Reviewerly/exHarmony huggingface dataset:
Authenticated Selective Disclosure of Credentials in Hybrid-Storage Blockchain

The digital representation of credentials has become a necessary way in all aspects of human life, such as healthcare, education, etc. However, the current digital credentials sharing solutions tend to overlook the problem of over-disclosure. The data presentation of credentials is an all-or-nothing process, which results in the leakage of unnecessary data and threatens the privacy of the holder. In this paper, to achieve authenticated selective disclosure of credentials, we first design a hybrid storage model incorporating erasure coding (EC), where the raw data are outsourced to an off-chain distributed storage service provider while only small digest information are stored on-chain to maintain data integrity. Moreover, under the storage model, we propose an authenticated data structure (ADS) which integrates EC and the Merkle B-tree to minimize data sharing. Based on this ADS, a verifiable object (VO) can be generated, which is used to provide proof of the disclosed data without exposing the other data of the credentials. At last, we prove the security of the proposed ADS scheme and the experimental results show that, compared to a baseline solution, the proposed ADS reduces the average building and verification time, without sacrificing much of the transmission cost.
CluFL: Cluster-driven Weighted FL Model Aggregation Strategy

Federated learning (FL) has become a promising machine learning (ML) paradigm for training machine learning models over distributed datasets, owing to its low communication costs and privacy preserving property. To date, the most commonly adopted model fusion mechanism in FL is average aggregation. However, it has been shown that this average aggregation mechanism performs poorly in heterogeneous systems, especially for non-independent and identically distributed (NonIID) data. In order to address this challenge, we propose a weighted FL model aggregation strategy for each client based on clustering, termed CluFL. Specifically, CluFL first measures the similarities among uploaded models from clients through their parameters using a spectral clustering algorithm. Then, CluFL assigns aggregation weights according to the similarity of the intra-cluster global model for each cluster and the average model across the clusters. Further, we derive a convergence bound on the CluFL algorithm considering a practical nonconvex setting of neural network training. This bound reveals that the proposed CluFL algorithm can achieve a convergence speed in the order of O(1/T). Extensive experiments have been conducted on both FashionMNIST and CIFAR-10 datasets and show that CluFL outperforms the state-of-the-art FL algorithms in terms of accuracy and communication efficiency.
Tereis: A Package-Based Scheduling in Deep Learning Systems

Deep learning (DL) systems are typically used to accelerate training DL jobs. Training DL models requires feeding mass input data. It takes a long time to transfer training data from the storage nodes to the compute nodes. However, the computational resources of the GPUs are idle during the data transmission period, which results in a waste of computing resources. In DL systems, a large number of short-term jobs are queuing longer than their own execution times. Meanwhile, many multi-GPU jobs are suffering a long-queuing time due to not enough free GPU. To the best of our knowledge, no studies try to use the idle computation resources of GPU in the data transmission period.We propose Tereis, a package-based scheduler to make full use of GPU. Tereis predicts a DL job’s execution time and data transmission time, then safely package two jobs on the same GPU. One of the packaged jobs will be completed before the other job ends transferring data, which is what ‘safely’ means. This ‘safe’ Packaging does not cause GPU contention. Tereis also designs multi-level queues to prevent starvation. We implemented Tereis in python on the actual cluster and evaluated its performance. The experiment results show that Tereis decreases the average waiting time by 3.2× to10.5×, and has an improvement of 18% to 42% on makespan compared to other methods. Furthermore, we have made large-scale simulations to explore the sensitivities of Tereis. We find that Tereis performs better in the scenario where the job set has a distribution of large dataset size and jobs are submitted frequently.
Rewriting Deep Learning Models for Maximizing Edge TPU Utilization

The Google Edge TPU is an ASIC designed to accelerate inference of deep learning models on edge devices. Edge TPU only supports a limited set of operations. In those deep learning models containing unsupported operations, the Edge TPU compiler maps the unsupported operations and their succeeding operations to execute on the CPU, even if the succeeding operations can be executed on the Edge TPU. As a result, the Edge TPU is under-utilized and the performance significantly degrades. To overcome this issue, we have developed a model rewriting tool, which leverages MLIR to replace unsupported operations in the model with supported ones while maintaining the same functionality. We also propose a general method to approximate arbitrary continuous functions to any precision using the ReLU operation. Experimental results show that our transformation achieves an average speedup of 1.66× and 4.44× over the models without rewriting on the server and edge platforms, respectively.
Sexism Classification in Social Media Using Machine Learning Algorithms

