Reviewerly/exHarmony huggingface dataset:
The Dark Web: What Is It, How to Access It, and Why We Need to Study It

The dark web is a subsection of the deep web that conventional search engines cannot index. As an encrypted network of websites, the dark web can only be accessed using special browsers such as Tor. Tor, formerly an acronym for “The Onion Router,” is a free and open-source software intended to protect the personal privacy of its users and keep their internet activities unmonitored. While the dark web is known for both legitimate and illegitimate purposes, it remains notorious for facilitating illegal and deviant activities ranging from drug dealing to child pornography, human trafficking, arms dealing, and extremist recruitment. Accordingly, researching and understanding the dark web is a critical and essential step in fighting and preventing cybercrime. However, studying the dark web poses unique challenges. This special issue seeks to provide a platform for researchers and criminologists to share and discuss research designs and methods that help shed light on the actual activity going on in the dark web’s shadowy realms.
Better Localness for Non-Autoregressive Transformer

The Non-Autoregressive Transformer, due to its low inference latency, has attracted much attention from researchers. Although, the performance of the non-autoregressive transformer has been significantly improved in recent years, there is still a gap between the non-autoregressive transformer and the autoregressive transformer. Considering the success of localness on the autoregressive transformer, in this work, we consider incorporating localness into the non-autoregressive transformer. Specifically, we design a dynamic mask matrix according to the query tokens, key tokens, and relative distance, and unify the localness module for self-attention and cross-attention module. We conduct experiments on several benchmark tasks, and the results show that our model can significantly improve the performance of the non-autoregressive transformer.
Compressed Zero-Knowledge Proofs for Lattice-Based Accumulator

Abstract The lattice-based cryptographic accumulators, which enable short zero-knowledge arguments of membership, have numerous applications in post-quantum privacy-preserving protocols. However, most efficient quantum-safe zero-knowledge arguments are PCP-based systems and rely on non-falsifiable assumptions. For non-PCP-based constructions using the state-of-the-art techniques on compressing lattice-based zero-knowledge proofs, the concrete size of the resulting proof for accumulators with $2^{32}$ members is at least 500 KB. In this paper, we propose a compact non-PCP zero-knowledge proof for the lattice-based Merkle-tree, which leads to an efficient post-quantum cryptographic accumulator. The complexity of our construction is logarithmic in $l\cdot n_{s}$, where $l$ and $n_{s}$ denote the depth of the underlying Merkle-tree and the size of a node, respectively, and the concrete size is only $143.7\ $KB when $l=32$. In particular, we provide an improved lattice-based Bulletproof with efficient knowledge extraction, which allows large challenge space but small soundness slack. Furthermore, the amortized technique can be applied to the Bulletproof without breaking the knowledge soundness due to our improved knowledge extraction. As a direct application, we present a practical lattice-based ring signature, which can achieve logarithmical signing/verifying computational complexity with the number of the ring, while the state-of-the-art constructions (CRYPTO 21) have linear computational complexity.
Human-centred design research during the pandemic: using an online survey to inform personas of women at risk of hip fracture

During the global pandemic, social distancing rules made it difficult to conduct human-centred research into hip fracture protection, and in-person focus groups were abandoned. An online survey was developed to safely gather insights from people living with osteoporosis who are at risk of fragility fractures. The survey data informed the design of five personas that provide a critical lens to drive innovation in hip protector garment design and evaluate ongoing concept development. Although the pandemic disrupted people-facing research, the use of survey data provided a greater breadth of voices to inform the personas than focus groups alone.
A Novel Optimized Perturbation-Based Machine Learning for Preserving Privacy in Medical Data

In recent times, providing privacy to the medical dataset has been the biggest issue in medical applications. Since, in hospitals, the patient's data are stored in files, the files must be secured properly. Thus, different machine learning models were developed to overcome data privacy issues. But, those models faced some problems in providing privacy to medical data. Therefore, a novel model named Honey pot-based Modular Neural System (HbMNS) was designed in this paper. Here, the performance of the proposed design is validated with disease classification. Also, the perturbation function and the verification module are incorporated into the designed HbMNS model to provide data privacy. The presented model is implemented in a python environment. Moreover, the system outcomes are estimated before and after fixing the perturbation function. A DoS attack is launched in the system to validate the method. At last, a comparative assessment is made between executed models with other models. From the comparison, it is verified that the presented model achieved better outcomes than others.
Emerging Modularity During the Evolution of Neural Networks

