Reviewerly/exHarmony huggingface dataset:
FMRNet: A fused network of multiple tumoral regions for breast tumor classification with ultrasound images

Recent studies have illustrated that the peritumoral regions of medical images have value for clinical diagnosis. However, the existing approaches using peritumoral regions mainly focus on the diagnostic capability of the single region and ignore the advantages of effectively fusing the intratumoral and peritumoral regions. In addition, these methods need accurate segmentation masks in the testing stage, which are tedious and inconvenient in clinical applications. To address these issues, we construct a deep convolutional neural network that can adaptively fuse the information of multiple tumoral-regions (FMRNet) for breast tumor classification using ultrasound (US) images without segmentation masks in the testing stage.To sufficiently excavate the potential relationship, we design a fused network and two independent modules to extract and fuse features of multiple regions simultaneously. First, we introduce two enhanced combined-tumoral (EC) region modules, aiming to enhance the combined-tumoral features gradually. Then, we further design a three-branch module for extracting and fusing the features of intratumoral, peritumoral, and combined-tumoral regions, denoted as the intratumoral, peritumoral, and combined-tumoral module. Especially, we design a novel fusion module by introducing a channel attention module to adaptively fuse the features of three regions. The model is evaluated on two public datasets including UDIAT and BUSI with breast tumor ultrasound images. Two independent groups of experiments are performed on two respective datasets using the fivefold stratified cross-validation strategy. Finally, we conduct ablation experiments on two datasets, in which BUSI is used as the training set and UDIAT is used as the testing set.We conduct detailed ablation experiments about the proposed two modules and comparative experiments with other existing representative methods. The experimental results show that the proposed method yields state-of-the-art performance on both two datasets. Especially, in the UDIAT dataset, the proposed FMRNet achieves a high accuracy of 0.945 and a specificity of 0.945, respectively. Moreover, the precision (PRE = 0.909) even dramatically improves by 21.6% on the BUSI dataset compared with the existing method of the best result.The proposed FMRNet shows good performance in breast tumor classification with US images, and proves its capability of exploiting and fusing the information of multiple tumoral-regions. Furthermore, the FMRNet has potential value in classifying other types of cancers using multiple tumoral-regions of other kinds of medical images.
TSS: Transformation-Specific Smoothing for Robustness Certification

As machine learning (ML) systems become pervasive, safeguarding their security is critical. However, recently it has been demonstrated that motivated adversaries are able to mislead ML systems by perturbing test data using semantic transformations. While there exists a rich body of research providing provable robustness guarantees for ML models against Lp bounded adversarial perturbations, guarantees against semantic perturbations remain largely underexplored. In this paper, we provide TSS-a unified framework for certifying ML robustness against general adversarial semantic transformations. First, depending on the properties of each transformation, we divide common transformations into two categories, namely resolvable (e.g., Gaussian blur) and differentially resolvable (e.g., rotation) transformations. For the former, we propose transformation-specific randomized smoothing strategies and obtain strong robustness certification. The latter category covers transformations that involve interpolation errors, and we propose a novel approach based on stratified sampling to certify the robustness. Our framework TSS leverages these certification strategies and combines with consistency-enhanced training to provide rigorous certification of robustness. We conduct extensive experiments on over ten types of challenging semantic transformations and show that TSS significantly outperforms the state of the art. Moreover, to the best of our knowledge, TSS is the first approach that achieves nontrivial certified robustness on the large-scale ImageNet dataset. For instance, our framework achieves 30.4% certified robust accuracy against rotation attack (within ±30°) on ImageNet. Moreover, to consider a broader range of transformations, we show TSS is also robust against adaptive attacks and unforeseen image corruptions such as CIFAR-10-C and ImageNet-C.
Data Augmentation for Scene Text Recognition

