Reviewerly/exHarmony huggingface dataset:
Scalable fully coupled annealing processing system and multi-chip FPGA implementation

This work proposes a scalable method for a large-scale fully coupled annealing processing system that utilizes multi-chip operation in parallel to solve the combinatorial optimization problems facing a wide range of applications. We previously proposed an architecture for the LSI implementation of a fully coupled annealing processor that updates the value of spin based on an energy calculated using a one-dimensional spin array and a two-dimensional spin-to-spin interaction array. In the present work, expanding this method, we proposed a new method of dividing the energy and calculating it on several identical first chips and then taking the sum of them on a second chip to obtain the updated spin value. The amount of data communication between the first and second chips is quite small with only 4 pins used for chip connection each. This enables us to create a fully coupled annealing processing system with a scalable number of spins. The results of an FPGA demonstration confirmed that these small amounts of communication by multi-chip operation were possible while operating as a fully coupled system. In the demonstration, a 384-spin fully coupled annealing processing system board was implemented using 16 first FPGA chips and one second FPGA chip. The current consumption of the entire system board was 950 mA when idling and 1050 mA when running fully coupled annealing processing at 10 MHz. The results also showed that a 92-node graph coloring problem and 384-node max-cut problem could be solved by using this system. For the max-cut problem, our system board was 584 times faster and 46 times more energy efficient than a 4-GHz CPU PC when solving the same problem.
Opportunistic Algorithmic Double-Spending:

In this paper, we outline a novel form of attack we refer to as Opportunistic Algorithmic Double-Spending (OpAl). OpAl attacks avoid equivocation, i.e., do not require conflicting transactions, and are carried out automatically in case of a fork. Algorithmic double-spending is facilitated through transaction semantics that dynamically depend on the context and ledger state at the time of execution. Hence, OpAl evades common double-spending detection mechanisms and can opportunistically leverage forks, even if the malicious sender themselves is not responsible for, or even actively aware of, any fork. Forkable ledger designs with expressive transaction semantics, especially stateful EVM-based smart contract platforms such as Ethereum, are particularly vulnerable. Hereby, the cost of modifying a regular transaction to opportunistically perform an OpAl attack is low enough to consider it a viable default strategy. While Bitcoin’s stateless UTXO model, or Cardano’s EUTXO model, appear more robust against OpAl, we nevertheless demonstrate scenarios where transactions are semantically malleable and thus vulnerable. To determine whether OpAl-like semantics can be observed in practice, we analyze the execution traces of $$922\,562$$ transactions on the Ethereum blockchain. Hereby, we are able to identify transactions, which may be associated with frontrunning and MEV bots, that exhibit some of the design patterns also employed as part of the herein presented attack.
Automatic Model Adaption Method based on Few-Shot Incremental Learning for $\text{IoT}$ Applications

The dynamic Internet-of-Things application scenarios can lead to model expiration. Machine learning models need to update adaptively and efficiently. In most cases, models are expected to learn new classes from consecutive tasks. Attempts to develop an incremental learning system have been impeded by a chronic problem called “catastrophic forgetting”. This problem will become worse when the number of samples of the new classes is low. To alleviate the effect of catastrophic forgetting, it's essential to retain the previously learned knowledge and accommodate new knowledge over time. Inspired by the dual-coding theory, we propose the SE, a novel computationally efficient model adaption framework with a cooperative model architecture consisting of a sampling model (sampler) and an incremental classifier (extractor). With these two components (the sampler, the extractor), knowledge can be easily accumulated, and incremental classifier can be achieved. Extensive experiments have been carried out to demonstrate the effectiveness and efficiency of the proposed SE approach.
Multi-task Federated Learning based on Client Scheduling in Mobile Edge Computing

With the gradual enrichment of mobile edge computing scenarios, more mobile edge smart devices are involved in edge computing. And due to the requirement for data privacy for multi-party machine learning, federated learning has emerged as a secure multi-party machine learning framework. In order to fix the problem of model accuracy due to client data heterogeneity, we propose a multi-task federated learning framework based on client scheduling for mobile edge computing in this paper, which uses client scheduling to reduce the statistical heterogeneity of data and computational redundancy caused by multi-task learning. In addition, an alternating optimization strategy is proposed to solve the unfairness problem of clients in multi-task learning. We conduct a series of experiments and compare them with popular federated learning frameworks, and the results demonstrate that the methods we proposed can significantly improve local model accuracy and solve the client-side fairness problem.
An adaptive seam line panoramic video stitching algorithm

