Reviewerly/exHarmony huggingface dataset:
A complementary dual-backbone transformer extracting and fusing weak cues for object detection in extremely dark videos

Reliable object detection under dark environment is of wide applications but severely challenged by heavy noise washing out informative features and uneven radiance caused by nighttime illuminations. These unique features of dark videos would largely degenerate the performance of existing detectors. To address this issue, specially designed algorithms being able to extract and fuse the weak features buried in the low-quality videos are of vital importance. Bearing these in mind, we propose illumination-aware spatio-temporal feature fusion modules for low-light video object detection and implement a Dark Video Detector under a TRansformer network structure, dubbed as DVD-TR. Firstly, we use a dual-backbone Transformer to extract separate complementary features and fuse them to strengthen the network’s feature extraction capability. Secondly, we incorporate a spatio-temporal sampling mechanism to aggregate features from multiple frames, which can enhance detection accuracy in dark videos. Thirdly, we use a small encoder–decoder network to obtain irradiance distribution which is further incorporated for illumination-aware feature fusion. Extensive experiments on large-scale multi-illuminance dark video benchmark show that DVD-TR outperforms state-of-the-art video detectors by a large margin and validate the effectiveness of the proposed approach.
A Multi-Key with Partially Homomorphic Encryption Scheme for Low-End Devices Ensuring Data Integrity

In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. Hardware and software limitations of IoT devices can be mitigated using the massive on-demand cloud resources. However, IoT cloud-based solutions pose some security and privacy concerns, specifically when an untrusted cloud is used. This calls for strong encryption schemes that allow operations on data in an encrypted format without compromising the encryption. This paper presents an asymmetric multi-key and partially homomorphic encryption scheme. The scheme provides the addition operation by encrypting each decimal digit of the given integer number separately using a special key. In addition, data integrity processes are performed when an untrusted third party performs homomorphic operations on encrypted data. The proposed work considers the most widely known issues like the encrypted data size, slow operations at the hardware level, and high computing costs at the provider level. The size of generated ciphertext is almost equal to the size of the plaintext, and order-preserving is ensured using an asymmetrical encryption version.
Combating fraudulent returns using blockchain technology

Abstract Fraudulent returns have become increasingly frequent in the development of online retail platforms, leading to huge losses for retailers. A blockchain‐based system can help eliminate this problem primarily because the data stored in a blockchain network cannot be modified. In this paper, we study the impact of blockchain technology that aims to eliminate fraudulent return behavior on retailers and their upstream suppliers. We develop a game‐theoretic model that comprises a supplier, a retailer, and customers and analyze the equilibrium outcomes. The results show that the retailer would (not) adopt blockchain technology if the efficiency of the supplier's investment in the innovation of its products is high (low). Moreover, if the retailer's loss from the return is low (high), the adoption of blockchain technology decreases (increases) the supplier's investment and the wholesale and retail prices. In terms of its effect on profitability, we reveal that if the fraudulent return losses are high, using blockchain technology always benefits the supplier; otherwise, it increases retailer profitability and supply chain performance but may make the supplier suffer losses in profits. Interestingly, the retailer would adopt blockchain technology to ensure profitability in situations where the number of customers without fraudulent returns and the intensity of losses are low; however, this weakens supplier profitability and meanwhile reduces supply chain performance.
Exploring the Process and Strategies of Chinese–English Abstract Writing Using Machine Translation Tools

The present study explores English as a foreign language (EFL) learners’ processes and strategies when using machine translation (MT) tools in academic abstract writing. Eight EFL graduate students were introduced to translation-friendly writing strategies using Google Translate and were required to produce an English abstract with the aid of a machine translation tool. The study used qualitative and quantitative approaches in data collection and analysis. A triangulation process was developed and implemented, including think-aloud protocols during the writing session, surveys, and individual interviews after the writing session. The findings suggested that the translation-friendly writing strategies introduced to the participants were useful in enhancing the quality of their writing. Each participant demonstrated individual strategic uses of MT. Among the various strategies reported, back translation was the most commonly adopted one; that is, they first composed an abstract in Chinese (L1) and engaged in multiple rounds of translation between Chinese and English using MT; when problems were identified in the English abstract, they modified the Chinese abstract using translation-friendly writing strategies to enhance the quality of MT translation output. Most of the translation problems identified by the participants were related to non-academic expressions. While participants were satisfied with the quality of the abstracts produced with the aid of MT, they raised ethical concerns regarding the use of MT in academic writing. These findings suggest that MT has fundamentally changed the process of academic writing in English and call for the re-examination of the purpose of academic writing instruction and the approaches employed.
Gaussian similarity-based adaptive dynamic label assignment for tiny object detection

