Reviewerly/exHarmony huggingface dataset:
Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data. Given an acceptable level of privacy guarantee, the goal of FL is to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-to-accuracy</i> of model training. Compared with distributed ML in data centers, there are four distinct challenges to achieving short time-to-accuracy in FL training, namely the lack of information for optimization, the tradeoff between statistical and system utility, client heterogeneity, and large configuration space. In this paper, we survey recent works in addressing these challenges and present them following a typical training workflow through three phases: client selection, configuration, and reporting. We also review system works including measurement studies and benchmarking tools that aim to support FL developers.
A Proposed Federated Learning Model for Vaccination Tweets

This work examines the dissemination of erudition about the COVID-19 vaccination tweets. Social media offers us a platform to share our thoughts freely. With the implementation of sentimental analysis, this paper intends to obtain acumen into their feelings. We present to train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for Opinion Mining of COVID-19 Vaccination Tweets. Federated Learning will be one of the biggest trends in mobile development in upcoming years. The protocols in Federated Learning help to maintain the security and privacy of user data. Since Recurrent Neural Networks are used for sequential data, and we can represent language in the sequential data structure, it facilitates the usage of the Recurrent Neural Networks Language Model for Natural Language Processing. We have compared the results of Convolutional Neural Network and Recurrent Neural Network and found out that Recurrent Neural Network performs better for Opinion Mining.
Balancing fraud analytics with legal requirements: Governance practices and trade-offs in public administrations

Abstract Fraud analytics refers to the use of advanced analytics (data mining, big data analysis, or artificial intelligence) to detect fraud. While fraud analytics offers the promise of more efficiency in fighting fraud, it also raises legal challenges related to data protection and administrative law. These legal requirements are well documented but the concrete way in which public administrations have integrated them remains unexplored. Due to the complexity of the techniques applied, it is crucial to understand the current state of practice and the accompanying challenges to develop appropriate governance mechanisms. The use of advanced analytics in organizations without appropriate organizational change can lead to ethical challenges and privacy issues. The goal of this article is to examine how these legal requirements are addressed in public administrations and to identify the challenges that emerge in doing so. For this, we examined two case studies related to fraud analytics from the Belgian Federal administration: the detection of tax frauds and social security infringements. This article details 15 governance practices that have been used in administrations. Furthermore, it highlights the complexity of integrating legal requirements with advanced analytics by identifying six key trade-offs between fraud analytics opportunities and legal requirements.
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and Reasoning

Scene graph generation aims to capture the semantic elements in images by modelling objects and their relationships in a structured manner, which are essential for visual understanding and reasoning tasks including image captioning, visual question answering, multimedia event processing, visual storytelling and image retrieval. The existing scene graph generation approaches provide limited performance and expressiveness for higher-level visual understanding and reasoning. This challenge can be mitigated by leveraging commonsense knowledge, such as related facts and background knowledge, about the semantic elements in scene graphs. In this paper, we propose the infusion of diverse commonsense knowledge about the semantic elements in scene graphs to generate rich and expressive scene graphs using a heterogeneous knowledge source that contains commonsense knowledge consolidated from seven different knowledge bases. The graph embeddings of the object nodes are used to leverage their structural patterns in the knowledge source to compute similarity metrics for graph refinement and enrichment. We performed experimental and comparative analysis on the benchmark Visual Genome dataset, in which the proposed method achieved a higher recall rate ( $$R@K = 29.89, 35.4, 39.12$$ for $$K = 20, 50, 100$$ ) as compared to the existing state-of-the-art technique ( $$R@K = 25.8, 33.3, 37.8$$ for $$K = 20, 50, 100$$ ). The qualitative results of the proposed method in a downstream task of image generation showed that more realistic images are generated using the commonsense knowledge-based scene graphs. These results depict the effectiveness of commonsense knowledge infusion in improving the performance and expressiveness of scene graph generation for visual understanding and reasoning tasks.
PERMANENT: Publicly Verifiable Remote Attestation for Internet of Things Through Blockchain

