Reviewerly/exHarmony huggingface dataset:
An empirical study on real bug fixes from solidity smart contract projects

Smart contracts are pieces of code that reside inside the blockchains and can be triggered to execute any transaction when specifically predefined conditions are satisfied. Being commonly used for commercial transactions in blockchain makes the security of smart contracts particularly important. Over the last few years, we have seen a great deal of academic and practical interest in detecting and fixing the bugs in smart contracts written by Solidity. But little is known about the real bug fixes in Solidity smart contract projects. To understand the bug fixes and enrich the knowledge of bug fixes in real-world projects, we conduct an empirical study on historical bug fixes from 46 real-world Solidity smart contract projects in this paper. We provide a multi-faceted discussion and mainly explore the following four questions: File Type and Amount, Fix Complexity, Bug distribution, and Fix Patches. We distill four findings during the process to explore these four questions. Finally, based on these findings, we provide actionable implications to improve the current approaches to fixing bugs in Solidity smart contracts from three aspects: Automatic repair techniques, Analysis tools, and Solidity developers.
MorphAttnNet: An Attention-based morphology framework for lung cancer subtype classification

Lung cancer is recognized as the most life-threatening cancer among other type of cancers all over the world. Early stage recognition and proper diagnosis can increase the five-year survival rate and also save the patient life. Accurate identification of lung cancer subtype from histopathological images plays an important role and help doctors to take necessary decisions for lung cancer treatment. Therefore, in this work, a new deep learning (DL) framework based on image morphology is developed for lung cancer subtype classification. The proposed Morphology-based Attention Network, (MorphAttnNet) can classify lung benign, adenocarcinoma (ADC), and squamous cell carcinoma (SCC) from histopathology images. The framework is designed based on convolution and morphological operations. Attention-based mechanism is incorporated to select important features from histopathology images. The framework with its morphology-based path combined with attention blocks is able to capture morphological variations of lung cancer subtypes accurately and effectively. Finally, the extracted deep features from convolution and morphological paths are combined and used for lung cancer subtype classification. The performance of the proposed framework is analyzed on publicly available LC25000 dataset and achieved a sensitivity, specificity, average accuracy, precision, and f1-score of 98.33%, 97.76%, 98.96%, 99.12%, and 98.72% respectively for lung cancer subtype classification. The proposed system is also compared with existing state-of-the-art systems and achieved considerable performance indices for lung cancer subtype classification.
Deepfakes: evolution and trends

Abstract This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.
Memristive chaotic system-based hybrid image encryption application with AES and RSA algorithms

Abstract The widespread use of information and communication tools today facilitates information access and highlights the significance of information and data security. In recent years, chaos-based encryption systems have emerged as a promising approach for protecting the confidentiality of transmitted images. In particular, memristor-based hyperchaotic systems have attracted significant attention because of their robustness and complexity. In this paper, we propose an image encryption model that employs a two-stage encryption method using various chaotic systems, including the logistic map, Lorenz chaotic system, and memristor-based hyperchaotic system, with AES and RSA encryption algorithms. The proposed hybrid scheme applies bit-based pixel diffusion and confusion techniques to improve the security of encrypted images. Statistical and security tests are conducted to compare the performance of the different encryption systems and algorithms and to present the measurement values obtained from the analysis. Our experimental results demonstrate the effectiveness of the proposed image encryption scheme in terms of security, speed, and reliability and provide valuable insights for the development of future chaos-based encryption systems.
ARist: An effective API argument recommendation approach