Benefiting from the advanced deep learning techniques, significant achievements have been made in generic object detection. Tiny object detection (TOD) is a challenging task in computer vision due to the low resolution, insufficient geometric cues, and high noise levels. A recent trend for detectors is introducing more granular label assignment strategies to provide promising supervision information for classification and regression. However, most previous Intersection-Over-Union (IoU) based methods suffer from two main drawbacks, including (1) low tolerance of IoU for bounding box deviations in tiny objects and (2) deficient guidance for optimization caused by inter-sample and intra-sample imbalance. We propose two novel components to address these problems: the Gaussian probabilistic distribution-based fuzzy similarity metric (GPM) and the adaptive dynamic anchor mining strategy (ADAS). GPM aims to address the issue of inaccurate similarity measurement between small bounding boxes and pre-defined anchors, providing a more accurate basis for label assignment. ADAS adopts a dynamically adjusted strategy for label assignment to address the distribution bias between positive and negative samples, ensuring that the label assignment is consistent with the distribution of objects in the image. Extensive experiments are conducted on AI-TODv2 and other tiny object detection datasets to evaluate the proposed ADAS-GPM method’s performance. The results demonstrate that incorporating ADAS-GPM into an anchor-based object detector yields significant outperformance over state-of-the-art methods on the challenging AI-TODv2 benchmark. The proposed ADAS-GPM method exhibits good results, clearly demonstrating its validity and potential.
palaeoverse: A community‐driven R package to support palaeobiological analysis

Abstract The open‐source programming language ‘R' has become a standard tool in the palaeobiologist's toolkit. Its popularity within the palaeobiological community continues to grow, with published articles increasingly citing the usage of R and R packages. However, there are currently a lack of agreed standards for data preparation and available frameworks to support the implementation of such standards. Consequently, data preparation workflows are often unclear and not reproducible, even when code is provided. Moreover, due to a lack of code accessibility and documentation, palaeobiologists are often forced to ‘reinvent the wheel’ to find solutions to issues already solved by other members of the community. Here, we introduce palaeoverse , a community‐driven R package to aid data preparation and exploration for quantitative palaeobiological research. The package is freely available and has three core principles: (1) streamline data preparation and analyses; (2) enhance code readability; and (3) improve reproducibility of results. To develop these aims, we assessed the analytical needs of the broader palaeobiological community using an online survey, in addition to incorporating our own experiences. In this work, we first report the findings of the survey, which shaped the development of the package. Subsequently, we describe and demonstrate the functionality available in palaeoverse and provide usage examples. Finally, we discuss the resources we have made available for the community and our future plans for the broader Palaeoverse project. palaeoverse is a community‐driven R package for palaeobiology, developed with the intention of bringing palaeobiologists together to establish agreed standards for high‐quality quantitative research. The package provides a user‐friendly platform for preparing data for analysis with well‐documented open‐source code to enhance transparency. The functionality available in palaeoverse improves code reproducibility and accessibility, which is beneficial for both the review process and future research.
Remote Sensing Image Retrieval by Deep Attention Hashing With Distance-Adaptive Ranking

With the joint advancement of numerous related fields of remote sensing, the amount of remote sensing data is growing exponentially. As an essential remote sensing Big Data management technique, content-based remote sensing image retrieval has attracted more and more attention. A novel deep attention hashing with distance-adaptive ranking (DAH) is proposed for remote sensing image retrieval in this article. First, a channel-spatial joint attention mechanism is employed for feature extraction of remote sensing images to make the proposed DAH method focus more on the critical details of the remote sensing images and suppress irrelevant regional responses. Second, a novel balanced pairwise weighted loss function is proposed to enable discrete hash codes to participate in neural network training, which contains pairwise weighted similarity loss, classification loss, and quantization loss. The pairwise weighted similarity loss is designed to decrease the impact of the imbalance of positive and negative sample pairs. The classification loss and quantization loss are added to the loss function to decrease background interference and information loss during the quantization phase, respectively. Finally, a distance-adaptive ranking strategy with category-weighted Hamming distance is presented in the retrieval phase to utilize the category probability information fully. Experiments on benchmark datasets compared with state-of-the-art methods demonstrate the effectiveness of the proposed DAH method.
Does Active Learning Reduce Human Coding?: A Systematic Comparison of Neural Network with nCoder