Scene text recognition (STR) is a challenging task in computer vision due to the large number of possible text appearances in natural scenes. Most STR models rely on synthetic datasets for training since there are no sufficiently big and publicly available labelled real datasets. Since STR models are evaluated using real data, the mismatch between training and testing data distributions results into poor performance of models especially on challenging text that are affected by noise, artifacts, geometry, structure, etc. In this paper, we introduce STRAug which is made of 36 image augmentation functions designed for STR. Each function mimics certain text image properties that can be found in natural scenes, caused by camera sensors, or induced by signal processing operations but poorly represented in the training dataset. When applied to strong base-line models using RandAugment, STRAug significantly increases the overall absolute accuracy of STR models across regular and irregular test datasets by as much as 2.10% on Rosetta, 1.48% on R2AM, 1.30% on CRNN, 1.35% on RARE, 1.06% on TRBA and 0.89% on GCRNN. The diversity and simplicity of API provided by STRAug functions enable easy replication and validation of existing data augmentation methods for STR. STRAug is available at https://github.com/roatienza/straug.
Taking DevOps mainstream: a critical review and conceptual framework

Agile development and operations (DevOps) is a model for delivering software as a service that comprises a set of practices and tools combining the responsibilities for both developing and operating the software into a single team. DevOps has been shown to offer advantages over traditional, waterfall-based software development methods. Agile software development practices have radically changed how developers realize software, breaking traditional structures. DevOps enables the team to continuously deliver software. The scope of DevOps is different, as it focuses on the relation between the development and operations teams. Both the academic literature and business press opine on DevOps implementation challenges and failures. The literature on DevOps is abundant but fails to convey with clarity what DevOps is. The lack of a homogeneous and clear conceptualization of DevOps is considered a major obstacle to the diffusion of this methodology.We perform a systematic literature review to identify, review, and synthesise all the relevant studies published in the field of information systems and software engineering between 2012 and 2020. As a result of our review, we derive a conceptual model providing a logical categorisation and a clear definition of the conceptual elements of DevOps.
IRANet: Identity-relevance aware representation for cloth-changing person re-identification

Existing person re-identification methods mainly focus on searching the target person across disjoint camera views in a short period of time. With this setting, these methods rely on the assumption that both query and gallery images of the same person have the same clothing. To tackle the challenges of clothing changes over a long duration, this paper proposes an identity-relevance aware neural network (IRANet) for cloth-changing person re-identification. Specifically, a human head detection module is designed to localize the human head part with the help of the human parsing estimation. The detected human head part contains abundant identity information, including facial features and head type. Then, raw person images in conjunction with detected head areas are respectively transformed into feature representation with the feed-forward network. The learned features of raw person images contain more attributes of global context, meanwhile the learned features of head areas contain more identity-relevance attributes. Finally, a head-guided attention module is employed to guide the global features learned by raw person images to focus more on the identity-relevance head areas. The proposed method achieves mAP accuracy of 25.4% on the Celeb-reID-light dataset, 19.0% on the Celeb-reID dataset, and 53.0% (Cloth-changing setting) on the PRCC dataset, which shows the superiority of our approach for the cloth-changing person re-identification task.
Aerogeophysical systematics applied to the geological reconnaissance of the Eastern Pernambuco-Alagoas Domain, Borborema Province: The Ipojuca-Atalaia Batholith

Over the past few decades, geological and geophysical studies in the Borborema Province (NE Brazil) have evolved considerably, especially in the North and Central subprovinces. However, the Pernambuco-Alagoas Domain, inserted in the South subprovince, does not have, up to now, detailed geophysical and geological studies, mainly due to a thick soil profile, which make difficulty the geological survey in the region. The aerogeophysical study of an area located in the Ipojuca-Atalaia Batholith, Eastern part of the Pernambuco-Alagoas Domain allowed the delimitation of the lithogeophysical and magnetic geophysical domains, main directions of the lineaments, main shear zones, and their behavior in depth through the upward continuation and the Euler Deconvolution. The set of integrated magnetometric data revealed that the region was intensely deformed by a set of transcurrent shear zones of NE-SW direction. Aerogammaspectrometric data enabled the improvement of the geological mapping (1) of the Western part of the Ipojuca-Atalaia Batholith granitic plutons and gneisses and (2) of the limits of the batholith with the adjoining crustal sub-domains. The integration of the whole set of aero magnetometric and aero gammaspectrometric data also provides a basis for a better understanding of the tectonic evolution of the studied area and the Ipojuca-Atalaia Batholith.
Is chatting with a sophisticated chatbot as good as chatting online or FTF with a stranger?

