Reviewerly/exHarmony huggingface dataset:
Cretaceous climates: Mapping paleo-Köppen climatic zones using a Bayesian statistical analysis of lithologic, paleontologic, and geochemical proxies

The Cretaceous Period (145 to 66 Ma) was a prolonged warmhouse to hothouse period characterized by high atmospheric CO2 conditions, elevated surface temperatures, and an enhanced global hydrologic cycle. It provides a case study for understanding how a hothouse climate system operates, and is an analog for future anthropogenic climate change scenarios. This study presents new quantitative temperature and precipitation proxy datasets for nine key Cretaceous time slices (Berriasian/Valanginian, Hauterivian/Barremian, Aptian, Albian, Cenomanian, Turonian, Coniacian/Santonian, Campanian, Maastrichtian), and a new geostatistical analysis technique that utilizes Markov Chain Monte Carlo algorithm and Bayesian hierarchical models to generate high resolution, quantitative global paleoclimate reconstructions from these proxy datasets, with associated uncertainties. Using these paleoclimate reconstructions, paleo-Köppen (-Geiger) climate zone maps are produced that provide new insights into the changing spatial and temporal climate patterns during the Cretaceous. These new paleoclimate reconstructions and paleo-Köppen climate maps provide new insight into the timing of the initiation of the Early Cretaceous equatorial humid belt over Gondwana and reveal temporal shifts in the width of the subtropical arid belts from the Early to mid- to Late Cretaceous. A comparison of these proxy-based reconstructions and model simulations of Cretaceous climate reveal continued proxy/model differences. In addition, the methodology developed for this study can be applied to other time periods, providing a framework for better understanding ancient climate, environments, and ecosystems.
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning

Abstract Class imbalance occurs when the class distribution is not equal. Namely, one class is under-represented (minority class), and the other class has significantly more samples in the data (majority class). The class imbalance problem is prevalent in many real world applications. Generally, the under-represented minority class is the class of interest. The synthetic minority over-sampling technique (SMOTE) method is considered the most prominent method for handling unbalanced data. The SMOTE method generates new synthetic data patterns by performing linear interpolation between minority class samples and their K nearest neighbors. However, the SMOTE generated patterns do not necessarily conform to the original minority class distribution. This paper develops a novel theoretical analysis of the SMOTE method by deriving the probability distribution of the SMOTE generated samples. To the best of our knowledge, this is the first work deriving a mathematical formulation for the SMOTE patterns’ probability distribution. This allows us to compare the density of the generated samples with the true underlying class-conditional density, in order to assess how representative the generated samples are. The derived formula is verified by computing it on a number of densities versus densities computed and estimated empirically.
Why optics needs thickness

This study shows why and when optical systems need thickness as well as width or area. Wave diffraction explains the fundamental need for area or diameter of a lens or aperture to achieve some resolution or number of pixels in microscopes and cameras. This work demonstrates that if we know what the optics is to do, even before design, we can also deduce the minimum required thickness. This limit comes from diffraction combined with a concept called overlapping nonlocality C that can be deduced rigorously from just the mathematical description of what the device is to do. C expresses how much the input regions for different output regions overlap. This limit applies broadly to optics, from cameras to metasurfaces, and to wave systems generally.
Vision-Transformer-Based Transfer Learning for Mammogram Classification

Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.
A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN

In terms of the Internet and communication, security is the fundamental challenging aspect. There are numerous ways to harm the security of internet users; the most common is phishing, which is a type of attack that aims to steal or misuse a user’s personal information, including account information, identity, passwords, and credit card details. Phishers gather information about the users through mimicking original websites that are indistinguishable to the eye. Sensitive information about the users may be accessed and they might be subject to financial harm or identity theft. Therefore, there is a strong need to develop a system that efficiently detects phishing websites. Three distinct deep learning-based techniques are proposed in this paper to identify phishing websites, including long short-term memory (LSTM) and convolutional neural network (CNN) for comparison, and lastly an LSTM–CNN-based approach. Experimental findings demonstrate the accuracy of the suggested techniques, i.e., 99.2%, 97.6%, and 96.8% for CNN, LSTM–CNN, and LSTM, respectively. The proposed phishing detection method demonstrated by the CNN-based system is superior.
Remote sensing of photovoltaic scenarios: Techniques, applications and future directions

