Reviewerly/exHarmony huggingface dataset:
Overlooked Trustworthiness of Saliency Maps

Various saliency visualization methods have been proposed to explain artificial intelligence (AI) models towards building the trustworthiness of AI-driven medical image computing applications. However, an important question has yet to be answered - are the saliency maps themselves trustworthy? The trustworthiness of saliency methods has largely been overlooked. This paper first proposes the criteria and methods to evaluate the trustworthiness of saliency maps. Then, a series of systematic studies are performed on a large-scale dataset with a commonly adopted deep neural network. The results show that: (i) Saliency maps may not be relevant to the model outputs; (ii) Saliency maps lack resistance and can be tampered without changing the model output. By demonstrating these risks of the current saliency methods, we suggest the community using saliency maps with caution when explaining AI models. Our source code is available at https://github.com/DIAL-RPI/Trustworthiness-of-Medical-XAI .
Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification

Advances in digital pathology and deep learning have enabled robust disease classification, better diagnosis, and prognosis. In real-world settings, readily available and inexpensive image-level labels from pathology reports are weak, which seriously degrades the performance of deep learning models. Weak image-level labels do not represent the complexity and heterogeneity of the analyzed WSIs. This work presents an importance-based sampling framework for robust histopathology image analysis, Uncertainty-Aware Sampling Framework (UASF). Our experiments demonstrate the effectiveness of UASF when used to grade a highly heterogeneous subtype of soft tissue sarcomas. Furthermore, our proposed model achieves better accuracy when compared to the baseline models by sampling the most relevant tiles.
A Study on Fuzzy Keywords Search Techniques and Incorporating Certificateless Cryptography

Cloud computing is preferred because of its numerous improvements, such as data security, low maintenance cost, unlimited storage capacity and consistent backups. However, legitimate users take advantage of cloud storage services for storing a considerable amount of sensitive data. After storing data on the cloud, data users pass on control over data to cloud administrators. Although for assuring data security, sensitive information needs to be encrypted before deploying it on the cloud server. In traditional searchable encryption, encrypted data can be searched using keywords on a cloud server without knowing data details, and users can retrieve certain specific files of interest after authentication. However, the results are only related to the exact matching keyword searches. This drawback affects system usability and efficiency, due to which existing encryption methods are unsuitable in cloud computing. To avoid the above problems, this study includes as follows: Firstly, we analyze all fuzzy keyword search techniques that are wildcard based, gram based and trie-traverse. Secondly, we briefly describe certificateless cryptography and suggest a certificateless searchable encryption scheme. Finally, this study gives easy access to developing a fuzzy keyword searchable system for a new researcher to combine the above two points. It provides easy access and efficient search results.
Comparing Moderation Strategies in Group Chats with Multi-User Chatbots

The increasing capabilities of chatbots will become more and more important in the near future. In this paper, we introduce a conversational system which connects a chatbot with a mainstream messaging service in a multi-user scenario. While there are already numerous options for single-user bots, ready-to-use systems for multiple users and group chats are scarce. This work thus aims to get insight into how such a group chatbot should behave during a multi-turn conversation. For this, we implemented and evaluated four different moderation strategies in an everyday use-case: the planning and negotiation of a joint appointment. In our subsequent user study with 40 participants, we investigated how the different strategies were perceived and what influence they had on the acceptance, usability and efficiency of the system. Our evaluation results show that users' perceptions of innovation and inventiveness of the bot were influenced by the moderation strategies.
Lexicon‐Augmented Cross‐Domain Chinese Word Segmentation with Graph Convolutional Network

Existing neural approaches have achieved significant progress for Chinese word segmentation (CWS). The performances of these methods tend to drop dramatically in the cross-domain scenarios due to the data distribution mismatch across domains and the out of vocabulary words problem. To address these two issues, proposes a lexicon-augmented graph convolutional network for cross-domain CWS. The novel model can capture the information of word boundaries from all candidate words and utilize domain lexicons to alleviate the distribution gap across domains. Experimental results on the cross-domain CWS datasets (SIGHAN-2010 and TCM) show that the proposed method successfully models information of domain lexicons for neural CWS approaches and helps to achieve competitive performance for cross-domain CWS. The two problems of cross-domain CWS can be effectively solved through various interactions between characters and candidate words based on graphs. Further, experiments on the CWS benchmarks (Bakeoff-2005) also demonstrate the robustness and efficiency of the proposed method.
A Machine Learning approach for classification of ovarian tumours

Abstract Ovarian tumours are abnormal growths in the ovaries which can be either cancerous or non-cancerous. Ovarian cancer is reported to cause more deaths than any other female reproductive cancers. In order to reduce the death rate, early diagnosis and treatment is required. In the recent days, Artificial Intelligence (AI) based Computer Aided Diagnosis(CAD) is being used performed in order to reduce human errors. This work is one such CAD based classification system to classify of benign and malignant ovarian tumours. Six popular Machine Learning(ML) algorithms have been applied on clinical ovarian bio-markers with an aim to classify the tumours. Also, a comparative study between the applied ML techniques is given in the result and discussion section.
Alexa, It’s Me! An Online Survey on the User Experience of Smart Speaker Authentication