Learning and remembering to use APIs are difficult. Several techniques have been proposed to assist developers in using APIs. Most existing techniques focus on recommending the right API methods to call, but very few techniques focus on recommending API arguments. In this paper, we propose ARist, a novel automated argument recommendation approach which suggests arguments by predicting developers’ expectations when they define and use API methods. To implement this idea in the recommendation process, ARist combines program analysis (PA), language models (LMs), and several features specialized for the recommendation task which consider the functionality of formal parameters and the positional information of code elements (e.g., variables or method calls) in the given context. In ARist, the LMs and the recommending features are used to suggest the promising candidates identified by PA. Meanwhile, PA navigates the LMs and the features working on the set of the valid candidates which satisfy syntax, accessibility, and type-compatibility constraints defined by the programming language in use. Our evaluation on a large dataset of real-world projects shows that ARist improves the state-of-the-art approach by 19% and 18% in top-1 precision and recall for recommending arguments of frequently-used libraries. For general argument recommendation task, i.e., recommending arguments for every method call, ARist outperforms the baseline approaches by up to 125% top-1 accuracy. Moreover, for newly-encountered projects, ARist achieves more than 60% top-3 accuracy when evaluating on a larger dataset. For working/maintaining projects, with a personalized LM to capture developers’ coding practice, ARist can productively rank the expected arguments at the top-1 position in 7/10 requests.
A collaborative gated attention network for fine-grained visual classification

Fine-grained image classification aims at subdividing large coarse-grained categories into finer-grained subcategories. Most existing fine-grained research methods use a single attention mechanism or multiple sub-networks to zoom in and find distinguishable local feature regions. These models seldom explore the intrinsic connections between cross-layer features with similar semantic features. This tends to show erratic performance in images with complex backgrounds. To this end, we propose a feature-semantic fusion module to enhance the diversity of global feature information. Second, we employ cross-layer spatial attention and channel attention modules, which can accurately locate local key regions of images. Finally, we propose a cross-gate attention module that can find rich discriminative features from key object regions of images to guide the final classification. Experiments show that the proposed model performs well on three datasets: CUB-200-2011, Stanford cars, and FGVC aircraft.
Human-Friendly RDF Graph Construction: Which One Do You Chose?

Knowledge Graphs (KGs) are a powerful mechanism to structure and organize data on the Web. RDF KGs are usually constructed by declaring a set of mapping rules, specified according to the grammar of a mapping language (e.g., RML), that relates the input data sources to a domain vocabulary. However, the verbosity and (manual) definition of these rules affect their global adoption. Several user-friendly serializations for different mapping languages were proposed to facilitate users with the definition of such rules, e.g., YARRRML, SMS2, XRM, or ShExML. Still, most of them do not cover all features of the mapping languages for RDF graph construction (e.g., constructing RDF-star), or they lack tooling support. In this paper, (i) we present a set of updates over the YARRRML serialisation to empower it with the latest necessities for constructing RDF graphs; (ii) we implement these new features in a new open-source translator, Yatter, currently used in different real-use cases and international projects; and (iii) we qualitatively compare our proposal against similar state-of-the-art serialisations, and their associated translators over a set of conformance test cases. Our proposal advances the declarative construction of RDF graphs and supports users in choosing an appropriate serialisation and translator for their use cases.
Blockchain-Based Lightweight Message Authentication for Edge-Assisted Cross-Domain Industrial Internet of Things

In edge-assisted cross-domain Industrial Internet of Things (IIoT), blockchain-based authentication is an effective way to build cross-domain trust and secure cross-domain data. However, existing authentication schemes still have serious challenges in terms of efficiency and security. In this paper, we propose a blockchain-based lightweight message authentication scheme. First, to address efficiency challenges, we build a blockchain-enabled edge-assisted lightweight authentication framework. This framework uses edge servers to assist smart devices in achieving cross-domain authentication and effectively reduce redundant interactions between entities. Second, to resolve the security challenges, we design a lightweight message authentication algorithm for cross-domain IIoT. The algorithm guarantees message security with low computational overhead and is suitable for multi-receiver cross-domain IIoT. The security proof and analysis demonstrate that the proposed scheme is secure under the random oracle model and can resist various attacks. The performance evaluation shows that our proposed scheme is superior in terms of computation and communication overhead when compared with other related schemes.
An Improved Reptile Search Algorithm with Ghost Opposition-based Learning for Global Optimization Problems