Emotionally-responsive chatbots are marketed as agents with which one can form emotional connections. They can also become weak ties in the outer layers of one's acquaintance network and available for social support. In this experiment, which was designed to study the acquaintance process, we randomly assigned 417 participants into three conditions: face-to-face (FTF) chat with a human, online chat with a human, and online chat with a commercially-available, emotionally-responsive chatbot, Replika. After a 20-min getting-acquainted chat, participants reported their affective state and relational evaluations of the chat. Additionally, all chats were recorded and text analyzed using the Linguistic Inquiry and Word Count (LIWC) program. In all conditions, participants reported moderate levels of positive emotions and low levels of negative emotions. Those who chatted FTF with a human reported significantly more negative emotions than those who chatted with a bot. However, those who chatted with a human also reported more homophily with and liking of their chat partner and that their partner was more responsive. Meanwhile, participants had fewest conversational concerns with the chatbot. These findings have implications for future computer-mediated interaction studies: conversations with chatbots appear to have different affordances and effects on chatter enjoyment and conversational concerns in getting-acquainted contexts. These results may help designers improve reception and marketability for chatbots in consumer markets.
GRAD-CAM Guided Channel-Spatial Attention Module for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is becoming an important research field, due to its wide applications and the rapid development of computer vision technologies. The current state-of-the-art (SOTA) methods in the FGVC usually employ attention mechanisms to first capture the semantic parts and then discover their subtle differences between distinct classes. The existing attention modules have significantly improved the classification performance but they are poorly guided since part-based detectors in the FGVC depend on the network learning ability without the supervision of part annotations. As obtaining such part annotations is labor-expensive, some visual localization and explanation methods, such as gradient-weighted class activation mapping (Grad-CAM), can be utilized for supervising the attention mechanism. In this paper, we propose a Grad-CAM guided channel-spatial attention module for the FGVC, which employs the Grad-CAM to supervise and constrain the attention weights by generating the coarse localization maps. To demonstrate the effectiveness of the proposed method, we conduct comprehensive experiments on three popular FGVC datasets, including CUB-200-2011, Stanford Cars, and FGVC-Aircraft datasets. The proposed method outperforms the SOTA attention modules in the FGVC task. In addition, visualizations of the feature maps demonstrate the superiority of the proposed method against the SOTA approaches.
Laplacian Feature Pyramid Network for Object Detection in VHR Optical Remote Sensing Images

Except for multiscale features, high-frequency features are also crucial for the identification of many objects in object detection for very high resolution optical remote sensing (VHR-ORS) images but have not been considered yet. Due to the fact that the Laplacian pyramid consists of high-frequency information at each level, we propose a Laplacian feature pyramid (FP) network (LFPN) considering both low-frequency features and high-frequency features based on FP structure to improve the object detection performance of VHR-ORS images. FP-based structures are efficient to represent multiscale features. But, in general, FP-based structures, high-frequency features are not specially considered. Such high-frequency features are important to distinguish many ground objects with sufficient details. For example, texture features are critical to distinguish basketball_court and tennis_court. The construction of LFPN consists of a bottom-up pathway, Laplacian pathway, and a fusion pathway, which generate low-frequency pyramid, high-frequency pyramid, and compound pyramid, respectively. The bottom-up pathway follows the computation flow of the backbone convolutional neural networks (CNNs) which is similar to general FP-based structures. The Laplacian pathway extracts the high-frequency features of objects through a trainable Laplacian operator. Finally, the low-frequency and high-frequency FPs are fused to generate the compound pyramid in efficient ways. To evaluate the performance of LFPN, we embed LFPN into both two-stage object detection (T-LFPN) systems and single-stage object detection (S-LFPN) systems to conduct experiments. Experiments on a public challenging ten-class data set NWPU VHR-10 demonstrate the superior performance of LFPN in both T-LFPN and S-LFPN systems and state-of-the-art performance of LFPN-based detectors.
News-based business sentiment and its properties as an economic index

This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years’ worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient r=0.937) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-of-the-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.
A Survey of Quantum Key Distribution (QKD) Network Simulation Platforms

