Reviewerly/exHarmony huggingface dataset:
Social-Emotional-Sensory Design Map for Affective Computing Informed by Neurodivergent Experiences

One of the grand challenges of artificial intelligence and affective computing is for technology to become emotionally-aware and thus, more human-like. Modeling human emotions is particularly complicated when we consider the lived experiences of people who are on the autism spectrum. To understand the emotional experiences of autistic adults and their attitudes towards common representations of emotions, we deployed a context study as the first phase of a Grounded Design research project. Based on community observations and interviews, this work contributes empirical evidence of how the emotional experiences of autistic adults are entangled with social interactions as well as the processing of sensory inputs. We learned that (1) the emotional experiences of autistic adults are embodied and co-constructed within the context of physical environments, social relationships, and technology use, and (2) conventional approaches to visually representing emotion in affective education and computing systems fail to accurately represent the experiences and perceptions of autistic adults. We contribute a social-emotional-sensory design map to guide designers in creating more diverse and nuanced affective computing interfaces that are enriched by accounting for neurodivergent users.
Toward Multi-FPGA Acceleration of the Neural Networks

High-throughput and low-latency Convolutional Neural Network (CNN) inference is increasingly important for many cloud- and edge-computing applications. FPGA-based acceleration of CNN inference has demonstrated various benefits compared to other high-performance devices such as GPGPUs. Current FPGA CNN-acceleration solutions are based on a single FPGA design, which are limited by the available resources on an FPGA. In addition, they can only accelerate conventional 2D neural networks. To address these limitations, we present a generic multi-FPGA solution, written in OpenCL, which can accelerate more complex CNNs (e.g., C3D CNN) and achieve a near linear speedup with respect to the available single-FPGA solutions. The design is built upon the Intel Deep Learning Accelerator architecture, with three extensions. First, it includes updates for better area efficiency (up to 25%) and higher performance (up to 24%). Second, it supports 3D convolutions for more challenging applications such as video learning. Third, it supports multi-FPGA communication for higher inference throughput. The results show that utilizing multiple FPGAs can linearly increase the overall bandwidth while maintaining the same end-to-end latency. In addition, the design can outperform other FPGA 2D accelerators by up to 8.4 times and 3D accelerators by up to 1.7 times.
PGRA: Projected graph relation-feature attention network for heterogeneous information network embedding

Graph neural networks (GNNs) have achieved superior performance and gained significant interest in various domains. However, most of the existing GNNs are considered for homogeneous graphs, whereas real-world systems are usually modeled as heterogeneous graphs or heterogeneous information networks (HINs). Designing a GNN to fully capture the rich semantic information of HINs is significantly challenging owing to the heterogeneity and incompatibility of relations in HINs. To address these issues while utilizing the power of GNNs, we propose a novel unsupervised embedding approach, named Projected Graph Relation-Feature Attention Network (PGRA). PGRA is based on three mechanisms: 1) specific-relation projection that projects the representation vector of each node to a relation-specific space, 2) aggregation with a relation-feature attention network that learns salient neighbors in the aggregation by considering the features of the nodes and compatibility between the connected and target relations, 3) an elegantly designed loss function that captures both the first- and second-order proximities between nodes. The results of extensive experiments on seven real-world datasets illustrate that PGRA outperforms the state-of-the-art methods by a large margin.
Random Space Division Sampling for Label-Noisy Classification or Imbalanced Classification

This article presents a simple sampling method, which is very easy to be implemented, for classification by introducing the idea of random space division, called "random space division sampling" (RSDS). It can extract the boundary points as the sampled result by efficiently distinguishing the label noise points, inner points, and boundary points. This makes it the first general sampling method for classification that not only can reduce the data size but also enhance the classification accuracy of a classifier, especially in the label-noisy classification. The "general" means that it is not restricted to any specific classifiers or datasets (regardless of whether a dataset is linear or not). Furthermore, the RSDS can online accelerate most classifiers because of its lower time complexity than most classifiers. Moreover, the RSDS can be used as an undersampling method for imbalanced classification. The experimental results on benchmark datasets demonstrate its effectiveness and efficiency. The code of the RSDS and comparison algorithms is available at: https://github.com/syxiaa/RSDS.
Do background characteristics matter in Children's mastery of digital literacy? A cognitive diagnosis model analysis

