Reviewerly/exHarmony huggingface dataset:
Clients in Simulated Teletherapy via Videoconference Compensate for Altered Eye Contact When Evaluating Therapist Empathy

Eye contact is frequently associated with an increased perception of empathy and telepresence, but the currently used videoconferencing (VC) technologies diminish the possibility of naturally conveying eye contact. This study compared the empathy, telepresence, and eye gaze patterns of clients in simulated VC teletherapy sessions where eye contact was altered or facilitated. Forty-two would-be clients met with one of four therapists in training for one 20-min simulated teletherapy session taking place via VC. The session either altered or facilitated eye contact perception by manipulating the positioning of the webcams and of the clients in their chair. Eye-tracking data focusing on the eyes, face, and general body regions of interest were obtained for 25 clients. The results show that facilitating eye contact in VC did not increase the clients' perceptions of empathy or telepresence. However, empathy was associated with greater time spent looking at the eyes and faces of the therapists, but only in the sessions facilitating eye contact. We suggest that clients successfully rely on other verbal and nonverbal cues to detect therapist empathy when eye contact is altered in teletherapy sessions.
The impact of network and social context on quality of experience for competitive multiplayer virtual reality games

With market trends showing an increased move towards multi-player gaming scenarios, key research challenges include evaluating the impact of various factors on player Quality of Experience (QoE). Further moves towards increased use of immersive media technologies are opening up new challenges related to the QoE assessment of multiplayer VR games, enabling rich social experiences and immersion levels that go beyond traditional gaming. In this paper we report on a subjective user study involving 32 participants grouped into pairs and taking part in two competitive VR games. We investigate the impact of various networks (4G, 5G, and Ethernet) and latency levels on overall QoE. We further analyze the impact of social context on the reported results of the conducted QoE study, investigating to what extent playing with friends/strangers impacts player experience. Findings provide novel insights with respect to methodological challenges and participant recruitment related to conducting QoE studies for competitive multiplayer VR games.
A Sociomaterial Lens on Crowdsourcing for Learning

Abstract Crowdsourcing is increasingly being applied in educational contexts to explore the ideation and problem-solving capacity of large, networked groups. Research is emerging on the use of crowdsourcing in education, yet little is known about how the particular affordances of crowdsourcing platforms facilitate student learning. This paper applies sociomaterial theory to analysing a case study of a crowdsourcing experiment undertaken at the University of Sydney. It reflects on the crowdsourcing experiment as an assemblage of different relations, dynamics and materials, building on a recent typology for analysing social learning software through a sociomaterial lens. We contribute to the growing discourse around sociomaterial approaches by exploring how the material affordances of a unique online learning environment participate to produce certain kinds of learning experiences. This supports future research into the potentialities of crowdsourcing pedagogies at a time when increased online and blended learning brought about by the Covid-19 pandemic has galvanised educators’ interest in exploring different online environments and approaches.
Balancing Cost and Quality: An Exploration of Human-in-the-Loop Frameworks for Automated Short Answer Scoring

Short answer scoring (SAS) is the task of grading short text written by a learner. In recent years, deep-learning-based approaches have substantially improved the performance of SAS models, but how to guarantee high-quality predictions still remains a critical issue when applying such models to the education field. Towards guaranteeing high-quality predictions, we present the first study of exploring the use of human-in-the-loop framework for minimizing the grading cost while guaranteeing the grading quality by allowing a SAS model to share the grading task with a human grader. Specifically, by introducing a confidence estimation method for indicating the reliability of the model predictions, one can guarantee the scoring quality by utilizing only predictions with high reliability for the scoring results and casting predictions with low reliability to human graders. In our experiments, we investigate the feasibility of the proposed framework using multiple confidence estimation methods and multiple SAS datasets. We find that our human-in-the-loop framework allows automatic scoring models and human graders to achieve the target scoring quality.
Provable convergence of Nesterov’s accelerated gradient method for over-parameterized neural networks

