Reviewerly/exHarmony huggingface dataset:
A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer

Deep learning has shown its great potential in the field of image classification due to its powerful feature extraction ability, which heavily depends on the number of available training samples. However, it is still a huge challenge on how to obtain an effective feature representation and further learn a promising classifier by deep networks when faced with few-shot classification tasks. This paper proposes a multi-features adaptive aggregation meta-learning method with an information enhancer for few-shot classification tasks, referred to as MFAML. It contains three main modules, including a feature extraction module, an information enhancer, and a multi-features adaptive aggregation classifier (MFAAC). During the meta-training stage, the information enhancer comprised of some deconvolutional layers is designed to promote the effective utilization of samples and thereby capturing more valuable information in the process of feature extraction. Simultaneously, the MFAAC module integrates the features from several convolutional layers of the feature extraction module. The obtained features then feed into the similarity module so that implementing the adaptive adjustment of the predicted label. The information enhancer and MFAAC are connected by a hybrid loss, providing an excellent feature representation. During the meta-test stage, the information enhancer is removed and we keep the remaining architecture for fast adaption on the final target task. The whole MFAML framework is solved by the optimization strategy of model-agnostic meta-learner (MAML) and can effectively improve generalization performance. Experimental results on several benchmark datasets demonstrate the superiority of the proposed method over other representative few-shot classification methods.
Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

Social media platforms such as Twitter, Facebook, and Flicker, and the evolution of digital image capturing devices have resulted in the generation of a massive number of images.Thus, we experienced exponential growth in digital image repositories in the last decade.Content-based image retrieval (CBIR) has been extensively employed to reduce the dependency on textual annotations for image searching.Effective feature descriptor is mandatory to retrieve the most relevant images from the repository.Additionally, CBIR methods often experience the semantic gap problem, which must be addressed.In this paper, we propose a novel texture feature descriptor, Directional Magnitude Local Hexadecimal Patterns (DMLHP), based on texture orientation and magnitude to retrieve the most relevant images.The objective of the proposed feature descriptor is to examine the relationship of neighboring pixels with their adjacent neighbors based on texture orientation and magnitude.Our DMLHP texture descriptor is capable of capturing the texture and semantic information of the images effectively with the same visual content.Furthermore, the proposed method employs a learning-based approach to lessen the semantic gap problem and to improve the understanding of the contents of query images to retrieve the relevant images.The presented descriptor provides remarkable results by achieving the average retrieval precision (ARP) of 66%, 92%, 83%, average retrieval recall (ARR) of 66%, 92%, 83%, average retrieval specificity (ARS) of 99%, 99%, 76%, and average retrieval accuracy (ARA) of 98%, 99%, 85% on the AT&T, MIT Vistex, and Brodatz Texture image repositories, respectively.Our experiments reveal that the proposed DMLHP descriptor achieves far better performance, i.e., 95% on AT&T, 92% on BT, and 99% on MIT Vistex, when used with a learning-based approach over a non-learning-based approach (similarity measure).Experimental results show that the proposed texture descriptor outperforms state-of-the-art descriptors such as LNIP, LTriDP, LNDP, LDGP, LEPSEG, and CSLBP for CBIR.
A collaborative decision support system for multi-criteria automatic clustering

Automatic clustering is a challenging problem, especially when the decision-maker has little or no information about the nature of the dataset and the criteria of interest. There is a lack of generalizability in the current validity indexes (VI) for automatic clustering algorithms, as each considers a limited number of objectives and mostly ignores the other aspects of clustering validation. The proposed framework benefits from collaboration among selected evolutionary algorithms. A mixed-integer non-linear programming model is developed, and a framework is proposed for a six-step decision support system to solve it. The decision-maker (DM) selects the quantitative (primary) VIs and the evolutionary algorithms. Given DM's knowledge on the dataset and VIs, DM can incorporate qualitative (secondary) VIs. DM determines the quality threshold for each VI and runs the evolutionary algorithms separately. The DSS then saves the best obtained value of VIs in order to prepare the input necessary to construct the aggregated function. Based on the selected primary VIs, a new normalized aggregated function is developed and solved repeatedly using the randomly selected or predefined weights of importance. Eventually, DM employs a proper DEA model to define the final clustering output among all possible solutions. Given multiple efficient solutions, the best-worst method and a multi-criteria decision-making approach are applied to find the final output. The applicability of the proposed approach is illustrated on a synthetic and two secondary datasets, and the result at each step is discussed in detail.
Reality Transform Adversarial Generators for Image Splicing Forgery Detection and Localization

