Reviewerly/exHarmony huggingface dataset:
An empirical study of issue-link algorithms: which issue-link algorithms should we use?

Abstract The accuracy of the SZZ algorithm is pivotal for just-in-time defect prediction because most prior studies have used the SZZ algorithm to detect defect-inducing commits to construct and evaluate their defect prediction models. The SZZ algorithm has two phases to detect defect-inducing commits: (1) linking issue reports in an issue-tracking system to possible defect-fixing commits in a version control system by using an issue-link algorithm (ILA) ; and (2) tracing the modifications of defect-fixing commits back to possible defect-inducing commits. Researchers and practitioners can address the second phase by using existing solutions such as a tool called . In contrast, although various ILAs have been proposed for the first phase, no large-scale studies exist in which such ILAs are evaluated under the same experimental conditions. Hence, we still have no conclusions regarding the best-performing ILA for the first phase. In this paper, we compare 10 ILAs collected from our systematic literature study with regards to the accuracy of detecting defect-fixing commits. In addition, we compare the defect prediction performance of ILAs and their combinations that can detect defect-fixing commits accurately. We conducted experiments on five open-source software projects. We found that all ILAs and their combinations prevented the defect prediction model from being affected by missing defect-fixing commits. In particular, the combination of a natural language text similarity approach, Phantom heuristics, a random forest approach, and a support vector machine approach is the best way to statistically significantly reduced the absolute differences from the ground-truth defect prediction performance . We summarized the guidelines to use ILAs as our recommendations.
What topics should or should not be included in software security education—Qualitative content analysis

Abstract Software security education is still not preparing students for the types of high‐skilled technical roles that represent the most severe workforce shortage. Recognizing this, academia has begun redefining the knowledge area concepts of software security curricula to meet the current workforce shortage. This article studies the software security courses in Arab Gulf academic programs and benchmarks their descriptions with the corresponding knowledge area from cybersecurity Curricula (CSEC). 2017, the first set of global cybersecurity curricular guidelines. Using content analysis, six concepts or essentials are investigated: security requirements, secure design principles, secure source code, analysis and testing, patch, and ethics. It was found that no course follows all the CSEC. 2017 essentials. The analysis and testing essential were considered by all courses, and security after deployment, including the patch essential, needs more attention because it was not included in any course. Similarly, the security requirements and ethics essentials were also considered by a few courses. However, software success depends on requirements, and ethics has become critical in the cybersecurity and information assurance fields that depend on law and forensics. The secure source code essential was covered by most courses. The well‐known types of code attacks were covered, and over half of the courses discussed secure design principles essential. However, security by design is an emerging development philosophy. The article discussed observations and recommendations that will assist program managers and their staff in making effective decisions about the essentials and concepts that should be included when they are developing the software security curriculum.
Blockchain technology and money laundering

In this paper, we want to break down certain prejudices against new blockchain technologies and cryptocurrencies, especially the Bitcoin, as instruments having mostly negative connotations and representing an opportunity for various criminal activities, including the cases of money laundering where money has been acquired in unethical and illegal ways. According to that aim, there were applied the methods of genetic, structural and functional analysis, the method of correlative variations, as well as the analogous and normative method. A significant part of the paper is dedicated to an introduction to DLT - (Distributed Ledger Technologies), i.e. a distributed book of records technologies, on which the blockchain and its most important exponent - the Bitcoin - rests. Also, we had to touch upon the second most important contribution to this technology, namely the Ethereum blockchain, which expands the perspectives opened by the Bitcoin, and thus the possibilities for misuse of this technology, primarily due to its constitutive principle of anonymity. In the paper, we have shown the fact that despite inadequate legislation, both nationally and globally, the blockchain and cryptocurrencies have not significantly supported the paths of illegal money laundering, especially not related to serious crimes, in particular drug trafficking and terrorism. We mostly see the contribution of this paper in the typologization of possible money laundering procedures, especially by using the NFT (Non-Fungible Tokens), non-exchangeable tokens whose hype, in last two years, might be the result of a perceived opportunity for a new way of money laundering. We conclude that we should not be afraid of the Bitcoin, but it should be accepted as an integral part of a peaceful and prosperous futurity for it opens new perspectives for humanity burdened with bigger problems than money laundering, which had existed to the same degree even before the appearance of the Bitcoin.
Trademarks, NFTs, and the Law of the Metaverse

