Reviewerly/exHarmony huggingface dataset:
LogContrast: A Weakly Supervised Anomaly Detection Method Leveraging Contrastive Learning

We propose a novel log-based anomaly detection method that leverages weakly supervised contrastive learning, named LogContrast. LogContrast aims to address the issues of limited and noisy labeled logs in real-world scenarios. During the training stage, LogContrast first augments the current log feature through dropout. Subsequently, it treats the current log feature and the augmented feature as a positive pair, while treating the current log feature and other log features in the same batch as negative pairs. The objective is to pull the positive pairs closer together and push the negative pairs farther apart, thereby encouraging similar logs to be closer to each other and dissimilar logs to be farther apart in the feature space. The experimental results demonstrate the excellent performance of LogContrast even with limited labeled logs and greater noise resistance compared to fully supervised methods. In addition, we explore the role of semantic features and demonstrate that semantic features have strong adaptability to constantly evolving logs.
Exhibition spaces in the metaverse: a novel design approach

In recent years, thanks to the development of digital and immersive technologies and the implementation of network connections, many applications within the Metaverse have been made. This new concept opens up increasingly innovative scenarios and perspectives on how we understand and experience virtual spaces and environments. The fields of application of the Metaverse can be infinite, one example being the world of art and culture. The Metaverse makes it possible to create new exhibition and museum spaces, interactive and virtual, which can be enjoyed by users worldwide and offer new entertainment opportunities. Starting from a study of the articles already published on these topics, the proposed work aims to identify the workflow to be followed for the realisation of a virtual exhibition space based on the metaverse concept, developing the creative process in six steps starting with the choice of the most suitable platform for the requirements, moving on to the choice of the layout and design of the exhibition space, which must be in line with the identity of the project and be conceived in such a way as to facilitate the enjoyment of the art or culture on display, and ending with a final phase of evaluation of the results to validate the system and the effectiveness of the project. The importance of adopting further measures, such as a marketing and promotion plan, privacy guarantee, and technical support for users, to ensure that the proposed space is increasingly used, updated and improved is also emphasised.
Automated Evaluation of Student Answers for Geometric Questions Based on the Theorem 'Angles on a Straight Line Add to 180±’

Evaluation of the answers for a geometry-related question is a much more challenging task due to the usage of multiple methods for solving this kind of problem. However, in this research, an automated system to evaluate the students' answers to geometric questions has been introduced. The image of the problem and the image of the student's answer are the inputs of this system. The system can evaluate student's answers by using the identified angles and creating equations in the system. Furthermore, the system allocates the marks for correct steps and provides feedback on whether the answer is correct or incorrect. Here, the set of rules using “sympy” has been used to create the solved answer by the system. Currently, the system evaluates the answers only for the problems related to the theorem” Angles on a Straight Line Add to 180°”. The system has been tested with 30 student answers for 10 questions. 18 were correct out of these 30 answers.
The Effect of Ethereum’s Proof of Stake Update on Ethereum Price Using the Long Short-Term Memory Method

Cryptocurrency is a digital or virtual currency secured by cryptography. Using cryptography, this digital currency is nearly impossible to counterfeit. All transaction records are stored on the blockchain. Ethereum is one of the cryptocurrencies, along with Bitcoin, etc. Ethereum was first created in 2013 by Vitalik Buterin, a professional programmer, with the goal of building decentralized applications. Ethereum was launched in 2015 with its own cryptocurrency called ether. Proof of Stake (PoS) is the underlying mechanism that activates validators after receiving sufficient stake. For Ethereum, users need to stake 32 ETH to become validators. The objectives of this research are to understand the general overview of ETH prices in the past two years, assess the effectiveness of transitioning from Proof of Work (PoW) to PoS, and explore the application of long-short-term memory (LSTM) in price prediction. The results of this research indicate that the LSTM model is well-suited for predicting prices with a sufficiently large and complex dataset. The evaluation parameters, including the root mean square error (RMSE) with a value of 130.109, the mean squared error (MSE) with a value of 16928.408, and the mean absolute error (MAE) with a value of 92.9926, show that the performance of the LSTM model is good.
Contextualizing Enhances Gradient Based Meta Learning for Few Shot Image Classification