Remote Attestation (RA) is a security mechanism that allows a centralized trusted entity (Verifier) to check the trustworthiness of a potentially compromised IoT device (Prover). With the tsunami of interconnected IoT devices, the advancement of swarm RA schemes that efficiently attest large IoT networks has become crucial. Recent swarm RA approaches work towards distributing the attestation verification from a centralized Verifier to many Verifiers. However, the assumption of trusted Verifiers in the swarm is not practical in large networks. In addition, the state-of-the-art RA schemes do not establish network-wide decentralized trust among the interacting devices in the swarm. This paper proposes PERMANENT, a Publicly Verifiable Remote Attestation protocol for Internet of Things through Blockchain, which stores the historical attestation results of all devices in a blockchain and allows each interacting device to obtain the attestation result. PERMANENT enables devices to make a trust decision based on the historical attestation results. This feature allows the interaction among trustworthy devices (or with a trust score over a certain threshold) without the computational overhead of attesting every participating device before each interaction. We validate PERMANENT with a proof-of-concept implementation, using Hyperledger Sawtooth as the underlying blockchain. The conducted experiments confirm the feasibility of the PERMANENT protocol.
Deep Learning-Based Modeling of Pedestrian Perception and Decision-Making in Refuge Island for Autonomous Driving

Pedestrian is the main Vulnerable Road User (VRU) that needs further support when designing enhanced Intelligent Transportation Systems (ITSs). The pedestrian’s perception and motion identification in real time and in different situations are still challenging tasks for the autonomous vehicle designer. Serious efforts have been invested in this field using recent Artificial Intelligence (AI) techniques such as deep and reinforcement learning. However, pedestrian refuge island perception and decision-making have not been investigated so far. In this paper, we propose a model to detect pedestrians and count their numbers in refuge islands using the Pedestrian Location Perception Network (P-LPN) technique. Also, our model uses the Long Short-Term Memory (LSTM) for updating the number of pedestrians over time and predict the desired decision.
A Robust Data Hiding Reversible Technique for Improving the Security in e-Health Care System

The authenticity and integrity of healthcare is the primary objective.Numerous reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection.A trade-off between robustness, imperceptibility, and embedded capacity is difficult to achieve with current algorithms due to limitations in their ability.Keeping this purpose insight, an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern.A key is generated by a random path with minimum bit flipping is selected in the 4 × 4 block to gain access to the data embedding patterns.The random path's complex structure ensures data security.Data of various sizes (8 KB, 16 KB, 32 KB) are used to analyze image imperceptibility and evaluate quality factors.The proposed reversible watermarking methodology performance is tested under standard structures PSNR, SSIM, and MSE.The results revealed that the MRI watermarked images are imperceptible, like the cover image when LSB is 3 bits plane.Our proposed reversible watermarking methodology outperforms other related techniques in terms of average PSNR (49.29).Experiment results show that the suggested reversible watermarking method improves data embedding capacity and imperceptibility compared to existing state-of-the-art approaches.
Rectified Adam Optimizer-Based CNN Model for Speaker Identification

Speaker identification (SI) is the process of recognizing the identity of a speaker according to the acoustic features extracted from a given utterance. Convolutional neural network (CNN) models have been widely used for solving SI tasks. However, the performance of CNN models significantly depends on how the loss function is optimized during the training process. In this paper, we propose a Rectified Adam (RAdam) optimizer-based CNN model for the speaker identification task. Mel-frequency cepstrum coefficient (MFCC) features are considered as the input features in this study. Moreover, in this work, the CNN architecture is improvised with one more dense layer with respect to the earlier CNN model to improve the feature learning ability. The experimental results showed the superiority of the proposed model over the state-of-the-art Adam optimizer-based CNN model and LSTM model in terms of accuracy for speaker identification.
Spatial-Temporal Graph Network for Video Crowd Counting

In recent years, researchers have developed many deep-learning-based methods to count crowd numbers in static images. However, much fewer works focus on video-based crowd counting, in which the critical challenge of temporal correlation has not been well explored. This paper proposes a Spatial-Temporal Graph Network (STGN) to achieve efficient and accurate crowd counting in videos via learning pixel-wise and patch-wise relations in local spatial-temporal domains. Specifically, we design a pyramid graph module to leverage multi-scale features. In each scale, we sequentially construct three graphs: spatial-temporal pixel graph, temporal patch graph, and spatial pixel graph, in which we apply the self-attention mechanism to capture pixel-wise relation, learn structure-aware relation, and aggregate local features, respectively. Furthermore, we propose spatial-aware channel-wise attention to effectively fuse multi-scale features. To demonstrate the effectiveness of the proposed method, we conduct experiments on five crowd counting datasets, including a large-scale video crowd dataset (FDST). Moreover, the proposed model is also applied in the vehicle counting dataset (TRANCOS). The results show that the proposed model outperforms existing spatial-temporal crowd counting models and achieves state-of-the-art. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuzhe71/STGN</uri>
An Image Steganography Technique Based on Fake DNA Sequence Construction