Developing solar photovoltaic (PV) systems is an effective way to address the problems of limited fossil fuel reserves, soaring world energy demand and global climate change. The earth observation information provides a promising perspective for estimating the PV energy potential and understanding the status of the PV system development, which is critical for making scientifically sound and cost-optimal sustainable planning strategies. Remote sensing (RS), a versatile technology that captures surface information at various temporal and spatial scales, is now widely applied in different fields of the PV development. However, despite the rapid growth of related research, there is still a lack of comprehensive review on the application of RS to different stages (i.e., planning, site selection, installation, maintenance, etc.) of the PV system development. This paper systematically reviews the research progress of RS technology applied throughout various stages of the PV system development. The reviewed literatures are organized as four major parts: i) PV potential estimation, ii) PV array detection, iii) PV fault monitoring and diagnosis, and iv) other cross-cutting areas where RS can facilitate PV development. We conclude that RS technology can bridge the gap caused by the traditional methods in effective assessment of resource potential, large-scale data analysis and PV health monitoring, which can provide strong support in assisting the planning, management, and decision-making of PV systems. Finally, we discuss future challenges and opportunities for RS technology in PV applications for advancing the research in this area.
Secret Data Transmission Using Advanced Morphological Component Analysis and Steganography

This paper mentions how morphological component analysis, deep learning, and steganography could be used to safeguard the dissemination, recognition, and affirmation of text-based pictures over one such Internet of Things–based link. Morphological component analysis has been utilized to extract features from text-based images. Every one of those attributes may indeed, in fact, also have different morphological components. A morphological component technique decreases not only redundant but also, moreover, statistically independent features, and the dimensions in bits of data of a text-based picture to an unexpected tier of pictorial performance. Instead of relying on a singular temporal feature descriptor, the ability to obtain detailed text-based picture texture (Guo et al., In Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV). France, Nice, 2003) characteristics proves to be an essential characteristic of the indicated methodology. For the purpose of constructing morphological components, four different textured aspects (which include horizontal and vertical) were postulated all across the whole paper. Without first being transmitted through the use of Internet of Things–based networks, the morphological portions of a concealed text-based picture may be further scattered and usually embedded into the least significant bit (LSB) of a cover pixel utilizing spatial obfuscation techniques. To fetch a message from stored images, a concealed key of such an embedding process can converse with such a text retrieval method at that recipient’s edge. Finally, a combined convolution neural network approach would be used to recognize an engrained text-based message. In addition, an optimization technique would be employed to improve the hybrid convolutional neural network (HCNN) effectiveness. The findings of the analyses reveal that perhaps the visual and statistical estimations of such a cover image of the morphological component of a textual image ever seen as insertion were indeed similar to a value system upon implantation, not only removing the possibility of a highly confidential utterance being found but also empowering encrypted systems. An experiment demonstrated that the recommended technique, which integrates morphological component analysis with Daubechies filters and steganography, outperforms Haar filtering methods and techniques in terms of peak signal-to-noise ratio, structural similarity index, and accuracy.
The rise of decentralized cryptocurrency exchanges: Evaluating the role of airdrops and governance tokens