Currently, social media is a place where everyone shares their perspective. Sexism mainly affects women which may eventually lead to physical and physiological abuse. It is similar to gender discrimination. For many years, sexism has prevailed in our society and affects social media too. In the proposed paper, the analysis of sexism is done using NLP and Machine Learning algorithms. In the world, male domination has taken a firm root, and women are trying hard to break the stereotypes that women cannot work or are only for giving birth and caring for children. The dataset used is EXIST-2022 and worked only in the English language. The classification uses machine learning algorithms such as Support Vector Machine, Random Forest, Naive Bayes and Logistic Regression. The proposed paper discusses the performance of multiclass and binary classification methods to work with various kinds of sexism on social platforms. The best accuracy obtained for multiclass classification and binary classification.
Re-visiting Reservoir Computing Architectures Optimized by Evolutionary Algorithms

For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the reservoir, and learning process is restricted to adjusting a linear parametric function. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.
FedDGIC: Reliable and Efficient Asynchronous Federated Learning with Gradient Compensation

Asynchronous federated learning is a distributed machine learning paradigm that may alleviate the impact of straggler nodes and improve the efficiency of federated training. However, some nodes can become sluggish, and node dropout may frequently happen for various reasons, such as network connection constraints, energy deficits, and system faults. Consequently, the global model may deviate from the desired convergence direction and lead to suboptimal results. This work proposes an asynchronous federated learning framework, FedDGIC, to mitigate the impact of the node dropout problem. The proposed framework can improve training efficiency by utilizing a dynamic grouping algorithm with gradient compensation. Experiments are performed in a real federated learning environment using two datasets, i.e., MNIST and CIFAR-10. Compared with three state-of-the-art methods, the proposed FedDGIC can significantly improve training efficiency and provide reliable asynchronous federated learning.
SMS Phishing Dataset for Machine Learning and Pattern Recognition

The reliability of the dataset is an essential factor for solving classification problems. Data is required for training, testing, classification, and evaluation of the machine learning models. SMS Phishing (Smishing) is a binary classification problem in which messages are categorized as malicious (Smishing) or legitimate (Ham). It is a fraudulent activity in which the attacker sends a malicious text message to the Smartphone user that causes financial or personal loss to the victim. Few research works have been proposed for the identification of smishing messages. According to the literature survey conducted, the smishing dataset is not publicly available yet. Hence, we have composed a smishing dataset that contains smishing messages extracted from different internet sources. We have formulated a dataset of 5971 text messages that contain 638 smishing messages, 489 spam messages, and 4844 ham messages. This SMS Phishing dataset can be used for the extraction of smishing features and classification of text messages using Machine Learning Algorithms. Experimental evaluation of the dataset for smishing message categorization using keyword classification is also presented in this paper. This smishing dataset can be used as a baseline for future research work corresponding to SMS Phishing.
DiTing: Edge Assisted Real-time ID-aware Visual Interaction for Multi-user Augmented Reality

Most existing multi-user Augmented Reality (AR) systems only support multiple co-located users to view a common set of virtual objects but lack the ability to enable each user to directly interact with other users appearing in his/her view. Such multi-user AR systems should be able to detect the human keypoints and share device poses (for identifying different users) in the meanwhile. However, due to the stringent low latency requirements and the intensive computation of the above two capabilities, previous research only enables either of the two capabilities for mobile devices even with the aid of the edge server. Integrating the above two capabilities is promising but non-trivial in terms of latency, accuracy, and matching. To fill this gap, we propose DiTing to achieve real-time ID-aware multi-device visual interaction for multi-user AR applications, which contains three key innovations: Shared On-device Tracking to merge the similar computation for optimized latency, Tightly Coupled Dual Pipeline to enhance the accuracy of each task through mutual assistance, Body Affinity Particle Filter to precisely match device poses with human bodies. We implement DiTing on four types of mobile AR devices and develop a multi-user AR game as a case study. Extensive experiments show that DiTing can provide high-quality human keypoint detection and pose estimation in real-time (30fps) for ID-aware multi-device interaction and outperform the SOTA baseline approaches.
Some Cases of Prediction and Inference with Uncertainty