Hiding information within a media such as photo, video, text, audio, etc. is known as steganography. This research proposes a new technique based on the strategy of DNA sequencing for concealing data into a picture with the help of the least-significant bit (LSB). Here, the DNA is broken down into four nucleotides: cytosine, guanine, thymine, and adenine. Whereas, the private bits are encrypted within the DNA sequence then concealed within the cover image. This procedure converts the original picture to a stego-picture that is fully reliable in avoiding the human eyes, and the sensitive information is difficult to identify. The proposed strategy produces 0.784 bpp of concealing power with an average peak-signal to noise-ratio (PSNR) of 56.24 dB, making it a powerful picture steganography technique, according to the experimental data.
Explainable Artificial Intelligence in Genomic Sequence for Healthcare Systems Prediction

Various Classification techniques have been developed in past years and applied on genomic sequence for the dynamic modelling. These methods have resulted to impressive answers in term of correctness and analytical capability. Most of their techniques and applications based on Black-box models that use more understandable methodologies that are supported and verified by the scientific world, thus limited the power of interpretations. Despite the development and application of many statistical and machine learning approaches to expose genomic sequence for disease prediction, integrative understanding of the massive statistical and ML remains a challenge. Hence, the introduction and application of Explainable Artificial Intelligence (XAI) paradigm has provides a solution for this problem, were rule-based methods are particularly well suited to explanatory purposes. Additional steps toward more explanatory and genomic sequence sound models include integrating the technique of data gathering with sequence analysis and route studies. Therefore, this chapter present the applicability of XAI in genomic sequence for healthcare system. Also, the chapter discusses the challenges facing using eXplainable AI in genomic sequence for disease prediction and diagnosis, and in the healthcare system generally.
Multiframe CenterNet Heatmap ROI Aggregation for Real-Time Video Object Detection

Though Two-stage video object detectors cannot perform detection in real time, the accuracy of them is normally higher than that of one-stage video object detectors. One essence is that two-stage video detectors can easily use feature information from adjacent frames to augment key frame features. How to extract and exploit temporal features in the video stream for one-stage detectors needs further exploration. CenterNet is an anchor-free one-stage object detector that regress bounding boxes from heatmap peaks. We propose to use detected peaks and regressed boxes which encompass peak points to determine the heatmap ROIs as the extracted object heatmap features. A new relation module is designed to evaluate the similarity of heatmap ROI features and output the relation features which can effectively augment the heatmap ROI features. In the video sequence the heatmap ROIs of multiple adjacent frames are aggregated to a heatmap ROI of the key frame. Compared to CenterNet and other CenterNet-based video object detectors, our method achieves improved online real-time performance on ImageNet VID dataset with 78.8% mAP at 36 FPS.
Learning Adaptive Patch Generators for Mask-Robust Image Inpainting

In this paper, we propose a Mask-Robust Inpainting Network (MRIN) approach to recover the masked areas of an image. Most existing methods learn a single model for image inpainting, under a basic assumption that all masks are from the same type. However, we discover that the masks are usually complex and exhibit various shapes and sizes at different locations of an image, where a single model cannot fully capture the large domain gap across different masks. To address this, we learn to decompose a complex mask area into several basic types and recover the damaged image in a patch-wise manner with a type-specific generator. More specifically, our MRIN consists of a mask-robust agent and an adaptive patch generative network. The mask-robust agent contains a mask selector and a patch locator, which generates mask attention maps to select a patch at each step. Based on the predicted mask attention maps, the adaptive patch generative network inpaints the selected patch with the generators bank, so that it sequentially inpaints each patch with different patch generators according to its mask type. Extensive experiments demonstrate that our approach outperforms most state-of-the-art approaches on the Place2, CelebA, and Paris Street View datasets.
IHEM Loss: Intra-Class Hard Example Mining Loss for Robust Face Recognition