Momentum methods, such as heavy ball method (HB) and Nesterov's accelerated gradient method (NAG), have been widely used in training neural networks by incorporating the history of gradients into the current updating process. In practice, they often provide improved performance over (stochastic) gradient descent (GD) with faster convergence. Despite their empirical success, a theoretical understanding of their accelerated convergence rates is still insufficient. Recently, some attempts have been made by analyzing the trajectories of gradient-based methods in an over-parameterized regime, where the number of the parameters is significantly larger than that of the training instances. However, the majority of existing theoretical work is mainly concerned with GD and the established convergence result of NAG is inferior to HB and GD, which fails to explain the practical success of NAG. In this paper, we take a step towards closing this gap by analyzing NAG in training a randomly initialized over-parameterized two-layer fully connected neural network with ReLU activation. Despite the fact that the objective function is non-convex and non-smooth, we show that NAG converges to a global minimum at a non-asymptotic linear rate (1−Θ(1/κ))t, where κ>1 is the condition number of a gram matrix and t is the number of the iterations. Compared to the convergence rate (1−Θ(1/κ))t of GD, our result provides theoretical guarantees for the acceleration of NAG in neural network training. Furthermore, our findings suggest that NAG and HB have similar convergence rate. Finally, extensive experiments on six benchmark datasets have been conducted to validate the correctness of our theoretical results.
An innovative document image binarization approach driven by the non-local p-Laplacian

Abstract Text image binarization is fairly a tedious task and a significant problem in document image analysis. This process, as a necessary pretreatment for noisy images with stains, non-uniform background, or degraded text characters, can successfully improve the quality of the image and facilitate the subsequent image processing steps. A theoretically well-motivated non-local method for document image binarization is addressed in this paper. This approach enhances degraded images by estimating and then removing the undesirable background. Extensive experiments conducted on degraded document images evince the greater effectiveness of the proposed non-local algorithm.
Towards automatic discovery and assessment of vulnerability severity in cyber–physical systems

Despite their wide proliferation, complex cyber–physical systems (CPSs) are subject to cybersecurity vulnerabilities and potential attacks. Vulnerability assessment for such complex systems are challenging, partly due to the discrepancy among mechanisms used to evaluate their cyber-security weakness levels. Several sources do report these weaknesses like the National Vulnerability Database (NVD), as well as manufacturer websites besides other security scanning advisories such as Cyber Emergency Response Team (CERT) and Shodan databases. However, these multiple sources are found to face inconsistency issues, especially in terms of vulnerability severity scores. We advocate an artificial intelligence based approach to streamline the computation of vulnerability severity magnitudes. This approach decreases the error rate induced by manual calculation processes, that are traditionally used in cybersecurity analysis. Popular repositories such as NVD and SecurityFocus are employed to validate the proposed approach, assisted with a query method to retrieve vulnerability instances. In doing so, we report discovered correlations among reported vulnerability scores to infer consistent magnitude values of vulnerability instances. The method is applied to a case study featuring a CPS application to illustrate the automation of the proposed vulnerability scoring mechanism, used to mitigate cybersecurity weaknesses.
Imagining Better Futures for Everybody – Sustainable Entrepreneurship Education for Future Design Protagonists

Previous research has shown the need for children to grow to ‘design protagonists’, developing skills and competences in technology design and being able to reflect on the role of technology in their own lives as well as in the society. Entrepreneurship understanding has been linked as one important competence for design protagonists, for having skills to plan for and reflect on a better future for all of us. To advance understanding on how to support children to grow to design protagonists in areas connected to technology, business, and sustainability, we conducted a study with 14-15-year-olds, educating them first about artificial intelligence (AI) and sustainable business thinking, and asking them then to create and present business ideas for sustainable innovations that use AI. Based on the findings, we indicate the need to link systems thinking approaches with this type of education and we provide some recommendations for practice.
Verifying Dynamic Trait Objects in Rust

Rust has risen in prominence as a systems programming language in large part due to its focus on reliability. The language's advanced type system and borrow checker eliminate certain classes of memory safety violations. But for critical pieces of code, teams need assurance beyond what the type checker alone can provide. Verification tools for Rust can check other properties, from memory faults in unsafe Rust code to user-defined correctness assertions. This paper particularly focuses on the challenges in reasoning about Rust's dynamic trait objects, a feature that provides dynamic dispatch for function abstractions. While the explicit dyn keyword that denotes dynamic dispatch is used in 37% of the 500 most-downloaded Rust libraries (crates), dynamic dispatch is implicitly linked into 70%. To our knowledge, our open-source Kani Rust Verifier is the first symbolic modeling checking tool for Rust that can verify correctness while supporting the breadth of dynamic trait objects, including dynamically dispatched closures. We show how our system uses semantic trait information from Rust's Mid-level Intermediate Representation (an advantage over targeting a language-agnostic level such as LLVM) to improve verification performance by 5%–15× for examples from open-source virtualization software. Finally, we share an open-source suite of verification test cases for dynamic trait objects.
Video frame prediction with dual-stream deep network emphasizing motions and content details

