Reviewerly/exHarmony huggingface dataset:
m <sup>3</sup> Track

Nowadays, the market of 3D human posture tracking has extended to a broad range of application scenarios. As current mainstream solutions, vision-based posture tracking systems suffer from privacy leakage concerns and depend on lighting conditions. Towards more privacy-preserving and robust tracking manner, recent works have exploited commodity radio frequency signals to realize 3D human posture tracking. However, these studies cannot handle the case where multiple users are in the same space. In this paper, we present a <u>mm</u>Wave-based <u>m</u>ulti-user 3D posture tracking system, m3Track, which leverages a single commercial off-the-shelf (COTS) mmWave radar to track multiple users' postures simultaneously as they move, walk, or sit. Based on the sensing signals from a mmWave radar in multi-user scenarios, m3Track first separates all the users on mmWave signals. Then, m3Track extracts shape and motion features of each user, and reconstructs 3D human posture for each user through a designed deep learning model. Furthermore. m3Track maps the reconstructed 3D postures of all users into 3D space, and tracks users' positions through a coordinate-corrected tracking method, realizing practical multi-user 3D posture tracking with a COTS mmWave radar. Experiments conducted in real-world multi-user scenarios validate the accuracy and robustness of m3Track on multi-user 3D posture tracking.
TN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training still need to be detected. We design an end-to-end deep transferable network TN-ZSTAD as the architecture for this solution. On the one hand, this network utilizes an activity graph transformer to predict a set of activity instances that appear in the video, rather than produces many activity proposals in advance. On the other hand, this network captures the common semantics of seen and unseen activities from their corresponding label embeddings, and it is optimized with an innovative loss function that considers the classification property on seen activities and the transfer property on unseen activities together. Experiments on the THUMOS'14, Charades, and ActivityNet datasets show promising performance in terms of detecting unseen activities.
It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy

To achieve high accuracy in machine learning (ML) systems, practitioners often use complex “black-box” models that are not easily understood by humans. The opacity of such models has resulted in public concerns about their use in high-stakes contexts and given rise to two conflicting arguments about the nature — and even the existence — of the accuracy-explainability trade-off. One side postulates that model accuracy and explainability are inversely related, leading practitioners to use black-box models when high accuracy is important. The other side of this argument holds that the accuracy-explainability trade-off is rarely observed in practice and consequently, that simpler interpretable models should always be preferred. Both sides of the argument operate under the assumption that some types of models, such as low-depth decision trees and linear regression are more explainable, while others such as neural networks and random forests, are inherently opaque.
Language models

A language modeling overview, highlighting basic concepts, intuitive explanations, technical achievements, and fundamental challenges.
Complementary Data Augmentation for Cloth-Changing Person Re-Identification

This paper studies the challenging person re-identification (Re-ID) task under the cloth-changing scenario, where the same identity (ID) suffers from uncertain cloth changes. To learn cloth- and ID-invariant features, it is crucial to collect abundant training data with varying clothes, which is difficult in practice. To alleviate the reliance on rich data collection, we reinforce the feature learning process by designing powerful complementary data augmentation strategies, including positive and negative data augmentation. Specifically, the positive augmentation fulfills the ID space by randomly patching the person images with different clothes, simulating rich appearance to enhance the robustness against clothes variations. For negative augmentation, its basic idea is to randomly generate out-of-distribution synthetic samples by combining various appearance and posture factors from real samples. The designed strategies seamlessly reinforce the feature learning without additional information introduction. Extensive experiments conducted on both cloth-changing and -unchanging tasks demonstrate the superiority of our proposed method, consistently improving the accuracy over various baselines.
Spintronic reservoir computing without driving current or magnetic field