Abstract Modularity is a feature of most small, medium and large–scale living organisms that has evolved over many years of evolution. A lot of artificial systems are also modular, however, in this case, the modularity is the most frequently a consequence of a handmade design process. Modular systems that emerge automatically, as a result of a learning process, are very rare. What is more, we do not know mechanisms which result in modularity. The main goal of the paper is to continue the work of other researchers on the origins of modularity, which is a form of optimal organization of matter, and the mechanisms that led to the spontaneous formation of modular living forms in the process of evolution in response to limited resources and environmental variability. The paper focuses on artificial neural networks and proposes a number of mechanisms operating at the genetic level, both those borrowed from the natural world and those designed by hand, the use of which may lead to network modularity and hopefully to an increase in their effectiveness. In addition, the influence of external factors on the shape of the networks, such as the variability of tasks and the conditions in which these tasks are performed, is also analyzed. The analysis is performed using the Hill Climb Assembler Encoding constructive neuro-evolutionary algorithm. The algorithm was extended with various module-oriented mechanisms and tested under various conditions. The aim of the tests was to investigate how individual mechanisms involved in the evolutionary process and factors external to this process affect modularity and efficiency of neural networks.
Modelling Supra-Classical Logic in a Boltzmann Neural Network: II Incongruence

Abstract Information present in any training set of vectors for machine learning can be interpreted in two different ways, either as whole states or as individual atomic units. In this paper, we show that these alternative information distributions are often inherently incongruent within the training set. When learning with a Boltzmann machine, modifications in the network architecture can select one type of distributional information over the other; favouring the activation of either state exemplar or atomic characteristics. This choice of distributional information is of relevance when considering the representation of knowledge in logic. Traditional logic only utilises preference that is the correlate of whole state exemplar frequency. We propose that knowledge representation derived from atomic characteristic activation frequencies is the correlate of compositional typicality, which currently has limited formal definition or application in logic. Further, we argue by counter-example, that any representation of typicality by ‘most preferred model semantics’ is inadequate. We provide a definition of typicality derived from the probability of characteristic features; based on neural network modelling.
On exploring weakly supervised domain adaptation strategies for semantic segmentation using synthetic data

Abstract Pixel-wise image segmentation is key for many Computer Vision applications. The training of deep neural networks for this task has expensive pixel-level annotation requirements, thus, motivating a growing interest on synthetic data to provide unlimited data and its annotations. In this paper, we focus on the generation and application of synthetic data as representative training corpuses for semantic segmentation of urban scenes. First, we propose a synthetic data generation protocol, which identifies key features affecting performance and provides datasets with variable complexity. Second, we adapt two popular weakly supervised domain adaptation approaches (combined training, fine-tuning) to employ synthetic and real data. Moreover, we analyze several backbone models, real/synthetic datasets and their proportions when combined. Third, we propose a new curriculum learning strategy to employ several synthetic and real datasets. Our major findings suggest the high performance impact of pace and order of synthetic and real data presentation, achieving state of the art results for well-known models. The results by training with the proposed dataset outperform popular alternatives, thus demonstrating the effectiveness of the proposed protocol. Our code and dataset are available at http://www-vpu.eps.uam.es/publications/WSDA_semantic/
Anomaly Detection in Yarn Tension Signal Using Independent Component Analysis

Finding patterns in data that defy expected behavior is what anomaly detection entails. In many application fields, these incorrect patterns are referred to as contaminants, abnormalities, exceptions, or outliers. The significance of anomaly detection is that it helps to identify irregularities in data across a range of application domains and turns them into valuable information. When the yarn tension signals are inspected, anomaly states in the signals are seen in situations where it defect for whatever reason. This distinction makes it possible to predict whether the twister is malfunctioning. So, a bigger issue is avoided. The employment of Cluster-Based Algorithms, Statistical Method Algorithms, and other techniques to identify anomalies is common in the literature. The yarn tension signals in the twisting machines have been analyzed in this work using independent component analysis, and the problematic signal locations have been identified. The proposed method has been contrasted with other ways, and it has produced the highest success rate.
Dynamic Linkages Among Cryptocurrencies: The Role of COVID-19

This paper investigates the impact of COVID-19 on the cryptocurrency market. It empirically examines the level of volatility and the dynamic conditional correlations among cryptocurrencies pre-COVID-19 and during COVID-19. We find significant dynamic conditional correlations among cryptocurrencies and that the level of volatility is higher during COVID-19 than pre-COVID-19.
Annotation Visualizer: A software visualization tool for code annotations

