient constructed (e.g., constructed inefficient cryptographic primitives), or have limited applicability. In this paper, we present ObliHub, an efficient payment channel scheme that conceals transaction direction information to the hubs. The core technique of ObliHub in achieving unlinkability is our tailored oblivious puzzle transfer protocol (OPT), which enables puzzle solving among the payer, the hub, and the receiver to be conducted in an oblivious manner – the hub center neither knows where a puzzle hint came from nor who acquired it. The implementation of ObliHub only requires efficient cryptographic primitives, and compared with A2L (a state-of-the-art Bitcoin-compatible PCH using homomorphic encryption), ObliHub saves 0.2 seconds in computation time over previous solutions and improves transfer throughput. Besides, our scheme is in accord with Universal Composability (UC) framework and we provide a comprehensive security analysis of it.