In quantitative ethnography (QE) studies which often involve large datasets that cannot be entirely hand-coded by human raters, researchers have used supervised machine learning approaches to develop automated classifiers. However, QE researchers are rightly concerned with the amount of human coding that may be required to develop classifiers that achieve the high levels of accuracy that QE studies typically require. In this study, we compare a neural network, a powerful traditional supervised learning approach, with nCoder, an active learning technique commonly used in QE studies, to determine which technique requires the least human coding to produce a sufficiently accurate classifier. To do this, we constructed multiple training sets from a large dataset used in prior QE studies and designed a Monte Carlo simulation to test the performance of the two techniques systematically. Our results show that nCoder can achieve high predictive accuracy with significantly less human-coded data than a neural network.
Examining logistics developments in post-pandemic Japan through sentiment analysis of Twitter data

The objective of this study is to utilize natural language processing technologies to examine data gathered from Twitter related to logistics in Japan during the COVID-19 pandemic. The Bidirectional Encoder Representations from Transformers (BERT) machine learning model is utilized to assess the sentiment of the content. The findings suggest a positive outlook on logistics during time frame analyzed. This research has four key implications: (1) the sentiment towards the term "logistics" is generally positive as per our analysis; (2) there is a trend of increasing interest in logistics in western Japan in 2022; (3) social media can be utilized as a tool to address the challenges faced by the logistics industry; and (4) our research highlights the potential of using social media data to provide a more timely and comprehensive analysis of logistics and transportation trends.
LSTM Neural Network Assisted Regex Development for Qualitative Coding

Regular expression (regex) based automated qualitative coding helps reduce researchers’ effort in manually coding text data, without sacrificing transparency of the coding process. However, researchers using regex based approaches struggle with low recall or high false negative rate during classifier development. Advanced natural language processing techniques, such as topic modeling, latent semantic analysis and neural network classification models help solve this problem in various ways. The latest advance in this direction is the discovery of the so called “negative reversion set (NRS)”, in which false negative items appear more frequently than in the negative set. This helps regex classifier developers more quickly identify missing items and thus improve classification recall. This paper simulates the use of NRS in real coding scenarios and compares the required manual coding items between NRS sampling and random sampling in the process of classifier refinement. The result using one data set with 50,818 items and six associated qualitative codes shows that, on average, using NRS sampling, the required manual coding size could be reduced by 50% to 63%, comparing with random sampling.
Demystifying Random Number in Ethereum Smart Contract: Taxonomy, Vulnerability Identification, and Attack Detection

Recent years have witnessed explosive growth in blockchain smart contract applications. As smart contracts become increasingly popular and carry trillion dollars worth of digital assets, they become more of an appealing target for attackers, who have exploited vulnerabilities in smart contracts to cause catastrophic economic losses. Notwithstanding a proliferation of work that has been developed to detect an impressive list of vulnerabilities, the bad randomness vulnerability is overlooked by many existing tools. In this article, we make the first attempt to provide a systematic analysis of random numbers in Ethereum smart contracts, by investigating the principles behind pseudo-random number generation and organizing them into a taxonomy. We also lucubrate various attacks against bad random numbers and group them into four categories. Furthermore, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RNVulDet</i> – a tool that incorporates taint analysis techniques to automatically identify bad randomness vulnerabilities and detect corresponding attack transactions. To extensively verify the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RNVulDet</i> , we construct three new datasets: i) 34 well-known contracts that are reported to possess bad randomness vulnerabilities, ii) 214 popular contracts that have been rigorously audited before launch and are regarded as free of bad randomness vulnerabilities, and iii) a dataset consisting of 47,668 smart contracts and 49,951 suspicious transactions. We compare <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RNVulDet</i> with three state-of-the-art smart contract vulnerability detectors, and our tool significantly outperforms them. Meanwhile, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RNVulDet</i> spends 2.98 s per contract on average, in most cases orders-of-magnitude faster than other tools. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RNVulDet</i> successfully reveals 44,264 attack transactions. Our implementation and datasets are released, hoping to inspire others.
A Specific Earthquake Processing Workflow for Studying Long‐Lived, Explosive Volcanic Eruptions With Application to the 2008 Okmok Volcano, Alaska, Eruption