When many forgery images become more and more realistic with help of image editing tools and convolutional neural networks (CNNs), authenticators need to improve their ability to verify these forgery images. The process of generating and detecting forgery images is the same as the principle of Generative Adversarial Networks (GANs). In this paper, since the retouching progress of forgery images requires to suppress the tampering artifacts and to keep the structural information, we consider this retouching progress as an image style transform, and then propose a fake-to-realistic transform generator G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf> . For detecting the tampered regions, a localization generator G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf> is proposed too, which is based on a multi-decoder-single-task strategy. By adversarial training two generators, the proposed α-learnable whitening and coloring transform α-learnable WCT) block in G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf> automatically suppress the tampering artifacts in the forgery images. Meanwhile, the detection and localization abilities of G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf> will be improved by learning the forgery images retouched by G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf> . The experiment results demonstrate that the proposed two generators in GAN can simulate confrontation between the faker and the authenticator well; the localization generator G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf> outperforms the state-of-the-art methods in splicing forgery detection and localization on four public datasets.
SegRecon: Learning Joint Brain Surface Reconstruction and Segmentation from Images

Commonly-used tools for cortical reconstruction and parcellation, such as FreeSurfer, are central to brain surface analysis but require extensive computation times. This paper proposes SegRecon, a fast learning approach where an integrated end-to-end deep learning method does simultaneously reconstruct and segment cortical surfaces directly from an MRI volume, all in a single step. We train a volume-based neural network to predict, for each voxel, the signed distance to the white-to-grey-matter interface along with its corresponding spherical representation in the registered atlas space. The continuous representation of the spherical coordinates enables our approach to naturally extract an implicit isolevel surface for its reconstruction and obtain the parcel labels from the spherical atlas. We illustrate the advantages of our method with thorough experiments on the MindBoggle dataset. Our parcellation results show more than 4% improvements in average Dice accuracy with respect to FreeSurfer and a drastic speed-up from hours to seconds of computation.
The Effects of Service Crises and Recovery Resources on Market Reactions: An Event Study Analysis on Data Breach Announcements

Building on the literatures on service failure and crisis seriousness, we develop a framework to understand the effects of a specific type of service crisis (i.e., data breaches) and organizational recovery resources on the reactions of the stock market. To do so, we conduct an event study analysis with a sample of 217 data breach announcements, as our empirical context. Our analyses reveal that a firm suffers from negative abnormal stock returns when either the outcome of the breach (e.g., the breach of financial data) or its causal process (e.g., hacker attack) indicates a high level of seriousness. Moreover, considering organizational recovery resources, we find that in the case of financial data breaches, age, size, profitability, liquidity, and brand familiarity are the primary resources that can help a firm’s recovery. For hacker attacks, these organizational recovery resources include size, profitability, and liquidity.
An empirical study of various candidate selection and partitioning techniques in the DIRECT framework

Over the last three decades, many attempts have been made to improve the DIRECT (DIviding RECTangles) algorithm's efficiency. Various novel ideas and extensions have been suggested. The main two steps of DIRECT-type algorithms are selecting and partitioning potentially optimal rectangles. However, the most efficient combination of these two steps is an area that has not been investigated so far. This paper presents a study covering an extensive examination of various candidate selection and partitioning techniques within the same DIRECT algorithmic framework. Twelve DIRECT-type algorithmic variations are compared on 800 randomly generated GKLS-type test problems and 96 box-constrained global optimization problems from DIRECTGOLib v1.1 with varying complexity. Based on these studies, we have identified the most efficient selection and partitioning combinations leading to new, more efficient, DIRECT-type algorithms. All these algorithms are included in the latest version of DIRECTGO v1.1.0 and are publicly available.
Cross-Patch Graph Convolutional Network for Image Denoising

