Reviewerly/exHarmony huggingface dataset:
A Survey on Attacks and Their Countermeasures in Deep Learning: Applications in Deep Neural Networks, Federated, Transfer, and Deep Reinforcement Learning

Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL techniques such as Deep Neural Networks (DNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL) are prone to adversarial attacks, which can make the DL techniques perform poorly. Developing such attacks and their countermeasures is the prerequisite for making artificial intelligence techniques robust, secure, and deployable. Previous survey papers only focused on one or two techniques and are outdated. They do not discuss application domains, datasets, and testbeds in detail. There is also a need to discuss the commonalities and differences among DL techniques. In this paper, we comprehensively discussed the attacks and defenses in four popular DL models, including DNN, DRL, FL, and TL. We also highlighted the application domains, datasets, metrics, and testbeds in these fields. One of our key contributions is to discuss the commonalities and differences among these DL techniques. Insights, lessons, and future research directions are also highlighted in detail.
“Too Small to Shine? Not Really!”: Developing Society 5.0 Adaptation Initiatives for SMEs

Society 5.0 is an extremely popular idea in current socioeconomic contexts. The transition process of evolving into this superintelligent society envisioned by the Japanese government is still ongoing, but this paradigm has already had an impact through recent changes in societies’ perspectives and ambitions. The academic contributions on this topic are still scarce, so further research is required particularly in the context of small- and medium-sized enterprises (SMEs). Society 5.0 encompasses various complex subjective aspects (i.e., variables) that require a constructivist approach to the associated decision-making processes. This study sought to overcome limitations identified in previous studies of Society 5.0’s varied dimensions by applying a combination of cognitive mapping and decision-making trial and evaluation laboratory techniques in a neutrosophic context. These methodologies provided a holistic perspective on the research topic, as well as identifying the most important variables to be included in the analysis system and defining their causal interrelationships. This comprehensive decision-support system was constructed by a panel of experts in related fields in order to mirror SME realities. The benefits and practical contributions of the proposed analysis system are discussed, and the results confirm that SMEs are not too small to shine in Society 5.0.
PSCVFinder: A Prompt-Tuning Based Framework for Smart Contract Vulnerability Detection

With the increasing security issues in the blockchain, smart contract vulnerability detection has gradually become the focus of research. Recently, many approaches have been proposed to detect smart contract vulnerabilities. Despite promising results, these approaches still have three drawbacks: 1) Symbolic execution and static analysis methods are constrained by predefined rules, which limits their adaptability to different vulnerabilities. 2) Most smart contract code contains abundant irrelevant information which is useless for vulnerability detection. 3) Pre-trained models fail to bridge the gap between pre-training and detecting smart contract vulnerabilities.To solve these problems, we propose an approach named PSCVFinder for detecting reentrancy vulnerability and times-tamp dependency vulnerability, which are two severe vulnerabilities in smart contract. To better detect these vulnerabilities, we propose CSCV which is a smart contract slicing method to reduce the irrelevant code. Unlike existing approaches, our model first learns the representation of programming language through the pre-training model, then fully exploits the capacity of large language model with prompt-tuning to precisely detect smart contract vulnerability. We conduct experiments on real-world dataset and the results reflect that PSCVFinder scores 93.83% and 93.49% on two kinds of vulnerabilities in F1-score, surpassing the state-of-the-art baseline by 1.14% and 4.02%, respectively.
Social Construction of Internet Fraud as Innovation among Youths in Nigeria

The proliferation of internet technologies has shaped interactions in contemporary society.Despite the pivotal importance of the internet to the global economy, it has several negative consequences such as internet fraud.This study examined the perception that young adults in Nigeria hold about internet fraud as an innovative means to economic survival rather than as a criminal enterprise.Robert Merton's Anomie/Strain Theory (AST) was adopted as the theoretical thrust of the study.Adopting a qualitative data collection method, 15 participants were selected using the non-probabilistic purposive and snowballing techniques while opinions were sampled through in-depth interviews in different locations within Ibadan metropolis of Nigeria.Study findings revealed that youths are engaged in internet fraud mainly to support themselves financially despite harsh economic realities as a form of deviant innovation.The study established that internet fraudsters in Nigeria are socially organized based on specializations.Furthermore, the study established that the accumulation of wealth and "clients" are the major basis for hierarchy.Although there are extant laws on the prohibition and prosecution of internet crimes in Nigeria, the fraudsters claimed that the compromised security apparatus of the state is often manipulated to allow them to continue in the "business".Only those who do not know their way are prosecuted while successful internet fraudsters gallivant and invest their illegal proceeds in legitimate businesses as cover-up.The study established the need for value reorientation for youths as well as creation of jobs.
Quantum Repeater for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>W</mml:mi></mml:math> States