Recent studies have shown that nonlinear magnetization dynamics excited in nanostructured ferromagnets are applicable to brain-inspired computing such as physical reservoir computing. The previous works have utilized the magnetization dynamics driven by electric current and/or magnetic field. This work proposes a method to apply the magnetization dynamics driven by voltage control of magnetic anisotropy to physical reservoir computing, which will be preferable from the viewpoint of low-power consumption. The computational capabilities of benchmark tasks in single MTJ are evaluated by numerical simulation of the magnetization dynamics and found to be comparable to those of echo-state networks with more than 10 nodes.
Effects of different service failure types and recovery strategies on the consumer response mechanism of chatbots

The increasing application of artificial intelligence to online retailing and the rapid deployment of online robots have made online robot service failures common. This study explores consumer response mechanisms to different types of service failures and recovery strategies of chatbots based on role congruity theory and psychological accounting theory. Questionnaires were used to collect data and test hypotheses. The study found the following. First, chatbot service failures can affect consumers' choice of different recovery strategies. After the functional failure of chatbots, consumers are more inclined for the chatbot to be involved in service recovery. After the nonfunctional failure of chatbots, consumers are more inclined toward human involvement in service recovery. Second, different service recovery strategies affect the level of perceived governance. Compared with humans involved in service recovery, robots have a higher level of perceived governance. Third, the level of perceived governance affects consumers’ willingness to forgive. Fourth, the level of robot intelligence plays a moderating role in the types of service failures affecting the choice of recovery strategies. The findings of this study can enrich the response mechanisms and boundary conditions of online robot service failure and provide important insights for online retail enterprises to effectively respond to robot service failure and make reasonable use of human-robot collaborative work.
Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.
Regularized discriminative broad learning system for image classification

Because of its simple network structure and efficient learning mode, the Broad Learning System (BLS) has achieved impressive performance in image classification tasks. Nevertheless, two deficiencies still exist which have severely limited its learning ability. First, the strict binary labeling strategy used in BLS-based models restricts the model’s flexibility. Second, the final broad features are inevitably redundant, which can cause useless features to be learned and reduce the recognition accuracy. In this paper, we propose three discriminative BLS-based models to address these mentioned problems. Specifically, we first integrate the ɛ-dragging technique into the framework of standard BLS to relax the regression targets and propose the ℓ2-norm based discriminative BLS (L2DBLS) model. Secondly, to avoid the negative effects of redundant features in L2DBLS, we utilize the ℓ2,1 regularizer to replace the Frobenius norm for feature selection. Furthermore, we propose to constrain the projection matrix of BLS by ℓ2 and ℓ2,1 regularization simultaneously. As a result, the obtained output weights can be more compact and smooth for recognition. Efficient iterative methods based on the alternating direction method of multipliers are derived to optimize the proposed models. Finally, various experiments on image databases are intended to demonstrate the outstanding recognition capability of our proposed models in comparison with other state-of-the-art classifiers.
An LDA–SVM Machine Learning Model for Breast Cancer Classification

Breast cancer is a prevalent disease that affects mostly women, and early diagnosis will expedite the treatment of this ailment. Recently, machine learning (ML) techniques have been employed in biomedical and informatics to help fight breast cancer. Extracting information from data to support the clinical diagnosis of breast cancer is a tedious and time-consuming task. The use of machine learning and feature extraction techniques has significantly changed the whole process of a breast cancer diagnosis. This research work proposed a machine learning model for the classification of breast cancer. To achieve this, a support vector machine (SVM) was employed for the classification, and linear discriminant analysis (LDA) was employed for feature extraction. We measured our model’s feature extraction performance in principal component analysis (PCA) and random forest for classification. A comparative analysis of the proposed model was performed to show the effectiveness of the feature extraction, and we computed missing values based on the classifier’s accuracy, precision, and recall. The original Wisconsin Breast Cancer dataset (WBCD) and Wisconsin Prognostic Breast Cancer dataset (WPBC) were used. We evaluated performance in two phases: In phase 1, rows containing missing values were computed using the mean, and in phase 2, rows containing missing values were computed using the median. LDA–SVM when median was used to compute missing values has better results, with accuracy of 99.2%, recall of 98.0% and precision of 98.0% on the WBCD dataset and an accuracy of 79.5%, recall of 76.0% and precision of 59.0% on the WPBC dataset. The SVM classifier had a better performance in handling classification problems when LDA was applied and the median was used as a method for computing missing values.
Service Robots in Long-Term Care: A Consumer-Centric View