Abstract By providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high‐noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single‐channel template matching combined with machine‐learning and fingerprint‐based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high‐resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption.
A deep learning architecture with an object-detection algorithm and a convolutional neural network for breast mass detection and visualization

This study presents an integrated deep learning architecture with an object-detection algorithm and a convolutional neural network (CNN) for breast mass detection and visualization. Mammograms are analyzed to identify and localize breast mass lesions to aid clinician review. Two complementary forms of deep learning are used to identify the regions of interest (ROIs). An object-detection algorithm, YOLO v5, analyzes the entire mammogram to identify discrete image regions likely to represent masses. Object detections exhibit high precision, but the object-detection stage alone has insufficient overall accuracy for a clinical application. A CNN independently analyzes the mammogram after it has been decomposed into subregion tiles and is trained to emphasize sensitivity (recall). The ROIs identified by each analysis are highlighted in different colors to facilitate an efficient staged review. The CNN stage nearly always detects tumor masses when present but typically occupies a larger area of the image. By inspecting the high-precision regions followed by the high-sensitivity regions, clinicians can quickly identify likely lesions before completing the review of the full mammogram. On average, the ROIs occupy less than 20% of the tissue in the mammograms, even without removing pectoral muscle from the analysis. As a result, the proposed system helps clinicians review mammograms with greater accuracy and efficiency.
Per-title and per-segment CRF estimation using DNNs for quality-based video coding

Nowadays, video content accounts for a large percentage of network traffic. Most streaming services use HTTP Adaptive Streaming by splitting the video in non-overlapping segments and by encoding each video segment independently (possibly with multiple representations to allow adaptation to the varying network conditions). In this work we propose an encoding scheme based on a Deep Neural Network (DNN), to perform a per-title and per-segment adaptive estimation of the encoding parameter to achieve a target video quality of the encoded video. A dataset has been prepared using 1212 segments obtained from 158 videos. The segments have been encoded with 19 Constant Rate Factor (CRF) values using the VP9 encoder, generating a total of 23028 encoded segments, and for each encoded video segment, its Video Multi-Method Assessment Fusion (VMAF) quality has been computed. Besides, from a 240p downscaled version of the segments a feature vector has been obtained, and an analysis of the dependency of the features with the resolution has been carried out. With this dataset a DNN has been trained to estimate the CRF to be applied to each segment to achieve a target VMAF quality. Results show that the trained network is able to provide the CRF value to be applied to each video segment to achieve the desired quality with low computational overhead. To validate the proposal, the network has been used to predict the CRF to encode 1840 two-second segments, not used during the training process, using the VP9 codec at Full High Definition (FHD) resolution, and four target quality values. Results show that the system adapts the CRF to each segment and that the final videos have a mean deviation of 1.84% with respect to the requested VMAF value.
A survey on privacy preservation techniques for blockchain interoperability

The prosperity of blockchain has spawned numerous blockchain-based platforms with different structures and consensus mechanisms, further increasing the attention of blockchain interoperability for the interactions of different platforms. The interoperability supports the advanced development of blockchain technology by enabling value and information interactions of different chains. Considering the characteristics of openness and zero-trust of blockchain, privacy preservation is essential for blockchain interoperability technology. However, the state-of-the-art still lacks a comprehensive investigation of existing privacy preservation techniques for blockchain interoperability. To fill the research gap, this paper conducts an extensive survey on current privacy-preserving techniques in hash-locking-based, sidechains/relays-based and notary-scheme-based interoperability technology. Specifically, we put forward a series of evaluation criteria, including confidentiality, anonymity, unlinkability, conditional traceability, auditability, scalability, universality, efficiency, fairness, atomicity, and fault tolerance, to judge the effectiveness and weaknesses of the surveyed techniques. In the end, we discuss unsolved problems in the existing literature and propose promising directions for future research.