Reviewerly/exHarmony huggingface dataset:
From writing to drawing: Examining visual composition in danmu-mediated textual communication

Danmu (anonymous superimposed video comments) is a popular form of communication on Chinese and Japanese video sharing sites. While previous studies primarily focused on the verbal aspects of danmu comments, there is a growing interest in exploring their multimodal features. This study investigates the unique potential of danmu comments to communicate visual meaning, interact with on-screen content, and thereby shape audience perception. Informed by a social semiotic approach to multimodality and relevant pragmatic theories, the study analyzed 50 screenshots of visually significant danmu comments to understand the resources used by commenters to craft visual comments and the relationship between these comments and the screen. Our findings revealed that four key resources were utilized to create visual comments: arrows, kaomoji, context-specific special characters and symbols, and ASCII art. Additionally, five types of relationships were identified between visual danmu comments and the screen, including deictic, emphasizing, complementing, extending, and independent. This study provides an up-to-date examination of the possibilities for visual expression in textual communication and extends previous research on semiotic resources in social media. It also discusses the role of danmu visual play as internet memes and the emergence of danmu visual grammar.
MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder

Rewrite systems [11, 16, 18] have been widely employing equality saturation [15], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one. As such, optimal results can be achieved by avoiding the phase-ordering problem. However, we observe that when the e-graph is not saturated, it cannot represent all possible rewrite opportunities and therefore the phase-ordering problem is re-introduced during the construction phase of the e-graph. To address this problem, we propose MCTS-GEB, a domain-general rewrite system that applies reinforcement learning (RL) to e-graph construction. At its core, MCTS-GEB uses a Monte Carlo Tree Search (MCTS) [4] to efficiently plan for the optimal e-graph construction, and therefore it can effectively eliminate the phase-ordering problem at the construction phase and achieve better performance within a reasonable time. Evaluation in two different domains shows MCTS-GEB can outperform the state-of-the-art rewrite systems by up to 49x, while the optimisation can generally take less than an hour, indicating MCTS-GEB is a promising building block for the future generation of rewrite systems.
DOME: Drone-assisted Monitoring of Emergent Events For Wildland Fire Resilience

We develop a Drone-assisted Monitoring system, DOME, that gathers real-time data for situational awareness in emergent and evolving events. The driving use case for this work is a prescribed burn event (Rx fire), often used to reduce hazardous fuels in forests. DOME coordinates the use of multiple heterogeneous drone platforms to support the observation of emergent physical phenomena (e.g., fire spread) by leveraging domain expert input and physics-based modeling/simulation methods. We propose an executable rule-based system for drone task generation; here, a high-level mission specification utilizes physics-based models for fire spread prediction and automatically generates monitoring instructions with locations, periods, and frequency for drones. DOME integrates algorithms for task allocation (mapping tasks to drones) and flight path planning while considering trade-offs between sensing coverage and accuracy. In addition, DOME will guide in-flight drones to store and upload data under challenged communication settings (out of transmission range, external signal blocking by trees). We evaluate the performance of DOME in real events (with expert-developed burn plans). We test the applicability of the DOME system using simulated Rx burns at the Blodgett Forest Research Station and evaluate our proposed algorithms by comparing their performance with multiple baseline algorithms. Our experiments show the effectiveness of the composite mechanisms in DOME that outperforms other approaches with higher rewards (capturing data of higher quality) and coverage (reduction of missed tasks).
Scalable High-Performance Architecture for Evolving Recommender System

Recommender systems are expected to scale to the requirement of the large number of recommendations made to the customers and to keep the latency of recommendations within a stringent limit. Such requirements make architecting a recommender system a challenge. This challenge is exacerbated when different ML/DL models are employed simultaneously. This paper presents how we accelerated a recommender system that contained a state-of-the-art Graph neural network (GNN) based DL model and a dot product-based ML model. The ML model was used offline, where its recommendations were cached, and the GNN-based model provided recommendations in real time. The merging of offline results with the results provided by the real-time session-based recommendation model again posed a challenge for latency. We could reduce the model's recommendation latency from 1.5 seconds to under 65 milliseconds with careful re-architecting. We also improved the throughput from 1 recommendation per second to 1500 recommendations per second on a VM with 16-core CPU and 64 GB RAM.
A Comparative Analysis of Linux Mandatory Access Control Policy Enforcement Mechanisms

