OpenRaiser/Intern-Atlas huggingface dataset:
Blink: Lightweight Sample Runs for Cost Optimization of Big Data Applications
Venue: arXiv cs.DC 2022
Year: 2022

Distributed in-memory data processing engines accelerate iterative applications by caching substantial datasets in memory rather than recomputing them in each iteration. Selecting a suitable cluster size for caching these datasets plays an essential role in achieving optimal performance. In practice, this is a tedious and hard task for end users, who are typically not aware of cluster specifications, workload semantics and sizes of intermediate data. We present Blink, an autonomous sampling-based framework, which predicts sizes of cached datasets and selects optimal cluster size without relying on historical runs. We evaluate Blink on a variety of iterative, real-world, machine learning applications. With an average sample runs cost of 4.6% compared to the cost of optimal runs, Blink selects the optimal cluster size in 15 out of 16 cases, saving up to 47.4% of execution cost compared to average costs.
Using Self Determination Theory to Design to Support Young Peoples Online Help-Seeking
Venue: arXiv cs.HC 2022
Year: 2022

The application of Self-Determination Theory to understand online help-seeking and the design of online help-seeking technologies presents an interesting avenue for investigation. Improving motivation to engage in the help-seeking process could be achieved using the Basic Psychological Needs Theory as a structure to guide the design of online help-seeking technologies and online mental health resources. Positive online help-seeking experiences have an important role to play in sustained help-seeking and improved health outcomes.
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
Venue: arXiv cs.LG 2022
Year: 2022

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.
A Comprehensive Review on Deep Supervision: Theories and Applications
Venue: arXiv cs.CV 2022
Year: 2022

Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network. This technique has been increasingly applied in deep neural network learning systems for various computer vision applications recently. There is a consensus that deep supervision helps improve neural network performance by alleviating the gradient vanishing problem, as one of the many strengths of deep supervision. Besides, in different computer vision applications, deep supervision can be applied in different ways. How to make the most use of deep supervision to improve network performance in different applications has not been thoroughly investigated. In this paper, we provide a comprehensive in-depth review of deep supervision in both theories and applications. We propose a new classification of different deep supervision networks, and discuss advantages and limitations of current deep supervision networks in computer vision applications.
Goal-Aware RSS for Complex Scenarios via Program Logic
Venue: arXiv cs.RO 2022
Year: 2022

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement -- in addition to collision avoidance as in the original RSS -- requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.
White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning
Venue: arXiv cs.CV 2022
Year: 2022

White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF). We directly utilize information from all points in a fiber tract, without the need to average data along the fiber as is traditionally required by diffusion MRI tractometry methods. Specifically, we represent the AF as a point cloud with microstructure measurements at each point, enabling adoption of point-based neural networks. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores. Finally, we propose a Critical Region Localization (CRL) algorithm to localize informative anatomical regions containing points with strong contributions to the prediction results. Our method is evaluated on data from 806 subjects from the Human Connectome Project dataset. Results demonstrate superior neuropsychological score prediction performance compared to baseline methods. We discover that critical regions in the AF are strikingly consistent across subjects, with the highest number of strongly contributing points located in frontal cortical regions (i.e., the rostral middle frontal, pars opercularis, and pars triangularis), which are strongly implicated as critical areas for language processes.
WE model: A Machine Learning Model Based on Data-Driven Movie Derivatives Market Prediction
Venue: arXiv cs.CY 2022
Year: 2022

The mature development and the extension of the industry chain make the income structure of the film industry. The income of the traditional film industry depends on the box office and also includes movie merchandising, advertisement, home entertainment, book sales etc. Movie merchandising can even become more profitable than the box office. Therefore, market analysis and forecasting methods for multi-feature merchandising of multi-type films are particularly important. Traditional market research is time-consuming and labour-intensive, and its practical value is restricted. Due to the limited research method, more effective predictive analysis technology needs to be formed. With the rapid development of machine learning and big data, a large number of machine learning algorithms for predictive regression and classification recognition have been proposed and widely used in product design and industry analysis. This paper proposes a high-precision movie merchandising prediction model based on machine learning technology: WE model. This model integrates three machine learning algorithms to accurately predict the movie merchandising market. The WE model learns the relationship between the movie merchandising market and movie features by analyzing the main feature information of movies. After testing, the accuracy rate of prediction and evaluation in the merchandising market reaches 72.5%, and it has achieved a strong market control effect.
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation
Venue: arXiv cs.CV 2022
Year: 2022

Recently, horizontal representation-based panoramic semantic segmentation approaches outperform projection-based solutions, because the distortions can be effectively removed by compressing the spherical data in the vertical direction. However, these methods ignore the distortion distribution prior and are limited to unbalanced receptive fields, e.g., the receptive fields are sufficient in the vertical direction and insufficient in the horizontal direction. Differently, a vertical representation compressed in another direction can offer implicit distortion prior and enlarge horizontal receptive fields. In this paper, we combine the two different representations and propose a novel 360° semantic segmentation solution from a complementary perspective. Our network comprises three modules: a feature extraction module, a bi-directional compression module, and an ensemble decoding module. First, we extract multi-scale features from a panorama. Then, a bi-directional compression module is designed to compress features into two complementary low-dimensional representations, which provide content perception and distortion prior. Furthermore, to facilitate the fusion of bi-directional features, we design a unique self distillation strategy in the ensemble decoding module to enhance the interaction of different features and further improve the performance. Experimental results show that our approach outperforms the state-of-the-art solutions with at least 10\% improvement on quantitative evaluations while displaying the best performance on visual appearance.
Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruning
Venue: arXiv cs.CL 2022
Year: 2022

