Reviewerly/exHarmony huggingface dataset:
Entropy Minimization Versus Diversity Maximization for Domain Adaptation

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.
Hybrid active contour model driven by optimized local pre-fitting image energy for fast image segmentation

In this paper, the hybrid active contour model driven by optimized local pre-fitting image (OLPFI) energy was proposed for fast image segmentation. Since the proposed model combines the local pre-fitted image (LPFI) function with region-based characteristic and the optimized edge indicator function with edge-based characteristic, the proposed model can not only correctly segment images with intensity inhomogeneity or weak boundaries, but also can deal with images whose target has large noise interference inside. The two pre-fitting functions are computed before curve evolution, so the computational cost of the proposed model reduces greatly. Furthermore, the evolution curve can adaptively select the motion direction according to image information because of energy sign function. Regularized function used in the proposed model can accelerate the segmentation speed of the proposed model. Moreover, the addition of the regularization term and length term helps to improve segmentation efficiency of the proposed model and eliminate the effect of sensitive parameters in traditional penalty and length terms. Experimental results show that the proposed model has great efficiency in segmenting images and great robustness on initialization and parameters.
Explainable Boosting Machine for Predicting Alzheimer’s Disease from MRI Hippocampal Subfields

Although automatic prediction of Alzheimer’s disease (AD) from Magnetic Resonance Imaging (MRI) showed excellent performance, Machine Learning (ML) algorithms often provide high accuracy at the expense of interpretability of findings. Indeed, building ML models that can be understandable has fundamental importance in clinical context, especially for early diagnosis of neurodegenerative diseases. Recently, a novel interpretability algorithm has been proposed, the Explainable Boosting Machine (EBM), which is a glassbox model based on Generative Additive Models plus Interactions GA2Ms and designed to show optimal accuracy while providing intelligibility. Thus, the aim of present study was to assess – for the first time – the EBM reliability in predicting the conversion to AD and its ability in providing the predictions explainability. In particular, two-hundred brain MRIs from ADNI of Mild Cognitive Impairment (MCI) patients equally divided into stable (sMCI) and progressive (pMCI) were processed with Freesurfer for extracting twelve hippocampal subfields volumes, which already showed good AD prediction power. EBM models with and without pairwise interactions were built on training set (80%) comprised of these volumes, and global explanations were investigated. The performance of classifiers was evaluated with AUC-ROC on test set (20%) and local explanations of four randomly selected test patients (sMCIs and pMCIs correctly classified and misclassified) were given. EBMs without and with pairwise interactions showed accuracies of respectively 80.5% and 84.2%, thus demonstrating high prediction accuracy. Moreover, EBM provided practical clinical knowledge on why a patient was correctly or incorrectly predicted as AD and which hippocampal subfields drove such prediction.
Viewpoint Invariant Dense Matching for Visual Geolocalization

In this paper we propose a novel method for image matching based on dense local features and tailored for visual geolocalization. Dense local features matching is robust against changes in illumination and occlusions, but not against viewpoint shifts which are a fundamental aspect of geolocalization. Our method, called GeoWarp, directly embeds invariance to viewpoint shifts in the process of extracting dense features. This is achieved via a trainable module which learns from the data an invariance that is meaningful for the task of recognizing places. We also devise a new self-supervised loss and two new weakly supervised losses to train this module using only unlabeled data and weak labels. GeoWarp is implemented efficiently as a re-ranking method that can be easily embedded into pre-existing visual geolocalization pipelines. Experimental validation on standard geolocalization benchmarks demonstrates that GeoWarp boosts the accuracy of state-of-the-art retrieval architectures. The code and trained models will be released upon acceptance of this paper.
A Survey: Approaches to Facial Detection and Recognition with Machine Learning Techniques