Unix---and by extension, Linux---traditionally uses a discretionary access control (DAC) paradigm. DAC mechanisms are decentralized by design, which makes it difficult to audit the security of a computer system. Furthermore, Unix systems have the concept of a root user who can bypass any DAC policies in place. These issues led to the development of mandatory access control (MAC) mechanisms, such as AppArmor, Security-Enhanced Linux (SELinux), and eBPF.
Actionable Data Insights for Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) have made tremendous progress in the recent decade and have become ubiquitous in almost all application domains. Many recent advancements in the ease-of-use of ML frameworks and the low-code model training automations have further reduced the threshold for ML model building. As ML algorithms and pre-trained models become commodities, curating the appropriate training datasets and model evaluations remain critical challenges. However, these tasks are labor-intensive and require ML practitioners to have bespoke data skills. Based on the feedback from different ML projects, we built ADIML (Actionable Data Insights for ML) - a holistic data toolset. The goal is to democratize data-centric ML approaches by removing big data and distributed system barriers for engineers. We show in several case studies how the application of ADIML has helped solve specific data challenges and shorten the time to obtain actionable insights.
Towards A Platform and Benchmark Suite for Model Training on Dynamic Datasets

Machine learning (ML) is often applied in use cases where training data evolves and/or grows over time. Training must incorporate data changes for high model quality, however this is often challenging and expensive due to large datasets and models. In contrast, ML researchers often train and evaluate ML models on static datasets or with artificial assumptions about data dynamics. This gap between research and practice is largely due to (i) the absence of an open-source platform that manages dynamic datasets at scale and supports pluggable policies for when and what data to train on, and (ii) the lack of representative open-source benchmarks for ML training on dynamic datasets. To address this gap, we propose to design a platform that enables ML researchers and practitioners to explore training and data selection policies, while alleviating the burdens of managing large dynamic datasets and orchestrating recurring training jobs. We also propose to build an accompanying benchmark suite that integrates public dynamic datasets and ML models from a variety of representative use cases.
A First Look at the Impact of Distillation Hyper-Parameters in Federated Knowledge Distillation

Knowledge distillation has been known as a useful way for model compression. It has been recently adopted in the distributed training domain, such as federated learning, as a way to transfer knowledge between already pre-trained models. Knowledge distillation in distributed settings promises advantages, including significantly reducing the communication overhead and allowing heterogeneous model architectures. However, distillation is still not well studied and understood in such settings, which hinders the possible gains. We bridge this gap by performing an experimental analysis of the distillation process in the distributed training setting, mainly with non-IID data. We highlight some elements that require special considerations when transferring knowledge between already pre-trained models: the transfer set, the temperature, the weight, and the positioning. Appropriately tuning these hyper-parameters can remarkably boost learning outcomes. In our experiments, around two-thirds of the participants require settings other than commonly used default settings in literature, and appropriate tuning can reach more than five times improvement on average.
Distributed Training for Speech Recognition using Local Knowledge Aggregation and Knowledge Distillation in Heterogeneous Systems

Data privacy and data protection are crucial issues for automatic speech recognition (ASR) system when relying on client generated data for training. The best protection is achieved when training is distributed fashion, close to the client local data, rather than centralising the training. However, distributed training suffers from system heterogeneity, due to clients having unequal computation resources, and data heterogeneity, due to training data being non-independent and identically distributed (non-IID). To tackle these challenges, we introduce FedKAD, a Federated Learning (FL) framework that uses local Knowledge Aggregation over top level feature maps and Knowledge Distillation. We show that our FedKAD achieves better communication efficiency than standard FL methods that use uniform models, due to transferring parameters of smaller size client models, and overall better accuracy than FedMD, an alternative KD-based approach designed for heterogeneous data. Our work enables faster, cheaper and more inclusive participation of clients in heterogeneous distributed training.
Dynamic path planning of intelligent robot in power equipment maintenance environment

Aiming at the problem that the traditional path planning algorithm does not have real-time planning performance in the dynamic and complex power equipment maintenance environment, or it is challenging to avoid dynamic obstacle obstacles due to poor real-time version, this paper proposes an artificial potential field method for real-time planning. The characteristics of dynamic obstacle in a dynamic complex environment, the dynamic obstacles are simplified as moving particles, and the repulsion function of the dynamic obstacles is added to avoid the collision. Within the influence range of dynamic obstacles in a busy complex environment, the robot avoids the dynamic obstacles through real-time local path planning under the influence of the repulsive force of the dynamic obstacle to reach the target point. At the same time, aiming at the problems of unevenness and fluctuation of the path generated by the traditional artificial potential field method, the non-uniform rational B-spline and moving average filtering algorithm are used to improve the local path planning algorithm of the conventional artificial potential field, and the generated path is smoother and smoother than the traditional algorithm.
On the modeling of cyber-attacks associated with social engineering: A parental control prototype

