Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition Using Pose-Guided Face Alignment and Discriminative Features Based on Deep Learning

Face expression recognition is a key technology of robot vision, which can help the robotic understand human emotions. However, interference from the real-world, such as light changes, face occlusion, and pose variation, reduces the recognition rate of the model. To solve above problems, in this paper, a novel deep model is proposed to improve the classification accuracy of facial expressions. The proposed model has the following merits: 1) A pose-guided face alignment method is proposed to reduce the intra-class difference, which can overcome the impact of environmental noise; 2) A hybrid feature representation method is proposed to obtain high-level discriminative facial features that achieves better results in classification networks; 3) A lightweight fusion backbone is designed, which combines the VGG-16 and the ResNet to achieve low-data and low-calculation training. Finally, to evaluate the proposed model, we conduct a series of experiments on four benchmark datasets, including the CK+, the JAFFE, the Oulu-CASIA, and the AR. The results show that the proposed model achieves state-of-the-art recognition rates, that is, 98.9%, 96.8%, 94.5%, and 98.7%, respectively. Comparing with the traditional methods and other advanced deep learning methods, the proposed model can comparable performance in a variety of tasks.
On Finite-Time Convergence of Actor-Critic Algorithm

Actor-critic algorithm and their extensions have made great achievements in real-world decision-making problems. In contrast to its empirical success, the theoretical understanding of the actor-critic seems unsatisfactory. Most existing results only show the asymptotic convergence, which is developed mainly based on approximating the dynamic system of the actor and critic using ordinary differential equations. However, the finite-time convergence analysis of the actor-critic algorithm remains to be explored. The main challenges lie in the nonconvexity of parameterized policies, the coupling of the updates for actor and critic, and the data sampling dependency in online settings. In this paper, we provide a finite-time convergence analysis for an online actor-critic algorithm under the infinite-horizon average reward setting. In the critic step, we give a theoretical analysis of the TD(0) algorithm for the average reward with dependent data in online settings. Besides, we show that the sequence of actor iterates converges in a sublinear rate to a stationary point up to some irremovable bias due to the value function approximation by linear functions. To the best of our knowledge, our work seems to provide the first finite-time convergence analysis for an online actor-critic algorithm with TD learning.
Better, Funner, Stronger: A Gameful Approach to Nudge People into Making Less Predictable Graphical Password Choices

Graphical user authentication (GUA) is a common alternative to text-based user authentication, where people are required to draw graphical passwords on background images. Such schemes are theoretically considered remarkably secure because they offer a large password space. However, people tend to create their passwords on salient image areas introducing high password predictability. Aiming to help people use the password space more effectively, we propose a gameful password creation process. In this paper, we present GamePass, a gamified mechanism that integrates the GUA password creation process. We provide the first evidence that it is possible to nudge people towards better password choices by gamifying the process. GamePass randomly guides participants' attention to areas other than the salient areas of authentication images, makes the password creation process more fun, and people are more engaged. Gamifying the password creation process enables users to interact better and make less predictable graphical password choices instead of being forced to use a strict password policy.
Factor-Bounded Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems. Traditional optimization methods, such as the Multiplicative Updating Algorithm (MUA), solves the NMF problem by utilizing an auxiliary function to ensure that the objective monotonically decreases. Although the objective in MUA converges, there exists no proof to show that the learned matrix factors converge as well. Without this rigorous analysis, the clustering performance and stability of the NMF algorithms cannot be guaranteed. To address this knowledge gap, in this article, we study the factor-bounded NMF problem and provide a solution algorithm with proven convergence by rigorous mathematical analysis, which ensures that both the objective and matrix factors converge. In addition, we show the relationship between MUA and our solution followed by an analysis of the convergence of MUA. Experiments on both toy data and real-world datasets validate the correctness of our proposed method and its utility as an effective clustering algorithm.
Contract-Less Mobile Data Access Beyond 5G: Fully-Decentralized, High-Throughput and Anonymous Asset Trading Over the Blockchain