Meta learning methods have found success when applied to few shot classification problems, in which they quickly adapt to a small number of labeled examples. Prototypical representations, each representing a particular class, have been of particular importance in this setting, as they provide a compact form to convey information learned from the labeled examples. However, these prototypes are just one method of representing this information, and they are narrow in their scope and ability to classify unseen examples. We propose the implementation of contextualizers, which are generalizable prototypes that adapt to given examples and play a larger role in classification for gradient-based models. We demonstrate how to equip meta learning methods with contextualizers and show that their use can significantly boost performance on a range of few shot learning datasets. We also present figures of merit demonstrating the potential benefits of contextualizers, along with analysis of how models make use of them. Our approach is particularly apt for low-data environments where it is difficult to update parameters without overfitting. Our implementation and instructions to reproduce the experiments, available at https://github.com/naveace/proto-context/, are thoroughly tested on MIT SuperCloud, and scalable to other state-of-the-art HPC systems.
Developmental Scaffolding with Large Language Models

Exploration and self-observation are key mechanisms of infant sensorimotor development. These processes are further guided by parental scaffolding to accelerate skill and knowledge acquisition. In developmental robotics, this approach has been adopted often by having a human acting as the source of scaffolding. In this study, we investigate whether Large Language Models (LLMs) can act as a scaffolding agent for a robotic system that aims to learn to predict the effects of its actions. To this end, an object manipulation setup is considered where one object can be picked and placed on top of or in the vicinity of another object. The adopted LLM is asked to guide the action selection process through algorithmically generated state descriptions and action selection alternatives in natural language. The simulation experiments that include cubes in this setup show that LLM-guided (GPT3.5-guided) learning yields significantly faster discovery of novel structures compared to random exploration. However, we observed that GPT3.5 fails to effectively guide the robot in generating structures with different affordances such as cubes and spheres. Overall, we conclude that even without fine-tuning, LLMs may serve as a moderate scaffolding agent for improving robot learning, however, they still lack affordance understanding which limits the applicability of the current LLMs in robotic scaffolding tasks.
Automatic Differentiation for Inverse Problems with Applications in Quantum Transport

A neural solver and differentiable simulation of the quantum transmitting boundary model is presented for the inverse quantum transport problem. The neural solver is used to engineer continuous transmission properties and the differentiable simulation is used to engineer current-voltage characteristics.
A Study on Formal Verification of Smart Contracts in Distributed Ledger Technology

This study paper explores the critical topic of formal verification of smart contracts in distributed ledger technology (DLT) systems. Smart contracts, self-executing code running on blockchain platforms, have gained widespread adoption in various industries due to their automation and transparency benefits. However, the decentralized and immutable nature of DLT raises concerns regarding smart contract security, leading to vulnerabilities and potential exploits. Formal verification methods offer a systematic approach to analyze smart contracts for correctness and safety. This paper provides an overview of smart contracts, DLT, and the importance of formal verification. It reviews various formal verification techniques and presents state-of-the-art tools and frameworks. The paper concludes with discussions on challenges, future research opportunities, and the role of formal verification in enhancing DLT-based applications' security and usability.
A Survey on the Impact of Blockchain in Effective Organ Transplantation

Last decade has witnessed revolutionary changes in the field of Information Technology & Health Care. The digitization of healthcare data has enabled the effective storage, retrieval and sharing of medical information of the patients. It simplifies the tedious paper work involved in traditional data storage approaches providing cost effective and better health assistance. Given the sensitivity of medical information, it must be handled with the utmost care while maintaining confidentiality and privacy. Organ donation and transplant process is the most complicated area of health sector. There are several factors like scarcity of organs, lack of awareness among public, complexities involved in organ removal and implantation processes and black marketing that makes it challenging. To enable more transplants before patients on the organ waiting list pass away, an upsurge in the number of donors, handled by a more secure and accountable platform, is required. Blockchain with its distributed ledger technology offers a wider canvas for organ matching, breaking the regional barriers, with international waiting list databases. The security and immutability features of blockchain can ensure the privacy of the patients and reduces black market organ donations. This paper reviews the role that can be played by blockchain in improvising the existing organ transplantation procedure. Different systems implemented using blockchain technology are analyzed, highlighting their unique features.
Asymmetric Grouped Convolutions for Logarithmic Scale Efficient Convolutional Neural Networks

