Reviewerly/exHarmony huggingface dataset:
Cloak: Transitioning States on Legacy Blockchains Using Secure and Publicly Verifiable Off-Chain Multi-Party Computation

In recent years, the confidentiality of smart contracts has become a fundamental requirement for practical applications. While many efforts have been made to develop architectural capabilities for enforcing confidential smart contracts, a few works arise to extend confidential smart contracts to Multi-Party Computation (MPC), i.e., multiple parties jointly evaluate a transaction off-chain and commit the outputs on-chain without revealing their secret inputs/outputs to each other. However, existing solutions lack public verifiability and require O(n) transactions to enable negotiation or resist adversaries, thus suffering from inefficiency and compromised security.
Edge-Cooperative Privacy-Preserving Object Detection Over Random Point Cloud Shares for Connected Autonomous Vehicles

Connected autonomous vehicles (CAVs) employ the point cloud data captured by LiDAR to enhance the capability of object recognition and detection. Edge computing with its inherent advantages can help CAVs alleviate resource constraints and enable faster situational awareness and data processing. However, the point cloud data contains private information, such as vehicle identity, location and trajectory, directly uploading the raw point cloud to the edge nodes or other vehicle will lead to serious privacy leakage. To the best of our knowledge, we are the first to try to tackle this challenge and propose a privacy-preserving object detection framework over random point cloud shares for CAVs (referred to SecPCV), aiming to guarantee the privacy of both point cloud and object detection results. In SecPCV, CAVs split point cloud into two random shares based on additive secret sharing (ASS) and upload them to two competing edge nodes, respectively, which greatly compress the computational load of CAVs. Without changing the object detection network in plaintext environment, the edge nodes can cooperatively and securely extract, regress, and classify over point cloud shares. Theoretical analysis ensure the efficiency and security of the SecPCV framework. Experimental results with the real KITTI point cloud dataset indicate that SecPCV can achieve the consistent object detection accuracy as that in plaintext environment, and provide a feasible solution for CAVs secure sharing of point cloud data.
Perceptions of the Helpfulness of Unexpected Agent Assistance

Much prior work on creating social agents that assist users relies on preconceived assumptions of what it means to be helpful. For example, it is common to assume that a helpful agent just assists with achieving a user's objective. However, as assistive agents become more widespread, human-agent interactions may be more ad-hoc, providing opportunities for unexpected agent assistance. How would this affect human notions of an agent's helpfulness? To investigate this question, we conducted an exploratory study (N=186) where participants interacted with agents displaying unexpected, assistive behaviors in a Space Invaders game and we studied factors that may influence perceived helpfulness in these interactions. Our results challenge the idea that human perceptions of the helpfulness of unexpected agent assistance can be derived from a universal, objective definition of help. Also, humans will reciprocate unexpected assistance, but might not always consider that they are in fact helping an agent. Based on our findings, we recommend considering personalization and adaptation when designing future assistive behaviors for prosocial agents that may try to help users in unexpected situations.
US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images

Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis.We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set.Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy.The proposed framework can help improve the accuracy of ultrasound diagnosis.
Crypto market responses to digital asset policies

We construct daily databases of crypto bans and policy statements concerning central bank digital currencies (CBDCs) to estimate their effect on crypto trading volumes for an unbalanced panel of 116 countries from November 2016 to December 2021. We find that trading volume falls by up to 55% in the week after the announcement of a ban, and by up to 25% after a CBDC-supportive speech by senior central bank officials. For the strictest bans, this reduction persists over the subsequent quarter, driven by a reduction in trading by institutional investors. The results suggest that crypto market participants pay significant attention to government policy on digital assets.
Center-VAE with discriminative and semantic-relevant fine-tuning features for generalized zero-shot learning

Many approaches in generalized zero-shot learning(GZSL) rely on the visual features extracted by CNN pre-trained on ImageNet and artificially designed semantic features. However, due to the cross-dataset bias, the quality of extracted visual features is hard to guarantee. In addition, due to the cognitive limitations of human beings, hand-collected attributes cannot traverse the whole visual space. Therefore, there will still be some discriminative features that are not focused on while designing the semantic space. In our work, we visualize the visual and semantic features in the benchmark datasets of GZSL, and we find that both visual and semantic features commonly suffer from entanglement problems. To this problem, we present a center-VAE to minimize the intra-class variations while preserving the inter-class relation and guiding the VAE to learn a more discriminative latent space. Besides, we deploy a regressor to reconstruct the corresponding semantic features via latent representations and guide the VAE to learn a latent space that preserves semantic-relevant information. After training of VAE, we concatenate the visual features and the corresponding latent representations to obtain the fine-tuning features, on which we train a softmax classifier. We evaluate our proposed method on several benchmark datasets and establish a new state-of-the-art. Moreover, we visualize the fine-tuning features, and the results demonstrate that our method can significantly alleviate the entanglement problem.
Arabic natural language processing for Qur’anic research: a systematic review

