Reviewerly/exHarmony huggingface dataset:
Learning to Class-Adaptively Manipulate Embeddings for Few-Shot Learning

In few-shot learning (FSL), meta-learning approach (MLA) mainly focuses on learning transferable knowledge from plenty of auxiliary FSL tasks to facilitate fast generalization to a new task. For a given FSL task, due to the inter-class distribution discrepancy, each class necessitates a specific embedding (i.e., a mapping function) to map samples into an ideal semantic space where samples from this class can be well separately from other classes. Moreover, these embeddings may vary with different tasks. Hence, one crucial knowledge for MLA is how to separately construct optimal embeddings for each class based on a few training samples given in a FSL task. However, most existing MLAs rarely consider this and thus show limited generalization capacity. To mitigate this problem, instead of directly construct class-adaptive embeddings, we present a new MLA that aims at learning to class-adaptively manipulate the features of samples for accurate classification in a new FSL task. In a specific, for a new FSL task, the proposed MLA first learns to generate some class-specific weights based on training samples via exploiting the inter-class distribution discrepancy between this class and the others. Then, the generated weights are utilized to compute the Hadamard product of features produced by a task-agnostic embedding module. By doing this, the proposed MLA can dynamically enhance or depress some specific semantic dimensions of sample features depending on the distribution of each class for accurate classification, and thus equals to constructing class-adaptive embeddings for each class but in a simpler way which can appropriately avoid over-fitting and is scalable to cases with extensive classes. To show its efficacy, we test the proposed MLA on four benchmark FSL datasets under various settings and report superior performance over existing state-of-the-arts.
Debiased Video-Text Retrieval via Soft Positive Sample Calibration

With the emergence of enormous videos on various video apps, semantic video-text retrieval has become a critical task for improving the user experience. The primary paradigm for video-text retrieval learns the semantic video-text representations in a common space by pulling the positive samples close to the query and pushing the negative samples away. However, in practice, the video-text datasets contain only the annotations of positive samples. The negative samples are randomly drawn from the entire dataset. There may exist soft positive samples, which are sampled as negatives but share the same semantics as positive samples. Indiscriminately enforcing the model to push all the negative samples away from the query leads to inaccurate supervision and then misleads the video-text feature representation learning. In this paper, we introduce debiased video-text retrieval objectives that calibrate the punishment of soft positive samples. In particular, we propose a novel uncertainty measure framework to estimate the credibility of negative samples for each instance. Then, the reliability of negative samples is used to find the soft positive samples and rescale their contribution within video-text retrieval losses, including triplet loss and contrastive loss. Experimental results on five widely used datasets demonstrate that our debiased video-text retrieval objectives achieve significant performance improvements and establish a new state-of-the-art.
Distributed Optical Fiber Sensing Event Recognition Based on Markov Transition Field and Knowledge Distillation

We propose a distributed optical fiber sensing event recognition scheme based on Markov Transition Field (MTF) and knowledge distillation. The event recognition algorithm has the advantages of being lightweight, fast, and high accuracy. The event data are converted into images by the MTF algorithm, which keeps the event signals correlated in the time domain while highlighting the visual differences between different events. A two-stage knowledge distillation model compression method is proposed, which effectively compresses the large-scale model into a lightweight model with optimal learning capability, ensuring the lightweight and efficient recognition of events by the compressed model (student model). The experimental results show that the student model improves the recognition rate of six events by 5.2% and achieves 96.6% event recognition accuracy by the two-stage knowledge distillation method. The size of the student model is only 1.4 MB, the number of parameters is only 0.35 M, and the FLOPs are only 0.17 G. The student model recognizes a single event in 0.129s on a low configuration device, which can meet the requirements of deployment and real-time monitoring of resource-limited devices.
Holistically-nested edge detection in chair ergonomic detection

To choose a chair that fits the anthropometry of the body, a system is needed that can determine whether the chair has good ergonomics for the body of its use or not. This research will apply Holistically-Nested Edge Detection (HED) for the edge detection process on each part of the chair, namely the back, seat, chair legs. The results of the HED method will be in the Find Contour process to get the dimensions of the chair. After obtaining the dimensions, it will be compared with the user's body parameters to determine the ergonomics of the chair. The optimal distance and camera angle were tested to determine the level of system accuracy in determining the dimensions of the chair, as well as testing in determining the level of ergonomics to determine the accuracy of the system in determining the ergonomics of the chair. In the optimal distance test, a distance of 1 meter has the highest accuracy, which is 99.43%. In the camera angle test, the 45 degree camera angle has the highest accuracy, which is 99.69%. In the test in determining the level of ergonomics of the chair, the system obtained an accuracy of 95.92%.
HAR-time: human action recognition with time factor analysis on worker operating time

