Reviewerly/exHarmony huggingface dataset:
Account Abstraction via Singleton Entrypoint Contract and Verifying Paymaster

Ethereum, a leading cryptocurrency, is advancing the way we handle digital transactions. One important concept in Ethereum is “account abstraction,” which could play a crucial role in making it accessible to a wider audience. Currently, Ethereum has two types of accounts: externally owned accounts (EOAs) and contract accounts (CAs). EOAs are controlled by a public address and private key, allowing users to initiate transactions and interact with smart contracts. However, losing the private key can result in the loss of funds, and EOAs are not quantum safe. On the other hand, CAs are controlled by the code written on the Ethereum Virtual Machine (EVM) and do not possess private keys. They rely on network storage, and their creation incurs a cost. To enhance the existing technology and make it more user-friendly, there is a proposal for “account abstraction” in the Ethereum protocol. This change would enable users to interact with smart contracts using smart contract wallets instead of externally owned accounts. This would bring greater flexibility and security to the management of user accounts, as well as open doors for new and innovative user experiences.
Research on smart contract evaluation method for open source blockchain

The smart contract can complete the agreement by automatic execution between the parties of the blockchain network. It provides the basis for various distributed application services. In recent years, the security loopholes of smart contracts have exploded frequently, caused huge economic losses. The research on its security inspection has become a hot topic and a key scientific problem that needs to be solved urgently in the field of blockchain security. In the paper, firstly, gives the vulnerability of open blockchain system smart contract, investigates the mainstream smart contract vulnerability detection methods; then proposes the vulnerability detection model of smart contract based on gated graph neural network, and tests its performance. Finally, draws a conclusion that graph neural network technology can improve the accuracy of smart contracts mainstream vulnerability testing, and plans the future research direction.
Online Digital Cheque Signature Verification using Deep Learning Approach

The use of a signature as a biometric authentication method is crucial in banking and financial systems. The two forms of signatures are offline and online, with offline signatures being the more popular choice because of how straightforward and distinctive they are. The payer and payee must both sign digital cheques, just like paper ones. In this proposed approach we build a security system, verifying entrance applications and evaluating password substitutes. The proposed approach will create a system that can authenticate digital signatures online while clearing checks for banks and detecting any flaws, guaranteeing confidentiality and preventing fraud. The goal is to successfully validate the validity of online digital checks that contain signatures.
An Enhanced Animal Species Classification and Prediction Engine using CNN

Animal species classification is a fundamental task in wildlife conservation, animal behavior studies, and biodiversity research. Convolutional Neural Networks (CNNs) have become a potent technique for automatic classification tasks in recent times. This abstract presents an overview of the use of CNNs for animal species classification. The proposed approach involves pre-processing of the input images, followed by feature extraction and classification using CNN architecture. The pre-processing step involves image resizing, normalization, and augmentation to enhance the resilience of the model. The feature extraction is performed by convolutional layers, followed by max-pooling layers, and fully connected layers for classification. Transfer learning is also utilized to leverage the pre-trained CNN models and fine-tune them for specific animal species classification tasks. The proposed approach achieves high accuracy of 98% and can be extended to various animal species classification tasks. Overall, CNNs provide an effective means for automated animal species classification, enabling more efficient and accurate animal behavior studies, and wildlife conservation efforts.
Enhancing Recommender Systems with NLP-based Biased Singular Value Decomposition

This research introduces a novel Natural Language Processing (NLP) based Biased Singular Value Decomposition (SVD) Recommender System that addresses the limitations of existing methods while maintaining high-quality recommendations. By incorporating advanced NLP techniques and Biased SVD, this paper introduces a controlled bias that accounts for both user and item biases, effectively enhancing the performance of the recommender system while maintaining a certain degree of fairness and transparency. In addition, neural network-driven sequential models are incorporated to enhance the recommender system's overall performance. These approaches contribute to the development of efficient, fair, and transparent recommender systems that cater to the diverse needs and preferences of users in the digital era.
ProMatch: Semi-Supervised Learning with Prototype Consistency

