OpenRaiser/Intern-Atlas huggingface dataset:
A Simple and Efficient Pipeline to Build an End-to-End Spatial-Temporal Action Detector
Venue: arXiv cs.CV 2022
Year: 2022

Spatial-temporal action detection is a vital part of video understanding. Current spatial-temporal action detection methods mostly use an object detector to obtain person candidates and classify these person candidates into different action categories. So-called two-stage methods are heavy and hard to apply in real-world applications. Some existing methods build one-stage pipelines, But a large performance drop exists with the vanilla one-stage pipeline and extra classification modules are needed to achieve comparable performance. In this paper, we explore a simple and effective pipeline to build a strong one-stage spatial-temporal action detector. The pipeline is composed by two parts: one is a simple end-to-end spatial-temporal action detector. The proposed end-to-end detector has minor architecture changes to current proposal-based detectors and does not add extra action classification modules. The other part is a novel labeling strategy to utilize unlabeled frames in sparse annotated data. We named our model as SE-STAD. The proposed SE-STAD achieves around 2% mAP boost and around 80% FLOPs reduction. Our code will be released at https://github.com/4paradigm-CV/SE-STAD.
An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussions
Venue: arXiv cs.CR 2022
Year: 2022

IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, ensuring the security of IoT devices is crucial. IoT devices can differ from traditional computing, thereby the design and implementation of proper security measures can be challenging in IoT devices. We observed that IoT developers discuss their security-related challenges in developer forums like Stack Overflow(SO). However, we find that IoT security discussions can also be buried inside non-security discussions in SO. In this paper, we aim to understand the challenges IoT developers face while applying security practices and techniques to IoT devices. We have two goals: (1) Develop a model that can automatically find security-related IoT discussions in SO, and (2) Study the model output to learn about IoT developer security-related challenges. First, we download 53K posts from SO that contain discussions about IoT. Second, we manually labeled 5,919 sentences from 53K posts as 1 or 0. Third, we use this benchmark to investigate a suite of deep learning transformer models. The best performing model is called SecBot. Fourth, we apply SecBot on the entire posts and find around 30K security related sentences. Fifth, we apply topic modeling to the security-related sentences. Then we label and categorize the topics. Sixth, we analyze the evolution of the topics in SO. We found that (1) SecBot is based on the retraining of the deep learning model RoBERTa. SecBot offers the best F1-Score of 0.935, (2) there are six error categories in misclassified samples by SecBot. SecBot was mostly wrong when the keywords/contexts were ambiguous (e.g., gateway can be a security gateway or a simple gateway), (3) there are 9 security topics grouped into three categories: Software, Hardware, and Network, and (4) the highest number of topics belongs to software security, followed by network security.
CitySpec: An Intelligent Assistant System for Requirement Specification in Smart Cities
Venue: arXiv cs.AI 2022
Year: 2022

An increasing number of monitoring systems have been developed in smart cities to ensure that real-time operations of a city satisfy safety and performance requirements. However, many existing city requirements are written in English with missing, inaccurate, or ambiguous information. There is a high demand for assisting city policy makers in converting human-specified requirements to machine-understandable formal specifications for monitoring systems. To tackle this limitation, we build CitySpec, the first intelligent assistant system for requirement specification in smart cities. To create CitySpec, we first collect over 1,500 real-world city requirements across different domains from over 100 cities and extract city-specific knowledge to generate a dataset of city vocabulary with 3,061 words. We also build a translation model and enhance it through requirement synthesis and develop a novel online learning framework with validation under uncertainty. The evaluation results on real-world city requirements show that CitySpec increases the sentence-level accuracy of requirement specification from 59.02% to 86.64%, and has strong adaptability to a new city and a new domain (e.g., F1 score for requirements in Seattle increases from 77.6% to 93.75% with online learning).
Utility of Equivariant Message Passing in Cortical Mesh Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