In order to improve the phenomenon of artifacts and loss when moving objects pass through the overlapping area in panoramic video stitching, an adaptive panoramic video stitching algorithm is proposed by combining the characteristics of panoramic video stitching. Firstly, extracting video frames, computing overlap areas, and the calculation method of the energy function and the search direction of the seam line are improved based on the traditional dynamic planning algorithm to find the seam line. The traditional method only judges the three adjacent points in the previous row of the current pixel point instead of all the pixel points in the previous row, and this improved method is used to find multiple seam lines in the overlapping area, detect the moving object target, and update the seam line in time. The experiments show that the improved algorithm can not only avoid the larger objects in the overlapping area when finding seam lines, but also improve the artifacts and loss caused by moving objects passing through the seam lines in the overlapping area in video stitching. At the same time, the GPU processing achieves real-time effect.
A Wiener Filter Denoising Based Intelligent Modulation Recognition System

In this paper, to improve the modulation recognition accuracy when the signal to noise ratio (SNR) is low, we propose a Wiener filter preprocessing aided intelligent modulation recognition method. In this design, the Wiener filter preprocessing is firstly conducted to reduce the noise of the received signal, then the signal cycle spectrum is calculated as the input to the deep neural network (DNN). Subsequently, the DNN classifier will extract the features of signals to recognize the modulation type. Simulation results show that when SNR is as low as -25dB, the average recognition accuracy of the proposed scheme is improved by about 40% compared with that of the scheme without applying the noise suppressing preprocessing operations. Moreover, compared with the benchmark scheme, the proposed scheme has achieved higher recognition accuracy in low SNR regions.
Stochastic Client Scheduling with Dynamic SINR Thresholds for Fast Federated Learning

Federated edge learning is often used in delay-sensitive big data machine learning. However, in cellular networks with limited communication resources, efficient scheduling of large groups of clients has become vital. There is evidence that the impact of the number of participating clients in initial communication rounds is less significant than that of subsequent communication rounds in global model aggregations. Inspired by this, in this paper, we propose a Stochastic Client Scheduling Strategy with Dynamic SINR Thresholds (STUART). In particular, we apply a higher SINR threshold for initial communication rounds in the base station to prevent low-rate clients from participating in the model aggregation, which can effectively reduce local model upload time. Thereafter, the SINR threshold is gradually lowered for the following communication rounds to allow more clients to take part in, hence improving convergence performance. With a convergence constraint, the optimal client scheduling problem is formulated and solved using the genetic algorithm. Finally, we verify the effectiveness of the proposed algorithm through extensive experiments. The results reveal that compared with the fixed threshold method, the STUART algorithm can reduce communication time by more than 1/3 while marginally degrading the convergence performance.
Proximal Policy Optimization-based Federated Client Selection for Internet of Vehicles

The introduction of artificial intelligence (AI) tech-nology to Internet of Vehicles (IoV) brings the promising potential of enabling intelligent services to moving vehicles. However, training of conventional centralized AI model suffers from long access latency to cloud server and data privacy leakage of vehicle clients. To address these issues, federated learning (FL) has been introduced to IoV for distributed privacy-preserving model training. Nevertheless, to optimize the system overhead and model accuracy of FL, the selection of federated clients has become a critical problem. To overcome this challenge, a proximal policy optimization (PPO)-based client selection scheme has been proposed in this work for the federated AI model training in IoV. Specifically, the federated client selection problem is formulated as a Markov decision process (MDP) first, and a PPO-based algorithm has been developed for the MDP. Subsequently, the dataflow of the proposed client selection scheme is presented in detail. Simulations have been conducted based on practical scenario settings. Simulation results indicate that the proposed client selection scheme outperforms the double deep Q network (DDQN)-based scheme and biased selection scheme on the con-vergence time and test accuracy of federated model training with more stable system performance.
Lithology Identification Through X-Ray Fluorescence (XRF) Analyses on Drill Cuttings While Drilling, in Santos Basin