Recent state-of-the-art semi-supervised learning (SSL) methods have made significant advancements by combining consistency-regularization and pseudo-labeling in a joint learning paradigm. The core concept of these methods is to identify consistency targets (pseudo-labels) by selecting predicted distributions with high confidence from weakly augmented unlabeled samples. However, they often face the problem of erroneous high confident pseudo-labels, which can lead to noisy training. This issue arises due to two main reasons: (1) when the model is poorly calibrated, the prediction of a single sample may be overconfident and incorrect, and (2) propagating pseudo-labels from unlabeled samples can result in error accumulation due to the margin between the pseudo-label and the ground-truth label. To address this problem, we propose a novel consistency criterion called Prototype Consistency (PC) to improve the reliability of pseudo-labeling by leveraging the prototype similarities between labeled and unlabeled samples. First, we instantiate semantic-prototypes (centers of embeddings) and prediction-prototypes (centers of predictions) for each category using memory buffers that store the features of labeled examples. Second, for a given unlabeled sample, we determine the most similar semantic-prototype and prediction-prototype by assessing the similarities between the features of the unlabeled sample and the prototypes of the labeled samples. Finally, instead of using the prediction of the unlabeled sample as the pseudo-label, we select the most similar prediction-prototype as the consistency target, as long as the predicted category of the most similar prediction-prototype, the ground-truth category of the most similar semantic-prototype, and the ground-truth category of the most similar prediction-prototype are equivalent. By combining the PC approach with the techniques developed by the MixMatch family, our proposed ProMatch framework demonstrates significant performance improvements compared to previous algorithms on datasets such as CIFAR-10, CIFAR-100, SVHN, and Mini-ImageNet.
Cost-effective QKD protocol upgrading for metropolitan quantum optical networks

Quantum key distribution (QKD) is a promising technology that can provide future-proof security. With the emergence of multiple advanced QKD protocols, the QKD protocol upgrading for a metropolitan quantum optical network (MQON) is critical for fulfilling the requirements of users for secret keys with a high security level. Conventionally, due to the lack of effective QKD protocol upgrading strategies for MQONs, massive amounts of resources have to be consumed during QKD protocol upgrading, resulting in high costs. In order to reduce the costs for practical implementation of QKD protocol upgrading, in this work, a new policy of QKD protocol upgrading is proposed, where the bypass technique is adopted to decouple multiple protocols from the linking patterns for efficient resource utilization. Moreover, we illustrate a multi-role QKD node structure for enabling the harmonious operation of multiple QKD protocols. A mixed integer linear programming (MILP) model and a novel divided-packet-based QKD protocol upgrading algorithm are designed to save costs by relying on flexible traffic management. We also propose a fixed-chain-based QKD protocol upgrading algorithm with fixed traffic management for achieving low costs. Simulation results indicate that the proposed heuristic algorithms are significantly more cost effective than the associated benchmark algorithms, while the MILP model can reduce the cost of protocol upgrading by 41% compared with the benchmark.
An overview of FOLIO: an open-source library management system

Purpose The overview of FOLIO in this paper will include a description of its background and development, architecture, work structure, benefits and core features. It will detail how FOLIO handles the challenges libraries confront and how it makes library administration more effective and integrated. Design/methodology/approach This paper conducted a comprehensive literature search using Google Scholar and ProQuest databases to identify relevant literature on FOLIO library management systems, open-source library systems and library services platforms. It screened and selected relevant literature related to FOLIO’s architecture, features, implementation, community involvement and impacts on libraries. The selected literature was organised into a systematic structure, and analysed and synthesised information was selected from studies. Critically reviewing the literature, insights from libraries, users or FOLIO developers were integrated, and data analysis was if accessible. This research paper was organised logically, presenting a coherent flow of information. Findings A strong alternative for conventional proprietary systems is the use of FOLIO as an open-source library management system. The versatility, usability and affordability of the platform have the potential to revolutionise library services and improve the entire library experience for both staff and users. FOLIO is a revolutionary tool that gives libraries the resources they need to succeed in the digital era as they continue to adjust to the shifting information landscape. The unique open-source platform FOLIO is expected to continue to succeed and improve through the library community’s collaborative efforts and continuous research and development. Originality/value Despite FOLIO’s growing popularity, empirical research on user attitudes and perceptions of them is still in its early stages. This paper is one of the first scholarly studies to attempt a thorough forecast of FOLIO adoption in technologically advanced libraries.
Poor miners and empty e-wallets: Latin American experiences with cryptocurrencies in crisis