The automated segmentation of cortical areas has been a long-standing challenge in medical image analysis. The complex geometry of the cortex is commonly represented as a polygon mesh, whose segmentation can be addressed by graph-based learning methods. When cortical meshes are misaligned across subjects, current methods produce significantly worse segmentation results, limiting their ability to handle multi-domain data. In this paper, we investigate the utility of E(n)-equivariant graph neural networks (EGNNs), comparing their performance against plain graph neural networks (GNNs). Our evaluation shows that GNNs outperform EGNNs on aligned meshes, due to their ability to leverage the presence of a global coordinate system. On misaligned meshes, the performance of plain GNNs drop considerably, while E(n)-equivariant message passing maintains the same segmentation results. The best results can also be obtained by using plain GNNs on realigned data (co-registered meshes in a global coordinate system).
Quantifying the Effects of Working in VR for One Week
Venue: arXiv cs.HC 2022
Year: 2022

Virtual Reality (VR) provides new possibilities for modern knowledge work. However, the potential advantages of virtual work environments can only be used if it is feasible to work in them for an extended period of time. Until now, there are limited studies of long-term effects when working in VR. This paper addresses the need for understanding such long-term effects. Specifically, we report on a comparative study (n=16), in which participants were working in VR for an entire week -- for five days, eight hours each day -- as well as in a baseline physical desktop environment. This study aims to quantify the effects of exchanging a desktop-based work environment with a VR-based environment. Hence, during this study, we do not present the participants with the best possible VR system but rather a setup delivering a comparable experience to working in the physical desktop environment. The study reveals that, as expected, VR results in significantly worse ratings across most measures. Among other results, we found concerning levels of simulator sickness, below average usability ratings and two participants dropped out on the first day using VR, due to migraine, nausea and anxiety. Nevertheless, there is some indication that participants gradually overcame negative first impressions and initial discomfort. Overall, this study helps lay the groundwork for subsequent research, by clearly highlighting current shortcomings and identifying opportunities for improving the experience of working in VR.
Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions
Venue: arXiv cs.CV 2022
Year: 2022

Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A well-established approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply variability to adapt the response of the grid. In solar energy, short-term changes in electricity production caused by occluding clouds can be predicted at different time scales from all-sky cameras (up to 30-min ahead) and satellite observations (up to 6h ahead). In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting. Both deterministic and probabilistic predictions are evaluated in different weather conditions (clear-sky, cloudy, overcast) and with different input configurations (sky images, satellite observations and/or past irradiance values). Our results show that the hybrid model benefits predictions in clear-sky conditions and improves longer-term forecasting. This study lays the groundwork for future novel approaches of combining sky images and satellite observations in a single learning framework to advance solar nowcasting.
Improving Students' Academic Performance with AI and Semantic Technologies
Venue: arXiv cs.CY 2022
Year: 2022

Artificial intelligence and semantic technologies are evolving and have been applied in various research areas, including the education domain. Higher Education institutions strive to improve students' academic performance. Early intervention to at-risk students and a reasonable curriculum is vital for students' success. Prior research opted for deploying traditional machine learning models to predict students' performance. In terms of curriculum semantic analysis, after conducting a comprehensive systematic review regarding the use of semantic technologies in the Computer Science curriculum, a major finding of the study is that technologies used to measure similarity have limitations in terms of accuracy and ambiguity in the representation of concepts, courses, etc. To fill these gaps, in this study, three implementations were developed, that is, to predict students' performance using marks from the previous semester, to model a course representation in a semantic way and compute the similarity, and to identify the prerequisite between two similar courses. Regarding performance prediction, we used the combination of Genetic Algorithm and Long-Short Term Memory (LSTM) on a dataset from a Brazilian university containing 248730 records. As for similarity measurement, we deployed BERT to encode the sentences and used cosine similarity to obtain the distance between courses. With respect to prerequisite identification, TextRazor was applied to extract concepts from course description, followed by employing SemRefD to measure the degree of prerequisite between two concepts. The outcomes of this study can be summarized as: (i) a breakthrough result improves Manrique's work by 2.5% in terms of accuracy in dropout prediction; (ii) uncover the similarity between courses based on course description; (iii) identify the prerequisite over three compulsory courses of School of Computing at ANU.
A Transparency Index Framework for AI in Education
Venue: arXiv cs.CY 2022
Year: 2022

Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education and demonstrate that transparency considerations are embedded in the entire AI development process from the data collection stage until the AI system is deployed in the real world and iteratively improved. We also demonstrate how transparency enables the implementation of other ethical AI dimensions in Education like interpretability, accountability, and safety. In conclusion, we discuss the directions for future research in this newly emerging field. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.
To incentivize or not: Impact of blockchain-based cryptoeconomic tokens on human information sharing behavior
Venue: arXiv cs.CY 2022
Year: 2022

