Reviewerly/exHarmony huggingface dataset:
The Challenges of Continuous Self-Supervised Learning

Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks in representation learning - the need for human annotations. As a result, SSL holds the promise to learn representations from data in-the-wild, i.e., without the need for finite and static datasets. Instead, SSL should exploit the continuous stream of data being generated on the internet or by agents exploring their environments. In this work, we investigate whether traditional self-supervised learning approaches would be effective deployed in-the-wild by conducting experiments on the continuous self-supervised learning problem. In this setup, models should learn from a continuous (infinite) non-IID data stream that follows a non-stationary distribution of visual concepts. The goal is to learn representations that are robust, adaptive yet not forgetful of concepts seen in the past. We show that a direct application of current methods to continuous SSL is 1) inefficient both computationally and in the amount of data required, 2) leads to inferior representations due to temporal correlations (non-IID data) in the streaming sources and 3) exhibits signs of catastrophic forgetting when trained on sources with non-stationary data distributions. We study the use of replay buffers to alleviate the issues of inefficiency and temporal correlations, and enhance them by actively maintaining the least redundant samples in the buffer. We show that minimum redundancy (MinRed) buffers allow us to learn effective representations even in the most challenging streaming scenarios (e.g., sequential frames obtained from a single embodied agent), and alleviates the problem of catastrophic forgetting.
DPICT: Deep Progressive Image Compression Using Trit-Planes

We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS). First, we transform an image into a latent tensor using an analysis network. Then, we represent the latent tensor in ternary digits (trits) and encode it into a compressed bitstream trit-plane by trit-plane in the decreasing order of significance. Moreover, within each trit-plane, we sort the trits according to their rate-distortion priorities and transmit more important information first. Since the compression network is less optimized for the cases of using fewer tritplanes, we develop a postprocessing network for refining reconstructed images at low rates. Experimental results show that DPICT outperforms conventional progressive codecs significantly, while enabling FGS transmission. Codes are available at https://github.com/jaehanlee-mcl/DPICT.
Cybersecurity risks and central banks’ sentiment on central bank digital currency: Evidence from global cyberattacks

This paper distinguishes two types of cybersecurity risks—emerging risks from the private sector and systematic risks to the financial system—and examines their impacts on central banks' sentiment on CBDC in a context of global cyberattacks. We find that central banks' sentiment toward CBDC shifted positively following increased losses from cyberattacks on crypto assets, which implies that central banks believe CBDC could be a public solution to protect consumers from risks arising from the private sector. However, their sentiment toward CBDC significantly declined following more active cyberwarfare attacks, which could pose systematic risks to the national financial system.
Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan

Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues.
Rate-1 Non-Interactive Arguments for Batch-NP and Applications

We present a rate-1 construction of a publicly verifiable non-interactive argument system for batch-NP (also called a BARG), under the LWE assumption. Namely, a proof corresponding to a batch of k NP statements each with an m-bit witness, has size $m+poly(\lambda, log k)$.In contrast, prior work either relied on non-standard knowledge assumptions, or produced proofs of size m. poly $(\lambda, \log k)$ (Choudhuri, Jain, and Jin, STOC 2021, following Kalai, Paneth, and Yang 2019).We show how to use our rate-l BARG scheme to obtain the following results, all under the LWE assumption:•A multi-hop BARG scheme for NP.•A multi-hop aggregate signature scheme (in the standard model).•An incrementally verifiable computation (IVC) scheme for arbitrary T-time deterministic computations with proof size poly $(\lambda, log T)$.Prior to this work, multi-hop BARGs were only known under non-standard knowledge assumptions or in the random oracle model; aggregate signatures were only known under indistinguishability obfuscation (and RSA) or in the random oracle model; IVC schemes with proofs of size poly $(\lambda, T^{\epsilon})$ were known under a bilinear map assumption, and with proofs of size poly $(\lambda, log T)$ under non-standard knowledge assumptions or in the random oracle model.
A Lightweight Object Detector Based on Spatial-Coordinate Self-Attention for UAV Aerial Images

Object detection is one of the most widespread applications for numerous Unmanned Aerial Vehicle (UAV) tasks. Due to the shooting angle and flying height of the UAV, compared with general scenarios, small objects account for a large proportion of aerial images, and common object detectors are not extremely effective in aerial images. Moreover, since the computing resources of UAV platforms are generally limited, the deployment of common detectors with a large number of parameters on UAV platforms is difficult. This paper proposes a lightweight object detector YOLO-UAVlite for aerial images. Firstly, the spatial attention module and coordinate attention module are modified and combined to form a novel Spatial-Coordinate Self-Attention (SCSA) module, which integrates spatial, location, and channel information to enhance object representation. On this basis, we construct a lightweight backbone, named SCSAshufflenet, which combines the Enhanced ShuffleNet (ES) network with the proposed SCSA module to improve feature extraction and reduce model size. Secondly, we propose an improved feature pyramid model, namely Slim-BiFPN, where we construct new lightweight convolutional blocks to reduce the information loss during the feature map fusion process while reducing the model weights. Finally, the localization loss function is modified to increase the bounding box regression rate while improving the localization accuracy. Extensive experiments conducted on the VisDrone-DET2021 dataset indicate that, compared with the YOLOv5-N baseline, the proposed YOLO-UAVlite reduces the number of parameters by 25.8% and achieves gains of 10.9% in mAP0.50. Compared with other lightweight detectors, both the mAP and the number of parameters are improved.
A Survey on 6G Enabled Light Weight Authentication Protocol for UAVs, Security, Open Research Issues and Future Directions