This research proposed a data analysis framework applying human action recognition (HAR) with filtering processing to recognize human workers’ actions and further calculate the operating time of each action. When applying HAR to recognize the worker’s operation actions on a manufacturing site, the repeated and reciprocating actions easily cause misrecognition. Without the separation of actions in advance, the evaluation of HAR in real-world manufacturing is different from the evaluation based on the open data in the literature. To resolve the practical issues of applying HAR on a manufacturing site, first, the spatial-temporal skeleton coordinates of human joints were generated as the input of the skeleton-based spatial-temporal graph convolutional neural network (ST-GCN) for training the recognition model. Once the human action is recognized in each frame, the filtering processing considering the reference correction and accumulative moving mode (AMM) was proposed to improve the recognition accuracy. The real-world case study of the forging industry was conducted, and the empirical result shows the proposed framework was able to detect human workers’ repeated actions with 89% accuracy. Also, the time analysis based on HAR-Time recognition can correctly detect the associated time features that have highly correlated to the low quality of the product.
Do Javanese textbooks convey relevant material? Evidence of readability and value of learning outcomes

This study is aimed to analyze the readability level of the fourth grade textbooks in Javanese language used in Madiun, East Java, Indonesia then then connect with student learning outcomes. The data were obtained from 18 students, comprising 6 male and 12 female students between 10 and 12 years old. The secondary data were obtained from textbooks and worksheets used in primary schools. The data were analyzed using the Fry's Graph and the Klose's technique to determine the readability level of the textbooks. The results of data analysis indicated that the Javanese language textbooks had a score of 9.075, meaning that the textbooks are too difficult for the fourth grade students. This results in students having difficulty absorbing the material, so that students' learning outcomes in Javanese are below the required standard. The success of learning the Javanese language depends on an appropriate level of readability.
UHated: hate speech detection in Urdu language using transfer learning

Social media has become a driving force for social change in the global society. Events that take place in one part of the world can quickly reverberate across the globe due to the vast amount of data generated on these platforms. However, developers of these platforms face numerous challenges in keeping cyberspace as inclusive and healthy as possible. In recent years, there has been an increase in offensive and hate speech on social media. Manual efforts to address this issue have been inadequate due to the vast scope of the problem. Therefore, there is a need for an automated technique that can detect and remove offensive and hateful comments before they can cause harm. In this research, we use transfer learning to utilize pre-trained FastText Urdu word embeddings and multi-lingual BERT embeddings (RoBERTa) for our task. We also develop an Urdu language hate lexicon and use it to create an annotated dataset of 7800 Urdu tweets. Our results show that RoBERTa is able to achieve a macro F1-score of 0.82 on our multi-class classification task, outperforming deep learning and machine learning baseline models.
Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions

Recent work presented a framework for space-time-resolved neurophysiological process imaging that augments existing electromagnetic source imaging techniques. In particular, a nonlinear Analytic Kalman filter (AKF) has been developed to efficiently infer the states and parameters of neural mass models believed to underlie the generation of electromagnetic source currents. Unfortunately, as the initialization determines the performance of the Kalman filter, and the ground truth is typically unavailable for initialization, this framework might produce suboptimal results unless significant effort is spent on tuning the initialization. Notably, the relation between the initialization and overall filter performance is only given implicitly and is expensive to evaluate; implying that conventional optimization techniques, e.g. gradient or sampling based, are inapplicable. To address this problem, a novel efficient framework based on blackbox optimization has been developed to find the optimal initialization by reducing the signal prediction error. Multiple state-of-the-art optimization methods were compared and distinctively, Gaussian process optimization decreased the objective function by 82.1% and parameter estimation error by 62.5% on average with the simulation data compared to no optimization applied. The framework took only 1.6[Formula: see text]h and reduced the objective function by an average of 13.2% on 3.75[Formula: see text]min 4714-source channel magnetoencephalography data. This yields an improved method of neurophysiological process imaging that can be used to uncover complex underpinnings of brain dynamics.
NFT Legal and Market Challenges in Permissioned Blockchain Networks

This work analyses the distributed-ledger technology as a digital framework for the representation and exercise of tokenized rights, as well as the private-law characterization and EU regimen applicable to non-fungible tokens (NFTs). After a brief introduction to permissioning in blockchain and to permissioned distributed ledgers (PDLs), we reflect on its relevance to NFT contracting and markets, focusing on the legal and economic consequences of NFT trading in PDLs, with particular emphasis on public permissioned networks (PPDLs). We then discuss key challenges about law-compliant trading for NFTs, such as proper legal qualification of their nature, determination of optimal conditions for the legal trading of NFT underlying assets and investing applicable regime, including specific primary-market and essential public surveillance and intellectual-property (IP) issues.
A Gradient-Based Particle-Bat Algorithm for Stochastic Configuration Network