The new “status quo” created by the emerging integration of Multi-Access Edge Computing (MEC) and Radio Access Network (RAN) infrastructures, combined with the openness of the 5G service market, have set new standards on how the multitude of 5G service domains should be incentivized and coordinated to comply with the performance requirements set on a per session basis. In this paper, we revisit the predominant offline contract-driven mobile data access model enabling users to gain access to the infrastructure of only a few network operators and propose a new blockchain-backed mobile data access model where the key 5G stakeholders can trade, share and consume mobile edge network assets (access to the internet, spectrum, processing, storage, local content etc.) in a fully decentralized, anonymous and highly-robust fashion. Blockchain-backed mobile data access should address critical practical challenges of blockchain decentralization, scalability and security in the context of 5G and Beyond networks. Accordingly, we provide a meticulous survey of existing solutions in the aforementioned three areas and present a comprehensive holistic framework of protocols to address the key challenges identified, including a delegated Proof-of-Stake (DPoS) for distributed blockchain consensus over 5G and Beyond mobile data networks, a future-proof smart-contract enabled payment relay service enabling instant off-chain payments in a highly robust fashion as well as a hybrid mixing protocol that enables payment relays to act as anonymous payment hubs while addressing the unique challenges opposed by the joint blockchain and network level interaction of the 5G and Beyond service components. A wide range of practical implications and mitigation measures for dishonest operation of the blockchain nodes are investigated and sophisticated yet highly-robust incentive engineering mechanisms are derived. Detailed numerical results also accompany the paper, bringing to light the unique performance trade-offs and valuable design guidelines towards blockchain-backed mobile data access for 5G and Beyond mobile data networks.
Biases in AI Systems

This article provides an organization of various kinds of biases that can occur in the AI pipeline starting from dataset creation and problem formulation to data analysis and evaluation. It highlights the challenges associated with the design of bias-mitigation strategies, and it outlines some best practices suggested by researchers. Finally, a set of guidelines is presented that could aid ML developers in identifying potential sources of bias, as well as avoiding the introduction of unwanted biases. The work is meant to serve as an educational resource for ML developers in handling and addressing issues related to bias in AI systems.
Bringing the Jury to the Scene of the Crime: Memory and Decision-Making in a Simulated Crime Scene

This paper investigates the use of immersive virtual reconstructions as an aid for jurors during a courtroom trial. The findings of a between-participant user study on memory and decision-making are presented in the context of viewing a simulated hit-run-death scenario. Participants listened to the opening statement of a prosecutor and a defence attorney before viewing the crime scene in Virtual Reality (VR) or as still images. We compare the effects on cognition and usability of using VR over images presented on a screen. We found several significant improvements, including that VR led to more consistent decision-making among participants. This shows that VR could provide a promising solution for the court to present crime scenes when site visitations are not possible.
Adaptive Linear Span Network for Object Skeleton Detection

Conventional networks for object skeleton detection are usually hand-crafted. Despite the effectiveness, hand-crafted network architectures lack the theoretical basis and require intensive prior knowledge to implement representation complementarity for objects/parts in different granularity. In this paper, we propose an adaptive linear span network (AdaLSN), driven by neural architecture search (NAS), to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN is formulated with the theory of linear span, which provides one of the earliest explanations for multi-scale deep feature fusion. AdaLSN is materialized by defining a mixed unit-pyramid search space, which goes beyond many existing search spaces using unit-level or pyramid-level features. Within the mixed space, we apply genetic architecture search to jointly optimize unit-level operations and pyramid-level connections for adaptive feature space expansion. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off compared with the state-of-the-arts. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction. Code is available at https://github.com/sunsmarterjie/SDL-Skeletongithub.com/sunsmarterjie/SDL-Skeleton.
Learn to Navigate Autonomously Through Deep Reinforcement Learning

In this article, we propose a deep reinforcement learning (DRL) algorithm as well as a novel tailored neural network architecture for mobile robots to learn navigation policies autonomously. We first introduce a new feature extractor to better acquire critical spatiotemporal features from raw depth images. In addition, we present a double-source scheme so that the experiences are collected from both the proposed model and a conventional planner alternatively based on a switching criterion to provide more diverse and comprehensive samples for learning. Moreover, we also propose a dual-soft-actor–critic architecture to train two sets of networks with different purposes simultaneously. Specifically, the primary network aims to learn the autonomous navigation policy, while the secondary network aims to learn the depth feature extractor. In this way, the learning performance can be improved through decoupling representation learning from policy learning and training the feature extractor separately with more specific goals. Experimental results have demonstrated the remarkable performance of the proposed model. It outperforms both the conventional and the state-of-the-art DRL-based methods consistently in terms of success rate. Meanwhile, it also exhibits higher trajectory quality and better generalization capability compared to existing DRL-based methods. Videos of our experiments are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/evjU6bOU3UY</uri> or OneDrive.
Cross-Project Defect Prediction via Landmark Selection-Based Kernelized Discriminant Subspace Alignment