The design of convolutional neural networks has been increasingly focused on small and efficient models to meet the modern demands of edge devices. Thus, analyzing the theoretical limits of convolutional layers of previously unexplored complexities is critical. Here, we present a logarithmic-scale efficient convolutional neural network architecture. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: an asymmetric grouped convolution and a depthwise fast wavelet transform layer. By applying asymmetry in channel dimensions and applying a provably optimal fast algorithm we shrink the complexity of convolutional blocks by an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{O}(\log D/D)$</tex> factor (from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(D^{2})$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{O}(D\log D))$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> is the number of channels. Experiments on CIFAR-10, CIFAR100 and ImageNet classification show superior/comparable performances of our model to classical strong baseline models such as MobileNetV2 and ShuffleNet. The new convolutional layers that we propose could serve a variety of applications, from designing efficient models by hand to augmenting the search space of AutoML architectures.
Black-box Speech Adversarial Attack with Genetic Algorithm and Generic Attack Ideas

Deep learning technology has been widely used in many fields related to people's life. However, current research shows that speech recognition models designed based on deep neural networks are extremely susceptible to adversarial samples. Black-box attack methods are closer to the actual situation, but the black-box attack techniques now in use have drawbacks such as low attack success rate, easy perception of the adversarial samples by humans, and more iterations required to generate the adversarial samples. Therefore, constructing black-box adversarial examples for speech recognition systems continues to be an extremely difficult challenge. In this paper, we propose a blackbox speech adversarial attack method based on genetic algorithm and generalized attack idea to address the problem of low attack success rate of existing black-box speech attack methods. By effectively combining a speech attack method based on a genetic algorithm design with a general speech attack method, at the same time, we designe a switching condition to switch the genetic algorithm module to a general attack method module to continue the attack process under certain conditions. This effectively improves the success rate of targeted attacks on DeepSpeech, a speech recognition model with good current speech recognition, and provides a new idea for the design of black-box speech adversarial attack methods in the future.
Manifold Transfer Networks for Lens Distortion Rectification

Convolutional neural networks (CNNs), well-known for their translational invariance property on translational man-ifolds, are not guaranteed to generalize to images on other types of manifolds. Existing works extending CNNs' translational invariance property are limited to linear transformations such as rotation. We propose a novel framework, the Manifold Transfer Network, with an embedded inductive bias for any specified nonlinear manifold. Our model maps a nonlinear transformation to a linear translation on a translational manifold, making it suitable for a CNN to learn and predict. We design such a map through the solutions of a particular class of partial differential equations. We empirically apply our method to the domain of radial lens distortion rectification. In our experiments on the CelebA dataset we demonstrate superior performance of our model compared to conventional baselines.
Applying Hoeffding Tree Algorithms for Effective Stream Learning in IoT DDoS Detection

The constant evolution of botnets to cause DDoS and the non-existence of storage devices in IoT environments with high capacity to save the packets traveling on the network claim an IDS capable of constantly learning about new attacks without the need to save all the IoT traffic. A good solution to this scenario is to use stream learning, but it is important to guarantee that the learning model will be adaptable to concept drift. In this sense, Hoeffding Trees can be employed as the core of an IDS. This paper evaluates the effectiveness of using different Hoeffding Tree algorithms to detect DDoS against IoT devices. The work advances the state of the art by comparing different algorithms, adapting the basic algorithm to the considered scenario, evaluating a recently created dataset, and showing how the algorithms react to concept drift. Results show that the basic Hoeffding Tree algorithm is the most effective option when compared against the Hoeffding Adaptive Tree and ensembles of these trees (0.96/0.12 of average/standard deviation F1-Score vs. 0.90/0.26 and 0.91/0.24 respectively). As an additional contribution, all the code and data produced in the investigation are shared as open-source software and open data.
Privacy-Preserving Trainer Recruitment in Model Marketplace of Federated Learning