This paper demonstrates a broad exploration of existing authentication and secure communication of unmanned aerial vehicles (UAVs) in a ‘6G network’. We begin with an overview of existing surveys that deal with UAV authentication in 6G and beyond communications, standardization, applications and security. In order to highlight the impact of blockchain and UAV authentication in ‘UAV networks’ in future communication systems, we categorize the groups in this review into two comprehensive groups. The first group, named the Performance Group (PG), comprises the performance-related needs on data rates, latency, reliability and massive connectivity. Meanwhile, the second group, named the Specifications Group (SG), is included in the authentication-related needs on non-reputability, data integrity and audit ability. In the 6G network, with blockchain and UAV authentication, the network decentralization and resource sharing would minimize resource under-utilization thereby facilitating PG targets. Furthermore, through an appropriate selection of blockchain type and consensus algorithms, the SG’s needs of UAV authentication in 6G network applications can also be readily addressed. In this study, the combination of blockchain and UAV authentication in 6G network emergence is reviewed as a detailed review for secure and universal future communication. Finally, we conclude on the critical identification of challenges and future research directions on the subject.
Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions

Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users’ emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users’ emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems.
A comprehensive survey of the R-CNN family for object detection

Object detection using deep learning, one of the most challenging problems in computer vision, seeks to locate instances of objects from a large number of predefined categories in natural images. Given this period of rapid evolution, the main contribution of this paper is to provide a comprehensive survey of the region-based convolutional neural network (R-CNN) family (R-CNN, Fast R-CNN, and Faster R-CNN). In comparison to the R-CNN and Fast R-CNN, simulation results show that the faster R-CNN improves not only accuracy but also detection speed. For robust object detection, it has been found that the Faster R-CNN is particularly suited for this purpose. We conclude with several open issues and challenges that are keys to the design of future work.
Efficient neural codes naturally emerge through gradient descent learning

Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from the less frequent diagonal ones. Here we find that artificial neural networks trained to recognize objects also have patterns of sensitivity that match the statistics of features in images. To interpret these findings, we show mathematically that learning with gradient descent in neural networks preferentially creates representations that are more sensitive to common features, a hallmark of efficient coding. This effect occurs in systems with otherwise unconstrained coding resources, and additionally when learning towards both supervised and unsupervised objectives. This result demonstrates that efficient codes can naturally emerge from gradient-like learning.
Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images

Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtype classification study. In this paper, we propose a convolutional neural network (CNN)-based breast cancer classification method for hematoxylin and eosin (H&E) whole slide images (WSIs). The proposed method incorporates fused mobile inverted bottleneck convolutions (FMB-Conv) and mobile inverted bottleneck convolutions (MBConv) with a dual squeeze and excitation (DSE) network to accurately classify breast cancer tissue into binary (benign and malignant) and eight subtypes using histopathology images. For that, a pre-trained EfficientNetV2 network is used as a backbone with a modified DSE block that combines the spatial and channel-wise squeeze and excitation layers to highlight important low-level and high-level abstract features. Our method outperformed ResNet101, InceptionResNetV2, and EfficientNetV2 networks on the publicly available BreakHis dataset for the binary and multi-class breast cancer classification in terms of precision, recall, and F1-score on multiple magnification levels.
CWOA: A novel complex-valued encoding whale optimization algorithm

The whale optimization algorithm (WOA) is inspired by humpback whales’ bubble-net assaulting mechanism and imitates behaviors such as shrinking and enveloping prey, spiral bubble-net assaulting prey, and variational searching for prey to determine the best solution. However, the basic WOA has the disadvantages of low computation precision, slow convergence rate and easily falling into search stagnation. To strengthen the optimization quality and search reliability, this paper presents a distinctive complex-valued encoding WOA (CWOA) to satisfy the function optimization and engineering design. The complex-valued methodology utilizes a diploid structure to encode individual whales, and the real and imaginary components are added to the basic WOA to revise the position of each humpback whale, which converts the two-dimensional encoding area to a one-dimensional expression area and utilizes the real and imaginary components to illustrate a feasible solution with inherent parallelism. This methodology enriches the population diversity, furthers the individual information, elevates the general search ability, avoids premature convergence and promotes convergence efficiency. The CWOA not only utilizes the characteristics of the complex-valued methodology to avoid slipping into local optima but also exhibits excellent adaptability and robustness to determine the accurate value. Sixteen benchmark test functions and eight engineering designs are used to verify the applicability and practicability of the CWOA. The experimental results demonstrate that the optimization productivity and search performance of the CWOA are superior to those of other algorithms. In addition, the CWOA is a more successful and efficacious method that completely stabilizes exploration and exploitation to establish a quicker convergence rate, higher computation precision, greater resilience and stability.
HNERec: Scientific collaborator recommendation model based on heterogeneous network embedding