The present scenario in biometrics is very complex and challenging to classify the facial recognition and authentication. This article described a detailed review on machine learning. Author describes different databases and multiple approaches to overcome the issue of face identification and recognition. This paper offers a description of the work of numerous researchers on the recognition of faces and identity. It focuses on the facial recognition and recognition method in which minimal and unregulated faces processed for individual photographs, and videos are recognized and authenticated. Author describes several facial files, real-time pictures, and videos to discuss sophisticated approaches for their identification and recognition applications. The introduction of a machine learning approach with multiple image datasets also increases the efficiency of the classifier in order to predict face detection and recognition-related content. In conclusion, author elaborated the various approaches of machine learning and deep learning related to facial recognition and identification.
T-BFA: <u>T</u>argeted <u>B</u>it-<u>F</u>lip Adversarial Weight <u>A</u>ttack

Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack.Recently, another dimension of adversarial attack, namely, attack on DNN weight parameters, has been shown to be very powerful. Asa representative one, the Bit-Flip based adversarial weight Attack (BFA) injects an extremely small amount of faults into weight parameters to hijack the executing DNN function. Prior works of BFA focus on un-targeted attacks that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory. This paper proposes the first work oftargetedBFA based (T-BFA) adversarial weight attack on DNNs, which can intentionally mislead selected inputs to a target output class. The objective is achieved by identifying the weight bits that are highly associated with classification of a targeted output through a class-dependent weight bit searching algorithm. Our proposed T-BFA performance is successfully demonstrated on multiple DNN architectures for image classification tasks. For example, by merely flipping 27 out of 88 million weight bits of ResNet-18, our T-BFA can misclassify all the images from Hen class into Goose class (i.e., 100% attack success rate) in ImageNet dataset, while maintaining 59.35% validation accuracy.
Securing higher education against cyberthreats: from an institutional risk to a national policy challenge

This article problematises the understudied nature of cybersecurity in the higher education sector as an area of research and policy analysis, despite the exponential rise in cyberthreats against colleges and universities worldwide, particularly following the Covid-19 pandemic. It explains how the peculiarities of higher education as a target for cyberthreats are often overlooked due to the dominance of high-profile cyber incidents as opposed to mundane, everyday threats in policymaking and academic discourses on cybersecurity. In contrast to some studies that approach cyberthreats against educational institutions and technologies as an information technology (IT) issue in which risks are transferred to targeted institutions, this article explores the complexities of securing higher education against cyberthreats as a national policy challenge that requires national strategies and policies to address. This sectoral approach to cybersecurity also discusses possible measures that governments can adopt to improve the higher education sector's resilience against cyberthreats and to better prepare for the inherent security vulnerabilities that come with increasing digitisation.
Toward Text Data Augmentation for Sentiment Analysis

A significant part of natural language processing (NLP) techniques for sentiment analysis is based on supervised methods, which are affected by the quality of data. Therefore, sentiment analysis needs to be prepared for data quality issues, such as imbalance and lack of labeled data. Data augmentation methods, widely adopted in image classification tasks, include data-space solutions to tackle the problem of limited data and enhance the size and quality of training datasets to provide better models. In this work, we study the advantages and drawbacks of text augmentation methods such as easy data augmentation, back-translation, BART, and pretrained data augmentor) with recent classification algorithms (long short-term memory, convolutional neural network, bidirectional encoder representations of transformers, support vector machine, gated recurrent units, random forests, and enhanced language representation with informative entities, that have attracted sentiment-analysis researchers and industry applications. We explored seven sentiment-analysis datasets to provide scenarios of imbalanced datasets and limited data to discuss the influence of a given classifier in overcoming these problems, and provide insights into promising combinations of transformation, paraphrasing, and generation methods of sentence augmentation. The results revealed improvements from the augmented dataset, mainly for reduced datasets. Furthermore, when balanced by augmenting the minority class, the datasets were found to have improved quality, leading to more robust classifiers. The contributions to this article include the taxonomy of NLP augmentation methods and their efficiency over several classifiers from recent research trends in sentiment analysis and related fields.
A QoS-Based routing algorithm over software defined networks