The exponential development in Quantum Computing is becoming a threat to the existing asymmetric cryptographic algorithms like RSA (Rivest, Shamir, Adleman). A threat to these public key cryptographic algorithms is a threat to the entire Internet itself, as algorithms like RSA are the basic building blocks of existing secure Internet communication. Thus, a new area of quantum cryptography emerged, that lead to the development of Post-Quantum Cryptography (PQC) algorithms and Quantum-safe Internet protocol Quantum Key Distribution (QKD). These systems, which are generally known as Quantum Cryptographic Systems, have many practical limitations that make them complex and expensive to be implemented or even experimented with, in a real-life scenario. Therefore, to develop new protocols and analyze the feasibility of the existing protocols in a cost-effective manner, we need efficient simulation frameworks. So, in this paper, we are profiling some of the recent Quantum Cryptographic based networks and Quantum Key Distribution (QKD) simulation frameworks and conducting a systematic and thorough analysis of the implementation and functionalities of these experimental platforms, with an aim to build a universal quantum testbed in the future.
Explainable neural network-based approach to Kano categorisation of product features from online reviews

The Kano model is an extensively used technique for understanding different types of customer preferences. It classifies product features based on the effects of their performance on the overall customer satisfaction. Compared to surveys, numerous online reviews can be easily collected at a lower cost. This paper proposes an explainable neural network-based approach for the Kano categorisation of product features from online reviews. First, product feature words are identified by clustering nouns based on word embedding. Subsequently, the sentiments of the product feature words are determined by conducting the Vader sentiment analysis. Finally, the effects of the sentiments of each product feature on the star rating are estimated using explainable neural networks. Based on their effects, the product features are classified into the Kano categories. A case study of three Fitbit models is performed to validate the proposed approach. The Kano categorisation by the proposed approach is compared with the results of a previous product feature word clustering and ensemble neural network-based method. The results exhibit that the former presents a more reliable performance than the latter. The proposed approach is automated after providing several hyperparameters and can assist companies in conducting the Kano analysis with increased speed and efficiency.
Adversarial Attacks against LiDAR Semantic Segmentation in Autonomous Driving

Today, most autonomous vehicles (AVs) rely on LiDAR (Light Detection and Ranging) perception to acquire accurate information about their immediate surroundings. In LiDAR-based perception systems, semantic segmentation plays a critical role as it can divide LiDAR point clouds into meaningful regions according to human perception and provide AVs with semantic understanding of the driving environments. However, an implicit assumption for existing semantic segmentation models is that they are performed in a reliable and secure environment, which may not be true in practice. In this paper, we investigate adversarial attacks against LiDAR semantic segmentation in autonomous driving. Specifically, we propose a novel adversarial attack framework based on which the attacker can easily fool LiDAR semantic segmentation by placing some simple objects (e.g., cardboard and road signs) at some locations in the physical space. We conduct extensive real-world experiments to evaluate the performance of our proposed attack framework. The experimental results show that our attack can achieve more than 90% success rate in real-world driving environments. To the best of our knowledge, this is the first study on physically realizable adversarial attacks against LiDAR point cloud semantic segmentation with real-world evaluations.
Single image super-resolution based on a modified U-net with mixed gradient loss

Single image super-resolution (SISR) is the task of inferring a high-resolution image from a single low-resolution image. Recent research on super-resolution has achieved great progress because of the development of deep convolutional neural networks in the field of computer vision. Existing super-resolution methods have high performances in mean square error (MSE), but most methods fail to reconstruct an image with shape edges. To solve this problem, the mixed gradient error, which is composed by MSE and mean gradient error, is proposed and applied to a modified U-net network. The modified U-net removes all batch normalization layers and one of the convolution layers in each block. The operation reduces the parameter number and therefore accelerates the model. Compared with the existing SISR algorithms, the proposed method has better performance and time consumption. The experiments demonstrate that modified U-net with mixed gradient loss yields high-level results on three widely used datasets, SET14, BSD300 and Manga109, and outperforms other state-of-the-art methods on the text dataset, ICDAR2003. Code is available online