This study aims to investigate the mastery profiles of digital literacy skills of Hong Kong primary students using a general cognitive diagnosis model (CDM) framework. In particular, the relationship between the mastery of each digital skill and a number of students' background characteristics is explored using a three-step approach. The current study analyzes data collected from 642 Grade 3 students in Hong Kong using a newly developed digital literacy assessment (DLA). CDMs are fitted to the data to determine students' mastery profiles of five digital skills, as well as test properties; subsequently latent logistic regression analyses were implemented to determine the relationship between skill mastery and the covariates. Results indicate that CDM analysis is an appropriate method to analyze the DLA performance data, which exhibited measurement invariance across gender and socioeconomic status (SES). Despite low mastery proportions for all digital skills, students' skill mastery can be accurately classified. Finally, the latent logistic regression results indicate that children's background characteristics (i.e., gender, educational aspiration, home language, SES, and access to digital devices) are differentially related to their mastery of each digital skill. • Cognitive diagnosis model analysis provided a finer grain differentiation of students' digital literacy (DL) achievements. • Educational aspiration, SES, device access were significant predictors of Primary 3 students' DL skills mastery level. • Children's DL masteries were not significantly predicted by gender and home language. • Shared access to computers at home was positively related to DL mastery, but exclusive access was not.
Resisting Racism in Tech Design

Speech-based interfaces that feature conversational agents have a diverse population of users, including children. While recent HCI research has investigated child-agent interactions in the context of speech-based interfaces with rare mention of sample populations' racial makeup, scant research has examined how racially minoritized groups, in particular Black youth, interact with such technologies. Subsequently, issues of race and racism continue to be largely neglected within the HCI community, contributing to technology that marginalizes Black people. Leveraging Black feminist epistemology as a critical framework, we engage Black students in the ideation process of conversational agents as speech-based interfaces, drawing attention to how race, gender, and class influence their design choices. Our findings reveal that Black students prefer custom-designed conversational agents that are modeled after Black women, possessing knowledge of Black culture and serving as life coaches to help them achieve their goals. We also confirm that the current design of speech-based interfaces perpetuates racism, reinforcing whiteness as the norm.
A Selective Mitigation Technique of Soft Errors for DNN Models Used in Healthcare Applications: DenseNet201 Case Study

Deep neural networks (DNNs) have been successfully deployed in widespread domains, including healthcare applications.DenseNet201 is a new DNN architecture used in healthcare systems (i.e., presence detection of the surgical tool).Specialized accelerators such as GPUs have been used to speed up the execution of DNNs.Nevertheless, GPUs are prone to transient effects and other reliability threats, which can impact DNN models' reliability.Safety-critical systems, such as healthcare applications, must be highly reliable because minor errors might lead to severe injury or death.In this paper, we propose a selective mitigation technique that relies on in-depth analysis.First, we inject the DenseNet201 model implemented on a GPU via NVIDIA's SASSIFI fault injector.Second, we perform a comprehensive analysis from the perspective of kernel and layer to identify the most vulnerable portions of the injected model.Finally, we validate our technique by applying it to the top-vulnerable kernels to selectively protect the only sensitive portions of the model to avoid unnecessary overheads.Our experiments demonstrate that our mitigation technique achieves a significant reduction in the percentage of errors that cause malfunction (errors that lead to misclassification) from 6.463% to 0.21%.Moreover, the performance overhead (the execution time) of our technique is compared with the well-known protection techniques: Algorithm-Based Fault Tolerance (ABFT), Double Modular Redundancy (DMR), and Triple Modular Redundancy (TMR).The proposed solution shows only 0.3035% overhead compared to these techniques while correcting up 84.8% of the SDC errors in DenseNet201, remarkably improving the healthcare domain's model reliability.
Long-term prediction of time series using fuzzy cognitive maps

As a powerful recognized knowledge modeling tool, fuzzy cognitive maps (FCMs) have been investigated for time series modeling and forecasting problems. This methodology performs well in one-step-ahead or short-term prediction but poorly in terms of long-term prediction because of the potentially complex interaction between different ensuing steps. In this article, a sound conceptual method is proposed for long-term time series prediction with FCMs, which melds FCMs, time series segmentation and fuzzy clustering. A time series is divided into suitable and internally homogeneous segments. Dynamic time warping is introduced to evaluate the distance between segments. Subsequently, modified fuzzy c-means based on dynamic time warping is utilized to fuzzify these segments such that the segments are transformed into fuzzy time series and semantic vectors. The convex optimization based method is utilized with intent to rapidly and robustly learn FCMs. Consequently, the weight of FCMs can be obtained on the basis of the fuzzy time series. Eventually, the forecasting time segment will be capable of inference according to the formed FCMs and the semantic vectors. In addition, the semantic vectors can intuitively reflect the main characteristics and change tendencies of the time series. To demonstrate the long-term prediction ability of our method, we test it on both synthetic and real-life datasets in comparison with other representative and up-to-date forecasting methods; the superior performance of our method exhibits its excellent capability in forecasting future values.
Classroom Digital Twins with Instrumentation-Free Gaze Tracking