This Article discusses the early encounters of NFTs, trademarks, and the United States legal system. It will examine the areas where NFT creators are getting into or are likely to get into trouble with the law and policy of trademark infringement, trademark dilution, and the related legal causes of action of false endorsement and false designation of origin. United States law is being applied in lawsuits that already are being adjudicated in the real world concerning subject matter that exists only in cyberspace, the pre-cursor to the metaverse. The cases of Yuga Labs v. Ryder Ripps, Nike v. StockX, Hermès Int’l v. Rothschild, and Miramax v. Tarantino are discussed.
A Contrastive Learning Transformer for Change Detection in High-Resolution Sar Images

Compared with medium and low resolution Synthetic Aperture Radar (SAR) images, high resolution SAR images are more seriously interfered by target complexity and imaging noise, which present a greater challenge for using high resolution SAR images to detect changes. In this paper, we proposed a self-supervised contrastive representation learning method based on convolution-enhanced Transformer to identify the changes from high resolution SAR images. First, contrastive self-supervised representation learning framework is constructed on unlabeled SAR data without introducing any human-annotated labels, where the hierarchical representation is computed with convolution-enhanced Transformer. The key design of this Transformer lies in dividing patches and limiting self-attention calculation within local windows, which lead to linear computational complexity. However, the lack of across-window connections limits its representation power. Thus, we propose convolution-based module to build the information interactions across windows. As a result, rich local-global context information can be modeled more powerfully. Then, the changes are identified in the learned representation space. To improve the adaptability of the algorithm to different data sets, the final detection map is generated by decision level fusion. In addition, we propose sparse sampling strategy to reduce the amount of data and enable our method to be friendly to large-scale HR images. Experiments on simulated and real data sets demonstrate the effectiveness and superiority of the proposed method.
Gaussian Processes for Missing Value Imputation

Missing values happen when a particular attribute of an instance of a machine learning problem is not recorded. They are common in many real-life datasets. However, most of the current machine learning methods can not handle missing values. This means that they should be imputed beforehand. Gaussian Processes (GPs) are non-parametric models with accurate uncertainty estimates that combined with sparse approximations and stochastic variational inference scale to large data sets. Sparse GPs can be used to compute a predictive distribution for missing data. Here, we present a hierarchical composition of sparse GPs that is used to predict missing values ateach dimension using all the variables from the other dimensions. We call the approach missing GP (MGP). MGP can be trained simultaneously to impute all observed missing values. Specifically, MGP outputs a predictive distribution for each missing value that is then used in the imputation of other missing values. We evaluate MGP in one private clinical data set and four UCI datasets with a different percentage of missing values. We compare the performance of MGP with other state-of-the-art methods for imputing missing values, including variants based on sparse GPs and deep GPs. The results obtained show that the performance of MGP is significantly better.
Secure distributed data integrity auditing with high efficiency in 5G-enabled software-defined edge computing

In edge computing, the idle resources of the devices in the network can be virtualized into a platform that provides clients with storage resource and computing capability. Note that the service response of edge computing is faster than that of cloud computing. The service provision speed and the distributed resources utilization rate of edge computing will be further improved when integrated with 5G and software definition paradigm in the design of the network system. However, the issues of data storage security and edge devices’ trustworthiness seriously restrict the development of edge computing. To enhance the security of the data storage in edge computing, a secure distributed data integrity auditing is proposed. The proposed auditing scheme in this paper can be used to guarantee the correctness and the completeness of the stored data in 5G-enabled software-defined edge computing. The auditing results of the distributed data in the proposed scheme can be used as an important basis for evaluating the trustworthiness of the edge devices. Due to the utilization of certificateless cryptography in the design of the proposed scheme, the computational cost of the terminal side can be highly reduced. Security analysis of the proposed scheme demonstrates that the properties of key exposure resistance and privacy-preserving are provided in data auditing. Simulation results of the time cost of the server side and the terminal side show that the proposed scheme is highly efficient compared to previous schemes.
Transformer Union Convolution Network for visual object tracking

