Reviewerly/exHarmony huggingface dataset:
SOCI: A Toolkit for Secure Outsourced Computation on Integers

Secure outsourced computation is a key technique for protecting data security and privacy in the cloud. Although fully homomorphic encryption (FHE) enables computations over encrypted data, it suffers from high computation costs in order to support an unlimited number of arithmetic operations. Recently, secure computations based on interactions of multiple computation servers and partially homomorphic encryption (PHE) were proposed in the literature, which enable an unbound number of addition and multiplication operations on encrypted data more efficiently than FHE and do not add any noise to encrypted data; however, these existing solutions are either limited in functionalities (e.g., computation on natural numbers only) or leak information of the underlying data. To tackle these shortcomings, this paper proposes Secure Outsourced Computation on Integers (SOCI) based on PHE and a twin-server architecture. Compared with the existing solutions, SOCI supports computations on encrypted integers (vs. natural numbers) and greatly improves the security and correctness of the computations. Results of theoretical analysis and experimental evaluation show that SOCI outperforms existing solutions in computation and communication efficiencies.
Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs

Unstructured pruning is well suited to reduce the memory footprint of convolutional neural networks (CNNs), both at training and inference time. CNNs contain parameters arranged in K x K filters. Standard unstructured pruning (SP) reduces the memory footprint of CNNs by setting filter elements to zero, thereby specifying a fixed subspace that constrains the filter. Especially if pruning is applied before or during training, this induces a strong bias. To overcome this, we introduce interspace pruning (IP), a general tool to improve existing pruning methods. It uses filters represented in a dynamic interspace by linear combinations of an underlying adaptive filter basis (FB). For IP, FB coefficients are set to zero while un-pruned coefficients and FBs are trained jointly. In this work, we provide mathematical evidence for IP's superior performance and demonstrate that IP outperforms SP on all tested state-of-the-art unstructured pruning methods. Especially in challenging situations, like pruning for ImageNet or pruning to high sparsity, IP greatly exceeds SP with equal runtime and parameter costs. Finally, we show that advances of IP are due to improved trainability and superior generalization ability.
Robot Co-design Can Help Us Engage Child Stakeholders in Ethical Reflection

Children are stakeholders of robotic technologies who deserve to have their voices heard in the design process just as much as adult stakeholders. This is especially true for robotic technologies explicitly designed for child-robot interaction, in areas like education, healthcare, and therapy. Researchers face the challenge of cultivating children's critical awareness on the design of robots and accompanying ethical concerns, as the types of exercises typically used to engage with adult stakeholders can be ineffective with children. This requires developmentally appropriate methods for understanding children's perspectives that also address the imbalanced power dynamics between children and adults-such that children feel comfortable sharing their ideas. In this work, we demonstrate that participatory design research techniques already accepted in the Human Robot Interaction (HRI) community can fulfill this purpose. Specifically, through the design and analysis of two co-design workshops with children of different ages at a school in Denver, Colorado, we demonstrate that co-design workshops can be used to effectively understand how children make sense of robotic technologies and to facilitate children's critical reflection on the ethical dilemmas surrounding their own relationships with robots.
TDASS: Target Domain Adaptation Speech Synthesis Framework for Multi-speaker Low-Resource TTS

Recently, synthesizing personalized speech by text-to-speech (TTS) application is highly demanded. But the previous TTS models require a mass of target speaker speeches for training. It is a high-cost task, and hard to record lots of utterances from the target speaker. Data augmentation of the speeches is a solution but leads to the low-quality synthesis speech problem. Some multi-speaker TTS models are proposed to address the issue. But the quantity of utterances of each speaker imbalance leads to the voice similarity problem. We propose the Target Domain Adaptation Speech Synthesis Network (TDASS) to address these issues. Based on the backbone of the Tacotron2 model, which is the high-quality TTS model, TDASS introduces a self-interested classifier for reducing the non-target influence. Besides, a special gradient reversal layer with different operations for target and non-target is added to the classifier. We evaluate the model on a Chinese speech corpus, the experiments show the proposed method outperforms the baseline method in terms of voice quality and voice similarity.
ESCNet: Gaze Target Detection with the Understanding of 3D Scenes