Abstract The present research propouses a methodology using XRF analysis on drill cuttings samples, obtained during well drilling, for characterizing pre-salt reservoir formations in the Santos Basin, southeast of Brazil in order to identify composition variations in carbonates, clay zones and potential igneous rocks. In this study 16 off-set wells were analyzed where XRF analysis was performed on drill cuttings samples. Aftwerwards the results were compared with lithogeochemical and gamma-ray spectral logs, determining the consistency of the data. The lithological interpretations were based on macroscopic (sidewall core samples - SWC) and microscopic (thin section) descriptions and geochemical analyses from SWC samples. This permitted us to comprehend the variations observed in the carbonate reservoir and to identify possible igneous rocks. From the results, a pattern of responses could be established for the XRF method for each lithology within the Pre-salt section, using the major elements (Mg, Ca, Si, k, Fe and Al) and some minor elements and trace elements (Sr, Rb, Y, Zr, Ti, Nb, Ga). The variations of the major elements were best observed in radar and bar charts, that use only the major elements, which enabled the separation of the lithological section into six main sets: limestones, dolomitic carbonates, silicified carbonates, carbonates with magnesian clay, siliciclastic rocks (shale/siltstone/sandstone) and basic igneous rocks (basalt/diabase). For quality control, still during drilling, comparative analysis by three approaches was proposed: a) the systematic comparison of the proportion of elements composing the same mineral, such as Ca and Sr, K and Rb, and Al and Ga; b) the comparison of XRF data with XRD data; and c) comparison with calcimetry data in a cutting sample. Furthermore, in order to support the interpretations, four crossplots (Ca × Si; K.Rb × Al.Zr; Rb × Sr; Zr × Fe) and one crossover (Rb × Sr) were generated aiming to individualize the six lithological types described, as well as diagrams from the literature to identify the igneous rock type. The use of XRF on cuttings to determine lithologies during the drilling of petroleum wells is new in the literature, as well as the proposed quality control, being useful for the characterization of complex reservoirs such as Santos Basin pre-salt, being a methodology already used since 2018 by Petrobras.
Evaluation of College Students’ IPE Environment Based on User Rating Sparsity and Big Data Technology

Big data improves opportunities for enhancing and improving university students' IPE. In order to improve the accessibility of IPE for university students, this study integrates big-data techniques into the IPE (Ideological and Political Education) model of university students and builds the IPE platform. This study presents the idea of user rating sparsity and employs a two-step training strategy to address the issues of user cold start and sparse data in light of the drawbacks of conventional methods. This algorithm produces a very small data structure, which saves a lot of storage space. This study also uses hybrid recommendation technology, which effectively enables platform users to select customized update resources based on their interest information. According to test results, this method's suggestion accuracy can reach 95.69%, and it has a high user rating. This demonstrates that the method is reliable and accomplishes the desired result. This paper fully utilizes mega data to improve the accessibility of IPE for university students.
We Can Hear Your PIN Drop: An Acoustic Side-Channel Attack on ATM PIN Pads

Personal Identification Numbers (PINs) are the most common user authentication method for in-person banking transactions at ATMs. The US Federal Reserve reported that, in 2018, PINs secured 31.4 billion transactions in the US, with an overall worth of US$ 1.19 trillion. One well-known attack type involves the use of cameras to spy on the ATM PIN pad during PIN entry. Countermeasures include covering the PIN pad with a shield or with the other hand while typing. Although this protects PINs from visual attacks, acoustic emanations from the PIN pad itself open the door for another attack type. In this paper, we show the feasibility of an acoustic side-channel attack (called $$\mathcal PinDrop$$ ) to reconstruct PINs by profiling acoustic signatures of individual keys of a PIN pad. We demonstrate the practicality of $$\mathcal PinDrop$$ via two sets of data collection experiments involving two commercially available metal PIN pad models and 58 participants who entered a total of 5,800 5-digit PINs. We simulated two realistic attack scenarios: (1) a microphone placed near the ATM (0.3 m away) and (2) a real-time attacker (with a microphone) standing in the queue at a common courtesy distance of 2 m. In the former case, we show that $$\mathcal PinDrop$$ recovers 96% of 4-digit, and up to 94% of 5-digits, PINs. Whereas, at 2 m away, it recovers up to 57% of 4-digit, and up to 39% of 5-digit PINs in three attempts. We believe that these results are both significant and worrisome.
A Reconstruction of Ewens’ Sampling Formula via Lists of Coins

This overview paper starts from an elementary fact about lists of numbers (coins) for which a simple arithmetical proof is lacking. The paper does provide a proof, but via probabilistic reasoning, using iterations of probabilistic functions (channels), or equivalently, using iteration of transitions in a probabilistic automaton. The formulas involved capture mutations, with a rate parameter, as developed some fifty years ago in population biology by Warren Ewens. Here, this formula is reconstructed, in a theoretical computer science setting, first for lists and then also for multisets—like in the original work. The methods for describing such mutations have wider significance, beyond biology, for instance in machine learning, when the number of clusters in a classification problem may grow.
From Languages to Behaviors and Back

We present two formalisms for describing behaviors of procedural systems, i.e., systems that consist of multiple procedures that can mutually call each other. Systems of procedural transition systems (SPTSs) provide a fine-grained formalism for the step-wise semantics of reactive systems whereas the equally expressive systems of behavioral automata (SBAs) provide a language-based characterization that can be used by active automata learning (AAL). Based on the concepts of our previous work on systems of procedural automata (SPAs), we present an AAL algorithm for SBAs and provide an open-source implementation of the algorithm that is publicly available for experimentation. In a synthetic benchmark evaluation, we show that our approach of learning behaviors can even out-perform our previous approaches for holistic system models.
Playing Wordle with Uppaal Stratego