Abstract In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.
ChatGPT in the Classroom: A Preliminary Exploration on the Feasibility of Adapting ChatGPT to Support Children’s Information Discovery

The influence of ChatGPT and similar models on education is being increasingly discussed. With the current level of enthusiasm among users, ChatGPT is envisioned as having great potential. As generative models are unpredictable in terms of producing biased, harmful, and unsafe content, we argue that they should be comprehensively tested for more vulnerable groups, such as children, to understand what role they can play and what training and supervision are necessary. Here, we present the results of a preliminary exploration aiming to understand whether ChatGPT can adapt to support children in completing information discovery tasks in the education context. We analyze ChatGPT responses to search prompts related to the 4th grade classroom curriculum using a variety of lenses (e.g., readability and language) to identify open challenges and limitations that must be addressed by interdisciplinary communities.
ISS Monocular Depth Estimation Via Vision Transformer

Monocular depth estimation can be used as an energy-efficient and lightweight backup system to the onboard light detection and ranging (LiDAR) instrument. Unfortunately, the monocular depth estimation is an ill-posed problem. Therefore typical methods resort to statistical distributions of image features. In this work, a deep neural network that exploits Vision Transformer in the encoder is trained in a supervised fashion to solve the regression problem. Specifically, the problem considered is monocular depth estimation with images taken at different positions and distances from the International Space Station (ISS) to measure the network performance in a rendezvous maneuver.
Automatic Modulation Classification for Adaptive OFDM Systems Using Convolutional Neural Networks With Residual Learning

Automatic modulation classification (AMC) is becoming a promising technique for future adaptive wireless transceiver systems. The existing blind modulation classification (BMC) methods for orthogonal frequency division multiplexing (OFDM) fail to achieve the required performance by using statistical-based methods. Thus, the modulation classification research community is trying to adopt the deep learning (DL) method to improve the modulation classification accuracy. However, most of the existing DL methods for AMC of OFDM that involve the extraction of statistical features from the signal do not work for adaptive transceiver systems where the signal parameters are changed dynamically. In this paper, we design and implement AMC for adaptive OFDM systems by using a convolutional neural network (CNN) with residual learning. The proposed AMC can identify the modulation format of the received OFDM signal with different number of subcarriers, randomized carrier frequency offset (CFO), symbol timing offset (STO), phase offset, and unknown channel state information. We use residual learning to mitigate the effect of varying CFO, STO, and AWGN noise on the received OFDM signal. A larger pool of modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> /4-QPSK, minimum shift keying, 8-PSK, 16-quadrature amplitude modulation (QAM), and 64-QAM are being considered for the proposed AMC for OFDM system in a dynamic environment. The performance and complexity of the proposed AMC are compared with the existing statistical feature-based and DL-based approaches. The proposed AMC for the OFDM system is also verified on the real-time data set generated from the universal software radio peripheral testbed setup.
Real-Time Pedestrian Detection using YOLO

Detecting pedestrians in a crowded scene in real time is a challenging task in monitoring and managing crowd. Many researchers around the world have addressed this task and managed to achieve satisfactory results. However, the problem of automating detection of pedestrians in the crowd is still an open issue depending on the density of crowd in a scene. To ensure safety and security, automating the crowd detection and tracking process in real time is necessary in designing a robust and secure system. Detecting and localizing objects has successfully aided in identifying the major problems with detecting pedestrians and has been a major step forward in managing crowd automatically. In this paper, we have used tiny YOLOv4. YOLO (You Only Look Once) has proved quite useful in detecting and localizing objects in an image with impressive response speed. YOLO network usually scales an entire image into fixed sized grids and then identifies and detects the region into these grids using bounding boxes. Using transfer learning on an already trained YOLO inception model on COCO dataset, detection of pedestrians in surveillance videos is handled. The paper discusses the implementation and detection performance of the proposed YOLOv4 tiny model on the UCSD pedestrian Detection dataset with promising results.
Real-Time Face Mask Detector