W states are a valuable resource for various quantum information tasks, and several protocols to generate them have been proposed and implemented. We introduce a quantum repeater protocol to efficiently distribute three-qubit W states over arbitrary distances in a two-dimensional triangular quantum network with polylogarithmic overhead, thereby enabling these applications between remote parties. The repeater protocol combines two ingredients that we establish: probabilistic entanglement swapping with three copies of three-qubit W states to a single long-distance three-qubit W state, and an improved entanglement purification protocol. The latter not only shows a better performance, but also an enlarged purification regime as compared to previous approaches. We show that the repeater protocol allows one to deal with errors resulting from imperfect channels or state preparation, and noisy operations, and we analyze error thresholds, achievable fidelities, and overheads.Received 2 May 2023Revised 8 September 2023Accepted 4 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040323Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum networksQuantum protocolsQuantum repeatersQuantum Information, Science & Technology
Predicting Price Trends Using Sentiment Analysis: A Study of StepN’s SocialFi and GameFi Cryptocurrencies

The cryptocurrency market, specifically the non-fungible token (NFT) market, has been gaining popularity with the rise of social finance, game finance, metaverse, and web 3.0 technologies. With the increasing interest in cryptocurrency, it is essential to develop a comprehensive understanding of the market dynamics to aid investment decisions. This paper aims to analyze the impact of news sentiment on the prices of two cryptocurrencies, Green Satoshi Token (GST) and Green Metaverse Token (GMT). The sentiment analysis model used in this study is Finance Bidirectional Encoder Representations from Transformers (FinBERT), a pre-trained deep neural network model designed for financial sentiment analysis. Additionally, we introduce the use of the Extreme Gradient Boosting (XGBoost) algorithm to evaluate the sentiment result on the model’s performance. The study period covered from March 2022 to April 2022, and the sentiment score of the result generated by FinBERT on crypto, stock market, and finance news was found to be correlated with the prices of GST and GMT. The findings suggest that the sentiment score of GST reflects changes in the price earlier than GMT. These findings have significant implications for decision-making strategies and can aid investors in making more informed decisions. The research highlights the importance of sentiment analysis in understanding the market dynamics and its potential impact on the prices of cryptocurrencies. The use of FinBERT and XGBoost algorithms provides valuable insights into market trends and can aid investors in making informed decisions.
A lightweight convolutional neural network with end-to-end learning for three-dimensional mineral prospectivity modeling: A case study of the Sanhetun Area, Heilongjiang Province, Northeastern China

With the continuous exploitation of surface and shallow mineral resources, the global demand for concealed ore deposit exploration is increasing. However, concealed mineral prospectivity modeling (MPM) requires significant efforts, particularly in terms of methods and technologies for three-dimensional modeling. In this study, we propose a lightweight three-dimensional convolutional neural network (3D CNN) for MPM, which adopts the inception structure of GoogleNet and combines the idea of end-to-end learning. We replace the fully connected layer in the conventional CNN with deconvolution, which can greatly reduce the required parameters during the training process and accelerate the convergence. The proposed method overcomes the disadvantage that other shallow machine learning methods cannot extract spatial neighborhood information, while it can extract cross-correlations among geological factors and generates less parameters by a lightweight network when facing massive data. Additionally, compared with the patch-wise method commonly used in previous studies, we use the pixel-wise method for end-to-end learning, which not only overcomes the drawbacks of random sampling but also considers the influence of each voxel when calculating the loss function. The three-dimensional multi-source geoscientific characteristics obtained from the geophysical inversion and 3D geological models are not discretized in order to promote effective CNN training while facilitating the ore-controlling representation. Comparing the predicted results between the 3D weight of evidence (WofE) and our proposed 3D CNN method for MPM, our proposed method and WofE delineated 100% of the known mineralization in high-favorability areas with voxel numbers of 70% and 95%, respectively. A case study of a structure-controlled hydrothermal gold deposit in the Sanhetun area of Heilongjiang Province demonstrates that the proposed 3D CNN method performs better than WofE in terms of prediction effectiveness and efficiency and effectively reveals the correlation between mineralization and adjacent ore-controlling characteristics. Moreover, the proposed 3D CNN method can simulate non-linear metallogenic processes and mine hidden relationships to reveal complex ore-controlling characteristics. In conclusion, the proposed 3D CNN method can reduce the exploration effort in 3D MPM, thus greatly improving the efficiency of discovering concealed ore deposits.
Contrastive Registration for Unsupervised Medical Image Segmentation

Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using deep learning supervised techniques, they assume a large and well-representative labeled set. This is a strong assumption in the medical domain where annotations are expensive, time-consuming, and inherent to human bias. To address this problem, unsupervised segmentation techniques have been proposed in the literature. Yet, none of the existing unsupervised segmentation techniques reach accuracies that come even near to the state-of-the-art of supervised segmentation methods. In this work, we present a novel optimization model framed in a new convolutional neural network (CNN)-based contrastive registration architecture for unsupervised medical image segmentation called CLMorph. The core idea of our approach is to exploit image-level registration and feature-level contrastive learning, to perform registration-based segmentation. First, we propose an architecture to capture the image-to-image transformation mapping via registration for unsupervised medical image segmentation. Second, we embed a contrastive learning mechanism in the registration architecture to enhance the discriminative capacity of the network at the feature level. We show that our proposed CLMorph technique mitigates the major drawbacks of existing unsupervised techniques. We demonstrate, through numerical and visual experiments, that our technique substantially outperforms the current state-of-the-art unsupervised segmentation methods on two major medical image datasets.
Transformer based Natural Language Generation for Question-Answering

This paper explores Natural Language Generation within the context of Question-Answering task. The several works addressing this task only focused on generating a short answer or a long text span that contains the answer, while reasoning over a Web page or processing structured data. Such answers’ length are usually not appropriate as the answer tend to be perceived as too brief or too long to be read out loud by an intelligent assistant. In this work, we aim at generating a concise answer for a given question using an unsupervised approach that does not require annotated data. Tested over English and French datasets, the proposed approach shows very promising results.
SPT: Learning to Selectively Insert Prompts for Better Prompt Tuning

Prompt tuning prepends a soft prompt to the input embeddings or hidden states and only optimizes the prompt to adapt pretrained models (PTMs) to downstream tasks. The previous work manually selects prompt layers which are far from optimal and failed to exploit the potential of prompt tuning. In this work, we propose a novel framework, Selective Prompt Tuning (SPT), that learns to select the proper prompt layers by inserting a prompt controlled by a learnable probabilistic gate at each intermediate layer. We further propose a novel bi-level optimization framework, SPT-DARTS, that can better optimize the learnable gates and improve the final prompt tuning performances of the learned prompt layer settings. We conduct extensive experiments with ten benchmark datasets under the full-data and few-shot scenarios. The results demonstrate that our SPT framework can perform better than the previous state-of-the-art PETuning baselines with comparable or fewer tunable parameters.
Revisiting Automated Topic Model Evaluation with Large Language Models

Topic models help us make sense of large text collections. Automatically evaluating their output and determining the optimal number of topics are both longstanding challenges, with no effective automated solutions to date. This paper proposes using large language models (LLMs) for these tasks. We find that LLMs appropriately assess the resulting topics, correlating more strongly with human judgments than existing automated metrics. However, the setup of the evaluation task is crucial — LLMs perform better on coherence ratings of word sets than on intrustion detection. We find that LLMs can also assist us in guiding us towards a reasonable number of topics. In actual applications, topic models are typically used to answer a research question related to a collection of texts. We can incorporate this research question in the prompt to the LLM, which helps estimating the optimal number of topics.
Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{will}$</tex> focus on model deployment for onboard real-time inference and control of human-robot walking.
The W3C Data Catalog Vocabulary, Version 2: Rationale, Design Principles, and Uptake