Language model debiasing has emerged as an important field of study in the NLP community. Numerous debiasing techniques were proposed, but bias ablation remains an unaddressed issue. We demonstrate a novel framework for inspecting bias in pre-trained transformer-based language models via movement pruning. Given a model and a debiasing objective, our framework finds a subset of the model containing less bias than the original model. We implement our framework by pruning the model while fine-tuning it on the debiasing objective. Optimized are only the pruning scores - parameters coupled with the model's weights that act as gates. We experiment with pruning attention heads, an important building block of transformers: we prune square blocks, as well as establish a new way of pruning the entire heads. Lastly, we demonstrate the usage of our framework using gender bias, and based on our findings, we propose an improvement to an existing debiasing method. Additionally, we re-discover a bias-performance trade-off: the better the model performs, the more bias it contains.
Multi-area Target Individual Detection with Free Drawing on Video
Venue: arXiv cs.CV 2022
Year: 2022

This paper has provided a novel design idea and some implementation methods to make a real time detection of multi-areas with multiple detecting areas that are generated by the real time drawing on the screen display of the video. The drawing on the video will remain the output as polylines, and the colors of the outlines will change when the stage of drawing or detecting is changed. The shape of the drawn area is free to be customized and real-time effective. The configuration of the drawn areas can be renewed and the detecting areas are working individually. The detection result should be shown with a GUI designed by Tkinter. The object recognition model was developed on YOLOv5 but can be changed to others, which means the core design and implementation idea of this paper is model-independent. With PIL and OpenCV and Tkinter, the drawing effect is real time and efficient. The design and code of this research is basic and can be extended to be implemented in numerous monitoring and detecting situations.
Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging
Venue: arXiv cs.CV 2022
Year: 2022

Reconstructing ghosting-free high dynamic range (HDR) images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion and occlusions, leading to visible artifacts using existing methods. To address this problem, we propose a deep network that tries to learn multi-scale feature flow guided by the regularized loss. It first extracts multi-scale features and then aligns features from non-reference images. After alignment, we use residual channel attention blocks to merge the features from different images. Extensive qualitative and quantitative comparisons show that our approach achieves state-of-the-art performance and produces excellent results where color artifacts and geometric distortions are significantly reduced.
Edge-Of-Chaos Learning Achieved by Ion-Electron Coupled Dynamics in an Ion-Gating Reservoir
Venue: arXiv cs.ET 2022
Year: 2022

Physical reservoir computing has recently been attracting attention for its ability to significantly reduce the computational resources required to process time-series data. However, the physical reservoirs that have been reported to date have had insufficient expression power, and most of them have a large volume, which makes their practical application difficult. Herein we describe the development of a Li+-electrolyte based ion-gating reservoir (IGR), with ion-electron coupled dynamics, for use in high performance physical reservoir computing. A variety of synaptic responses were obtained in response to past experience, which responses were stored as transient charge density patterns in an electric double layer, at the Li+-electrolyte/diamond interface. Performance, which was tested using a nonlinear autoregressive moving-average (NARMA) task, was found to be excellent, with a NMSE of 0.023 for NARMA2, which is the highest for any physical reservoir reported to date. The maximum Lyapunov exponent of the IGR was 0.0083: the edge of chaos state enabling the best computational capacity. The IGR described herein opens the way for high-performance and integrated neural network devices.
Gaze-Vergence-Controlled See-Through Vision in Augmented Reality
Venue: arXiv cs.CV 2022
Year: 2022

Augmented Reality (AR) see-through vision is an interesting research topic since it enables users to see through a wall and see the occluded objects. Most existing research focuses on the visual effects of see-through vision, while the interaction method is less studied. However, we argue that using common interaction modalities, e.g., midair click and speech, may not be the optimal way to control see-through vision. This is because when we want to see through something, it is physically related to our gaze depth/vergence and thus should be naturally controlled by the eyes. Following this idea, this paper proposes a novel gaze-vergence-controlled (GVC) see-through vision technique in AR. Since gaze depth is needed, we build a gaze tracking module with two infrared cameras and the corresponding algorithm and assemble it into the Microsoft HoloLens 2 to achieve gaze depth estimation. We then propose two different GVC modes for see-through vision to fit different scenarios. Extensive experimental results demonstrate that our gaze depth estimation is efficient and accurate. By comparing with conventional interaction modalities, our GVC techniques are also shown to be superior in terms of efficiency and more preferred by users. Finally, we present four example applications of gaze-vergence-controlled see-through vision.
Planning Courses for Student Success at the American College of Greece
Venue: arXiv cs.AI 2022
Year: 2022