The Annotation Visualizer (AVisualizer) is a software visualization tool for displaying code annotations distribution in a given target Java-based software system. Implemented as a web application, it can extract annotations usage from the target source code and display it using a hierarchical circle packing approach. Using a dedicated suite of software metrics, it can display size-related information and code responsibilities associated with annotations usage. The tool provides three different views of the analyzed system, each with different granularity. The AVisualizer is a tool that helps improve code comprehension.
Greedy stochastic configuration networks for ill-posed problems

The inequality constraint in stochastic configuration networks (SCNs) is key to their universal approximation capability. SCNs incrementally add new units to neural networks under a supervisory mechanism. However, with an increasing number of hidden nodes, the output weights which are evaluated by the least squares method, are frequently unstable. This instability phenomenon (arbitrarily small perturbations leading to large changes in the solutions) can be classified as an ill-posed problem. Instability is one of the most important manifestations of ill-posed problems, and the Tikhonov regularization method, which is widely used to address ill-posed problems, is undesirable. This study examined the supervisory mechanism of SCNs and algebraic properties of the hidden output matrix, thereby proposing a new greedy stochastic configuration network (GSCN) for ill-posed problems. First, under the premise of ensuring universal approximation capability, GSCN tries to optimize the randomly assigned input weights and biases based on the hunter–prey optimization (HPO) algorithm. The optimized hidden parameters which can reduce residual error the most are selected for the newly added hidden nodes. Second, singular value decomposition (SVD) and orthogonal–triangular (QR) decomposition with column pivoting are introduced to extract a linearly independent subset of the hidden output matrix for ill-posed problems in SCNs. Finally, one function approximation, six benchmark regressions, two classification datasets, and bearing fault diagnosis are employed to evaluate the performance of GSCN. Experimental results indicate that GSCN shows superior performance with respect to fast convergence, generalization, and stability compared with other contrast methods. This code is available at https://github.com/Gzu-public-bigdata/GSCN.
A quasi-solution on a lattice graph for an initial state reconstruction

This note considers a graph-based initial state reconstruction in image denoising. Our particular focus is on developing a quasi-model by a combination of boundary perturbation methods. The corresponding spectral quasi-solution enables one to stably approximate the original pixelated image after the standard forward–backward denoising process. Convergence estimates are derived with respect to the deterministic noise level. Some numerical examples are provided to verify the performance of the method.
A novel signal representation in SEI: Manifold

Specific emitter identification (SEI), as an important problem in situational awareness, identifies emitters via unique characteristics. However, current SEI methods mostly suffer from appropriately setting the trade-off between comprehensiveness and efficiency when extracting fingerprint features. To address the issue, this paper provides a novel SEI framework with a separate representation module. Within the novel framework, manifolds are proposed to be signal representations and multi-level manifold features are extracted as fingerprint features. We first build the SEI model from the nonlinear dynamic perspective, where the SEI process identifies the nonlinear systems via a measurement sequence. Then, we demonstrate that manifolds can represent emitters equivalently and prove the one-to-one correspondence between manifolds and emitter individuals. Hence, manifolds can highlight unique nonlinear dynamic characteristics and simultaneously describe comprehensive system working processes. The coordinate delayed technique and manifold learning methods are employed to reconstruct the phase space and manifold, respectively. For accomplishing the identification task, multi-level manifold features, comprising intrinsic dimension, topological features, conformal features, and Riemannian metric features, are extracted from the reconstructed manifolds and input to an ensemble learning scheme, named Adaboost. Extensive simulation and real-world experiments agree with our analytical conclusions and confirm the proposed method’s efficiency. The results also demonstrate that the proposed method achieves a high recognition accuracy, outstanding adaptability, and strong robustness.
Short: Certifiably Robust Perception Against Adversarial Patch Attacks: A Survey

The physical-world adversarial patch attack poses a security threat to AI perception models in autonomous vehicles.To mitigate this threat, researchers have designed defenses with certifiable robustness.In this paper, we survey existing certifiably robust defenses and highlight core robustness techniques that are applicable to a variety of perception tasks, including classification, detection, and segmentation.We emphasize the unsolved problems in this space to guide future research, and call for attention and efforts from both academia and industry to robustify perception models in autonomous vehicles.
"I didn't click": What users say when reporting phishing

When people identify potential malicious phishing emails one option they have is to contact a help desk to report it and receive guidance.While there is a great deal of effort put into helping people identify such emails and to encourage users to report them, there is relatively little understanding of what people say or ask when contacting a help desk about such emails.In this work, we qualitatively analyze a random sample of 270 help desk phishing tickets collected across nine months.We find that when reporting or asking about phishing emails, users often discuss evidence they have observed or gathered, potential impacts they have identified, actions they have or have not taken, and questions they have.Some users also provide clear arguments both about why the email really is phishing and why the organization needs to take action about it.
Planning and Execution of the Library's use of Barcode Technology

