Reviewerly/exHarmony huggingface dataset:
Detecting Toxic Flow

This paper develops a framework to predict toxic trades that a broker receives from her clients. Toxic trades are predicted with a novel online Bayesian method which we call the projection-based unification of last-layer and subspace estimation (PULSE). PULSE is a fast and statistically-efficient online procedure to train a Bayesian neural network sequentially. We employ a proprietary dataset of foreign exchange transactions to test our methodology. PULSE outperforms standard machine learning and statistical methods when predicting if a trade will be toxic; the benchmark methods are logistic regression, random forest, and a recursively-updated maximum-likelihood estimator. We devise a strategy for the broker who uses toxicity predictions to internalise or to externalise each trade received from her clients. Our methodology can be implemented in real-time because it takes less than one millisecond to update parameters and make a prediction. Compared with the benchmarks, PULSE attains the highest PnL and the largest avoided loss for each of the deploy horizons we consider.
GA-Stereo: A Real-Time Stereo Network Based on the Gradient Flow Shunting Strategy and the Atrous Pyramid Network

While stereo matching methods have great progress in recent years, they still face several formidable challenges. These challenges mainly include how to fully utilize global context and depth semantic information for depth feature extraction while maintaining low computational cost, and how to accurately predict disparity in object details and ill-posed regions of stereo images. In this paper, we propose GA-Stereo, an end-to-end stereo network aiming at fast and more accurate disparity regression from rectified stereo images. Firstly, we propose GA-Net, a depth feature extraction network for stereo networks; it utilizes a gradient flow shunting strategy and aggregated residual transformation to calculate the matching cost of stereo images. Secondly, we propose an Atrous Pyramid Network, which implements convolution kernels adaptive sampling to assist the initial disparity map optimize, on the basis of more feature information and larger receptive field. Finally, we propose a collaborative supervised learning strategy. By using the collaborative supervised learning strategy during training stage, our GA-Stereo can not only calculate loss from the Ground Truth, but also calculate the loss from perceptual reconstruction loss and edge-loss smoothing loss to obtain better training results and more accurate predicted disparity map than other stereo networks. Our GA-Stereo achieve highly competitive results in matching accuracy and computational efficiency on stereo benchmarks, including SceneFlow, KITTI2012, and KITTI2015. This advancement represents a significant step forward in real-time stereo matching methods.
Cryptodata Multi-Level Artificial Intelligence System (Cryptodata MCAI-System)

The rationale behind the Cryptodata Multi-Level Analytical Intelligence System (Cryptodata MCAI-System) lies in the effectiveness of multi-level mega-data analysis frameworks, combined with the utilization of multidimensional graphs as a powerful tool for artificial intelligence learning. This approach enables a comprehensive understanding and resolution of economic, financial, and business challenges. The primary goal in developing the Cryptodata MCAI-System is to provide a new artificial intelligence learning tool capable of efficiently evaluating dynamic and complex economic, financial, and business issues, thereby facilitating a wide range of potential solutions. As such, the Cryptodata MCAI-System seeks to present academics, researchers, and policymakers with an alternative artificial intelligence approach. It introduces a suite of different types of multidimensional artificial intelligence learning tools, including the mega-dynamic disks coordinate space (vertical and horizontal position) learning machine tool, and the mega-disks networks mapping (MDN-Mapping) learning machine tool.
Proof of Humanity DAO: No crypto-utopia… yet

Proof of Humanity is a protocol designed to combat Sybil attacks in blockchain networks. Like other Proof of Personhood implementations, it allows users to prove their uniqueness on the blockchain, enabling new applications such as democratic governance in Decentralized Autonomous Organizations (DAOs) where each user has one vote. The Proof of Humanity DAO, the governance body of the protocol, serves as an example of this democratic approach. However, the protocol has faced challenges such as exploitation, internal conflicts, and the declining value of its Universal Basic Income token experiment, hindering its development and growth. These challenges offer valuable lessons for other Proof of Personhood implementations and DAOs wanting to adopt more democratic governance principles.
Deep Learning-Based Standard Sign Language Discrimination