Video streaming requires reliable transmission and a steady network to satisfy the video users and applications QoS needs. The network service must provide and meet a certain level of performance to accommodating these QoS, specially in Smart City environments, but current Internet best-effort service does not guarantee QoS. Software-defined networking (SDN) is an approach intended to separate the network control and data (forwarding) planes to get better management and optimization, which clearly enhance the planning and development of smart urban communities. In this paper, we propose a dynamic QoS algorithm over SDN to select the optimum path that meets video QoS in order to optimize the quality of experience (QoE). We differentiate the traffic based on video resolution QoE parameters to Standard Definition (SD) and High Definition (HD). The results demonstrate that the proposed method obtained better viewing quality for an smart community environment and increases the overall network throughput.
Accelerating at the Edge: A Storage-Elastic Blockchain for Latency-Sensitive Vehicular Edge Computing

The application of blockchain to Vehicular Edge Computing (VEC) has attracted significant interests. As the Internet of Things plays an essential and fundamental role for data collecting, data analyzing, and data management in VEC, it is vital to guarantee the security of the data. However, the resource-constraint nature of edge node makes it challenging to meet the needs to maintain long life-cycle IoT data since vast volumes of IoT data quickly increase. In this paper, we propose Acce-chain, a storage-elastic blockchain based on different storage capacities at the edge. Acce-chain supports re-write operation to re-write the historical block with a newly generated block without breaking the hash links between the blocks. As a result, Acce-chain ensures that the hot data can be efficiently accessed at the edge without incurring much communication costs or increasing the total size of the chain. To guarantee the security of the re-write process, we propose a new cryptographic primitive named Dynamic Threshold Trapdoor Chameleon Hash (DTTCH). To guarantee the verifiability of query operation, we design a novel storage structure named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HybridStore</i> to ensure the verifiable query for on-chain/off-chain IoT data. As a result, Acce-chain achieves both authorized re-write and verifiable query simultaneously. We provide security analysis for the DTTCH scheme and the IoT data query algorithms. We evaluate Acce-chain through experiments and the results show that the performance of the re-write operation is feasible in real-world VEC settings, and the query efficiency can achieve up to several magnitudes better than which of the baseline. The results also demonstrate that Acce-chain can provide high service quality for the latency-sensitive VEC systems.
Dynamic Hand Gesture Recognition in In-Vehicle Environment Based on FMCW Radar and Transformer

Hand gesture recognition technology plays an important role in human-computer interaction and in-vehicle entertainment. Under in-vehicle conditions, it is a great challenge to design gesture recognition systems due to variable driving conditions, complex backgrounds, and diversified gestures. In this paper, we propose a gesture recognition system based on frequency-modulated continuous-wave (FMCW) radar and transformer for an in-vehicle environment. Firstly, the original range-Doppler maps (RDMs), range-azimuth maps (RAMs), and range-elevation maps (REMs) of the time sequence of each gesture are obtained by radar signal processing. Then we preprocess the obtained data frames by region of interest (ROI) extraction, vibration removal algorithm, background removal algorithm, and standardization. We propose a transformer-based radar gesture recognition network named RGTNet. It fully extracts and fuses the spatial-temporal information of radar feature maps to complete the classification of various gestures. The experimental results show that our method can better complete the eight gesture classification tasks in the in-vehicle environment. The recognition accuracy is 97.56%.
DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning

Federated learning (FL) has become an emerging machine learning technique lately due to its efficacy in safeguarding the client’s confidential information. Nevertheless, despite the inherent and additional privacy-preserving mechanisms (e.g., differential privacy, secure multi-party computation, etc.), the FL models are still vulnerable to various privacy-violating and security-compromising attacks (e.g., data or model poisoning) due to their numerous attack vectors which in turn, make the models either ineffective or suboptimal. Existing adversarial models focusing on untargeted model poisoning attacks are not enough stealthy and persistent at the same time because of their conflicting nature (large scale attacks are easier to detect and vice versa) and thus, remain an unsolved research problem in this adversarial learning paradigm. Considering this, in this paper, we analyze this adversarial learning process in an FL setting and show that a stealthy and persistent model poisoning attack can be conducted exploiting the differential noise. More specifically, we develop an unprecedented DP-exploited stealthy model poisoning (DeSMP) attack for FL models. Our empirical analysis on both the classification and regression tasks using two popular datasets reflects the effectiveness of the proposed DeSMP attack. Moreover, we develop a novel reinforcement learning (RL)-based defense strategy against such model poisoning attacks which can intelligently and dynamically select the privacy level of the FL models to minimize the DeSMP attack surface and facilitate the attack detection.
Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm

With the development of power systems, fault diagnosis in infrared images has become increasingly important for ensuring the stability of these systems. In this paper, we propose a multi-objective multilevel threshold image segmentation method based on the boost marine predators algorithm (BMPA) for infrared-image fault diagnosis. As fault points are small, it is difficult to detect the target in an infrared image using existing segmentation methods. To address this, we use 9DKapur as the fitness function and obtain a many-objective optimization problems (MaOPs). Then, we use adaptive weights and opposition-based learning to boost the optimization ability of the MPA and solve the MOP so that a Pareto front is obtained. The DTLZ and WFG test suits are used as benchmarks to evaluate the performance of the BMPA, and electric equipment in infrared images was used to assess the fault-diagnostic ability of the proposed method. The results demonstrate that the BMPA performs better than other optimization algorithms in terms of uniformity measurement, peak signal-to-noise ratio, feature similarity, hypervolume, spacing, and CPU time. On the actual test data, the recall rate and fault detection accuracy of the proposed method were 94.29% and 92.38%, respectively. Insulator faults under various circumstances can be correctly detected in infrared images.
Comprehensive analysis of embeddings and pre-training in NLP

The amount of data and computing power has drastically increased over the last decade, which leads to the development of several new fronts in the field of Natural Language Processing (NLP). In addition to that, the entanglement of embeddings and large pre-trained models have pushed the field forward, covering a wide variety of tasks starting from machine translation to more complex tasks such as contextual text classification. This paper covers the underlying idea behind all embeddings and pre-trained models and provides an insight into fundamental strategies and implementation details of innovative embeddings. Further, it imparts the pros and cons of each specific embedding design and the associated impact on the result. It also comprehends the comparison of all the different strategies, datasets, architectures discussed in different papers with the help of standard metrics used in NLP. The content covered in this review work aims to shed light on different milestones reached in NLP, allowing the reader to deepen their understanding of NLP, which would motivate to explore the field further.
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition

Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones. The benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions. We set the benchmark’s first state-of-the-art and show there is massive scope for further innovation, holding the potential to impact a broad range of real-world vision applications including tools for the blind/low-vision community. We release the dataset at https://doi.org/10.25383/city.14294597 and benchmark code at https://github.com/microsoft/ORBIT-Dataset.
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework

Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main communication strategies of FL: synchronous FL and asynchronous FL. The advantages of synchronous FL are that the model has high precision and fast convergence speed. However, this synchronous communication strategy has the risk that the central server waits too long for the devices, namely, the straggler effect which has a negative impact on some time-critical applications. Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash. Therefore, we combine the advantages of these two strategies to propose a clustered semi-asynchronous federated learning (CSAFL) framework. We evaluate CSAFL based on four imbalanced federated datasets in a non-IID setting and compare CSAFL to the baseline methods. The experimental results show that CSAFL significantly improves test accuracy by more than +5% on the four datasets compared to TA-FedAvg. In particular, CSAFL improves absolute test accuracy by +34.4% on non-IID FEMNIST compared to TA-FedAvg.
Contrastive Information Extraction With Generative Transformer

Information extraction tasks such as triple extraction and event extraction are of great importance for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end information extraction task for sequence generation. Since generative information extraction may struggle to capture long-term dependencies and generate unfaithful triples, we introduce a novel model, contrastive information extraction with a generative transformer. Specifically, we introduce a single shared transformer module for an encoder-decoder-based generation. To generate faithful results, we propose a novel triplet contrastive training object. Moreover, we introduce two mechanisms to further improve model performance (i.e., batch-wise dynamic attention-masking and triple-wise calibration). Experimental results on five datasets (i.e., NYT, WebNLG, MIE, ACE-2005, and MUC-4) show that our approach achieves better performance than baselines.