Probability and statistical methods are a better tool for making scientific inferences and handling uncertainty in empirical contexts. We show how the uncertainties happen in inferences and predictions, and how to handle them easily in some cases. Starting with a couple of probability paradoxes, for giving the reader an idea about how tricky the application of the probability can be, a potential uncertainty in statistical significance testing is shown. How the predictions can be done effectively with the probabilistic approach while handing the uncertainties is presented. And finally, what the analyst needs to consider when doing discrete predictions is discussed through application of Simpson’s paradox. For the task effective use of causal assumptions is discussed.
MHMC: Real-Time Hand Motion Capture Using Millimeter-Wave Radar

Hand motion capture is essential for emerged augmented reality (AR) and virtual reality (VR) applications. We propose MHMC, a real-time hand motion capture system using millimeter-wave (mmWave) radar with the advantages of lighting independence, privacy protection and good user experience. When implementing such a system, we design a novel vertical neural network architecture to infer complex hand motion from limited resolution data of mmWave radar, a vision-based labeling method to acquire adequate labeled data, and effective loss functions to avoid abnormal output. The experimental results demonstrate that MHMC achieves similar accuracy as visionbased methods, along with fast processing speed, i.e., 7. 3ms per frame, for supporting real-time motion capture.
Improving Federated Learning on Heterogeneous Data via Serial Pipeline Training and Global Knowledge Regularization

Federated learning is a distributed machine learning paradigm that resolves the conflict between training requirements and client data privacy. There are some challenges for federated learning, such as data heterogeneity and communication load, which lead to the global model bias and slow convergence. In this work, we address the problem of data heterogeneity and communication load from a novel perspective, which is named FedSPARK. 1) We propose a new federated learning interaction training strategy, serial pipeline training (SPT). SPT changes the local training of a single client to serial training of multiple clients, which improves the performance of the global model on heterogeneous data. 2) We propose the global knowledge regularization (GKR) which is inspired by continuous learning. GKR can reduce the bias of the client local model by building global knowledge. Through theoretical analysis and experiments on multiple datasets, we show that our approaches greatly reduce the computation of clients and the amount of communication between clients and server, and improve the efficiency of federated learning compared to existing methods.
Classification of Brand Images Using Convolutional Neural Networks

This paper investigates the classification of brand images using convolutional neural networks. Traditionally, the images must be manually named, classified, and tagged, which is a laborious and time-consuming task. Nowadays, these processes can be addressed with the help of computer vision for brand classification. An approach to create a Convolutional Neural Network (CNN) model with a high accuracy is proposed and discussed, in which the images in the classification process are automatically tagged with the predicted class name.
Facial Landmark, Head Pose, and Occlusion Analysis Using Multitask Stacked Hourglass

In this study, we proposed a multitask network architecture for three attributes, landmark, head pose, and occlusion, from a face image. A 2-stacked hourglass with three task-specific heads is the proposed network architecture. We also designed three auxiliary components for the network. First is the feature pyramid fusion module, which plays a crucial role in facilitating contextual information from various receptive fields. Second is the interlevel occlusion-aware fusion module, which explicitly fuses intermediate occlusion prediction between subnetworks. The third is the gimbal-lock-free head pose head, which outputs a rotation matrix from a 6D rotation representation. We conducted an ablative study of these auxiliary components to determine their impacts on the network. Additionally, we introduced the landmark heatmap scaling approach to avoid falling local minima. We trained the proposed network with a 300W-LP dataset for landmark and head pose and a C-CM dataset for occlusion. Then, we fine-tuned the network using the 300W or WFLW dataset, instead of the 300W-LP dataset for the landmark task. This 2-stage training method contributes to enhancing the landmark detection accuracy and that of other tasks. In the experiments, we assessed the proposed network using eight test datasets and task-specific metrics.
Missing Types Prediction in Linked Data Using Deep Neural Network with Attention Mechanism: Case Study on DBpedia and UniProt Datasets