We model the problem of optimizing the schedule of courses a student at the American College of Greece will need to take to complete their studies. We model all constraints set forth by the institution and the department, so that we guarantee the validity of all produced schedules. We formulate several different objectives to optimize in the resulting schedule, including fastest completion time, course difficulty balance, and so on, with a very important objective our model is capable of capturing being the maximization of the expected student GPA given their performance on passed courses using Machine Learning and Data Mining techniques. All resulting problems are Mixed Integer Linear Programming problems with a number of binary variables that is in the order of the maximum number of terms times the number of courses available for the student to take. The resulting Mathematical Programming problem is always solvable by the GUROBI solver in less than 10 seconds on a modern commercial off-the-self PC, whereas the manual process that was installed before used to take department heads that are designated as student advisors more than one hour of their time for every student and was resulting in sub-optimal schedules as measured by the objectives set forth.
Low-Level Force-Control of MR-Hydrostatic Actuators
Venue: arXiv cs.RO 2022
Year: 2022

Precise and high-fidelity force control is critical for new generations of robots that interact with humans and unknown environments. Mobile robots, such as wearable devices and legged robots, must also be lightweight to accomplish their function. Hydrostatic transmissions have been proposed as a promising strategy for meeting these two challenging requirements. In previous publications, it was shown that using magnetorheological (MR) actuators coupled with hydrostatic transmissions provides high power density and great open-loop human-robot interactions. Still, the open-loop force fidelity at low and high frequencies are decreased by the transmission's dynamics and by nonlinear friction. This letter compares control strategies for MR-hydrostatic actuator systems to increase its torque fidelity, defined as the bandwidth (measured vs desired torque reference) and transparency (minimizing the undesired forces reflected to the end effector when backdriving the robot). Four control approaches are developed and compared experimentally: (1) Open-loop control with friction compensation; (2) non-collocated pressure feedback; (3) collocated pressure feedback; (4) LQGI state feedback. A dither strategy is also implemented to smoothen ball screw friction. Results show that approaches (1), (2) and (3) can increase the performances but are facing compromises, while approach (4) can simultaneously improve all metrics. These results show the potential of using control schemes for improving the force control performance of robots using tethered architectures, addressing issues such as transmission dynamics and friction.
Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware. As an efficient method to obtain deep SNN, the conversion method has exhibited high performance on various large-scale datasets. However, it typically suffers from severe performance degradation and high time delays. In particular, most of the previous work focuses on simple classification tasks while ignoring the precise approximation to ANN output. In this paper, we first theoretically analyze the conversion errors and derive the harmful effects of time-varying extremes on synaptic currents. We propose the Spike Calibration (SpiCalib) to eliminate the damage of discrete spikes to the output distribution and modify the LIPooling to allow conversion of the arbitrary MaxPooling layer losslessly. Moreover, Bayesian optimization for optimal normalization parameters is proposed to avoid empirical settings. The experimental results demonstrate the state-of-the-art performance on classification, object detection, and segmentation tasks. To the best of our knowledge, this is the first time to obtain SNN comparable to ANN on these tasks simultaneously. Moreover, we only need 1/50 inference time of the previous work on the detection task and can achieve the same performance under 0.492$\times$ energy consumption of ANN on the segmentation task.
Characterizing disruptions in online gaming behavior following software patches
Venue: arXiv cs.HC 2022
Year: 2022

Multiplayer online games are ideal settings for studying the effects of technological disruptions on social behavior. Software patches to online games cause significant changes to the game's rules and require players to develop new strategies to cope with these disruptions. We surveyed players, analyzed the content of software patch notes, and analyzed changes to the character selection behaviors in more than 53 million matches of Dota 2 in the days before and after software patches over a 30-month period. We found that the severity of patches is correlated with the magnitude of behavioral changes following a patch. We discuss the opportunities of leveraging software patches to online games as a valuable but overlooked empirical instrument for measuring behavioral dynamics.
Open Science and Authorship of Supplementary Material. Evidence from a Research Community
Venue: arXiv cs.DL 2022
Year: 2022

Authorship of scientific articles has profoundly changed from early science until now. While once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks, it turned out to be a rather hot-button topic from which a significant portion of academic disputes stems. However, the novel Open Science practices could be an opportunity to disrupt such dynamics and diversify the credit of the different scientific contributors involved in the diverse phases of the lifecycle of the same research effort. In fact, a paper and research data (or software) contextually published could exhibit different authorship to give credit to the various contributors right where it feels most appropriate. As a preliminary study, in this paper, we leverage the wealth of information contained in Open Science Graphs, such as OpenAIRE, and conduct a focused analysis on a subset of publications with supplementary material drawn from the European Marine Science (MES) research community. The results are promising and suggest our hypothesis is worth exploring further as we registered in 22% of the cases substantial variations between the authors participating in the publication and the authors participating in the supplementary dataset (or software), thus posing the premises for a longitudinal, large-scale analysis of the phenomenon.