When the COVID-19 pandemic was present we all have faced various challenges in our everyday lives. Pandemic created worldwide emergency which led to the lockdown in most of the countries resulting into disruption in world trade and movement. To reduce the covid cases WHO recommended to wear mask and maintain social distancing and then these steps became mandatory for some places, such as restaurants, hospitals, grocery stores etc., The purpose of limiting interaction with individuals as much as possible in such situations is simply defeated by having a staff personally check each person to make sure their mask is on. To avoid this dilemma, it became necessary to build a project named ‘Real-time face mask detector’, This reveals whether a mask-wearing individual is there. This project needed the use of some python libraries like TensorFlow, Keras, OpenCV. Using this project, we will be able to Detect mask-on faces in live video clip with accuracy of 97-98 %.
Cutout with patch-loss augmentation for improving generative adversarial networks against instability

Generative adversarial networks heavily rely on large datasets and carefully chosen model parameters to avoid model overfitting or mode collapse. Cutout with patch-loss augmentation, a dataset augmentation designed for generative adversarial networks that applies cutout to both the discriminator and the generator with a patch-loss structure and a new loss function, is proposed as a solution to the issue. It can enhance the performance of generative adversarial networks on full datasets and promote better convergence and stability on limited datasets. Additionally, the tensor value clamp is proposed, accelerating training speed without compromising quality. The proposed method can be successfully used with various generative adversarial networks, according to experiments. The performance of generative adversarial networks trained with full data on CIFAR-10 is matched by our method with only 20% of the training data. Finally, combined with our approach, StyleGAN2-ADA's Fréchet Inception Distance (FID) results on the CIFAR-10, LSUN-CAT, and FFHQ-256 datasets can be further enhanced.
Monetary policy and Bitcoin

I empirically study the impact of monetary policy on Bitcoin, and show that is has evolved over time. First, based on high-frequency data, the paper documents that Bitcoin prices did not immediately respond to US monetary policy announcements in the past; they only started doing so in late 2020, in a manner similar to other risky asset prices. Second, based on a structural VAR analysis I study the impact of monetary policy over longer horizons. I confirm the contractionary impact of a US monetary tightening in the post-2020 time period, but show that, historically, a US tightening used to persistently increase rather than decrease Bitcoin prices. To explain this result, I exploit spreads in Bitcoin valuations across currencies and blockchain data and link increased Bitcoin demand to East Asian economies subject to capital controls, particularly China. I discuss implications for the discussion of where demand for Bitcoin stems from: its recent responses to monetary policy confirm its role as a primarily speculative asset; yet, at least historically Bitcoin seems to have derived part if its value from enabling cross-border value transfers and capital flight.
The impact of bitcoin on gold, the volatility index (VIX), and dollar index (USDX): analysis based on VAR, SVAR, and wavelet coherence

Purpose Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX). Design/methodology/approach The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed. Findings The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not. Originality/value BTC’s diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold.
Sound and complete causal identification with latent variables given local background knowledge

Great efforts have been devoted to causal discovery from observational data, and it is well known that introducing some background knowledge attained from experiments or human expertise can be very helpful. However, it remains unknown that what causal relations are identifiable given background knowledge in the presence of latent confounders. In this paper, we solve the problem with sound and complete orientation rules when the background knowledge is given in a local form. Furthermore, based on the solution to the problem, this paper proposes two applications that are of independent interests. One is that we give a maximal ancestral graph (MAG) listing algorithm, to output all the MAGs consistent to the observational data in the presence of latent variables. The other application is that we present a general active learning framework for causal discovery in the presence of latent confounders, where we propose a baseline criterion to select the intervention variable with a Metropolis-Hastings MAG-sampling method. Experiments validate the efficiency of the proposed MAG listing method and the effectiveness of the active learning framework.