Classroom sensing is an important and active area of research with great potential to improve instruction. Complementing professional observers – the current best practice – automated pedagogical professional development systems can attend every class and capture fine-grained details of all occupants. One particularly valuable facet to capture is class gaze behavior. For students, certain gaze patterns have been shown to correlate with interest in the material, while for instructors, student-centered gaze patterns have been shown to increase approachability and immediacy. Unfortunately, prior classroom gaze-sensing systems have limited accuracy and often require specialized external or worn sensors. In this work, we developed a new computer-vision-driven system that powers a 3D “digital twin” of the classroom and enables whole-class, 6DOF head gaze vector estimation without instrumenting any of the occupants. We describe our open source implementation, and results from both controlled studies and real-world classroom deployments.
Self-Checking Deep Neural Networks in Deployment

The widespread adoption of Deep Neural Networks (DNNs) in important domains raises questions about the trustworthiness of DNN outputs.Even a highly accurate DNN will make mistakes some of the time, and in settings like self-driving vehicles these mistakes must be quickly detected and properly dealt with in deployment.Just as our community has developed effective techniques and mechanisms to monitor and check programmed components, we believe it is now necessary to do the same for DNNs.In this paper we present DNN self-checking as a process by which internal DNN layer features are used to check DNN predictions.We detail SelfChecker, a self-checking system that monitors DNN outputs and triggers an alarm if the internal layer features of the model are inconsistent with the final prediction.SelfChecker also provides advice in the form of an alternative prediction.We evaluated SelfChecker on four popular image datasets and three DNN models and found that SelfChecker triggers correct alarms on 60.56% of wrong DNN predictions, and false alarms on 2.04% of correct DNN predictions.This is a substantial improvement over prior work (SELFORACLE, DISSECTOR, and ConfidNet).In experiments with self-driving car scenarios, SelfChecker triggers more correct alarms than SELFORACLE for two DNN models (DAVE-2 and Chauffeur) with comparable false alarms.Our implementation is available as open source.
YOLOv4 Object Detection Algorithm with Efficient Channel Attention Mechanism

Channel attention mechanism has been widely used in object detection algorithms because of its strong feature representation ability. The real-time object detection algorithm YOLOv4 has fast detection speed and high accuracy, but it still has some shortcomings, such as inaccurate bounding box positioning and poor robustness. Therefore, we introduced channel attention mechanism into the YOLOv4 algorithm to enhance the feature representation ability of images, and proposed a object detection algorithm with channel attention mechanism. This module firstly carries out global average pooling operation on the features extracted by YOLOv4, and then carries out local cross-channel interactive operation on the feature channels through one-dimensional convolution to enhance the correlation between the features of channels, so as to improve the positioning accuracy of YOLOv4. Our method has achieved good results in the PASCAL VOC dataset. Compared with the original YOLOv4 algorithm, the mAP of this algorithm in the PASCAL VOC test set is improved by 0.62%.
Prediction of user outage under typhoon disaster based on multi-algorithm Stacking integration

Prediction of user outage under typhoon disaster is of great significance for power grid disaster prevention and mitigation. Based on the idea of Stacking integration in machine learning, this paper constructs a forecasting model of user outage under typhoon disaster. It includes base learner layer and meta learner layer. In the base learner layer, random forest, adaptive boosting, extremely tree, gradient boosting decision tree, support vector machine and logistic regression are selected. Then the XGBoost algorithm is selected in the meta learner layer. Taking one of the most frequently struck area Xuwen County of Guangdong, China as the research object, the model is verified by typhoon “Rammasun(2014)”,“Kalmaegi(2014)” and “Mujigae(2015)”. The results show that the accuracy and recall of the prediction model based on multi-algorithm Stacking integration can reach 0.7678 and 0.9059 respectively. It can well realize the prediction of user outage under typhoon disaster.
The impact of client use of blockchain technology on audit risk and audit approach—An exploratory study

Blockchain is claimed to disrupt the external audit function by enhancing the reliability of both external and internal audit evidence. This study examined the impact of client use of blockchain technology on audit risk and audit approach. It referred to the Australian Auditing Standard ASA 315 Identifying and Assessing the Risks of Material Misstatement and van Buuren et al.'s continuum of audit approaches to frame semi‐structured interviews with 28 blockchain stakeholders including audit partners. The study found that blockchain clients are perceived to be riskier than other clients and that inherent and control risks are amplified. The audit approach is not definitive with two likely approaches: a combination of direct, indirect, account‐level and entity‐level evidence and an increase in indirect and entity‐level evidence. This study's findings are useful for audit practitioners, standard setters and regulators.
Kalmannet: Data-Driven Kalman Filtering