Recently, deep learning-based image denoising methods have achieved significant improvements over traditional methods. Due to the hardware limitation, most deep learning-based image denoising methods utilize cropped small patches to train a convolutional neural network to infer the clean images. However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image. In this paper, we propose Cross-Patch Net (CPNet), which is the first deep-learning-based real image denoising method for HR (high resolution) input. Furthermore, we design a novel loss guided by the noise level map to obtain better performance. Compared with the vanilla patch-based training strategies, our approach effectively exploits the cross-patch contextual dependency. Besides, owing to the difficulty in capturing real noisy and noise-free image paired training data, we propose an effective method to generate realistic sRGB noisy images from their corresponding clean sRGB images for denoiser training. Denoising experiments on real-world sRGB images show the effectiveness of the proposed method. More importantly, our method achieves state-of-the-art performance on practical sRGB noisy image denoising.
Exploring functions of and recollections with photos in the age of smartphone cameras

People often report taking photos to aid memory. Two mixed-method surveys were used to investigate participants’ reasons for taking photos, focusing specifically on memory-related reasons, which were split into two sub-types: photos taken as mementos, and photos taken as a means of offloading information. Participants reported their motivations for taking a sample of photos and then rated their recollective experience of each photographed event. Across both studies, participants reported recollecting events associated with a memento goal more vividly, more positively, and with more emotional intensity than events associated with an offloading goal. As expected, events photographed with a memento goal were also rated by participants to be more reflective of a shared memory system between the participants and the camera than were events photographed with an offloading goal. These findings suggest that people’s motivations when taking photos tend to be associated with different types of recollective experiences, as well as different judgments about where personal information is located in a blended human-camera memory system.
From SARS to COVID-19: Digital infrastructures of surveillance and segregation in exceptional times

The COVID-19 pandemic, an exceptional crisis, sparked the introduction of new digital infrastructure to halt the novel coronavirus's spread. This paper explores how such digital infrastructure's impact might reverberate over the long term, by comparing Singapore, Hong Kong, and mainland China's utilization of digital technology in response to the 2003 SARS outbreak, and their responses to the 2020 COVID-19 pandemic. We find that advancements in digital technology since 2003 have boosted governments' surveillance and segregation abilities substantially-most dramatically so in China. Even though some of these new digital interventions are ostensibly designed to be temporary ones to address the needs of the immediate crisis, we argue that the resultant extensions of state power experienced during COVID-19 are likely to have profound long-term effects because they fundamentally affect sociopolitical contexts, institutional capabilities, and digital cultures. We also find that the extent to which governments can extend digital surveillance and segregation abilities during the pandemic is contingent on their respective sociopolitical, institutional, and digital cultural contexts.
Loss functions for pose guided person image generation

Pose guided person image generation aims to transform a source person image to a target pose. It is an ill-posed problem as we often need to generate pixels that are invisible in the source image. Recent works focus on designing new architectures of deep neural networks and show promising performance. However, they simply adopt loss functions widely used in generic image generation tasks, e.g., adversarial loss, L1-norm loss, perceptual loss, and style loss, which fail to consider the unique structural patterns of a person. In addition, it remains unclear how each individual loss and their combinations impact the generated person images. The goal of this paper is to have a comprehensive study of loss functions for pose guided person image generation. After revisiting these generic loss functions, we consider the structural similarity (SSIM) index as a loss function since it is widely used as the evaluation metric and can capture the perceptual quality of generated images. In addition, motivated by the observation that a person can be divided into part regions with homogeneous pixel values or texture, we extend the SSIM loss into a novel Part-based SSIM (PSSIM) loss to explicitly account for the articulated body structure. A new PSSIM metric is then proposed naturally to access the quality of generated person images. In order to have a deep investigation of loss functions, we conduct extensive experiments including single-loss analysis, multi-loss combination analysis, optimal loss combination search, and comparison with state-of-the-art methods. Both quantitative and qualitative results indicate that (1) using different loss functions significantly impacts the generated person images, (2) the combination of adversarial loss, perceptual loss, and PSSIM loss is the optimal choice for person image generation, and (3) the proposed PSSIM loss is complementary to prior losses and helps improve the performance of state-of-the art methods. We have made the source code publicly available at https://github.com/shyern/Pose-Transfer-pSSIM.git.
Refactoring Techniques for Improving Software Quality: Practitioners’ Perspectives