Scientific collaboration contributes to the innovation of science and technology. For individual scholars, it is valuable to find potential and valuable collaborators to improve the quality and quantity of research. Previous research mainly recommends collaborators by exploring one kind of relationship between authors, such as the research topics or co-authorship. Some research tries to utilize more relationships, but they simply integrate the recommendation results of different relationships rather than integrating them. This study proposes a novel Heterogeneous Network Embedding Recommendation (HNERec) model for scientific collaborator recommendation. The HNERec model employs a random walk to develop a heterogeneous network considering four kinds of meta-path illustrating the topic relationship, authorship, citation relationship, and venue relationship. It utilizes the skip-gram model to embed the nodes, and finally generates a recommendation list based on the similarity between the corresponding node vectors. Experiments on a DBLP dataset and an AMiner dataset demonstrate the reliability of the HNERec model. The HNERec model outperforms the mainstream collaborator recommendation baselines in metrics such as F1, MRR, and nDCG. The results can not only be applied to the selection of collaborators by individual scholars but also allow scientific and conference institutions to recommend collaborators.
Semantic Communication System Based on Semantic Slice Models Propagation

Traditional communication systems treat messages’ semantic aspects and meaning as irrelevant to communication, revealing its limitations in the era of artificial intelligence (AI), such as communication efficiency and intent-sharing among different entities. Through broadening the scope of the traditional communication system and the AI-based encoding techniques, in this manuscript, we present a novel semantic communication system, which involves the essential semantic information exploration, transmission and recovery for more efficient communications. Compared to other state-of-the-art semantic communication-related works, our proposed semantic communication system is characterized by the “flow of the intelligence” via the propagation of the model. Besides, the concept of semantic slice-models (SeSM) is proposed to enable flexible model-resembling under the different requirements of the model performance, channel situation and transmission goals. Specifically, a layer-based semantic communication system for images (LSCI) is built on the simulation platform to demonstrate the feasibility of the proposed system and a novel semantic metric called semantic service quality (SS) is proposed to evaluate the semantic communication systems. We evaluate the proposed system on Cityscapes and Open Images datasets, resulting in averaged 10% and 2% bit rate reduction over JPEG and JPEG2000, respectively. In comparison to LDPC, the proposed channel coding scheme can averagely save 2dB and 5dB in AWGN channel and Rayleigh fading channel, respectively.
Machine learning can aid in prediction of IDH mutation from H&amp;E-stained histology slides in infiltrating gliomas

Abstract While Machine Learning (ML) models have been increasingly applied to a range of histopathology tasks, there has been little emphasis on characterizing these models and contrasting them with human experts. We present a detailed empirical analysis comparing expert neuropathologists and ML models at predicting IDH mutation status in H&amp;E-stained histology slides of infiltrating gliomas, both independently and synergistically. We find that errors made by neuropathologists and ML models trained using the TCGA dataset are distinct, representing modest agreement between predictions (human-vs.-human κ = 0.656; human-vs.-ML model κ = 0.598). While no ML model surpassed human performance on an independent institutional test dataset (human AUC = 0.901, max ML AUC = 0.881), a hybrid model aggregating human and ML predictions demonstrates predictive performance comparable to the consensus of two expert neuropathologists (hybrid classifier AUC = 0.921 vs. two-neuropathologist consensus AUC = 0.920). We also show that models trained at different levels of magnification exhibit different types of errors, supporting the value of aggregation across spatial scales in the ML approach. Finally, we present a detailed interpretation of our multi-scale ML ensemble model which reveals that predictions are driven by human-identifiable features at the patch-level.
The effect of audio on the experience in virtual reality: a scoping review

The use of virtual reality (VR) has seen significant recent growth and presents opportunities for use in many domain areas. The use of head-mounted displays (HMDs) also presents unique opportunities for the implementation of audio feedback congruent with head and body movements, thus matching intuitive expectations. However, the use of audio in VR is still undervalued and there is a lack of consistency within audio-centedd research in VR. To address this shortcoming and present an overview of this area of research, we conducted a scoping review (n = 121) focusing on the use of audio in HMD-based VR and its effects on user/player experience. Results show a lack of standardisation for common measures such as pleasantness and emphasize the context-specific ability of audio to influence a variety of affective, cognitive, and motivational measures, but are mixed for presence and generally lacking for social experiences and descriptive research.