This article examines how cryptocurrencies are increasingly entangled with crises in Latin American political discourse and everyday economic life. In an effort of interdisciplinary integration, combining human geography with political economy and cultural anthropology, we critically assess the linkages between cryptocurrency, economic crisis and forms of political and economic precarity and exploitation. Drawing on experiences in Latin America, mostly on the cases of El Salvador and Venezuela, we explore how cryptocurrencies have rapidly emerged and expanded during periods of economic and political crises. We ground this discussion on social theories of money and critical analysis of blockchain and cryptocurrencies that question the apolitical assumptions of these apparent “trustless” infrastructures. The article contends that cryptocurrencies have the capacity to create potential niches for makeshift economic survival, speculation and quick profit, while at the same time reproducing historical conditions of vulnerability, inequality and ‘crypto-colonialism’. Though cryptocurrencies are surrounded by stories of freedom and decentralised community control, our ethnographic data on El Salvador and Venezuela suggest they often rely on free market fundamentalism and conditions of political corruption by authoritarian state-backed elites.
Demonstrating the use of a professional skills framework to support the assessment of dispositions in IT education

Abstract The skills and competencies of IT professionals are often described using employer-led skills frameworks. They express competencies as technical knowledge and skills combined with a range of personal qualities. Employers have indicated the importance of developing such qualities for new graduates. In response, recent ACM/IEEE curricular recommendations have shifted their emphases from bodies of knowledge to the development of competencies. The IT2017 ACM/IEEE Curriculum Guidelines for Baccalaureate Degree Programs proposed a model of IT competency comprising three interrelated components: content knowledge, skills, and dispositions, where dispositions represent personal qualities desirable in the workplace. The ACM/IEEE Computing Curricula 2020 (CC2020) report enriched the IT2017 disposition concept by identifying eleven dispositions that all computing programs should include for the career preparation of their graduates. However, developing and assessing dispositions in a degree program remain challenges, often involving internships, work placements and similar student opportunities. A recent mapping of the eleven CC2020 dispositions to the responsibility characteristics of the Skills Framework for the Information Age (SFIA), a widely used professional skills framework, suggested a promising approach to addressing this challenge. Inspired by this mapping, this paper aims to help educators assess students’ achievement of CC2020 dispositions by mapping real-world experiences they have recorded in individual portfolios against the SFIA responsibility characteristics. First, the selection of SFIA to operationalize the CC2020 dispositions is validated by demonstrating that alternative frameworks pose significant challenges for any assessment approach that needs to be independent of particular technical skills. A tool is described that maps demonstration of SFIA responsibility characteristics to CC2020 dispositions, applying a simple, consistent assessment algorithm. Finally, the assessment process and outcomes are illustrated using a fictional student portfolio, constructed to reflect one author’s experience of work placement students’ achievements.
Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning

Long-horizon task planning is essential for the development of intelligent assistive and service robots. In this work, we investigate the applicability of a smaller class of large language models (LLMs), specifically GPT-2, in robotic task planning by learning to decompose tasks into subgoal specifications for a planner to execute sequentially. Our method grounds the input of the LLM on the domain that is represented as a scene graph, enabling it to translate human requests into executable robot plans, thereby learning to reason over long-horizon tasks, as encountered in the ALFRED benchmark. We compare our approach with classical planning and baseline methods to examine the applicability and generalizability of LLM-based planners. Our findings suggest that the knowledge stored in an LLM can be effectively grounded to perform long-horizon task planning, demonstrating the promising potential for the future application of neuro-symbolic planning methods in robotics.
Regression tree-based active learning

Machine learning algorithms often require large training sets to perform well, but labeling such large amounts of data is not always feasible, as in many applications, substantial human effort and material cost is needed. Finding effective ways to reduce the size of training sets while maintaining the same performance is then crucial: one wants to choose the best sample of fixed size to be labeled among a given population, aiming at an accurate prediction of the response. This challenge has been studied in detail in classification, but not deeply enough in regression, which is known to be a more difficult task for active learning despite its need in practice. Few model-free active learning methods have been proposed that detect the new samples to be labeled using unlabeled data, but they lack the information of the conditional distribution between the response and the features. In this paper, we propose a standard regression tree-based active learning method for regression that improves significantly upon existing active learning approaches. It provides impressive results for small and large training sets and an appreciably low variance within several runs. We also exploit model-free approaches, and adapt them to our algorithm to utilize maximum information. Through experiments on numerous benchmark datasets, we demonstrate that our framework improves existing methods and is effective in learning a regression model from a very limited labeled dataset, reducing the sample size for a fixed level of performance, even with many features.
Matrix Encryption Walks for Lightweight Cryptography