Aim: The main aim of this research paper is to create consciousness among the librarians to use Barcode technology in the library. Design: This research paper briefly converses the explanation of the application of Barcode technology in libraries, advantages and its working mechanism. It also tried to discover the technical features of Barcode technology in the library. Findings: Barcode technology is mostly used in the motion system of a library and most successful due to its speed, accuracy and reliability. Though barcode is relatively an old technology but it is one of the important steps in library automation and is still not extensively used in libraries. Value: Barcode is very cost-effective technology can be used by every library.
ESSAY: Computers Are Useless ... They Only Give Us Answers

While the title of this essay, taken from a quotation from Picasso [1], is somewhat facetious, it contains more than a grain of truth. We have all experienced results that left us scratching our heads with Evolutionary Algorithms, but often these algorithms lead us to the most fruitful results. It can be hard to create the right conditions to find good answers. To quote Shakespeare: “I can call the spirits from the vasty deep!” to which the response in the text is “Why so can I! And so can any [person], but will they come when you doth call?”
Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks

The use of correlation as a fitness function is explored in symbolic regressionSymbolic regression tasks and its performance is compared against a more typical RMSE fitness function. Using correlation with an alignment step to conclude the evolution led to significant performance gains over RMSE as a fitness function. Employing correlation as a fitness function led to solutions being found in fewer generations compared to RMSE. We also found that fewer data points were needed in a training set to discover correct equations. The Feynman Symbolic RegressionSymbolic regression Benchmark as well as several other old and recent GP benchmark problems were used to evaluate performance.
GP-Based Generative Adversarial Models

We explore the use of Artificial Neural Network (ANN)-guided Genetic Programming (GP) to generate images that the guiding network classifies as belonging to a specific class. The experimental results demonstrate the ability of GP to perform such a task but also the inadequacy of most of the generated images, which can be considered false positives. Based on these findings and following an approach analogous to Generative Adversarial Networks (GANs), we propose an generative adversarial model where GP replaces the traditional GAN’s generator. The experimental results illustrate the advantages of this approach, highlighting the expressive power of GP, its capacity to perform online learning, thus adapting to a dynamic fitness landscape, and its ability to create novel imagery that fits the target classes.
The Influence of Chatgpt on Ai-Related Crypto Assets: Evidence from a Synthetic Control Analysis

The introduction of OpenAI’s large language model, ChatGPT, catalyzed investor attention towards artificial intelligence (AI) technologies, including AI-related crypto assets not directly related to ChatGPT. Utilizing the synthetic difference-in-difference methodology, we identify significant "ChatGPT effects” with returns of AI-related crypto assets experiencing average returns ranging between 10.7% and 15.6% (35.5% to 41.3%) in the one-month (two-month) period after the ChatGPT launch. Furthermore, Google search volumes, a proxy for attention to AI, emerged as critical pricing indicators for AI-related crypto post-launch. We conclude that investors perceived AI-assets as possessing heightened potential or value after the launch, resulting in higher market valuations.
Rules Are Rules: Rhetorical Figures and Algorithms

Rhetorical figures are form/function alignments in which the form (1) serves to convey the function(s) and (2) supports their computational detection; therefore, (3) they are particularly rich for various text mining activities and other Natural Language Understanding purposes. The figures which are especially valuable in these ways are known as schemes, figures that are defined by their material (phonological, orthographical, morpholexical, or syntactic) form, in distinction particularly from tropes, which are defined by their conceptual (semantic) form. Rhetorical schemes, however, have been almost universally ignored by linguists, including computational linguists. This article illustrates form/function alignments for a small handful of rhetorical schemes, with some examples of how they communicate specific meanings. The communicative functions of rhetorical schemes rely not so much on individual schemes as on certain collocations of schemes (sometimes with tropes or other figures as well). These collocations, in turn, are coordinated by linguistic features such that the relevant expressions fit the notion of a construction as understood in the Construction Grammar framework. Examples are drawn from epanalepsis, as well as antimetabole, mesodiplosis, and parison, which collate frequently as a group and also collectively with the trope, antithesis. The communicative functions explored include Semantic-Feature Promotion, Reciprocal Specification, Reciprocal Energy, Irrelevance of Order/Rank, Subclassification, and Reject-Replace. Rhetorical figure detection is also discussed in some detail with respect to figural and grammatical collocation.