The Kalman filter (KF) is a celebrated signal processing algorithm, implementing optimal state estimation of dynamical systems that are well represented by a linear Gaussian state-space model. The KF is model-based, and therefore relies on full and accurate knowledge of the underlying model. We present KalmanNet, a hybrid data-driven/model-based filter that does not require full knowledge of the underlying model parameters. KalmanNet is inspired by the classical KF flow and implemented by integrating a dedicated and compact neural network for the Kalman gain computation. We present an offline training method, and numerically illustrate that KalmanNet can achieve optimal performance without full knowledge of the model parameters. We demonstrate that when facing inaccurate parameters KalmanNet learns to achieve notably improved performance compared to KF.
UMLAUT: Debugging Deep Learning Programs using Program Structure and Model Behavior

Training deep neural networks can generate non-descriptive error messages or produce unusual output without any explicit errors at all. While experts rely on tacit knowledge to apply debugging strategies, non-experts lack the experience required to interpret model output and correct Deep Learning (DL) programs. In this work, we identify DL debugging heuristics and strategies used by experts, andIn this work, we categorize the types of errors novices run into when writing ML code, and map them onto opportunities where tools could help. We use them to guide the design of Umlaut. Umlaut checks DL program structure and model behavior against these heuristics; provides human-readable error messages to users; and annotates erroneous model output to facilitate error correction. Umlaut links code, model output, and tutorial-driven error messages in a single interface. We evaluated Umlaut in a study with 15 participants to determine its effectiveness in helping developers find and fix errors in their DL programs. Participants using Umlaut found and fixed significantly more bugs and were able to implement fixes for more bugs compared to a baseline condition.
Pruning CNN filters via quantifying the importance of deep visual representations

The achievement of convolutional neural networks (CNNs) in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts to evaluate the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a semantic concept and individual hidden unit representations is proposed to quantitatively evaluate the importance of feature maps. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining, crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines. Furthermore, we evaluate our pruning method on CIFAR-10, CUB-200, and ImageNet (ILSVRC 2012) datasets. The experimental results show that the proposed method efficiently achieves a 50% FLOPs reduction on CIFAR-10, with only 0.86% accuracy drop on the VGG-16 model. Meanwhile, ResNet pruned on CIFAR-10 achieves a 30% reduction in FLOPs with only 0.12% and 0.02% drops in accuracy on ResNet-20 and ResNet-32 respectively. For ResNet-50 on ImageNet, our pruned model achieves a 50% reduction in FLOPs with only a top-5 accuracy drop of 0.27%, which significantly outperforms state-of-the-art methods.
Optimization of Advanced Encryption Standard on Graphics Processing Units

Graphics processing units (GPUs) are specially designed for parallel applications and perform parallel operations much faster than central processing units (CPUs). In this work, we focus on the performance of the Advanced Encryption Standard (AES) on GPUs. We present optimizations which remove bank conflicts in shared memory accesses and provide 878.6 Gbps throughput for AES-128 encryption on an RTX 2070 Super, which is equivalent to 4.1 Gbps per Watt. Our optimizations provide more than 2.56x speed-up against the best GPU results in the literature. Our optimized AES implementations on GPUs even outperform any CPU using the hardware level AES New Instructions (AES-NI) and legacy FPGA-based cluster architectures like COPACOBANA and RIVYERA. Even on a low-end GPU like MX 250, we obtained 60.0 Gbps throughput for AES-256 which is generally faster than the read/write speeds of solid disks. Thus, transition from AES-128 to AES-256 when using GPUs would provide military grade security with no visible performance loss. With these breakthrough performances, GPUs can be used as a cryptographic co-processor for file or full disk encryption to remove performance loss coming from CPU encryption. With a single GPU as a co-processor, busy SSL servers can be free from the burden of encryption and use their whole CPU power for other operations. Moreover, these optimizations can help GPUs to practically verify theoretically obtained cryptanalysis results or their reduced versions in reasonable time.
Spatial-Aware GAN for Unsupervised Person Re- identification

The recent person re-identification research has achieved great success by learning from a large number of labeled person images. On the other hand, the learned models often experience significant performance drops when applied to images collected in a different environment. Unsupervised domain adaptation (UDA) has been investigated to mitigate this constraint, but most existing systems adapt images at pixel level only and ignore obvious discrepancies at spatial level. This paper presents an innovative UDA-based person re-identification network that is capable of adapting images at both spatial and pixel levels simultaneously. A novel disentangled cycle-consistency loss is designed which guides the learning of spatial-level and pixel-level adaptation in a collaborative manner. In addition, a novel multi-modal mechanism is incorporated which is capable of generating images of different geometry views and augmenting training images effectively. Extensive experiments over a number of public datasets show that the proposed UDA network achieves superior person re-identification performance as compared with the state-of-the-art.