Recently, angular margin-based methods have become the mainstream approach for unconstrained face recognition with remarkable success. However, robust face recognition still remains a challenge, as the face is subject to variations in pose, age, expression, occlusion, and illumination, especially in unconstrained scenarios. Since the training dataset are always collected in unconstrained scenarios, it is inevitable that there’re significant number of hard examples in the training process. In this paper, we design a hard example selection function to effectively identify hard examples in the training procedure with the supervision of angular margin-based losses. Furthermore, a novel Intra-class Hard Example Mining (IHEM) loss function is proposed, which penalizes the cosine distance between the hard examples and their class centers to enhance the discriminative power of face representations. To ensure high performance for face recognition, we combine the supervision of angular margin-based loss and IHEM loss for model training. Specifically, during the training procedure, the angular margin-based loss guarantees the power of feature discrimination for face recognition, while the IHEM loss further encourages the intra-class compactness of hard example. Extensive results demonstrate the superiority of our approach.
CTL-Based Adaptive Service Composition in Edge Networks

With the recent adoption of edge computing, <i>I</i> nternet of <i>T</i> hings ( <i>IoT</i> ) devices collaborate at the network edge to facilitate edge-native applications. In this setting, <i>IoT</i> devices are typically encapsulated as <i>IoT</i> services to encode their functionalities, and their collaboration is achieved through <i>IoT</i> service composition. Due to the continuous resource occupancy, release, and consumption of <i>IoT</i> devices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certain <i>IoT</i> services may significantly downgrade in their <i>Q</i> uality-of- <i>S</i> ervices ( <i>QoS</i> ). To guarantee the compatibility of compositions with <i>QoS</i> variations, this article proposes an adaptive composition mechanism leveraging <i>C</i> omputation <i>T</i> ree <i>L</i> ogic ( <i>CTL</i> ) specifications. Specifically, we formalize the composition as a temporal task, and convert it to <i>CTL</i> formulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted by <i>CTL</i> semantics during the execution of compositions. Besides, we construct a <i>QoS</i> <i>D</i> ependency <i>G</i> raph ( <i>QoSDG</i> ) to capture <i>QoS</i> variations, and achieve adaptive composition with dynamic <i>QoS</i> satisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higher <i>QoS</i> dependencies ranging from 0.3 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> to 27.8 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .
A Method of Chinese-Vietnamese Bilingual Corpus Construction for Machine Translation

A bilingual corpus is vital for natural language processing problems, especially in machine translation. The larger and better quality the corpus is, the higher the efficiency of the resulting machine translation is. There are two popular approaches to building a bilingual corpus. The first is building one automatically based on resources that are available on the internet, typically bilingual websites. The second approach is to construct one manually. Automated construction methods are being used more frequently because they are less expensive and there are a growing number of bilingual websites to exploit. In this paper, we use automated collection methods for a bilingual website to create a bilingual Chinese-Vietnamese corpus. In particular, the bilingual website we use to collect the data is the website of a multilingual dictionary (https://glosbe.com). We collected the Chinese-Vietnamese corpus from this website that includes more than 400k sentence pairs. We chose 100,000 sentence pairs in this corpus for machine translation experiments. From the corpus, we built five datasets consisting of 20k, 40k, 60k, 80k, and 100k sentence pairs, respectively. In addition, we built five additional datasets, applying word segmentation on the sentences of the original datasets. The experimental results showed that: (1) the quality of the corpus is relatively good with the highest BLEU score of 19.8, although there are still some issues that need to be addressed in future works; (2) the larger the corpus is, the higher the machine translation quality is; and (3) the untokenized datasets help train better translation models than the tokenized datasets.
Causal Feature Selection With Efficient Spouses Discovery

Causal feature selection has recently attracted much more attention because it can improve the interpretability of predictive models. However, the existing causal feature selection framework needs to discover the PC (i.e., parents and children) of each variable in the PC of a target variable for spouses discovery, which is time-consuming on high-dimensional data. To tackle this issue, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ausal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> eature <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> election framework with efficient spouses discovery, called CFS. Specifically, by exploiting the dependency change property between a variable and its non-PC, the proposed framework only discovers the PC of the variables in some children of the target variable for spouses discovery. Furthermore, based on the proposed CFS framework and existing PC discovery algorithms, we propose four new causal feature selection algorithms. Using benchmark Bayesian networks and real-world datasets, we experimentally validated the efficiency and accuracy of the proposed algorithms compared with seven state-of-the-art causal feature selection algorithms.