Cryptoeconomic incentives in the form of blockchain-based tokens are seen as an enabler of the sharing economy that could shift society towards greater sustainability. Nevertheless, knowledge of the impact of these tokens on human sharing behavior is still limited and this poses a challenge to the design of effective cryptoeconomic incentives. This study applies the theory of self-determination to investigate the impact of such tokens on human behavior in an information-sharing scenario. By utilizing an experimental methodology in the form of a randomized control trial with a 2x2 factorial design involving 132 participants, the effects of two token incentives on human information-sharing behavior are analyzed. Individuals obtain these tokens in exchange for their shared information. Based on the collected tokens, individuals receive a monetary payment and build reputation. Besides investigating the effect of these incentives on the quantity of shared information, the study includes quality characteristics of the information, such as accuracy and contextualization. The focus on quantity while excluding quality has been identified as a limitation in previous work. In addition to confirming previously known effects such as a crowding-out of intrinsic motivation by incentives, which also exists for blockchain-based tokens, the findings of this paper point to a hitherto unreported interaction effect between multiple tokens when applied simultaneously. The findings are critically discussed and put into the context of recent work and ethical considerations. The theory-based-empirical study is of interest to those investigating the effect of cryptoeconomic tokens or digital currencies on human behavior and supports the community in the design of effective personalized incentives for sharing economies.
Analyzing the impact of feature selection on the accuracy of heart disease prediction
Venue: arXiv cs.LG 2022
Year: 2022

Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade. In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an essential factor. Recently we have seen the usage of non-invasive medical procedures, such as artificial intelligence-based techniques in the field of medical. Specially machine learning employs several algorithms and techniques that are widely used and are highly useful in accurately diagnosing the heart disease with less amount of time. However, the prediction of heart disease is not an easy task. The increasing size of medical datasets has made it a complicated task for practitioners to understand the complex feature relations and make disease predictions. Accordingly, the aim of this research is to identify the most important risk-factors from a highly dimensional dataset which helps in the accurate classification of heart disease with less complications. For a broader analysis, we have used two heart disease datasets with various medical features. The classification results of the benchmarked models proved that there is a high impact of relevant features on the classification accuracy. Even with a reduced number of features, the performance of the classification models improved significantly with a reduced training time as compared with models trained on full feature set.
6G-AUTOR: Autonomic CSI-Free Transceiver via Realtime On-Device Signal Analytics
Venue: arXiv cs.NI 2022
Year: 2022

Next-generation wireless systems aim at fulfilling diverse application requirements but fundamentally rely on point-to-point transmission qualities. Aligning with recent AI-enabled wireless implementations, this paper introduces autonomic radios, 6G-AUTOR, that leverage novel algorithm-hardware separation platforms, softwarization of transmission (TX) and reception (RX) operations, and automatic reconfiguration of RF frontends, to support link performance and resilience. As a comprehensive transceiver solution, our design encompasses several ML-driven models, each enhancing a specific aspect of either TX or RX, leading to robust transceiver operation under tight constraints of future wireless systems. A data-driven radio management module was developed via deep Q-networks to support fast-reconfiguration of TX resource blocks (RB) and proactive multi-agent access. Also, a ResNet-inspired fast-beamforming solution was employed to enable robust communication to multiple receivers over the same RB, which has potential applications in realisation of cell-free infrastructures. As a receiver the system was equipped with a capability of ultra-broadband spectrum recognition. Apart from this, a fundamental tool - automatic modulation classification (AMC) which involves a complex correntropy extraction, followed by a convolutional neural network (CNN)-based classification, and a deep learning-based LDPC decoder were added to improve the reception quality and radio performance. Simulations of individual algorithms demonstrate that under appropriate training, each of the corresponding radio functions have either outperformed or have performed on-par with the benchmark solutions.
About Digital Twins, agents, and multiagent systems: a cross-fertilisation journey
Venue: arXiv cs.MA 2022
Year: 2022