Stochastic configuration network (SCN) is a mathematical model of incremental generation under a supervision mechanism, which has universal approximation property and advantages in data modeling. However, the efficiency of SCN is affected by some network parameters. An optimized searching algorithm for the input weights and biases is proposed in this paper. An optimization model with constraints is first established based on the convergence theory and inequality supervision mechanism of SCN; Then, a hybrid bat-particle swarm optimization algorithm (G-BAPSO) based on gradient information is proposed under the framework of PSO algorithm, which mainly uses gradient information and local adaptive adjustment mechanism characterized by pulse emission frequency to improve the searching ability. The algorithm optimizes the input weights and biases to improve the convergence rate of the network. Simulation results over some datasets demonstrate the feasibility and validity of the proposed algorithm. The training RMSE of G-BAPSO-SCN increased by 5.57×10−5 and 3.2×10−3 compared with that of SCN in the two regression experiments, and the recognition accuracy of G-BAPSO-SCN increased by 0.07% on average in the classification experiments.
A Systematic Literature Review of Latent Variable Mixture Modeling in Communication Scholarship

Recently, latent variable mixture modeling has gained traction in many disciplines, given its unique ability to discover unknown groups within a broader population. Indeed, this method assumes that a finite number of mixtures (i.e. unknown groups) exist within the population and can be discovered by evaluating participants’ response patterns to a set of manifest indicators. Despite the intuitive approach, recommendations have been proposed to overcome some methodological concerns associated with latent variable mixture modeling. The primary purpose of this study was to understand the characteristics of latent variable mixture modeling in communication research and to evaluate the extent to which the existing research meets these recommendations. Ninety-five manuscripts published between 2010 and 2022 in 18 communication journals were identified and systematically analyzed. The review found that (1) the use of latent variable mixture modeling has increased; (2) latent class analysis and latent profile analysis are the most common models; and (3) most manuscripts did not meet the proscribed standards for random start values, auxiliary variable procedures, indicator requirements, and missing data procedures. These findings are discussed more in comparison with the proscribed standards. In addition, conceptual and applicable recommendations are provided to improve communication scholarship.
Optimal blending of multiple independent prediction models

We derive blending coefficients for the optimal blend of multiple independent prediction models with normal (Gaussian) distribution as well as the variance of the final blend. We also provide lower and upper bound estimation for the final variance and we compare these results with machine learning with counts, where only binary information (feature says yes or no only) is used for every feature and the majority of features agreeing together make the decision.
Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding.
Event Log Privacy Based on Differential Petri Nets

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Deterrence, Backup, or Insurance: Game-Theoretic Modeling of Ransomware

In this paper, we present a game-theoretic analysis of ransomware. To this end, we provide theoretical and empirical analysis of a two-player Attacker-Defender (A-D) game, as well as a Defender-Insurer (D-I) game; in the latter, the attacker is assumed to be a non-strategic third party. Our model assumes that the defender can invest in two types of protection against ransomware attacks: (1) general protection through a deterrence effort, making attacks less likely to succeed, and (2) a backup effort serving the purpose of recourse, allowing the defender to recover from successful attacks. The attacker then decides on a ransom amount in the event of a successful attack, with the defender choosing to pay ransom immediately, or to try to recover their data first while bearing a recovery cost for this recovery attempt. Note that recovery is not guaranteed to be successful, which may eventually lead to the defender paying the demanded ransom. Our analysis of the A-D game shows that the equilibrium falls into one of three scenarios: (1) the defender will pay the ransom immediately without having invested any effort in backup, (2) the defender will pay the ransom while leveraging backups as a credible threat to force a lower ransom demand, and (3) the defender will try to recover data, only paying the ransom when recovery fails. We observe that the backup effort will be entirely abandoned when recovery is too expensive, leading to the (worst-case) first scenario which rules out recovery. Furthermore, our analysis of the D-I game suggests that the introduction of insurance leads to moral hazard as expected, with the defender reducing their efforts; less obvious is the interesting observation that this reduction is mostly in their backup effort.
Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy-a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method-a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.
Viewpoint planning with transition management for active object recognition

Active object recognition (AOR) provides a paradigm where an agent can capture additional evidence by purposefully changing its viewpoint to improve the quality of recognition. One of the most concerned problems in AOR is viewpoint planning (VP) which refers to developing a policy to determine the next viewpoints of the agent. A research trend is to solve the VP problem with reinforcement learning, namely to use the viewpoint transitions explored by the agent to train the VP policy. However, most research discards the trained transitions, which may lead to an inefficient use of the explored transitions. To solve this challenge, we present a novel VP method with transition management based on reinforcement learning, which can reuse the explored viewpoint transitions. To be specific, a learning framework of the VP policy is first established via the deterministic policy gradient theory, which provides an opportunity to reuse the explored transitions. Then, we design a scheme of viewpoint transition management that can store the explored transitions and decide which transitions are used for the policy learning. Finally, within the framework, we develop an algorithm based on twin delayed deep deterministic policy gradient and the designed scheme to train the VP policy. Experiments on the public and challenging dataset GERMS show the effectiveness of our method in comparison with several competing approaches.