General sign language recognition models are only designed for recognizing categories, i.e., such models do not discriminate standard and nonstandard sign language actions made by learners. It is inadequate to use in a sign language education software. To address this issue, this paper proposed a sign language category and standardization correctness discrimination model for sign language education. The proposed model is implemented with a hand detection and standard sign language discrimination method. For hand detection, the proposed method utilizes flow-guided features and acquires relevant proposals using stable and flow key frame detections. This model can resolve the inconsistency between the forward optical flow and the box center point offset. In addition, the proposed method employs an encoder-decoder model structure for sign language correctness discrimination. The encoder model combines 3D convolution and 2D deformable convolution results with residual structures, and it implements a sequence attention mechanism. A Sign Language Correctness Discrimination dataset (SLCD dataset) was also constructed in this study. In this dataset, each sign language video has two recognition labels, i.e., sign language category and standardization category. The semi-supervised learning method was employed to generate pseudo hand position labels. The hand detection model was getting sufficiently high hand detection result. The sign language correctness discrimination model was tested with hand patches or full images. SLCD dataset is available at https://dx.doi.org/10.21227/p9sn-dz70.
Scene-Adaptive 3-D Semantic Segmentation Method Based on Multiphase Edge Enhancement for Intelligent Vehicles

The 3-D semantic segmentation is crucial for enabling intelligent vehicles to accurately perceive and interpret their environment. Current state-of-the-art (SOTA) methods have achieved competitive results in indoor and typical urban traffic scenes. However, they commonly generate inaccurate edge detections, especially in those complex and variable scenarios. In order to improve edge awareness and robustness, we propose an efficient scene-adaptive method based on multiphase edge enhancement. First, the multitask segmentation module could simultaneously learn to predict edge detections and semantic segmentation labels using a two-branch structure. Second, considering the strong correlation between the two tasks, a cross-dimensional feature-fusion module is presented to implement the interaction of shared features for hierarchical supervision while retaining respective private features for jointly refining their predictions. Third, an edge-guided module (EGM) is induced to ensure the consistency on edges between the two tasks and realize the region-level edge awareness explicitly. Finally, joint learning is utilized to optimize the whole network by combining segmentation losses of two branches and region-energy loss of the EGM. We perform extensive evaluations on urban dataset SemanticKITTI and Nuscenes, off-road dataset RELLIS-3D, and our unstructured test set. The experimental results demonstrate that the proposed method has competitive performance in improving efficiency, accuracy, and scene-adaptivity.
Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting

With the growing use of artificial neural networks (ANNs) in temporal data processing tasks, the cost of training for complex ANNs is an increasing concern.Physical reservoir computing (RC), a variation of recurrent neural networks (RNNs), obviates the need for most data intensive matrix vector multiplication in the recurrent layer by evolving the RC's internal states with the inherent nonlinear dynamics and short-term memory.In this study, we show that magnetic skyrmion confined in a fixed geometry forming the soft layer of a magnetic tunnel junction (MTJ) can work as a RC and perform autonomous long-term prediction of temporal data.Our proposed skyrmion reservoir allows for manipulation of spin dynamics with ultra energy efficient voltage controlled magnetic anisotropy (VCMA) modulation method.Furthermore, the boundary effect on the skyrmion from the geometric edges provides necessary consistency property of the reservoir.We employ our proposed reservoir for the modeling and prediction of the chaotic time series such as Mackey-Glass and dynamic time-series data, such as household building energy loads.For autonomous run, the predicted output is fed to the input of the reservoir.By comparing our spintronic physical RC approach with energy load forecasting algorithms, such as long short-term memory (LSTM) and RNNs, we conclude that the proposed framework presents good performance in achieving high predictions accuracy, while also requiring low memory and energy both of which are at a premium in hardware resource and power constrained edge applications.Further, the proposed approach is shown to require very small training datasets and at the same time being at least 16× energy efficient compared to the sequence to sequence LSTM for accurate household load predictions.Higher endurance, fast processing and well-established technology (i.e., MTJ) to integrate with CMOS technology makes such spintronic reservoir attractive over other emergent memory device-based reservoirs.
SAR–Optical Image Matching With Semantic Position Probability Distribution

We propose a deep learning framework of Semantic Position Probability Distribution for SAR-optical image matching, termed as SPPD. Unlike the pixel-by-pixel searching matching method, a correspondence is directly obtained by an outputted matching position probability distribution. First, multiscale pyramidal features are created for each pixel in the SAR and optical images by using two weight-sharing ResNet-50 + Feature Pyramid Network (FPN) networks. The features containing high-level semantic information are then embedded into the proposed image Position Attention Module to obtain the spatial position dependencies between two images. Then, we present a loss function for semantic position matching to optimize the network from both semantic information and pixel alignment perspectives, converting the probability distribution of semantic matching positions into a point-to-point matching problem. In this paper, the SAR and optical images are set as the sensed and reference images. The effects of different image sizes, training label types, and loss function weights on matching accuracy are explored to obtain the optimal parameter settings for matching. The experimental results show that the proposed method is insensitive to image deformation and achieves cross-modal matching for SAR-optical images with high accuracy compared with the best matching method on different scene images, with several orders of magnitude faster inferences time.
A System on E - Health Care Card Using QR Code