In this paper, we propose a new symmetric stream cipher encryption algorithm based on Graph Walks and 2-dimensional matrices, called Matrix Encryption Walks (MEW). We offer example Key Matrices and show the efficiency of the proposed method, which operates in linear complexity with an extremely large key space and low-resource requirements. We also provide the Proof of Concept code for the encryption algorithm and a detailed analysis of the security of our proposed MEW. The MEW algorithm is designed for low-resource environments such as IoT or smart devices and is therefore intended to be simple in operation. The encryption, decryption, and key generation time, along with the bytes required to store the key, are all discussed, and similar proposed algorithms are examined and compared. We further discuss the avalanche effect, key space, frequency analysis, Shannon entropy, and chosen/known plaintext-ciphertext attacks, and how MEW remains robust against these attacks. We have also discussed the potential for future research into algorithms such as MEW, which make use of alternative structures and graphic methods for improving encryption models.
Quantum illumination using polarization-path entangled single photons for low reflectivity object detection in a noisy background

Detecting object with low reflectivity embedded within a noisy background is a challenging task. Quantum correlations between pairs of quantum states of light, though are highly sensitive to background noise and losses, offer advantages over traditional illumination methods. Instead of using correlated photon pairs which are sensitive, we experimentally demonstrate the advantage of using heralded single-photons entangled in polarization and path degree of freedom for quantum illumination. In the study, the object of different reflectivity is placed along the path of the signal in a variable thermal background before taking the joint measurements and calculating the quantum correlations. We show the significant advantage of using non-interferometric measurements along the multiple paths for single photon to isolate the signal from the background noise and outperform in detecting and ranging the low reflectivity objects even when the signal-to-noise ratio is as low as 0.03. Decrease in visibility of polarization along the signal path also results in similar observations. This will have direct relevance to the development of single-photon based quantum LiDAR and quantum imaging.
Double constrained discriminative least squares regression for image classification

Discriminative Least Squares Regression (DLSR) is an approach that is more popularly used in multi-classification tasks, which employs an ε-draggings technique to enhance intra-class similarity. However, this approach fails to take into account the fact that the increased intra-class compactness may also widen the distance between similar classes. For addressing this problem, we propose a novel method called Double Constrained Discriminative Least Squares Regression (DCDLSR), which incorporates both inter- and intra-class distances to improve classification performance. First, the constrained relaxation labels of Fisher criterion are introduced into the DLSR model, which shortens the interval of similar samples. Second, we place the operation of matrix decomposition inside the label space to preserve as much of its manifold structure as possible. Additionally, we introduce l2,1-norm constraints to the noise matrix to mitigate the impact of outliers. Our experiments demonstrate that improving the double constraint and matrix decomposition significantly overcomes the limitations of DLSR and achieves better performance on multi-classification tasks.
An ensemble learning approach for resampling forgery detection using Markov process

Resampling is an extremely well-known technique performed for Image forgery detection. It includes the changes in the content of a picture in terms of rotation, stretching/zooming, and shrinking, to frame a forged picture that is a localized forgery in comparison to the original picture. With the wrong intention, resampling forgery has been increased day by day, and its negative impact has been increased in criminology, law enforcement, forensics, research etc. Accordingly, the interest in the algorithm of image resampling forgery detection is significantly developed in image forensics. In this paper, a novel image resampling forgery detection technique has been proposed. In the proposed technique, two types of Markov feature with spatial and Discrete Cosine Transform domains have been extracted to recognize the resampling operation. The spatial domain gives the information for the distribution of the pixels and DCT gives the edge information. Further, these Markov features are consolidated. Due to high dimensionality hard thresholding technique is used for reducing the dimensionality. Then, these Markov features are applied to the set of models of different classifiers. With the utilization of classifiers, weighted majority voting values have been calculated during the ensemble classification. Unlike the other techniques, these weighted voting boundaries have been consequently balanced during the training process until the best accuracy has been obtained. However, it is very difficult to get best accuracy so for getting best accuracy this research needs to do lots of iterations and trained the dataset. For the comparative study very few research has been found for this resampling forgery technique with different interpolation techniques and classifier. Still, comparison has been done with some latest research work. The comparative analysis shows that the proposed ensemble learning-based algorithm provides the best outcomes with the accuracy of 99.12% for bicubic, 98.89% for bilinear, and 98.23% for lanczos3 kernel with considerably less complexity and high speed in comparison to prior techniques which are using single support vector machine for classification. Moreover, the proposed algorithm also detects a very low probability of error of 0.44% and detects the type of interpolation kernel, size of the forgery, and the type of resampling, whether it is up sampling and down sampling, using Graphical User Interface which has not been detected previously with multiple forgery detection.