Abstract DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. Since its first release in 2014 as a W3C Recommendation, DCAT has seen a wide adoption across communities and domains, particularly in conjunction with implementing the FAIR data principles (for findable, accessible, interoperable and reusable data). These implementation experiences, besides demonstrating the fitness of DCAT to meet its intended purpose, helped identify existing issues and gaps. Moreover, over the last few years, additional requirements emerged in data catalogs, given the increasing practice of documenting not only datasets but also data services and APIs. This paper illustrates the new version of DCAT, explaining the rationale behind its main revisions and extensions, based on the collected use cases and requirements, and outlines the issues yet to be addressed in future versions of DCAT.
The investigation of physical dan mechanical properties of Nipah-based particle board

The excessive use of wood as a raw material in furniture industries has raised environmental concerns that have attracted the attention of many individuals. Consequently, various innovations have been explored in developing alternative materials for the furniture industry. One promising resource that has the potential to be developed as a raw material for furniture applications is Nipah palm husk. Nipah palm husk is classified as an agricultural waste that is barely used within society and industries. Hence, in this study, Nipah palm husk will be utilized as the primary material to fabricate particle board by involving tapioca as an adhesive. This research aimed to investigate the effect of tapioca concentrations on water absorption, modulus elasticity, modulus of rupture, and screw-holding strength of the produced Nipah palm husk particleboard. The results of this study showed that the particle board produced with a 40% tapioca adhesive concentration exhibited the most favorable physical and mechanical properties with a water absorption rate of 25%, an elastic modulus of 21188.93 kg/cm2, a modulus of rupture of 55.53 kg/cm2, and a screw holding power of 7.53 kg. The findings indicated that Nipah-based particle board has the potential to be developed as an alternative for the furniture industry.
Localizing Object-level Shape Variations with Text-to-Image Diffusion Models

Text-to-image models give rise to workflows which often begin with an exploration step, where users sift through a large collection of generated images. The global nature of the text-to-image generation process prevents users from narrowing their exploration to a particular object in the image. In this paper, we present a technique to generate a collection of images that depicts variations in the shape of a specific object, enabling an object-level shape exploration process. Creating plausible variations is challenging as it requires control over the shape of the generated object while respecting its semantics. A particular challenge when generating object variations is accurately localizing the manipulation applied over the object’s shape. We introduce a prompt-mixing technique that switches between prompts along the denoising process to attain a variety of shape choices. To localize the image-space operation, we present two techniques that use the self-attention layers in conjunction with the cross-attention layers. Moreover, we show that these localization techniques are general and effective beyond the scope of generating object variations. Extensive results and comparisons demonstrate the effectiveness of our method in generating object variations, and the competence of our localization techniques.
Efficient Diffusion Training via Min-SNR Weighting Strategy

Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence. In this paper, we discovered that the slow convergence is partly due to conflicting optimization directions between timesteps. To address this issue, we treat the diffusion training as a multi-task learning problem, and introduce a simple yet effective approach referred to as Min-SNR-γ. This method adapts loss weights of timesteps based on clamped signal-to-noise ratios, which effectively balances the conflicts among timesteps. Our results demonstrate a significant improvement in converging speed, 3.4× faster than previous weighting strategies. It is also more effective, achieving a new record FID score of 2.06 on the ImageNet 256 × 256 benchmark using smaller architectures than that employed in previous state-of-the-art. The code is available at https://github.com/TiankaiHang/Min-SNR-Diffusion-Training.
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing

Despite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results. For example, generation approaches usually fail to synthesize multiple images of the same objects/characters but with different views or poses. Meanwhile, existing editing methods either fail to achieve effective complex nonrigid editing while maintaining the overall textures and identity, or require time-consuming fine-tuning to capture the image-specific appearance. In this paper, we develop MasaCtrl, a tuning-free method to achieve consistent image generation and complex non-rigid image editing simultaneously. Specifically, MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency. To further alleviate the query confusion between foreground and background, we propose a mask-guided mutual self-attention strategy, where the mask can be easily extracted from the cross-attention maps. Extensive experiments show that the proposed MasaCtrl can produce impressive results in both consistent image generation and complex non-rigid real image editing.