The publication and use of linked data in various fields has become commonplace. However, data published under the linked data principles suffer from issues such as uncertainty, incompleteness, imprecision, etc. We distinguish the problem of missing types for RDF entities among the causes of data incompleteness. In this paper, we propose a deep learning approach for detecting missing types. Our approach consists of using an encoder-decoder model with an attention mechanism, in which we use embedding layers to improve data representation and GRU cells to increase efficiency when processing the different sequences in input and output. The main goal of this work is to improve the quality of the literature results and to take into account the various triples in order to detect the correct type for each entity. This allows us to detect the types of entities and thus deduce other connections with other entities. As a result, we will be able to address a portion of the problem of incompleteness, allowing the various applications that use this data to produce more relevant results. This work only considers types. The other semantic links between entities are not considered. We conducted a case study on the UniProt dataset to evaluate the quality of our approach, which is a large database of protein sequences and annotations. We used our model to generate the missing types in two datasets: DBpedia and UniProt. The effectiveness of our approach has been demonstrated by the evaluation results.
Improving Confidence of Uncertain Knowledge Graphs by Crowdsourcing with Limited Budget

Knowledge graphs (KGs), either constructed automatically from texts or collected manually from crowdsourcing workers, may contain uncertainty. The uncertainty may propagate into the knowledge graph embedding and downstream tasks, which is potentially harmful, especially for those confidencesensitive applications such as medical diagnostic suggestion. Crowdsourcing workers with domain knowledge can help improve the data quality of knowledge graphs, by knowledge checking. However, due to the large scale of knowledge graphs and the limitation of adequate crowdsourcing workers, it is unrealistic to check all triplets in a knowledge graph to improve the data quality. Therefore, in this paper, we propose a crowdsourcing framework that efficiently improves the confidence of knowledge graphs with limited budget. We instantiate the framework in the medical domain and conduct a series of experiments with realworld medical data. We deploy the framework for knowledge graph embedding UKGE and corresponding downstream tasks. The experimental results show that the proposed method efficiently improves the quality of the knowledge graphs, and hence improves the performance of probabilistic knowledge graph embedding in the downstream tasks.
Object Detection: Literature Review

A subclass of computer vision, object detection is a computerized technique for detecting target objects in an image. Forming a tight coordinate (boundary) around these things and linking the relevant object category with each boundary is the approach to the object detection challenge. Deep learning is the most advanced way for detecting objects. This paper entails brief description, advantages and disadvantages and review of some traditional and deep learning algorithms that have been used by some researchers to implement and apply object detection. This paper describes the functioning of Histogram of Gradients, Scale Invariant Feature Transform, Convolutional Neural Network, Region Based Convolutional Neural Networks, Fast Region Based Convolutional Neural Network, Faster Region Based Convolutional Network, You Only Look Once and Single-Shot Detector algorithms. It also briefs about the steps involved in traditional as well as deep learning approaches to object detection.
Modality and Camera Factors Bi-Disentanglement for NIR-VIS Object Re-Identification

Aiming to match object identities across different modalities of images, the challenging task namely cross-modality object re-identification (NIR-VIS object Re-ID), has attracted increasing attention due to its wide application in low-light scenes. However, dramatic modality-dependent and camera-related discrepancies between Near-InfraRed-spectrum (NIR) and VISible-spectrum (VIS) images lead to a considerable intra-class gap in feature space. To address the problem, we propose a novel Modality and Camera factors Bi-Disentanglement (MCBD) model to learn modality-independent and camera-unrelated features for NIR-VIS object Re-ID. Our model consists of three key modules, including Confused Modality Generation (CMG), Modality-independent Information Distillation (MID), and Cameras Factor Disentanglement (CFD). Firstly, aiming at aligning image style between NIR and VIS data, the CMG utilizes a designed channel-interactive generator to generate confused modality images which preserves the structure information of original images. Besides, CMG is trained with confused adversarial learning which bridges the modality gap at the image level. Nevertheless, training the model with the confused modality images discards identity-related information such as color and contrast, which is not conducive to the extraction of distinctive features. To solve this problem, the MID is presented to distill out modality-independent information by feeding the original images to the model and reconstructing corresponding NIR and VIS modalities features to the confused modality images. Finally, due to the complexity of camera-related information in images, identity representation inevitably contains camera-related elements such as background and perspective information, which may interfere the matching process. To address this issue, the CFD is introduced to disentangle camera-related factors by the designed three-streams network and two factor decoupling losses, i.e., Camera-Camera Factor loss (CCF) and Identity-Camera Factor (ICF) loss. Comprehensive experiments are carried out on two cross-modality pedestrian Re-ID datasets and a cross-modality vehicle Re-ID dataset to demonstrate that the MCBD is effective in cross-modality object Re-ID task.