This paper aims to address the single image gaze target detection problem. Conventional methods either focus on 2D visual cues or exploit additional depth information in a very coarse manner. In this work, we propose to explicitly and effectively model 3D geometry under challenging scenario where only 2D annotations are available. We first obtain 3D point clouds of given scene with estimated depth and reference objects. Then we figure out the front-most points in all possible 3D directions of given person. These points are later leveraged in our ESCNet model. Specifically, ESCNet consists of geometry and scene parsing modules. The former produces an initial heatmap inferring the probability that each front-most point has been looking at according to estimated 3D gaze direction. And the latter further explores scene contextual cues to regulate detection results. We validate our idea on two publicly available dataset, GazeFollow and VideoAttentionTarget, and demon-strate the state-of-the-art performance. Our method also beats the human in terms of AUC on GazeFollow. Our code can be found here https://github.com/bjj9/ESCNet.
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo

Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural inverse rendering method for MVPS based on implicit representation. Given multi-view images of a non-Lambertian object illuminated by multiple unknown directional lights, our method jointly estimates the geometry, materials, and lights. Our method first employs multi-light images to estimate per-view surface normal maps, which are used to regularize the normals derived from the neural radiance field. It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer. After optimization, the reconstructed object can be used for novel-view rendering, relighting, and material editing. Experiments on both synthetic and real datasets demonstrate that our method achieves far more accurate shape reconstruction than existing MVPS and neural rendering methods. Our code and model can be found at https://ywq.github.io/psnerf .
Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation

This study compares the performance of Pytorch-based Deep Learning, Multiple Perceptron Neural Networks with Multiple Linear Regression in terms of software effort estimations based on function point analysis.This study investigates Adjusted Function Points, Function Point Categories, Industry Sector, and Relative Size.The ISBSG dataset (version 2020/R1) is used as the historical dataset.The effort estimation performance is compared among multiple models by evaluating a prediction level of 0.30 and standardized accuracy.According to the findings, the Multiple Perceptron Neural Network based on Adjusted Function Points combined with Industry Sector predictors yielded 53% and 61% in terms of standardized accuracy and a prediction level of 0.30, respectively.The findings of Pytorch-based Deep Learning are similar to Multiple Perceptron Neural Networks, with even better results than that, with standardized accuracy and a prediction level of 0.30, 72% and 72%, respectively.The results reveal that both the Pytorch-based Deep Learning and Multiple Perceptron model outperformed Multiple Linear Regression and baseline models using the experimental dataset.Furthermore, in the studied dataset, Adjusted Function Points may not contribute to higher accuracy than Function Point Categories.
An Invisible Black-Box Backdoor Attack Through Frequency Domain

Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined conditions, i.e., triggers, but behave normally otherwise. The triggers of existing attacks are mainly injected in the pixel space, which tend to be visually identifiable at both training and inference stages and detectable by existing defenses. In this paper, we propose a simple but effective and invisible black-box backdoor attack FTrojan through trojaning the frequency domain. The key intuition is that triggering perturbations in the frequency domain correspond to small pixel-wise perturbations dispersed across the entire image, breaking the underlying assumptions of existing defenses and making the poisoning images visually indistinguishable from clean ones. Extensive experimental evaluations show that FTrojan is highly effective and the poisoning images retain high perceptual quality. Moreover, we show that FTrojan can robustly elude or significantly degenerate the performance of existing defenses.
Multi-Objective Diverse Human Motion Prediction with Knowledge Distillation

Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving. Recent research has ex-plored several techniques to enhance diversity and maintain the accuracy of human motion prediction at the same time. However, most of them need to define a combined loss, such as the weighted sum of accuracy loss and diversity loss, and then decide their weights as hyperparameters before training. In this work, we aim to design a prediction frame-work that can balance the accuracy sampling and diversity sampling during the testing phase. In order to achieve this target, we propose a multi-objective conditional variational inference prediction model. We also propose a short-term oracle to encourage the prediction framework to explore more diverse future motions. We evaluate the performance of our proposed approach on two standard human motion datasets. The experiment results show that our approach is effective and on a par with state-of-the-art performance in terms of accuracy and diversity.
Semi-Supervised Few-shot Learning via Multi-Factor Clustering