Digital Twins (DTs) are rapidly emerging as a fundamental brick of engineering cyber-physical systems, but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or application domains (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and cyber-physical systems, and the former could both fill in some gaps in agent-oriented engineering and benefit from an agent-oriented interpretation -- in a cross-fertilisation journey.
Embedded Systems Education in the 2020s: Challenges, Reflections, and Future Directions
Venue: arXiv cs.CY 2022
Year: 2022

Embedded computing systems are pervasive in our everyday lives, imparting digital intelligence to a variety of electronic platforms used in our vehicles, smart appliances, wearables, mobile devices, and computers. The need to train the next generation of embedded systems designers and engineers with relevant skills across hardware, software, and their co-design remains pressing today. This paper describes the evolution of embedded systems education over the past two decades and challenges facing the designers and instructors of embedded systems curricula in the 2020s. Reflections from over a decade of teaching the design of embedded computing systems are presented, with insights on strategies that show promise to address these challenges. Lastly, some important future directions in embedded systems education are highlighted.
Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
Venue: arXiv cs.NE 2022
Year: 2022

In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision making, learning, and navigation in biological organisms. In utilizing RL, cognitive scientists often handcraft environments and agents to meet the needs of their particular studies. On the other hand, artificial intelligence researchers often struggle to find benchmarks for neurally and biologically plausible representation and behavior (e.g., in decision making or navigation). In order to streamline this process across both fields with transparency and reproducibility, Neuro-Nav offers a set of standardized environments and RL algorithms drawn from canonical behavioral and neural studies in rodents and humans. We demonstrate that the toolkit replicates relevant findings from a number of studies across both cognitive science and RL literatures. We furthermore describe ways in which the library can be extended with novel algorithms (including deep RL) and environments to address future research needs of the field.
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms
Venue: arXiv cs.LG 2022
Year: 2022

Abnormalities of the sewage pipe network will affect the normal operation of the whole city. Therefore, it is important to detect the abnormalities early. This paper propose an early abnormal-detection method. The abnormalities are detected by using the conventional algorithms, such as isolation forest algorithm, two innovations are given: (1) The current and historical data measured by the sensors placed in the sewage pipe network (such as ultrasonic Doppler flowmeter) are taken as the overall dataset, and then the general dataset is detected by using the conventional anomaly detection method to diagnose the anomaly of the data. The anomaly refers to the sample different from the others samples in the whole dataset. Because the definition of anomaly is not through the algorithm, but the whole dataset, the construction of the whole dataset is the key to propose the early abnormal-detection algorithms. (2) A bagging strategy for a variety of conventional anomaly detection algorithms is proposed to achieve the early detection of anomalies with the high precision and recall. The results show that this method can achieve the early anomaly detection with the highest precision of 98.21%, the recall rate 63.58% and F1-score of 0.774.
Deep Learning-based Finite Element Analysis (FEA) surrogate for sub-sea pressure vessel
Venue: arXiv cs.LG 2022
Year: 2022

During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. The pressure vessel contains dry electronics, power sources, and other sensors that can not be flooded. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations and optimizing the design to find the best suitable design which meets the requirement. Running these FEAs are computationally very costly for any optimization process and it becomes difficult to run even hundreds of evaluation. In such a case, a better approach is the surrogate design with the goal of replacing FEA-based prediction with some learning-based regressor. Once the surrogate is trained for a class of problem, then the learned response surface can be used to analyze the stress effect without running the FEA for that class of problem. The challenge of creating a surrogate for a class of problems is data generation. Since the process is computationally costly, it is not possible to densely sample the design space and the learning response surface on sparse data set becomes difficult. During experimentation, we observed that a Deep Learning-based surrogate outperforms other regression models on such sparse data. In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simulation process. By creating the surrogate we speed up the prediction on the other design much faster than direct Finite element Analysis. We also compared our DL-based surrogate with other classical Machine Learning (ML) based regression models( random forest and Gradient Boost regressor). We observed on the sparser data, the DL-based surrogate performs much better than other regression models.
Concentration bounds for SSP Q-learning for average cost MDPs
Venue: arXiv cs.LG 2022
Year: 2022

We derive a concentration bound for a Q-learning algorithm for average cost Markov decision processes based on an equivalent shortest path problem, and compare it numerically with the alternative scheme based on relative value iteration.