Verifying the identify of the speaker is a crucial requirement for security-critical voice-based services on smart speakers, such as transferring money or making online purchases. Whilst various studies have explored novel authentication mechanisms for voice-based services, there is little research on the user experience of respective authentication methods. To address this gap, we conducted a comprehensive online survey (n=696). We compared five authentication methods (spoken PIN, biometrics, app with button/voice confirmation, card reader) regarding their perceived efficiency, security, ease of use, and error susceptibility. Additionally, we investigated users' willingness to use security-critical services in banking and government. We found an overall preference to confirm actions triggered by voice by pressing a button on a mobile authentication app followed by PIN-based authentication. In contrast, biometric authentication by voice is considered unreliable, while applying a card reader is regarded secure, yet less convenient.
Developing a play-anywhere handheld AR storytelling app using remote data collection

Immersive story experiences like immersive theater productions and escape rooms have grown in popularity in recent years, offering the audience a more active role in the events portrayed. However, many of these activities were forced to close at the start of the COVID-19 pandemic, arising from restrictions placed on group activities and travel. This created an opportunity for a story experience that users could take part in around their local neighborhoods. Five mobile applications (apps) were developed toward this goal, aiming to make effective use of available local map data, alongside virtual content overlaid on users' surroundings through Augmented Reality (AR), to offer additional story features not present in the real environment. The first two apps investigated the feasibility of such an approach, including the remote field testing of the apps, where participants used their own devices across a variety of locations. Two follow-up apps further aimed to offer an improved user experience, also adopting a more standardized testing procedure, to better ensure each app was completed in an intended manner by those participating remotely. Participants rated their experience through immersion and engagement questionnaire factors that tested for their appropriateness to rate such experiences, in addition to providing their feedback. A final app applied the same AR story implementation to a curated site-specific study, once pandemic restrictions had eased. This combination of remote studies and subsequent curated study offered a reverse methodology to much previous research in this field, but was found to offer advantages in corroborating the results of the remote studies, and also in offering new insights to further improve such an AR story app, that is designed to be used at an outdoor location of the user's choosing. Such an app offers benefits to those who may prefer the opportunity to take part in such an activity solo or close to home, as well as for storytellers to develop an outside story for use at a variety of locations, making it available to a larger audience, without the challenges and costs in migrating it to different locations.
A Comprehensive Study on Robots in Health and Social Care

The world have experienced a severe human-health crisis as a result of the emergence of a novel coronavirus (COVID-19), which was declared a global pandemic by WHO. As close human-to-human contact can spread the COVID-19 causing virus, keeping social distance is now an absolute necessity as a preventative measure. At a time of global pandemic, there is a huge need to treat patients with little patient–doctor interaction by using robots. Robots can be characterized as machines that can execute a wide range of tasks with greater autonomy and degree of freedom (DoF) than humans, making it difficult to identify them from other machines. A wide range of equipment, sensors, and information and communication technology (ICT) are now part of the healthcare system, which has become increasingly complicated. Protecting front-line personnel from virus exposure is the primary goal of using robots in health care. The aim of this study is to emphasize the evolving importance of robotics applications in health care and related fields. This paper examines in depth the design and operation of a wide range of healthcare robots in use around the world.
INSightR-Net: Interpretable Neural Network for Regression Using Similarity-Based Comparisons to Prototypical Examples

Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks. However, conventional CNNs are not able to explain their reasoning process, therefore limiting their adoption in clinical practice. In this work, we propose an inherently interpretable CNN for regression using similarity-based comparisons (INSightR-Net) and demonstrate our methods on the task of diabetic retinopathy grading. A prototype layer incorporated into the architecture enables visualization of the areas in the image that are most similar to learned prototypes. The final prediction is then intuitively modeled as a mean of prototype labels, weighted by the similarities. We achieved competitive prediction performance with our INSightR-Net compared to a ResNet baseline, showing that it is not necessary to compromise performance for interpretability. Furthermore, we quantified the quality of our explanations using sparsity and diversity, two concepts considered important for a good explanation, and demonstrated the effect of several parameters on the latent space embeddings.
Causal Narrative Comprehension: A New Perspective for Emotion Cause Extraction

Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding.
Facial Expression Translation Using Landmark Guided GANs

We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expressionto-expression translation using a single image, which is an important and challenging task in computer vision since the expression-toexpression translation is a non-linear and non-aligned problem.Moreover, it requires a high-level semantic understanding between the input and output images since the objects in images can have arbitrary poses, sizes, locations, backgrounds, and self-occlusions.To tackle this problem, we propose utilizing facial landmark information explicitly.Since it is a challenging problem, we split it into two sub-tasks, (i) category-guided landmark generation, and (ii) landmark-guided expression-to-expression translation.Two sub-tasks are trained in an end-to-end fashion that aims to enjoy the mutually improved benefits from the generated landmarks and expressions.Compared with current keypoint-guided approaches, the proposed LandmarkGAN only needs a single facial image to generate various expressions.Extensive experimental results on four public datasets demonstrate that the proposed LandmarkGAN achieves better results compared with state-of-the-art approaches only using a single image.
Disentangled Graph Contrastive Learning With Independence Promotion