Service robots with advanced intelligence capabilities can potentially transform servicescapes. However, limited attention has been given to how consumers experiencing vulnerabilities, particularly those with disabilities, envisage the characteristics of robots’ prospective integration into emotionally intense servicescapes, such as long-term care (LTC). We take an interdisciplinary approach conducting three exploratory studies with consumers with disabilities involving Community Philosophy, LEGO ® Serious Play ® , and Design Thinking methods. Addressing a lack of consumer-centric research, we offer a three-fold contribution by 1) developing a conceptualization of consumer-conceived value of robots in LTC, which are envisaged as a supporting resource offering consumers opportunities to realize value; 2) empirically evidencing pathogenic vulnerabilities as a potential value-destruction factor to underscore the importance of integrating service robots research with a service inclusion paradigm; and 3) providing a theoretical extension and clarification of prior characterizations of robots’ empathetic and emotion-related AI capabilities. Consumers with disabilities conceive robots able to stimulate and regulate emotions by mimicking cognitive and behavioral empathy, but unable to express affective and moral empathy, which is central to care experience. While providing support for care practices, for the foreseeable future, service robots will not, in themselves, actualize the experience of “being cared for.”
How artificiality and intelligence affect voice assistant evaluations

Abstract Widespread, and growing, use of artificial intelligence (AI)–enabled voice assistants (VAs) creates a pressing need to understand what drives VA evaluations. This article proposes a new framework wherein perceptions of VA artificiality and VA intelligence are positioned as key drivers of VA evaluations. Building from work on signaling theory, AI, technology adoption, and voice technology, the authors conceptualize VA features as signals related to either artificiality or intelligence, which in turn affect VA evaluations. This study represents the first application of signaling theory when examining VA evaluations; also, it is the first work to position VA artificiality and intelligence (cf. other factors) as key drivers of VA evaluations. Further, the paper examines the role of several theory-driven and/ or practice-relevant moderators, relating to the effects of artificiality and intelligence on VA evaluations. The results of these investigations can help firms suitably design their VAs and suitably design segmentation strategies.
A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition

The use of drones in various applications has now increased, and their popularity among the general public has increased. As a result, the possibility of their misuse and their unauthorized intrusion into important places such as airports and power plants are increasing, threatening public safety. For this reason, accurate and rapid recognition of their types is very important to prevent their misuse and the security problems caused by unauthorized access to them. Performing this operation in visible images is always associated with challenges, such as the small size of the drone, confusion with birds, the presence of hidden areas, and crowded backgrounds. In this paper, a novel and accurate technique with a change in the YOLOv4 network is presented to recognize four types of drones (multirotors, fixed-wing, helicopters, and VTOLs) and to distinguish them from birds using a set of 26,000 visible images. In this network, more precise and detailed semantic features were extracted by changing the number of convolutional layers. The performance of the basic YOLOv4 network was also evaluated on the same dataset, and the proposed model performed better than the basic network in solving the challenges. Compared to the basic YOLOv4 network, the proposed model provides better performance in solving challenges. Additionally, it can perform automated vision-based recognition with a loss of 0.58 in the training phase and 83% F1-score, 83% accuracy, 83% mean Average Precision (mAP), and 84% Intersection over Union (IoU) in the testing phase. These results represent a slight improvement of 4% in these evaluation criteria over the YOLOv4 basic model.
Integration of remote sensing and geophysical data for the extraction of hydrothermal alteration zones and lineaments; Gabal Shilman basement area, Southeastern Desert, Egypt