In visual object tracking, the existing Siamese-like neural network utilizes the superposition of different convolution modules to extract feature maps, which makes the network unable to obtain sufficient global information. Inspired by Transformer, we present a novel method named Transformer Union Convolution Network (TUC-Net) to take the advantage of its encoder–decoder structure. Through the combination of attention mechanism and convolution, the interaction of local and global information between image patches is realized. Specifically, the proposed method includes a feature interaction stage based on feature perception head followed by encoder and a feature fusion stage based on U-Net-like convolution structure and decoder. Experiments shows that our TUC-Net achieves promising results on five challenging datasets compared with state-of-the-art methods. The average running speed of our tracker is approximatively 20 fps on GPU. Besides, we design a dual-resolution camera system with high resolution and large field of view. TUC-Net can be well-equipped with the system and get excellent tracking results.
Non-parametric discretization for probabilistic labeled data

Probabilistic label learning is a challenging task that arises from recent real-world problems within the weakly supervised classification framework. In this task algorithms have to deal with datasets where each instance has associated a set of probabilities belonging to different class labels. In this paper, we propose a supervised univariate non-parametric discretization algorithm based on kernel density estimation that can deal with probabilistic labeled data. The algorithm takes advantage of the estimation of the class conditional densities to produce different sets of cut points according to different smoothing parameters of the kernel. Then, the best set of cut points is selected according to a given supervised classification performance measure. The computational complexity is O(NlogN), where N is the number of instances. The proposal is tested on simulated probabilistic labeled data, which allows assessing the behavior with different noise degrees. The results show that the algorithm outperforms other discretization algorithms and is robust to different degrees of uncertainty.
Algebraic Attacks on Grain-Like Keystream Generators

This paper analyses the resistance of certain keystream generators against algebraic attacks, namely generators consisting of a nonlinear feedback shift register, a linear feedback shift register and a filter function. We show that poorly chosen filter functions make such designs vulnerable to new algebraic attacks, using a divide and conquer approach that targets the LFSR first. This approach provides efficient LFSR initial state recovery followed by partial NFSR initial state recovery. We apply our algebraic attacks to modified versions of the Grain family of stream ciphers. Our analysis shows that, despite the highly nonlinear filter functions used in these variants, the LFSR state can be recovered using our algebraic attack much faster than exhaustive search. Following this, the NFSR initial state can be partially recovered, leaving a smaller subset of NFSR stages to be exhaustively searched. This investigation highlights the importance of the filter function in keystream generators with a “Grain-like” structure, and demonstrates that many functions previously considered secure are vulnerable to this attack.
Collision-Resistant and Pseudorandom Function Based on Merkle-Damgård Hash Function

This paper presents a keyed hash function satisfying collision resistance and the pseudorandom-function (PRF) property. It is based on the Merkle-Damgård hash function. It is shown to satisfy collision resistance under the ideal assumption that the underlying compression function is a random oracle. It is also shown to be a secure PRF if the underlying compression function is a secure PRF against related-key attacks in two keying strategies. The novel feature of the proposed keyed hash function is its efficiency. It achieves the minimum number of calls to the underlying compression function for any message input. Namely, constructed with the compression function accepting a $$w$$ -bit message block, it processes any $$l(\ge 0)$$ -bit massage with $$\max \{1,\lceil l/w\rceil \}$$ calls to the compression function. Thus, it is more efficient than the standardized keyed hash function HMAC, which also satisfies both collision resistance and the PRF property, especially for short messages. The proposed keyed hash function, as well as HMAC, can be instantiated with the SHA-256 compression function.
Contrastive Learning with Complex Heterogeneity

With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multiple modalities and are characterized with multiple labels, thus exhibiting the co-existence of multiple types of heterogeneity. Although state-of-the-art techniques are good at modeling the complex heterogeneity with sufficient label information, such label information can be quite expensive to obtain in real applications. Recently, researchers pay great attention to contrastive learning due to its prominent performance by utilizing rich unlabeled data. However, existing work on contrastive learning is not able to address the problem of false-negative pairs, i.e., some 'negative' pairs may have similar representations if they have the same label. To overcome the issues, in this paper, we propose a unified heterogeneous learning framework, which combines both the weighted unsupervised contrastive loss and the weighted supervised contrastive loss to model multiple types of heterogeneity. We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false-negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue. Experimental results on real-world data sets demonstrate the effectiveness and the efficiency of the proposed framework modeling multiple types of heterogeneity.
Morphological Gradient Analysis and Contour Feature Learning for Locating Text in Natural Scene Images