While most cryptocurrency financial activity is conducted on centralized exchanges, decentralized finance (DeFi) has experienced a particular surge with roughly 90,000 users at the start of 2020 to 4.28 million by the end of 2021. Based on data collected from a popular crypto-asset data aggregation service and manually collected data, we document the rapid growth in decentralized exchanges and their differences in volume and price dynamics from centralized exchanges. Next, we investigate the role of airdrops and governance tokens as mechanisms for expanding the base of users and driving up the value of an exchange. While our results do not have a causal interpretation, they provide preliminary evidence that both mechanisms are effective for expanding and strengthening networks, particularly for decentralized exchanges. We also exploit two event studies that suggest the growth in decentralized exchanges is not driven by speculation, but at least partially by value-creating cybersecurity benefits.
Body Part-Based Representation Learning for Occluded Person Re-Identification

Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. However, training a part-based model is a challenging task for two reasons. Firstly, individual body part appearance is not as discriminative as global appearance (two distinct IDs might have the same local appearance), this means standard ReID training objectives using identity labels are not adapted to local feature learning. Secondly, ReID datasets are not provided with human topographical annotations. In this work, we propose BPBreID, a body part-based ReID model for solving the above issues. We first design two modules for predicting body part attention maps and producing body part-based features of the ReID target. We then propose GiLt, a novel training scheme for learning part-based representations that is robust to occlusions and non-discriminative local appearance. Extensive experiments on popular holistic and occluded datasets show the effectiveness of our proposed method, which outperforms state-of-the-art methods by 0.7% mAP and 5.6% rank-1 accuracy on the challenging Occluded-Duke dataset. Our code is available at https://github.com/VlSomers/bpbreid.
Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space

We propose a Cascaded Buffered IoU (C-BIoU) tracker to track multiple objects that have irregular motions and indistinguishable appearances. When appearance features are unreliable and geometric features are confused by irregular motions, applying conventional Multiple Object Tracking (MOT) methods may generate unsatisfactory results. To address this issue, our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space. In addition, to reduce the risk of overexpansion of the matching space, cascaded matching is employed: first matching alive tracks and detections with a small buffer, and then matching unmatched tracks and detections with a large buffer. Despite its simplicity, our C-BIoU tracker works surprisingly well and achieves state-of-the-art results on MOT datasets that focus on irregular motions and indistinguishable appearances. Moreover, the C-BIoU tracker is the dominant component for our 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> place solution in the CVPR’22 SoccerNet MOT and the ECCV’22 MOTComplex DanceTrack challenges. Finally, we analyze the limitation of our C-BIoU tracker in ablation studies and discuss its application scope.
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

Most modern frame interpolation approaches rely on explicit bidirectional optical flows between adjacent frames, thus are sensitive to the accuracy of underlying flow estimation in handling occlusions while additionally introducing computational bottlenecks unsuitable for efficient deployment. In this work, we propose a flow-free approach that is completely end-to-end trainable for multi-frame video interpolation. Our method, FLAVR, leverages 3D spatio-temporal kernels to directly learn motion properties from unlabeled videos and greatly simplifies the process of training, testing and deploying frame interpolation models. As a result, FLAVR delivers up to 6× speed up compared to the current state-of-the-art methods for multi-frame interpolation while consistently demonstrating superior qualitative and quantitative results compared with prior methods on popular benchmarks including Vimeo-90K, Adobe-240FPS, and GoPro. Finally, we show that frame interpolation is a competitive self-supervised pre-training task for videos via demonstrating various novel applications of FLAVR including action recognition, optical flow estimation, and video object tracking. Code and trained models are provided in the supplementary material.
A Cooperative Vehicle-Infrastructure System for Road Hazards Detection With Edge Intelligence

Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (hypertarget ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.
Resolving content moderation dilemmas between free speech and harmful misinformation