In the Gabal Shulman area of the southeastern desert of Egypt, ASTER and geophysical aeromagnetic data provide useful information for detecting hydrothermal alteration zones and structural features at the surface and subsurface. The band ratio of the data from ASTER, constrained energy minimization (CEM) and Crósta algorithms help to identify new potential alteration zones in the study region. The aeromagnetic data were used to enhance the exploration study in the study area. The Horizontal Gradient Magnitude (HGM) filter was used to map the lineaments in the area. The statistical analysis of the lineaments shows that the main directions controlling the study area are northwest, north, north-northeast, and northeast. Finally, the results of ASTER and the geophysical aeromagnetic data show the presence of new hydrothermal alteration zones in the study region. These alteration zones are mostly associated with two main structures trending NNW-SSE and N-S, mainly along tectonic shear zones (thrust faults) and contacts between serpentinites and basic metavolcanics.
Learning Implicit Class Knowledge for RGB-D Co-Salient Object Detection With Transformers

RGB-D co-salient object detection aims to segment co-occurring salient objects when given a group of relevant images and depth maps. Previous methods often adopt separate pipeline and use hand-crafted features, being hard to capture the patterns of co-occurring salient objects and leading to unsatisfactory results. Using end-to-end CNN models is a straightforward idea, but they are less effective in exploiting global cues due to the intrinsic limitation. Thus, in this paper, we alternatively propose an end-to-end transformer-based model which uses class tokens to explicitly capture implicit class knowledge to perform RGB-D co-salient object detection, denoted as CTNet. Specifically, we first design adaptive class tokens for individual images to explore intra-saliency cues and then develop common class tokens for the whole group to explore inter-saliency cues. Besides, we also leverage the complementary cues between RGB images and depth maps to promote the learning of the above two types of class tokens. In addition, to promote model evaluation, we construct a challenging and large-scale benchmark dataset, named RGBD CoSal1k, which collects 106 groups containing 1000 pairs of RGB-D images with complex scenarios and diverse appearances. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method.
Attention-based anomaly detection in multi-view surveillance videos

Anomaly detection is one of the most challenging tasks in visual understanding because anomalous events are diverse and complicated. In this paper, we propose a future frame prediction framework and a Multiple Instance Learning (MIL) framework by leveraging attention schemes to learn anomalies. In both frameworks, we utilize the attention-based module to better localize anomalies. Further, we introduce a memory addressing module for the future frame prediction framework, and a novel loss function for the MIL framework, respectively. We also introduce a new multi-view dataset of 170 videos with 10 realistic anomalies that pose a serious threat to security such as intrusion, crowd, accident, weapon, arson, etc., as well as normal activities. The experimental results demonstrate the effectiveness of our methods on the proposed dataset and multiple benchmark datasets. The proposed dataset brings more opportunities and challenges to future work on anomaly detection.
On the evaluation of neural code summarization

Source code summaries are important for program comprehension and maintenance. However, there are plenty of programs with missing, outdated, or mismatched summaries. Recently, deep learning techniques have been exploited to automatically generate summaries for given code snippets. To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets. The evaluation results show that some important factors have a great influence on the model evaluation, especially on the performance of models and the ranking among the models. However, these factors might be easily overlooked. Specifically, (1) the BLEU metric widely used in existing work of evaluating code summarization models has many variants. Ignoring the differences among these variants could greatly affect the validity of the claimed results. Furthermore, we conduct human evaluations and find that the metric BLEU-DC is most correlated to human perception; (2) code pre-processing choices can have a large (from -18\% to +25\%) impact on the summarization performance and should not be neglected. We also explore the aggregation of pre-processing combinations and boost the performance of models; (3) some important characteristics of datasets (corpus sizes, data splitting methods, and duplication ratios) have a significant impact on model evaluation. Based on the experimental results, we give actionable suggestions for evaluating code summarization and choosing the best method in different scenarios. We also build a shared code summarization toolbox to facilitate future research.