Cross-project defect prediction (CPDP) refers to identifying defect-prone software modules in one project (target) using historical data collected from other projects (source), which can help developers find bugs and prioritize their testing efforts. Recently, CPDP has attracted great research interest. However, the source and target data usually exist redundancy and nonlinearity characteristics. Besides, most CPDP methods do not exploit source label information to uncover the underlying knowledge for label propagation. These factors usually lead to unsatisfactory CPDP performance. To address the above limitations, we propose a landmark selection-based kernelized discriminant subspace alignment (LSKDSA) approach for CPDP. LSKDSA not only reduces the discrepancy of the data distributions between the source and target projects, but also characterizes the complex data structures and increases the probability of linear separability of the data. Moreover, LSKDSA encodes label information of the source data into domain adaptation learning process and makes itself with good discriminant ability. Extensive experiments on 13 public projects from three benchmark datasets demonstrate that LSKDSA performs better than a range of competing CPDP methods. The improvement is 3.44%-11.23% in g-measure, 5.75%-11.76% in AUC, and 9.34%-33.63% in MCC, respectively.
Radi-Eye: Hands-Free Radial Interfaces for 3D Interaction using Gaze-Activated Head-Crossing

Eye gaze and head movement are attractive for hands-free 3D interaction in head-mounted displays, but existing interfaces afford only limited control. Radi-Eye is a novel pop-up radial interface designed to maximise expressiveness with input from only the eyes and head. Radi-Eye provides widgets for discrete and continuous input and scales to support larger feature sets. Widgets can be selected with Look & Cross, using gaze for pre-selection followed by head-crossing as trigger and for manipulation. The technique leverages natural eye-head coordination where eye and head move at an offset unless explicitly brought into alignment, enabling interaction without risk of unintended input. We explore Radi-Eye in three augmented and virtual reality applications, and evaluate the effect of radial interface scale and orientation on performance with Look & Cross. The results show that Radi-Eye provides users with fast and accurate input while opening up a new design space for hands-free fluid interaction.
Two-timescale neurodynamic approaches to supervised feature selection based on alternative problem formulations

Feature selection is a crucial step in data processing and machine learning. While many greedy and sequential feature selection approaches are available, a holistic neurodynamics approach to supervised feature selection is recently developed via fractional programming by minimizing feature redundancy and maximizing relevance simultaneously. In view that the gradient of the fractional objective function is also fractional, alternative problem formulations are desirable to obviate the fractional complexity. In this paper, the fractional programming problem formulation is equivalently reformulated as bilevel and bilinear programming problems without using any fractional function. Two two-timescale projection neural networks are adapted for solving the reformulated problems. Experimental results on six benchmark datasets are elaborated to demonstrate the global convergence and high classification performance of the proposed neurodynamic approaches in comparison with six mainstream feature selection approaches.
Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network

Artificial neural networks (ANNs) have been used over the last few decades to perform tasks by learning with comparisons. Fitting input-output models, system identification, control, and pattern recognition are some fields for ANN applications. However, problems involving uncertain situations could be challenging for them. The family of paraconsistent logics (PL) is a powerful tool that can deal with uncertainty and contradictory information, so getting attention from researchers for its implications and applications in artificial intelligence. This investigation describes a novel activation function reasoned on the paraconsistent annotated logic by two-value annotations (PAL2v) rules, a variation of PL, allowing the design of a new paraconsistent neural net (PNN), applied in model identification for control (I4C) of a closed-loop rotary inverted pendulum (RIP) system.
REDAT: Accent-Invariant Representation for End-To-End ASR by Domain Adversarial Training with Relabeling

Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address this problem by building an accent-robust RNN-T system with domain adversarial training (DAT). We unveil the magic behind DAT and provide, for the first time, a theoretical guarantee that DAT learns accent-invariant representations. We also prove that performing the gradient reversal in DAT is equivalent to minimizing the Jensen-Shannon divergence between domain output distributions. Motivated by the proof of equivalence, we introduce reDAT, a novel technique based on DAT, which relabels data using either unsupervised clustering or soft labels. Experiments on 23K hours of multi-accent data show that DAT achieves competitive results over accent-specific baselines on both native and non-native English accents but up to 13% relative WER reduction on unseen accents; our reDAT yields further improvements over DAT by 3% and 8% relatively on non-native accents of American and British English.
ToonNote: Improving Communication in Computational Notebooks Using Interactive Data Comics

Computational notebooks help data analysts analyze and visualize datasets, and share analysis procedures and outputs. However, notebooks typically combine code (e.g., Python scripts), notes, and outputs (e.g., tables, graphs). The combination of disparate materials is known to hinder the comprehension of notebooks, making it difficult for analysts to collaborate with other analysts unfamiliar with the dataset. To mitigate this problem, we introduce ToonNote, a JupyterLab extension that enables the conversion of notebooks into "data comics." ToonNote provides a simplified view of a Jupyter notebook, highlighting the most important results while supporting interactive and free exploration of the dataset. This paper presents the results of a formative study that motivated the system, its implementation, and an evaluation with 12 users, demonstrating the effectiveness of the produced comics. We discuss how our findings inform the future design of interfaces for computational notebooks and features to support diverse collaborators.
Anomalicious: Automated Detection of Anomalous and Potentially Malicious Commits on GitHub

Security is critical to the adoption of open source software (OSS), yet few automated solutions currently exist to help detect and prevent <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">malicious</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contributions</i> from infecting open source repositories. On GitHub, a primary host of OSS, repositories contain not only code but also a wealth of commitrelated and contextual metadata - <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">if</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">this</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">metadata</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">could</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">be</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">used</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">to</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatically</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">identify</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">malicious</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OSS</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contributions</i> ? In this work, we show how to use only commit logs and repository metadata to automatically detect anomalous and potentially malicious commits. We identify and evaluate several relevant factors which can be automatically computed from this data, such as the modification of sensitive files, outlier change properties, or a lack of trust in the commit's author. Our tool, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Anomalicious</i> , automatically computes these factors and considers them holistically using a rule-based decision model. In an evaluation on a data set of 15 malware-infected repositories, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Anomalicious</i> showed promising results and identified 53.33% of malicious commits, while flagging less than 1% of commits for most repositories. Additionally, the tool found other interesting anomalies that are not related to malicious commits in an analysis of repositories with no known malicious commits.
Exploring the Use of a Voice-based Conversational Agent to Empower Adolescents with Autism Spectrum Disorder

Voice-based Conversational Agents (VCA) have served as personal assistants that support individuals with special needs. Adolescents with Autism Spectrum Disorder (ASD) may also benefit from VCAs to deal with their everyday needs and challenges, ranging from self-care to social communications. In this study, we explored how VCAs could encourage adolescents with ASD in navigating various aspects of their daily lives through the two-week use of VCAs and a series of participatory design workshops. Our findings demonstrated that VCAs could be an engaging, empowering, emancipating tool that supports adolescents with ASD to address their needs, personalities, and expectations, such as promoting self-care skills, regulating negative emotions, and practicing conversational skills. We propose implications of using off-the-shelf technologies as a personal assistant to ASD users in Assistive Technology design. We suggest design implications for promoting positive opportunities while mitigating the remaining challenges of VCAs for adolescents with ASD.
A binary Sine Cosine-Modified Whale Optimization Algorithm for Feature Selection

The increase of many pillars within the dataset makes it needed to pick the best part of features. The feature selection approach directly influences the performance of the style in terms of integrity and computational information. The wrapper feature choice version deals with the function set to improve the category reliability. In this paper, a new wrapper feature selection binary formula is intended based upon the Sine Cosine Algorithm (SCA) and a modified Whale Optimization Algorithm (MWOA). This algorithm (Binary SC-MWOA) was associated with obtaining unassociated characteristics and selecting the optimum features. The proposed formula's attractive outcomes reveal the algorithm's performance for picking the best features. Ten different UCI Repository datasets are checked in the experiments.