Abstract The Qur’an is a fourteen centuries old divine book in Arabic language that is read and followed by almost two billion Muslims globally as their sacred religious text. With the rise of Islam, the Arabic language gained popularity and became the lingua franca for large swaths of the old world. Devout Muslims read the Qur’an daily seeking guidance and comfort. Though the Qur’an, as a text, is short, there is a huge volume of supporting work filling tens of thousands of volumes, e.g., commentaries, exegesis, etc. Recently, there has been a renewed interest in such religious texts by non-specialists. Many of which were fueled by the recent advances in computational and natural language processing (NLP) techniques. These techniques help the development of tools that benefit common people to gain knowledge easily. This paper surveys the different efforts in the field of Qur’anic NLP, serving as a synthesized compendium of works (tools, data sets, approaches) covering the gamut from automated morphological analysis to correction of Qur’anic recitation via speech recognition. Multiple approaches are discussed for several tasks, where appropriate. Finally, we outline future research directions in this field.
REDS: Random ensemble deep spatial prediction

Abstract There has been a great deal of recent interest in the development of spatial prediction algorithms for very large datasets and/or prediction domains. These methods have primarily been developed in the spatial statistics community, but there has been growing interest in the machine learning community for such methods, primarily driven by the success of deep Gaussian process regression approaches and deep convolutional neural networks. These methods are often computationally expensive to train and implement and consequently, there has been a resurgence of interest in random projections and deep learning models based on random weights—so called reservoir computing methods. Here, we combine several of these ideas to develop the random ensemble deep spatial (REDS) approach to predict spatial data. The procedure uses random Fourier features as inputs to an extreme learning machine (a deep neural model with random weights), and with calibrated ensembles of outputs from this model based on different random weights, it provides a simple uncertainty quantification. The REDS method is demonstrated on simulated data and on a classic large satellite data set.
American Sign Language Alphabet Recognition Using Inertial Motion Capture System with Deep Learning

Sign language is designed as a natural communication method for the deaf community to convey messages and connect with society. In American sign language, twenty-six special sign gestures from the alphabet are used for the fingerspelling of proper words. The purpose of this research is to classify the hand gestures in the alphabet and recognize a sequence of gestures in the fingerspelling using an inertial hand motion capture system. In this work, time and time-frequency domain features and angle-based features are extracted from the raw data for classification with convolutional neural network-based classifiers. In fingerspelling recognition, we explore two kinds of models: connectionist temporal classification and encoder-decoder structured sequence recognition model. The study reveals that the classification model achieves an average accuracy of 74.8% for dynamic ASL gestures considering user independence. Moreover, the proposed two sequence recognition models achieve 55.1%, 93.4% accuracy in word-level evaluation, and 86.5%, 97.9% in the letter-level evaluation of fingerspelling. The proposed method has the potential to recognize more hand gestures of sign language with highly reliable inertial data from the device.
Comparison of Decision Tree, Random Forest and Linear Discriminant Analysis Models in Breast Cancer Prediction

Abstract Breast cancer is one of the illnesses that has become a leading cause of female death worldwide, and early diagnosis of these diseases is a difficult and important undertaking. The intention of this paper is to design prediction models based on Decision Tree, Random Forest and Linear Discriminant Analysis respectively to forecast the breast cancer occurrence at an early stage. The design is focused on analyzing the minimum set of attributes from the clinical data set that have been chosen by experts, and then compare the performances of the models to find their advantages in each model. Breast Cancer Wisconsin Data Set is used in this paper to construct the expected prediction models. Classification accuracy which is determined by comparing real values with predicted values, and generalization ability which is reflected by Receiver Operating Characteristic curve, are used to gauge the performance of the models. The results confirm that the Random Forest model can achieve the highest accuracy to 99.4% and the best generalization ability. However, the Linear Discriminant Analysis model can keep better stability in prediction accuracy and has the fastest running speed.
Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography

In computer-aided diagnosis methods for breast cancer, deep learning has been shown to be an effective method to distinguish whether lesions are present in tissues. However, traditional methods only classify masses as benign or malignant, according to their presence or absence, without considering the contextual features between them and their adjacent tissues. Furthermore, for contrast-enhanced spectral mammography, the existing studies have only performed feature extraction on a single image per breast. In this paper, we propose a multi-input deep learning network for automatic breast cancer classification. Specifically, we simultaneously input four images of each breast with different feature information into the network. Then, we processed the feature maps in both horizontal and vertical directions, preserving the pixel-level contextual information within the neighborhood of the tumor during the pooling operation. Furthermore, we designed a novel loss function according to the information bottleneck theory to optimize our multi-input network and ensure that the common information in the multiple input images could be fully utilized. Our experiments on 488 images (256 benign and 232 malignant images) from 122 patients show that the method's accuracy, precision, sensitivity, specificity, and f1-score values are 0.8806, 0.8803, 0.8810, 0.8801, and 0.8806, respectively. The qualitative, quantitative, and ablation experiment results show that our method significantly improves the accuracy of breast cancer classification and reduces the false positive rate of diagnosis. It can reduce misdiagnosis rates and unnecessary biopsies, helping doctors determine accurate clinical diagnoses of breast cancer from multiple CESM images.
Leaps and bounds: Analyzing WebAssembly’s performance with a focus on bounds checking

WebAssembly is gaining more and more popularity, finding applications beyond the Web browser for which it was initially designed. However, its performance, which developers intended to be comparable with native, has not been extensively studied to identify overheads and pinpoint their causes. This paper identifies that WebAssembly’s bounds-checked memory access safety mechanism may introduce up to a 650% overhead, and requires further tuning. Based on that, we extend four popular WebAssembly runtimes with modern bounds checking mechanisms and compare the performance of each with native compiled code. The runtimes are evaluated on three different instruction set architectures: x86-64, Armv8, and RISC-V RV64GC. We show that, for simple numerical kernels from Poly-Bench/C, there are no significant differences in the bounds checking performance overheads across different instruction set architectures. With the default bounds checking mechanism, performance-oriented runtimes are able to achieve execution times within 20% of native on x86-64 platforms, within 35% on Armv8 platforms, and within 17% on RISC-V. We also show that, when scaling the tested runtimes to multiple threads, the default bounds checking approach taken by WAVM, Wasmtime, and V8 of using the mprotect syscall to resize memory can cause excessive locking in the Linux kernel. Such scaling might be used to quickly start up serverless instances for a single function without the overhead of spawning new processes. We present an alternative userfaultf-based solution to mitigate this issue. We share our results, tools, and scripts under an open source license for other researchers to replicate and use to monitor the progress that WebAssembly runtimes make as they evolve.
A Slice and Dice Approach to Accelerate Compound Sparse Attention on GPU

Transformer-based models are vital to various research domains, including NLP, computer vision, and recommendation systems. However, because the attention mechanism with quadratic complexity limits computation and memory footprint in long sequences, numerous sparse attention-based transformer models are proposed to alleviate these problems. To efficiently infer these models on GPUs, prior solutions such as Triton and Sputnik have accelerated sparse attention in the sparse transformers, including optimizations for sampled dense-dense matrix multiplication (SDDMM), sparse softmax (SpSoftmax), and sparse matrix-matrix multiplication (SpMM). Although the existing optimizations have achieved impressive improvement, they have shortcomings, including low data reuse, unnecessary computation, and unnecessary memory accesses in the latest sparse transformer models based on compound sparse attention.We propose Multigrain to accelerate sparse attention by applying compound sparse GPU kernels with multi-stream for compound sparse patterns. First, we take sparse patterns with high spatial locality and execute them on our customized coarse-grained kernels, which exploit data reuse and high-performance tensor core units. Second, we execute the sparse patterns with low spatial locality on the fine-grained kernels to reduce unnecessary computation and memory accesses. Last, we process the coarse-grained and fine-grained kernels in parallel using multi-stream for the SDDMM and SpMM. In the latest sparse transformer models such as Longformer and QDS-transformer, Multigrain achieved 2.07× and 1.55× end-to-end speedup, respectively, over Triton, which only uses the coarse-grained method. We also achieved 2.08× and 1.08× end-to-end speedup, respectively, compared to Sputnik, which only uses the fine-grained kernel.
Classification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods

The writing process, in which feelings and thoughts are expressed in writing, differs from person to person.Handwriting samples, which are very easy to obtain, are frequently used to identify individuals because they are biometric data.Today, with human-machine interaction increasing by the day, machine learning algorithms are frequently used in offline handwriting identification.Within the scope of this study, a dataset was created from 3250 handwritten images of 65 people.We tried to classify collected handwriting samples according to person and gender.In the classification made for person and gender recognition, feature extraction was done using 32 different transfer learning algorithms in the Python program.For person and gender estimation, the classification process was carried out using the random forest algorithm.28 different classification algorithms were used, with DenseNet169 yielding the most successful results, and the data were classified in terms of person and gender.As a result, the highest success rates obtained in person and gender classification were 92.46% and 92.77%, respectively.
Image encryption approach using improved chaotic system incorporated with differential evolution and genetic algorithm

Meta-heuristic techniques are widely used to solve different complex real-life problems in recent scenarios. In this work, traditional key-generation mechanisms have been combined with meta-heuristic techniques to derive new key generation models in chaos-based image cryptography. Initially, one among the key-strings has been generated using the logistic map approach that is known for its ability to produce effective chaotic performance. In this paper, the chaotic effect of the key-string generated by the logistic map has further been enhanced by the application of differential evolution (DE), a population-based meta-heuristic optimization algorithm. This improved key sequence is used to produce chaotic a map which has been applied to images for diffusion operation. The confusion operation on the other hand is performed by the transposition process using another key-string produced by the application of another meta-heuristic technique, namely, Genetic Algorithm (GA). After thorough experimental works, the results have been compared with the state-of-the-art and it is observed that the approach proposed here provides strong cryptographic behavior in terms of information entropy, key sensitivity, robustness against noise, and hereby stands against all kinds of statistical and differential attacks including brute force.
Polarization imaging for surface fiber orientation measurements of carbon fiber sheet molding compounds

The ability to measure the fiber orientation in fiber reinforced polymers plays a vital role in process and part design. Recently, it has been discovered that carbon fibers polarize light, providing an opportunity to detect the fiber orientation in carbon fiber-reinforced polymer parts. The Fraunhofer Institute for Integrated Circuits has developed a polarization camera system capable of measuring the polarization state of light reflected from objects in real-time. In this work, the camera system is used to measure the surface fiber orientation in carbon fiber reinforced Sheet Molding Compound semi-finished products at several stages of the processing chain. First, the fiber orientation of carbon fiber-reinforced Sheet Molding Compound semi-finished product is measured during continuous production, enabling real-time quality control and complete digitization of the material's fiber orientation information. Second, the measurement system is used to characterize static flat pressed carbon fiber reinforced Sheet Molding Compound specimens. In addition, the technique is widely applicable to many other carbon fiber based raw material forms (e.g. preforms, prepregs, tapes) and even composite parts.
SMOTE Classification and Random Oversampling Naive Bayes in Imbalanced Data : (Case Study of Early Detection of Cervical Cancer in Indonesia)

Imbalanced data was a problem that is often encountered when classifying, where the distribution of the majority class has more numbers than the minority class. The existence of imbalanced data makes the performance of classification methods in machine learning decrease. This study adopted SMOTE and Random Oversampling (ROS) sampling techniques to overcome imbalanced data combined with the Naive Bayes classification method in cases of detection of early cervical cancer in Indonesia. Cervical cancer is a disease of an abnormal cell group that growth in the cervix (mouth of the womb). Cervical cancer is the most common type and ranks number 2 as cancer suffered by Indonesian women. Various factors that influence the event include eating behavior, personal hygiene behavior, motivational strength, social support, empowerment of knowledge, abilities, and desires. The data used is secondary data with a sample of 72 patients and 20 attributes. A total of21 patients in the classification had cervical cancer and 51 patients did not have cervical cancer. The ratio of 30:70 are imbalanced data. Through this classification method, it is expected to know what factors influence the event of cervical cancer and gains the best performance of two classifications. The results point out that average performance of SMOTE Naive Bayes has a higher (81,73%) than Random Oversampling Naive Bayes which is 81,12%. Therefore, SMOTE Naive Bayes outperforms Random Oversampling Naïve Bayes.