We use text as the primary medium for providing precise information. Text could be major source of information for understanding of a scene imagery or video, once it is recognized. Although identifying and understanding textual information is fairly simple for us, it can be extremely complex task for machines. Variations such as color, orientation, scale, flow, lighting, noise, occlusion, language features and font can make this task of computer vision challenging. Detecting presence of text and precisely locating regions of text are vital for faster and precise recognition. Due to the complexity of the task, most of the popular techniques of today require an intense training phase and powerful computation infrastructure. In the proposed method, we have tried to minimize the amount of training required to achieve a decent text localization result. We have observed that, morphological gradient analysis enhances textual regions and contour feature analysis can help to eliminate non-textual components. Combination of these techniques produces promising results with small dataset, minimal training and limited computational ability. Also, the proposed detector can detect text across multiple languages and is fairly robust against the variations such as orientation and scale. The proposed method achieves an F-measure of 0.77 on MSRA-TD500 after the training with 300 images.
Channel Difference Based Regeneration Architecture for Fake Colorized Image Detection

Selling of counterfeit/fraud goods like publishing the fake images of the products is increasing now a days due to advancement of multimedia technology for image editing applications. This work aims to detect the fake colorized images with a novel observation of channel difference maps. Here, we propose a novel deep channel difference network for fake colorized image detection/classification. Initially, an effective channel difference map (CDM) based auto-encoder is proposed for image regeneration. Here, to get distinguishing edge and color information between real and fake colorized images, CDM is proposed. After effective regeneration, the abstract version of learned features can be used for classification. Thus, we have proposed the transfer learning based network for fake colorized image detection (FCID) with learned encoder features of regeneration network. To the best of our knowledge, this is the first work with CDM based auto-encoder for FCID. The performance of the proposed network is tested on benchmark datasets and compared with the existing state-of-the-art methods for FCID in terms of half total error rate (HTER). The experimental results demonstrate that the proposed network is superior than the state-of-the-art approaches for FCID.
Analysis of Loss Functions for Image Reconstruction Using Convolutional Autoencoder

In recent years, several loss functions have been proposed for the image reconstruction task of convolutional autoencoders (CAEs). In this paper, a performance analysis of a CAE with respect to different loss functions is presented. Quality of reconstruction is analyzed using the mean Square error (MSE), binary cross-entropy (BCE), Sobel, Laplacian, and Focal binary loss functions. To evaluate the performance of different loss functions, a vanilla autoencoder is trained on eight datasets having diversity in terms of application domains, image dimension, color space, and the number of images in the dataset. MSE, peak signal to noise ratio (PSNR), and structural similarity index (SSIM) metrics have been used as the performance measures on all eight datasets. The assessment shows that the MSE loss function outperforms two datasets with a small image dimension and a large number of images. At the same time, BCE excels on six datasets with high image dimensions and a small number of training samples in datasets compared with the Sobel and Laplacian loss functions.
Single Frame-Based Video Dehazing with Adversarial Learning

Hazy environmental conditions degrade the quality of captured videos which leads to poor visibility and color distortion in videos. Such deterioration of captured video quality is mainly because of the attenuation caused by the scattering of light due to the haze particles present in the environment. In this paper, we propose an adversarial learning based single frame video dehazing encoder-decoder network. The proposed method comprises of Dilated Residual Block (DRB) used as encoder and Skip connection. DRB module is used to gain more contextual information by achieving large receptive fields. Skip connections are established between each encoder and decoder, which helps to detect and give more attention to haze specific features by adjusting the weights of learned feature maps automatically. This helps to extract the haze-relevant feature maps and recovers the haze-free video frame by using Channel Attention Block (CAB) and Residual Block (ResB) respectively. An extensive quantitative and qualitative analysis of the proposed method is done on benchmark synthetic hazy video database namely DAVIS-16 and NYU depth. Experimental result shows that the proposed method outperforms the other existing state-of-the-art (SOTA) approaches for video dehazing.