Federated learning (FL) technologies enable trainers to collaboratively train machine learning (ML) models while maintaining data privacy, making them a crucial component of the next-generation model marketplace. However, several issues arise from the non-independent and identically distributed (non-IID) data among trainers, as well as the customers' diverse model orders. Consequently, it is necessary to design a trainer recruitment mechanism to select trainers that meet the customer's model requirements and improve the performance of purchased models. In this paper, we propose a privacy-preserving trainer recruitment scheme in a model marketplace of FL, which aims to recruit optimal trainers that meet the customer's requirements. Specifically, we propose a hierarchical recruitment mechanism to select trainers based on their task preferences, data distributions, and data sizes. Additionally, we prove the NP-hardness of the optimal trainer recruitment problem and propose a heuristic selection algorithm to provide an approximate solution. Extensive experiments demonstrate that our proposed scheme effectively improve the performance of purchased models, particularly in scenarios with highly non-IID data and limited budgets.
Efficient Scheduling for Multi-Job Federated Learning Systems with Client Sharing

Federated Learning (FL) has emerged as a promising learning approch for data distributed across edge devices. Existing research mainly focuses on single-job FL systems. However, in practical scenarios, multiple FL jobs are often submitted simultaneously. Simply applying single-job optimizations to multi-job FL systems results in sub-optimal system performance. Specifically, we find considerably low resource utilization on the client side due to device heterogeneity. In this paper, we exploit opportunities in multi-job FL systems to improve resource utilization by client sharing: (1) clients not selected for one FL job could be allocated to another FL job, and (2) clients that complete their tasks early in one FL job could be preemptively assigned to another job. We propose an efficient scheduling algorithm for multi-job FL systems, namely GMFL. This scheduling algorithm promptly assigns an available job to a client as soon as it becomes available. To ensure training convergence, we carefully select jobs for each client while considering several constraints. We conduct experiments using four popular models across four different datasets to evaluate the performance of the proposed scheduling algorithm. Experimental results show that our proposed scheduling algorithm significantly outperforms existing methods, with a performance improvement of up to 2.03×.
Procrustean entanglement concentration in dense wavelength-division multiplexing

We employ polarization-dependent loss emulators for Procrustean filtering of a noisy quantum state affected by classical crosstalk, resulting in a substantial improvement of two-qubit entangled state fidelities from approximately 75% to over 92%.
Understanding Video Scenes through Text: Insights from Text-based Video Question Answering

Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4-ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training.
Cascaded Quantum Well Structure Emitting Entangled Multiphoton States

Deterministic cascaded quantum well source spontaneously emits inter-entangled triple frequency multiphoton states. The entanglement stems from interference of indistinguishable quantum evolution paths in the cascaded coupled quantum wells. Novel controllable quantum entanglement and quantum error detection are two possible features of the multiphoton source.
Complex-valued Optical Neural Networks Enabled by Multimode Interferometers and Phase Shifters

We propose a compact, complex-valued optical neural network accelerator that is composed of multimode interferometers and phase shifters and is enabled by coherent detection technology. Simulation results confirm its performance in neural network calculations, showcasing an accuracy rate of around 98% for classifying nonlinear datasets.
Enhancing Real-Time Training of Heterogeneous UAVs Using a Federated Teacher-Student Self-Training Framework

The use of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, is expanding quickly across a wide range of industries, such as package delivery, defense, law enforcement, etc. UAVs equipped with machine learning (ML) capabilities can improve system performance by carrying out autonomous actions at the edge, which lowers the need for communication. A prominent area of research addresses the challenge of a UAV equipped with a pre-trained model adapting to rapidly evolving, previously unencountered situations in real-world applications. Such scenarios can arise in situations like emergency deployments of multiple drones with minimal setup time for initial training of the ML models. When the UAV is equipped with sensors that are less sophisticated than those used during the initial training phase, the probability of autonomous operation failure increases. This paper suggests using the Teacher-Student self-training architecture with federated ML capability for a situation involving several heterogeneous UAVs. In this work, we examine a UAV docking scenario where a UAV with a pre-trained model of a docking station is used to teach additional UAVs in real-time in order to show the applicability. The process is done in a distributed framework, where the Teacher and the Student nodes are updated with the newly trained model. The results of our experiments show that our proposed method can improve the performance of the entire system.