In this paper we model and solve the popular game Wordle using Uppaal Stratego. We model three different game-modes in terms of POMDPs, with more than 12,000 controllable actions. These constitute by far the largest models ever presented to Uppaal Stratego. Our experimental evaluation is encouraging: e.g. in the hard game-mode the partitioning-refinement learning method of Uppaal Stratego reduces the expected number of guesses from a baseline of 7.67 to 4.40 using 1 million training episodes. To better understand the convergence properties of our learning method we also study reduced versions of Wordle.
The role of blockchain technology in improving the efficiency of fuel &amp; energy companies

This article discusses the impact of new technologies, such as blockchain systems for the functioning of enterprises in the fuel and energy sector. Authors give the definition of blockchain system and split it into types. The article depicts how the blockchain technology affects the fuel and energy complex. Existing cases of technology in the economy has been analyzed in this work. Authors highlighted barriers and prospects of the technology development. The work combines the theoretical aspects of blockchain technology and analysis of practical applications, high operational problems and predicting the potential beneficial effect of a new technology introduction. The aim of this work is to give an economic definition of efficiency of blockchain implementation in modern world. In this article the author used following methods: analysis, synthesis, induction, deduction, thought experiment method, hypotheticaldeductive method and retrospective analysis. In the process of research, the author came to conclusion that the evolution of blockchain has been explored. Main sectors of economics for blockchain use have been emphasized and economic effectiveness of technology has been substantiated.
Ani-GIFs: A benchmark dataset for domain generalization of action recognition from GIFs

Deep learning models perform remarkably well for the same task under the assumption that data is always coming from the same distribution. However, this is generally violated in practice, mainly due to the differences in data acquisition techniques and the lack of information about the underlying source of new data. Domain generalization targets the ability to generalize to test data of an unseen domain; while this problem is well-studied for images, such studies are significantly lacking in spatiotemporal visual content—videos and GIFs. This is due to (1) the challenging nature of misalignment of temporal features and the varying appearance/motion of actors and actions in different domains, and (2) spatiotemporal datasets being laborious to collect and annotate for multiple domains. We collect and present the first synthetic video dataset of Animated GIFs for domain generalization, Ani-GIFs , that is used to study the domain gap of videos vs. GIFs, and animated vs. real GIFs, for the task of action recognition. We provide a training and testing setting for Ani-GIFs , and extend two domain generalization baseline approaches, based on data augmentation and explainability, to the spatiotemporal domain to catalyze research in this direction.
A Diverse Models Ensemble for Fashion Session-Based Recommendation

Session-based recommendation is an important task for domains like e-commerce, that suffer from the user cold-start problem due to anonymous browsing and for which users preferences might change considerably over time. The RecSys Challenge 2022, organized by Dressipi, is focused on the session-based recommendation problem for the fashion e-commerce domain. In this paper, the NVIDIA RAPIDS and NVIDIA Merlin teams present their solution that placed 3rd in the challenge. Among the most effective techniques we found sessions augmentation and ensembling a very diverse set of statistical, machine learning and deep learning models. Our recommendation pipeline is composed of three stages, where the first level is focused on candidate generation and the others refine the recommendation ranking for more robust and accurate recommendations.
Synthetic Minority with CutMix for Imbalanced Image Classification

The class-imbalanced distributions between different classes in the visual world pose great challenges for deep learning-based classification models particularly on correct prediction of minority classes. In this study, different from existing strategies to alleviate the data imbalance issue, a novel mechanism based on the CutMix regularization technique is proposed for imbalanced image classification. The novelty is from two aspects. First, a novel sampling strategy is proposed to create the synthetic training data with a more balanced distribution. Second, labels of synthetic images were assigned with a bias toward minority classes. With the novel sampling and label assignment, more synthetic images of minority classes can be obtained to balance the class distribution of training data. Experiments on three benchmark datasets justified that the proposed method consistently outperforms commonly used strategies to alleviate the class imbalance issue.
Plan Deordering with Conditional Effects

Plan deordering aids several tasks such as plan reuse, modification, and plan decomposition by preserving only the necessary orderings among the actions in a plan. Several previous approaches deorder plans to annotate and find partial orderings. However, these methods do not fully support conditional effects. Conditional effects provide an expressive and convenient way to model complex and intricate scenarios allowing a single action to have context-dependent effects. In comparison with other features of ADL, conditional effects are surprisingly tricky to handle. In this paper, We develop a new algorithm for plan deordering that supports conditional effects. The algorithm also preserves the essence of conditional effects while deordering so that the generated partial orderings can be further used or modified. Our algorithm can successfully deorder plans from the domains of international planning competitions.