Video frame prediction is both challenging and critical for computer vision. Though the research on predicting video frames has gradually shifted from pixel-law based methods to motion based ones, existing predictors often generate ambiguous future frames, especially for long-term predictions. This paper proposes a composed model to generate future frames with more details. First, to further exploit motion information, we design a single motion decoder to strengthen the efficiency of the motion encoder in the original motion-content network (MCnet). Second, to alleviate prediction ambiguousness, we use both edges with and without semantic meanings from the holistically-nested edge detection (HED) module as content details. Third, based on the conclusion that the mean squared error (MSE) loss and the traditional generative adversarial learning framework cause the unsatisfied predictions of MCnet, we design a composite loss function that can guide our model to simultaneously focus on motions and content details. Also, based on the abovementioned conclusion, we finally embed our model in an improved generative adversarial network, which further enhances its performance. Experimental results on the benchmark KTH and UCF101 datasets show that our model outperforms the state-of-the-art predictors, such as the basic MCnet, the predictive neural network (PredNet), and the PredNet with a reduced-gate convolutional network (rgc-PredNet), in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM), especially for long-term video frame prediction.
Towards Dynamic Visual Servoing for Interaction Control and Moving Targets

In this work we present our results on dynamic visual servoing for the case of moving targets while also exploring the possibility of using such a controller for interaction with the environment. We illustrate the derivation of a feature space impedance controller for tracking a moving object as well as an Extended Kalman Filter based on the visual servoing kinematics for increasing the rate of the visual information and estimating the target velocity for both the cases of PBVS and IBVS with image point features. Simulations are carried out to validate the estimator performance during a Peg-in-Hole insertion task with a moving part. Experiments are also conducted on a real redundant manipulator with a low-cost wrist-mounted camera. Details on several implementation issues encountered during implementation are also discussed.
A Model for Analyzing Teaching Quality Data of Sports Faculties Based on Particle Swarm Optimization Neural Network

In this paper, we use a particle swarm optimization neural network algorithm to analyze the teaching data of physical education faculties and evaluate the quality of teaching in physical education faculties. By studying and analyzing the optimization problem of the weight parameters of convolutional neural network training, this paper designs a hybrid algorithm combining the improved PSO algorithm and the traditional gradient descent in the framework of the BP algorithm by using the gradient information of the loss function and the principle of group cooperative search through PSO algorithm. The hybrid algorithm takes the loss function as the objective function, based on the principle of the PSO algorithm, and optimizes the objective function by combining the gradient information of the loss function of the convolutional neural network. The convergence speed and global search ability of the algorithm are effectively improved while ensuring an acceptable increase in computation. The weight values of the three-level indicators of teacher teaching behavior, student learning behavior, and teaching environment relative to the teaching quality of physical education classroom are 0.106, 0.634, and 0.260, respectively, which shows that the dimension of student learning behavior has the highest weight value in the evaluation of physical education classroom teaching quality, followed by teaching environment and finally teacher teaching behavior. Teachers' teaching ability will affect the effect of teaching methods, and the stronger the teaching ability is, the better the selection and utilization of teaching methods can be optimized.
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels

Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations. Previous segmentation arts of learning with noisy labels merely perform a pixel-wise manner to preserve semantics, such as pixel-wise label correction, but neglect the pair-wise manner. In fact, we observe that the pair-wise manner capturing affinity relations between pixels can greatly reduce the label noise rate. Motivated by this observation, we present a novel perspective for noisy mitigation by incorporating both pixel-wise and pair-wise manners, where supervisions are derived from noisy class and affinity labels, respectively. Unifying the pixel-wise and pair-wise manners, we propose a robust Joint Class-Affinity Segmentation (JCAS) framework to combat label noise issues in medical image segmentation. Considering the affinity in pair-wise manner incorporates contextual dependencies, a differentiated affinity reasoning (DAR) module is devised to rectify the pixel-wise segmentation prediction by reasoning about intra-class and inter-class affinity relations. To further enhance the noise resistance, a class-affinity loss correction (CALC) strategy is designed to correct supervision signals via the modeled noise label distributions in class and affinity labels. Meanwhile, CALC strategy interacts the pixel-wise and pair-wise manners through the theoretically derived consistency regularization. Extensive experiments under both synthetic and real-world noisy labels corroborate the efficacy of the proposed JCAS framework with a minimum gap towards the upper bound performance. The source code is available at https://github.com/CityU-AIM-Group/JCAS .
Puncturable identity-based and attribute-based encryption from lattices