Refactoring is a critical task in software maintenance and is commonly applied to improve system design or to cope with design defects. There are 68 different types of refactoring techniques and each technique has a particular purpose and effect. However, most prior studies have selected refactoring techniques based on their common use in academic research without obtaining evidence from the software industry. This is a shortcoming that points to the existence of a clear gap between academic research and the corresponding industry practices. Therefore, to bridge this gap, this study identified the most frequently used refactoring techniques, the commonly used programming language, and methods of applying refactoring techniques in the current practices of software refactoring among software practitioners in the industry, by using an online survey. The findings from the survey revealed the most used refactoring techniques, programming language, and the methods of applying the refactoring techniques. This study contributes toward the improvement of software development practices by adding empirical evidence on software refactoring used by software developers. The findings would be beneficial for researchers to develop reference models and software tools to guide the practitioners in using these refactoring techniques based on their effect on software quality attributes to improve the quality of the software systems as a whole.
Privacy-Preserving Coded Mobile Edge Computing for Low-Latency Distributed Inference

We consider a mobile edge computing scenario where a number of devices want to perform a linear inference <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${W}{x} $ </tex-math></inline-formula> on some local data <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {x}$ </tex-math></inline-formula> given a network-side matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {W}$ </tex-math></inline-formula> . The computation is performed at the network edge over a number of edge servers. We propose a coding scheme that provides information-theoretic privacy against <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> colluding (honest-but-curious) edge servers, while minimizing the overall latency—comprising upload, computation, download, and decoding latency—in the presence of straggling servers. The proposed scheme exploits Shamir’s secret sharing to yield data privacy and straggler mitigation, combined with replication to provide spatial diversity for the download. We also propose two variants of the scheme that further reduce latency. For a considered scenario with 9 edge servers, the proposed scheme reduces the latency by 8% compared to the nonprivate scheme recently introduced by Zhang and Simeone, while providing privacy against an honest-but-curious edge server.
Natural Language Processing based Human Assistive Health Conversational Agent for Multi-Users

Background: Most of the people are not medically qualified for studying or understanding the extremity of their diseases or symptoms. This is the place where natural language processing plays a vital role in healthcare. These chatbots collect patients' health data and depending on the data, these chatbot give more relevant data to patients regarding their body conditions and recommending further steps also. Purposes: In the medical field, AI powered healthcare chatbots are beneficial for assisting patients and guiding them in getting the most relevant assistance. Chatbots are more useful for online search that users or patients go through when patients want to know for their health symptoms. Methods: In this study, the health assistant system was developed using Dialogflow application programming interface (API) which is a Google's Natural language processing powered algorithm and the same is deployed on google assistant, telegram, slack, Facebook messenger, and website and mobile app. With this web application, a user can make health requests/queries via text message and might also get relevant health suggestions/recommendations through it. Results: This chatbot acts like an informative and conversational chatbot. This chatbot provides medical knowledge such as disease symptoms and treatments. Storing patients personal and medical information in a database for further analysis of the patients and patients get real time suggestions from doctors. Conclusion: In the healthcare sector AI-powered applications have seen a remarkable spike in recent days. This covid crisis changed the whole healthcare system upside down. So this NLP powered chatbot system reduced office waiting, saving money, time and energy. Patients might be getting medical knowledge and assisting ourselves within their own time and place.
Super-Resolved Recognition of License Plate Characters

Object detection and recognition are crucial in the field of computer vision and are an active area of research. However, in actual object recognition processes, recognition accuracy is often degraded due to resolution mismatches between training and test image data. To solve this problem, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique that improves object recognition accuracy. In detail, we collected a number of license plate training images through web-crawling and artificial data generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to image flips. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on representative test images and confirmed that the proposed super-resolution technique improves the accuracy of character recognition. For character recognition with the 4× magnification, the proposed method remarkably increased the mean average precision by 49.94% compared to the existing state-of-the-art method.