The scarcity of labeled data and the problem of model overfitting have been the challenges in few-shot learning. Recently, semi-supervised few-shot learning has been developed to obtain pseudo-labels of unlabeled samples for expanding the support set. However, the relationship between unlabeled and labeled data is not well exploited in generating pseudo labels, the noise of which will di-rectly harm the model learning. In this paper, we propose a Clustering-based semi-supervised Few-Shot Learning (cluster-FSL) method to solve the above problems in image classification. By using multi-factor collaborative representation, a novel Multi-Factor Clustering (MFC) is designed to fuse the information of few-shot data distribution, which can generate soft and hard pseudo-labels for unlabeled samples based on labeled data. And we exploit the pseudo labels of unlabeled samples by MFC to expand the support set for obtaining more distribution information. Furthermore, robust data augmentation is used for support set in the fine-tuning phase to increase the labeled samples' diversity. We verified the validity of the cluster-FSL by comparing it with other few-shot learning methods on three popular benchmark datasets, miniImageNet, tieredImageNet, and CUB-200-2011. The ablation experiments further demonstrate that our MFC can effectively fuse distribution information of labeled samples and provide high-quality pseudo-labels. Our code is available at: https://gitlab.com/smartllvlab/cluster-fsl
Pairing-Free Certificateless Aggregate Signcryption Scheme for Vehicular Sensor Networks

Smart vehicle applications play an increasingly significant role in constructing intelligent transportation systems. Vehicles with sensors are able to form temporary vehicular sensor networks dynamically, in which a mass of data are collected, shared, and aggregated to the traffic administration for improving road safety and efficiency. In such networks, vehicles exchange urgent or sensitive information with others in open wireless channels. To ensure secure and privacy-preserving communications, we develop a new pairing-free certificateless aggregate signcryption scheme based on the elliptic curve cryptosystem. The certificateless setting prevents the proposed scheme from both certificate management and key escrow issues. Aggregate signcryption without pairings realizes the aggregation and simultaneous authentication of multisource data by efficient cryptographic computing. The proposed scheme is proved to achieve confidentiality and unforgeability with the random oracle model. The security analysis shows that the proposed scheme provides forward secrecy, immunity to various known attacks, and pseudonym-based conditional privacy for vehicles. The performance analysis demonstrates that it satisfies more security properties with higher computation efficiency and lower communication overhead than state-of-the-art schemes.
SSformer: A Lightweight Transformer for Semantic Segmentation

It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer [2] may lack of inductive biases of local neighborhoods and possess a high time complexity. Recently, Swin Transformer [3] sets a new record in various vision tasks by using hierarchical architecture and shifted windows while being more efficient. However, as Swin Transformer is specifically designed for image classification, it may achieve suboptimal performance on dense prediction-based segmentation task. Further, simply combing Swin Transformer with existing methods would lead to the boost of model size and parameters for the final segmentation model. In this paper, we rethink the Swin Transformer for semantic segmentation, and design a lightweight yet effective transformer model, called SSformer. In this model, considering the inherent hierarchical design of Swin Transformer, we propose a decoder to aggregate information from different layers, thus obtaining both local and global attentions. Experimental results show the proposed SSformer yields comparable mIoU performance with state-of-the-art models, while maintaining a smaller model size and lower compute. Source code and pretrained models are available at: https://github.com/shiwt03/SSformer.
FLoX: Federated Learning with FaaS at the Edge

Federated learning (FL) is a technique for distributed machine learning that enables the use of siloed and distributed data. With FL, individual machine learning models are trained separately and then only model parameters (e.g., weights in a neural network) are shared and aggregated to create a global model, allowing data to remain in its original environment. While many applications can benefit from FL, existing frameworks are incomplete, cumbersome, and environment-dependent. To address these issues, we present FLoX, an FL framework built on the funcX federated serverless computing platform. FLoX decouples FL model training/inference from infrastructure management and thus enables users to easily deploy FL models on one or more remote computers with a single line of Python code. We evaluate FLoX using three benchmark datasets deployed on ten heterogeneous and distributed compute endpoints. We show that FLoX incurs minimal overhead, especially with respect to the large communication overheads between endpoints for data transfer. We show how balancing the number of samples and epochs with respect to the capacities of participating endpoints can significantly reduce training time with minimal reduction in accuracy. Finally, we show that global models consistently outperform any single model on average by 8%.
Deterministic Point Cloud Registration via Novel Transformation Decomposition

Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF). Simultaneously estimating these 6 DOF is time-consuming due to high-dimensional parameter space. To solve this problem, it is common to decompose 6 DOF, i.e. independently compute 3-DOF rotation and 3-DOF translation. However, high non-linearity of 3-DOF rotation still limits the algorithm efficiency, especially when the number of correspondences is large. In contrast, we propose to decompose 6 DOF into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(2+1)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1+2)\ DOF$</tex> . Specifically, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(2+1)DOF$</tex> represent 2-DOF rotation axis and 1-DOF displacement along this rotation axis. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1+2)\ DOF$</tex> indicate 1-DOF rotation angle and 2-DOF displacement orthogonal to the above rotation axis. To compute these DOF, we design a novel two-stage strategy based on inlier set maximization. By leveraging branch and bound, we first search for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(2+1)\ DOF$</tex> , and then the remaining <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1+2)\ DOF$</tex> . Thanks to the proposed transformation decomposition and two-stage search strategy, our method is deterministic and leads to low computational complexity. We extensively compare our method with state-of-the-art approaches. Our method is more accurate and robust than the approaches that provide similar efficiency to ours. Our method is more efficient than the approaches whose accuracy and robustness are comparable to ours.
Evaluation of the efficiency of state-of-the-art Speech Recognition engines

Speech Recognition is one of the several Artificial Intelligence applications. It helps us converting spoken words into text. It can be part of various daily use cases in order to deal with accessibility. Google Assistant and Amazon's Alexa are in the top of list of the well-known Speech Recognition tools. European companies cannot use these solutions as they should guarantee data sovereignty. Another important point is that these mentioned solutions are not customized. So that, it is not possible to deal with new accents or new vocabularies. To cope with these problems, one can either use European Automatic Speech Recognition (ASR) solutions or build his own personalized models using well-known open-source tools like Deep Speech or Kaldi. Choosing the best solution between both, Kaldi and DeepSpeech, is an important task. The criteria for judging the finest method are the Accuracy and the Inference Time. In this paper, we make theoretical and experimental study between DeepSpeech and Kaldi. Also, Vosk and LinTO, open-source solutions build in top of Kaldi, will be included in the comparison study.
Neural Volumetric Object Selection

We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a set of foreground and background 2D user scribbles in one view and automatically estimates a 3D segmentation of the desired object, which can be rendered into novel views. To achieve this result, we propose a novel voxel feature embedding that incorporates the neural volumetric 3D representation and multi-view image features from all input views. To evaluate our approach, we introduce a new dataset of human-provided segmentation masks for depicted objects in real-world multi-view scene captures. We show that our approach out-performs strong baselines, including 2D segmentation and 3D segmentation approaches adapted to our task.
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks

High-order decomposition is a widely used model compression approach towards compact convolutional neural networks (CNNs). However, many of the existing solutions, though can efficiently reduce CNN model sizes, are very difficult to bring considerable saving for computational costs, especially when the compression ratio is not huge, thereby causing the severe computation inefficiency problem. To overcome this challenge, in this paper we propose efficient High-Order DEcomposed Convolution (HODEC). By performing systematic explorations on the underlying reason and mitigation strategy for the computation inefficiency, we develop a new decomposition and computation-efficient execution scheme, enabling simultaneous reductions in computational and storage costs. To demonstrate the effectiveness of HODEC, we perform empirical evaluations for various CNN models on different datasets. HODEC shows consistently outstanding compression and acceleration performance. For compressing ResNet-56 on CIFAR-10 dataset, HODEC brings 67% fewer parameters and 62% fewer FLOPs with 1.17% accuracy increase than the baseline model. For compressing ResNet-50 on ImageNet dataset, HODEC achieves 63% FLOPs reduction with 0.31% accuracy increase than the uncompressed model.
Credit Card Fraud Detection Based on Hyperparameters ‎Optimization Using the Differential Evolution

Due to the emigration of world business to the internet, credit ‎cards have become a tool for ‎payments for both online and outline purchases. However, fraudsters try ‎to attack those systems ‎using various techniques, and credit card fraud has become dangerous. To ‎secure credit cards, ‎different methods are proposed in the academic paper based on artificial ‎intelligence. The proposed ‎solution in this paper aims at combining the robustness of three methods: ‎the differential evolution ‎algorithm (DE) for selecting the best hyperparameters, a resampling ‎technique for handling ‎imbalanced data issues, and the XGBoost technique for classification. Finally, ‎the fraudulent ‎transactions are classified using the optimized XGBoost algorithm. The proposed ‎solution is ‎evaluated using two real-world datasets: the European dataset and the UCI dataset. The ‎evaluation ‎in terms of accuracy, sensitivity, specificity, precision, and F-measure shows the ability and ‎the ‎superiority of the proposed approach in comparison with the state-of-the-art machine learning ‎‎models.‎