The healthcare system in nations that are developing is one of their major concerns, so technological advancement has grown progressively more essential today. Smart card systems are essential in unexpected occurrences like accidents and when specialists conclude that someone requires rapid treatment in challenging circumstances. A dossier is kept when a baby is born containing details like the blood type, the date of vaccinations, obstacles with allergic reactions, and many other vital details. The medical system in emerging economies is one of their biggest concerns, so technology for healthcare is becoming increasingly important today. Request for ways that reduce the cost of medical services has risen as a result of increased social insurance costs. The necessity of coordinating the latest advances in the capacity and exchange of therapeutic data constitutes one of the handful of participants of the two sides participating in the social claim over the best procedure for minimizing prices in the context of human care architecture.
Secure and Efficient Federated Learning by Combining Homomorphic Encryption and Gradient Pruning in Speech Emotion Recognition

Speech Emotion Recognition (SER) detects human emotions expressed in spoken language. SER is highly valuable in diverse fields; however, privacy concerns arise when analyzing speech data, as it reveals sensitive information like biometric identity. To address this, Federated Learning (FL) has been developed, allowing models to be trained locally and just sharing model parameters with servers. However, FL introduces new privacy concerns when transmitting local model parameters between clients and servers, as third parties could exploit these parameters and disclose sensitive information. In this paper, we introduce a novel approach called Secure and Efficient Federated Learning (SEFL) for SER applications. Our proposed method combines Paillier homomorphic encryption (PHE) with a novel gradient pruning technique. This approach enhances privacy and maintains confidentiality in FL setups for SER applications while minimizing communication and computation overhead and ensuring model accuracy. As far as we know, this is the first paper that implements PHE in FL setup for SER applications. Using a public SER dataset, we evaluated the SEFL method. Results show substantial efficiency gains with a key size of 1024, reducing computation time by up to 25% and communication traffic by up to 70%. Importantly, these improvements have minimal impact on accuracy, effectively meeting the requirements of SER applications.
LocKey: Location-Based Key Extraction from the WiFi Environment in the User’s Vicinity

We investigate extracting persistent information from semi-volatile signals in the user’s vicinity to extend existing authentication factors. We use WiFi as a representative of semi-volatile signals, as WiFi signals and WiFi receiver hardware are ubiquitous. WiFi hardware is mostly bound to a physical location and WiFi signals are semi-volatile by nature. By comparing different locations, we confirm our expectation that location-specific information is present in the received WiFi signals. In this work, we study whether and how this information can be transformed to satisfy the following properties of a cryptographic key so that we can use it as an extension of an authentication factor: it must be uniformly random, contain sufficient entropy, and the information must be secret. We further discuss two primary use cases in the authentication domain: using extracted low-entropy information (48 bits) for password hardening and using extracted high-entropy information (128 bits and 256 bits) as a location-specific key. Using the WiFi-signal composition as an authentication component increases the usability, introduces the factor of ‘location’ to the authentication claims, and introduces another layer of defense against key or password extraction attacks. Next to these advantages, it has intrinsic limitations, such as the need for the receiver to be in proximity to the signal and the reliance on WiFi signals, which are outside the user’s control. Despite these challenges, using signals in the proximity of a user works in situations with a fallback routine in place while increasing usability and transparency. LocKey is capable to extract low-entropy information at all locations measured, and high-entropy from 68% locations for 128-bit keys (48% of the locations respectively for 256-bit keys). We further show that with an initial measurement time of at most five minutes, we can reconstruct the key in at least 75% of the cases in less than 15, 30, and 40 s depending on the desired key strength.
ASYMPTOTIC DECOMPOSITION AND ASYMPTOTIC FORMULA FOR THE ROOT OF THE TRANSCENDENTAL EQUATION WITH A PARAMETER

We consider the classic finite equation containing a parameter. Under a certain condition on the left side of this equation after replacing the variable it is reduced to the kind that it is not difficult to classify the interrelations between its constituent parts. Every case entails a certain situation with the existence of the solution of the equation under study, and it is shown that it can have, in essence, the same standard form. For the latter one fundamental result of the construction of the asymptotic decomposition is given. Next, the proof of formula for coefficients of the desired decomposition is presented using inductive technique. Another approach to finding a solution of the specified equation is associated with the possibility of obtaining an asymptotic formula in appearance resembling an infinite continued fraction. At first, approximations are naturally constructed recursively as consistently refined inequalities for the solution, and then, the convergence of these approximations is strictly proved. The pointwise convergence of separately even and odd approximations is related to their monotony and limitations, and the additional condition of continuous differentiability of the equation’s incoming data also guarantees uniform convergence of approximations to the solution. In conclusion, a simple example of such continued fraction is given.
When MPC in the Head Meets VC