Self-supervised learning for graph neural networks has attracted considerable attention and shows notable successes in graph representation learning. However, the formation of a real-world graph typically arises from highly complex interactions of many latent factors. The existing self-supervised learning methods for GNNs are inherently holistic and neglect the entanglement of the latent factors, resulting in suboptimal learned representations for downstream tasks and difficult to be interpreted. Learning disentangled graph representations with self-supervised learning poses great challenges and remains largely ignored by the existing literature. In this paper, we introduce Independence Promoted Disentangled Graph Contrastive Learning ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IDGCL</b> ) method, which can learn disentangled graph-level representations with self-supervision. In particular, we first identify the latent factors of the input graph and derive its factorized representations. Then we propose a factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors. To further promote the independence between the representations, we employ the Hilbert-Schmidt Independence Criterion to eliminate the dependence among different representations, which is effectively integrated into the self-supervised framework as a regularizer. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines.
Generating Synthetic Depth Image Dataset for Industrial Applications of Hand Localization

In this paper, we focus on the problem of applying domain randomization to produce synthetic datasets for training depth image segmentation models for the task of hand localization.We provide new synthetic datasets for industrial environments suitable for various hand tracking applications, as well as ready-to-use pre-trained models.The presented datasets are analyzed to evaluate the characteristics of these datasets that affect the generalizability of the trained models, and recommendations are given for adapting the simulation environment to achieve satisfactory results when creating datasets for specialized applications.Our approach is not limited by the shortcomings of standard analytical methods, such as color, specific gestures, or hand orientation.The models in this paper were trained solely on a synthetic dataset and were never trained on real camera images; nevertheless, we demonstrate that our most diverse datasets allow the models to achieve up to 90% accuracy.The proposed hand localization system is designed for industrial applications where the operator shares the workspace with the robot.
Vehicle Detection Method for Remote Sensing Images Based on Feature Anti-Interference and Adaptive Residual Attention

Vehicle detection in remote sensing images is of great significance to urban traffic intelligence. Though existing vehicle detection methods for remote sensing images such as fully convolutional regression network, spatial density building net, and pretraining and random-initialized fusion network have made many efforts and progress on network structural optimization, their models remain being weak on the feature anti-interference, contextual information utilization, and neglect the loss of feature information during the down-sampling process. In this article, we propose feature anti-interference and adaptive residual attention-Net, a remote sensing image object detection algorithm based on feature anti-interference and adaptive residual attention. First, a feature interference module is constructed, fed with the shallow feature and random noise, and generates interference in the detection process, making the detector improve its anti-interference ability against the disturbance in the adversarial training process. Second, a novel adaptive residual attention module is introduced into the network to extract the adaptively contextual features and enhance the weak features. Finally, a cross level fusion module is designed to enhance the collaboration between multiscale feature layers to reduce the loss of small target feature information. The effectiveness of the method is verified by comparing the method proposed in this article with other mainstream methods on the UCAS-AOD, CARPK, and OVDS datasets. The code is freely available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hel2020/FICLAR-Net</uri> .
Multi-node Acceleration for Large-scale GCNs

Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount of time, due to the explosive size of graphs nowadays. Thus, large-scale GCNs call for a multi-node acceleration system (MultiAccSys) like TPU-Pod for large-scale neural networks. In this work, we aim to scale up single-node GCN accelerators to accelerate GCNs on large-scale graphs. We first identify the communication pattern and challenges of multi-node acceleration for GCNs on large-scale graphs. We observe that (1) coarse-grained communication patterns exist in the execution of GCNs in MultiAccSys, which introduces massive amount of redundant network transmissions and off-chip memory accesses; (2) overall, the acceleration of GCNs in MultiAccSys is bandwidth-bound and latency-tolerant. Guided by these two observations, we then propose MultiGCN, the first MultiAccSys for large-scale GCNs that trades network latency for network bandwidth. Specifically, by leveraging the network latency tolerance, we <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> propose a topology-aware multicast mechanism with a one <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">put</monospace> per <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multicast</monospace> message-passing model to reduce transmissions and alleviate network bandwidth requirements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , we introduce a scatter-based round execution mechanism which cooperates with the multicast mechanism and reduces redundant off-chip memory accesses. Compared to the baseline MultiAccSys, MultiGCN achieves 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 12\times$</tex-math></inline-formula> speedup using only 28% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 68% energy, while reducing 32% transmissions and 73% off-chip memory accesses on average. It not only achieves 2.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 8\times$</tex-math></inline-formula> speedup over the state-of-the-art multi-GPU solution, but also scales to large-scale graphs as opposed to single-node GCN accelerators.