Nowadays, the psychological techniques used to harass, intimidate, threaten, steal information are more common due to free access to technological resources and the digitization of communications. However, studies related to cybersecurity concerning social engineering techniques are still limited. Several factors such as access to specific databases on cyber-attacks, the unification of scientific criteria that evaluate the nature of the problem, or the absence of accurate proposals that prevent and mitigate this problem could motivate researchers’ lack of interest in information security to generate meaningful contributions. This research presents a cyber-attack modeling process defining its stages through topic modeling. This process is applied to model grooming and bullying cyber-attacks, in which the use of psychological manipulation techniques by the attackers was evidenced. The proposal allowed the detection of the attackers’ communicational intentions with high accuracy. Additionally, a functional parental control prototype that supports the proposed modeling process is presented.
Feature Extraction Method of EEG Signals Evaluating Spatial Cognition of Community Elderly With Permutation Conditional Mutual Information Common Space Model

In order to improve the traditional common space pattern (CSP) algorithm pattern in EEG feature extraction, this study proposes a feature extraction method of EEG signals based on permutation conditional mutual information common space pattern (PCMICSP), which used the sum of the permutation condition mutual information matrices of each lead to replacing the mixed spatial covariance matrix in the traditional CSP algorithm, and its eigenvectors and eigenvalues are used to construct a new spatial filter. Then the spatial features in the different time domains and frequency domains are combined to construct the two-dimensional pixel map, Finally, a convolutional neural network (CNN) is used for binary classification. The EEG signals of 7 community elderly before and after spatial cognitive training in virtual reality (VR) scenes were used as the test data set. The average classification accuracy of the PCMICSP algorithm for pre-test and post-test EEG signals is 98%, which was higher than that of CSP based on CMI (conditional mutual information), CSP based on MI (mutual information), and traditional CSP in the combination of four frequency bands. Compared with the traditional CSP method, PCMICSP can be used as a more effective method to extract the spatial features of EEG signals. Therefore, this paper provides a new approach to solving the strict linear hypothesis of CSP and can be used as a valuable biomarker for the spatial cognitive evaluation of the elderly in the community.
[A multi-behavior recognition method for macaques based on improved SlowFast network].

Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.猕猴作为药物安全性评估中常见的动物模型，其行为反映它用药前后的健康情况，可以揭示出药物产生的副作用。目前，研究人员通常利用人工对猕猴的行为进行观察，该方式无法实现不间断的24 h监测。因此，急需发展一种方法体系实现对猕猴行为的24 h观测和识别。针对此问题，本文构建了一个包含九类猕猴行为的视频数据集（MBVD-9），并在此数据集的基础上提出一种名为基于变换器（Transformer）增强的慢快网络（SlowFast）用于猕猴行为识别（TAS-MBR）。具体地说，TAS-MBR网络在SlowFast的基础上将其快支路输入的红绿蓝（RGB）色彩模式帧转化为残差帧，并在卷积操作之后引入了变换器模块，更有效地提取了运动信息。结果显示，TAS-MBR网络对猕猴行为的平均分类准确率为94.53%，较原有的SlowFast有明显提升，证明了本文所提方法在猕猴行为识别方面的有效性和优越性。这项工作为猕猴行为的不间断的观测和识别提供了新的思路，奠定了药物安全评价中进行用药前后猕猴行为计算的技术基础。.
Feasibility of a Cinematic-Virtual Reality Training Program for Health Professional Students: A Single-Arm Pre-Post Study

Cinematic-virtual reality (cine-VR) has demonstrated improvements in cultural self-efficacy, diabetes attitudes, and empathy among healthcare providers, but its impact on health professional students is unknown. The purpose of the single-arm pre-post study was to examine the feasibility of this cine-VR diabetes training program as well as to assess changes in cultural self-efficacy, diabetes attitudes, and empathy among health professional students.Participants viewed 12 cine-VR 12 simulations about a 72-year-old patient with type 2 diabetes. Pre-training and post-training, they completed the Transcultural Self-Efficacy Tool, Diabetes Attitude Scale-3, and Jefferson Scale of Empathy.All 92 participants completed the full training. No participants reported technological difficulties or adverse events. For the assessment, 66 participants completed the pre-post measures for a response rate of 71.7% (mean age = 21.1 ± 1.9 years, 82.6% [n = 57] women; 84.1% [n = 58] white). We observed positive improvements in all three cultural self-efficacy subscales: "Cognitive" (t value = -4.705, P < .001), "Practical" (mean change = -.99, t value = -4.240, P < .001), and "Affective" (t value = -2.763, P = .008). Similarly, we observed positive improvements in four of the five diabetes attitude subscales: "Need for special training" (Z = -4.281, P < .001), "Seriousness of type 2 diabetes" (Z = -3.951, P < .001), "Value of tight glucose control" (Z = -1.676, P = .094), "Psychosocial impact of diabetes" (Z = -5.892, P < .001), and "Attitude toward patient autonomy" (Z = -2.889, P = .005). Finally, we observed a positive improvement in empathy (t value = -5.151, P < .001).Findings suggest that the cine-VR diabetes training program has the potential to improve cultural self-efficacy, diabetes attitudes, and empathy among health professional students. A randomized controlled trial is needed to confirm its effectiveness.
DEMO: Self-Preserving Genetic Algorithms vs. Safe Reinforcement Learning in Discrete Action Spaces