In online content moderation, two key values may come into conflict: protecting freedom of expression and preventing harm. Robust rules based in part on how citizens think about these moral dilemmas are necessary to deal with this conflict in a principled way, yet little is known about people's judgments and preferences around content moderation. We examined such moral dilemmas in a conjoint survey experiment where US respondents (N = 2, 564) indicated whether they would remove problematic social media posts on election denial, antivaccination, Holocaust denial, and climate change denial and whether they would take punitive action against the accounts. Respondents were shown key information about the user and their post as well as the consequences of the misinformation. The majority preferred quashing harmful misinformation over protecting free speech. Respondents were more reluctant to suspend accounts than to remove posts and more likely to do either if the harmful consequences of the misinformation were severe or if sharing it was a repeated offense. Features related to the account itself (the person behind the account, their partisanship, and number of followers) had little to no effect on respondents' decisions. Content moderation of harmful misinformation was a partisan issue: Across all four scenarios, Republicans were consistently less willing than Democrats or independents to remove posts or penalize the accounts that posted them. Our results can inform the design of transparent rules for content moderation of harmful misinformation.
A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education

The development of artificial intelligence in recent years has promoted the use of chatbot technology in sustainable education. Many studies examined the effect of chatbots on learning outcomes. However, scant studies summarized the effectiveness of chatbots in education. The aim of the study is to investigate the effect of chatbot-assisted learning on various components and how different moderator variables influenced the effectiveness. This study, through a meta-analysis, reviewed 32 empirical studies with 2201 participants published between 2010 and 2022. The results showed that chatbot technology exerted a medium-to-high effect on overall learning outcomes regardless of moderator variables, i.e., intervention duration, chatbot roles, and learning content. In terms of learning components, chatbots could significantly improve explicit reasoning, learning achievement, knowledge retention, and learning interest despite negative findings in critical thinking, learning engagement, and motivation. Future research could expand chatbot research by including different learning components.
Embedded Control Gate Fusion and Attention Residual Learning for RGB–Thermal Urban Scene Parsing

The semantic segmentation of road scenes is an important task in autonomous driving. Deep learning has enabled the development of a variety of semantic segmentation networks using RGB and depth data. However, poor lighting conditions and long-distance sensing limit the applicability of RGB and depth cameras. Nevertheless, many existing methods still rely on precise depth maps for scene segmentation. Unlike depth information, thermal imaging provides a visual heat representation that remains accurate under a variety of lighting conditions and over longer distances. For robust and accurate segmentation of scenes collected during autonomous driving, we used the advanced MobileNetV2 network for feature extraction and a fusion strategy with an embedded control gate. In addition, we adopted an encoder–decoder scheme for semantic segmentation and developed an attention residual learning strategy to restore the resolution of the feature map. Finally, semantic and boundary supervision is introduced to optimize parameters of the proposed network. Experimental results show that the proposed network outperforms existing networks on segmentation of urban scenes, and our network can be generalized to depth data.
A secure lightweight fuzzy embedder based user authentication scheme for internet of medical things applications

The Internet of Medical Things (IoMT) is a network of medical devices, hardware infrastructure, and software that allows healthcare information technology to be communicated over the web. The IoMT sensors communicate medical data to server for the quick diagnosis. As, it handles private and confidential information of a user, security is the primary objective. The existing IoT authentication schemes either using two-factor(Username, password) or multi-factor (username, password, biometric) to authenticate a user. Typically the structural characteristics-based biometric trait like Face, Iris, Palm print or finger print is used as a additional factor. There are chances that these biometrics can be fabricated. Thus, these structural biometrics based authentication schemes are fail to provide privacy, security, authenticity, and integrity. The biodynamic-based bioacoustics signals are gained attention in the era of human-computer interactions to authenticate a user as it is a unique feature to each user. So, we use a frequency domain based bio-acoustics as a biometric input. Thus, this work propose a Secure Lightweight Bioacoustics based User Authentication Scheme using fuzzy embedder for the Internet of Medical Things applications. Also, the IoT sensors tends to join and leave the network dynamically, the proposed scheme adopts chinese remainder technique for generate a group secret key to protect the network from the attacks of former sensor nodes. The proposed scheme’s security is validated using the formal verification tool AVISPA(Automated Validation of Internet Security Protocols and Applications). The system’s performance is measured by comparing the proposed scheme to existing systems in terms of security features, computation and communication costs. It demonstrates that the proposed system outperforms existing systems.