The concept of puncturable encryption (PE) was introduced by Green and Miers at IEEE S&P, 2015. PE allows recipients to update their decryption keys to revoke decryption capability for selected messages without communicating with senders. From the first instantiation, PE shows its essence for many interesting applications, such as asynchronous messaging systems, group messaging systems, public-key watermarking schemes, secure cloud emails, and many more. To eliminate the necessity of having a costly certificate verification process, Wei et al. introduced puncturable identity-based encryption (PIBE) at ESORICS, 2019. Unfortunately, till today, there is no PIBE, which can withstand quantum attack. In this paper, we aim to fill this gap in the literature by presenting the first constructions of PIBE, for both selective and adaptive identity, which are secure in the standard model based on the hardness of learning with errors problem. Finally, we extend the design idea of selectively secure PIBE to provide the first construction of lattice-based puncturable key-policy attribute-based encryption (KP-PABE), which can facilitate fine-grained access mechanism with decryption revocation. Design ideas of proposed constructions can, also, be useful to construct other lattice-based expressive PE.
Construction of Wireless Sensor Network Video Surveillance System for Multimedia Classroom Education and Teaching under 5G Communication Network

Research on wireless sensor network video surveillance system is a hot field of media classroom education and teaching. Based on the 5G communication network theory, this paper constructs a wireless sensor network video monitoring system for multimedia classroom education and teaching, systematically analyzes the new distance education model under the condition of the integration of the two networks, and improves the interactive live classroom education model. For the classroom teaching mode, the model studies the isochronous transmission technology of data collection and video encoding and decoding, adopts hardware compression encoding, integrates key technologies such as reliable multicast and conditional access, and proposes the design of the system scheme, which solves the problem of multimedia classroom education and teaching digital issues. During the simulation process, the MATLAB software platform was used to study the effect of the number of 5G communication network nodes and node attributes (such as node position, node perception angle, instantaneous perception direction, or direction difference between neighboring nodes, node speed, etc.) on the target area or target point. The experimental results show that the performance of the joint optimization of transmission video quality and network lifetime performs well under multipath conditions. The total utility under multipath finally converges to 8.3, while the single path finally converges to 6.5, which further promotes the real-time performance of the multimedia network classroom teaching system.
Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis

An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance the analysis performance. We propose an unsupervised deep learning model for whole-slide image diagnosis, which uses stacked autoencoders simultaneously feeding multiple-image descriptors such as the histogram of oriented gradients and local binary patterns along with the original image to fuse the heterogeneous features. The pre-trained latent vectors are extracted from each autoencoder, and these fused feature representations are utilized for classification. We observed that training with additional descriptors helps the model to overcome the limitations of multiple variants and the intricate cellular structure of histopathology data by various experiments. Our model outperforms existing state-of-the-art approaches by achieving the highest accuracies of 87.2 for ICIAR2018, 94.6 for Dartmouth, and other significant metrics for public benchmark datasets. Our model does not rely on a specific set of pre-trained features based on classifiers to achieve high performance. Unsupervised spaces are learned from the number of independent multiple descriptors and can be used with different variants of classifiers to classify cancer diseases from whole-slide images. Furthermore, we found that the proposed model classifies the types of breast and lung cancer similar to the viewpoint of pathologists by visualization. We also designed our whole-slide image processing toolbox to extract and process the patches from whole-slide images.
Mutual information maximizing GAN inversion for real face with identity preservation

Recent generative adversarial networks (GANs) have yielded remarkable performance in face image synthesis. GAN inversion embeds an image into the latent space of a pretrained generator, enabling it to be used for real face manipulation. However, current inversion approaches for real faces suffer the dilemma of initialization collapse and identity loss. In this paper, we propose a hierarchical GAN inversion for real faces with identity preservation based on mutual information maximization. We first use a facial domain guaranteed initialization to avoid the initialization collapse. Furthermore, we prove that maximizing the mutual information between inverted faces and their identities is equivalent to minimizing the distance between identity features from inverted and original faces. Optimization for real face inversion with identity preservation is implemented on this mutual information-maximizing constraint. Extensive experimental results show that our approach outperforms state-of-the-art solutions for inverting and editing real faces, particularly in terms of face identity preservation.