Safe learning techniques are learning frameworks that take safety into consideration during the training process. Safe reinforcement learning (SRL) combines reinforcement learning (RL) with safety mechanisms such as action masking and run time assurance to protect an agent during the exploration of its environment. This protection, though, can severely hinder an agent's ability to learn optimal policies as the safety systems exacerbate an already difficult exploration challenge for RL agents. An alternative to RL is an optimization approach known as genetic algorithms (GA), which utilize operators that mimic biological evolution to evolve better policies. By combining safety mechanisms with genetic algorithms, this work demonstrates a novel approach to safe learning called Self-Preserving Genetic Algorithms.
Guiding Federated Learning with Inferenced Formal Logic Properties

Recent progressions in federated learning (FL) have facilitated the development of decentralized collaborative Internet-of-Things (IoT) applications. However, data-driven FL algorithms face the challenge of heterogeneity in participating IoT devices, including their deployment environment and calibration settings. Fail to follow these device-specific properties can degenerate the model performance. To address this issue, we present FedSTL in this poster abstract, which is a two-staged personalized FL framework with clustering for sequential prediction tasks in IoT. FedSTL first identifies client properties as Signal Temporal Logic (STL) specifications. Then, a partitioning component of FedSTL associates each client to an aggregation center, while the framework continues to infer properties for the cluster. At the training stage, both cluster and client models are encouraged to follow customized properties to achieve a hierarchical property enhancing strategy. Further, we show preliminary results of FedSTL in this poster abstract under a synthetic multitask IoT environment and a real-world traffic prediction scenario.
Key-value data collection and statistical analysis with local differential privacy

The collection and statistical analysis of simple data types (e.g., categorical, numerical and multi-dimensional data) under local differential privacy has been widely studied. Recently, researchers have focused on the collection of the key-value data, which is one of the main types of NoSQL data model. In the collection and statistical analysis of key-value data under local differential privacy, the frequency and mean of each key must be estimated simultaneously. However, achieving a good utility-privacy tradeoff is difficult, because key-value data has inherent correlation, and some users may have different numbers of key-value pairs. In this paper, we propose an efficient sampling based scheme for collecting and analyzing key-value data. Note that the more valid data collected, the higher the accuracy of statistical data under the same disturbance level and disturbance algorithm. Therefore, we make full use of probability sampling and the inherent correlation of key-value data to improve the probability of users submitting valid key-value data. Moreover, we optimize the budget allocation on key-value data, so that the overall variance of frequency and mean estimation is close to optimal. Detailed theoretical analysis and experimental results show that the proposed scheme is superior to existing schemes in accuracy.
Research on surface shape measurement of highly reflective surfaces based on stripe projection

Abstract Metal workpieces with highly reflective surfaces are particularly common in modern industrial manufacturing systems, and their three-dimensional dimensional measurement has great applications and needs in various fields. Among the various three-dimensional dimensional measurement technologies, projection three-dimensional measurement technology is adopted for its non-contact, low-cost, high-precision, and fast data processing characteristics. However, because of the high reflectance of the highly reflective surface, the structured light photos taken by the camera will be overexposed and underexposed, which will lead to the lack of three-dimensional information obtained. Due to the large range of changes in surface reflectance and the limited grayscale range of the camera, the traditional stripe projection method cannot correctly measure the three-dimensional morphology of highly reflective surfaces [1] . This paper introduces an adaptive sinusoidal stripe grating based on the reflectance of the surface of an object, which is used to compensate for the overexposure of the camera when shooting on a highly reflective surface. Finally, the generated adaptive stripes are projected onto the measured object, and the phase solution and three-dimensional surface shape reconstruction are carried out by the heterodyne multi-frequency phase shift method.