In this paper, we investigate zero-knowledge proof systems based on the “MPC-in-the-head” paradigm (MPCitH), which presents the advantage of offering fast proof generation and post-quantum security. However, current constructions suffer from the drawbacks of large proof sizes and high memory consumption. Particularly, as the underlying circuit increases in size, the proof size grows significantly, and the machine that executes MPCitH-based protocol quickly surpasses its memory bounds due to the multiple parallel executions of MPC. To overcome this challenge, we present the VC-then-MPCitH paradigm, which integrates verifiable computation (VC) techniques into MPCitH. We implement our protocol using concrete VC protocol Virgo++ and MPCitH protocol BN++. Leveraging the properties of the underlying protocols, we can embed Virgo++ into BN++ efficiently. The resulting protocol can significantly reduce the memory consumption and the cost of both computation and communication of MPCitH for large circuits. We conduct our evaluation on a circuit over the field $${\mathbb F}_{2^{128}}$$ consisting of 40,006 multiplication gates and almost 100000 gates in total. With soundness error of $$2^{-128}$$ , our protocol can generate proofs of size 8891 KB in 86 ms, and verify in 70 ms. Furthermore, our protocol outperforms BN++ with the same parameter settings by reducing the proof size by a factor of 10 and shortening both the prover and verifier time by 13 times. On a resource-constrained device that offers 10 GB of memory, our protocol can handle effectively circuits with up to 10 million gates, while BN++ only supports circuits with up to 330,000 gates.
Blockchain technology in foreign trade management: which blockchain alternative is more suitable?

Purpose This study aims to determine the order of importance of blockchain technology gains for foreign trade management. The second aim of the research is to select the most suitable blockchain type for the foreign trade process. Design/methodology/approach The data required for the research analysis were collected through comparison matrices scored by foreign trade experts. The data were analyzed with the analytical hierarchy process method, and the gains of blockchain technology for the foreign trade process were prioritized. Finally, the most suitable type of blockchain technology was selected with the PROMETHEE method. Findings As a result of the study, subject matter experts perceived that the most critical blockchain gain for the foreign trade process would be cost savings. In addition, it has been observed that the general trade process differs compared to the logistics and payment processes. Finally, open blockchain was the most suitable blockchain type for foreign trade processes. Research limitations/implications In this study, the positive gains of blockchain technology are considered. In future studies, it is recommended to consider the existing negative factors to be able to use blockchain technology in foreign trade processes. In addition, it is suggested to conduct a study by dividing foreign trade into two, import and export. Practical implications All stakeholders who want to integrate blockchain technology into their foreign trade processes, including foreign trade companies, software developers, policymakers and international institutions, can benefit from the results of this study. A blockchain technology software created for foreign trade management can be shaped according to the results of this study. Originality/value Although the gains that blockchain technology provides to foreign trade processes are frequently emphasized in the literature, which gain would be greater has not been examined. The lack of an answer to the issue of how blockchain technology should be designed for foreign trade processes has been the missing part of the relevant literature. To the best of the authors’ knowledge, this study is the first experimental study in the literature that prioritizes blockchain gains and selects the appropriate blockchain type for foreign trade processes.
LEES-Net: Fast, lightweight unsupervised curve estimation network for low-light image enhancement and exposure suppression

Images in dark scenes are affected not only by low-light conditions but also by mixed exposures, posing great challenges to human and machine vision. Most of the current methods predominantly focus on ameliorating low-exposure images. However, for mixed-exposure low-light images, the high-exposure areas of the images will be over-enhanced, resulting in losing some detail information and not getting a well visual effect. To address this problem, we design a new unsupervised low-light enhancement method, called LEES-Net, with good robustness for mixed exposure cases. LEES-Net can transform the problem of low-light enhancement and exposure suppression into a curve estimation problem, thereby effectively reducing the complexity associated with image enhancement. By adding the attention mechanisms, the low-exposure and high-exposure areas in the images can be targeted and dynamically adjusted, which makes the enhanced images have well visual effects. Through extensive experiments, our method outperforms other state-of-the-art unsupervised methods with good generalization ability, robustness and visual effects. Furthermore, we propose a more lightweight network with merely 4.464 K parameters and 0.002 s inference speed, called LEES-Net+, which can keep the enhanced performance of LEES-Net with smaller computational cost and parameters, and is more suitable for deployment on devices with limited resources and real-time requirements.