OpenRaiser/Intern-Atlas huggingface dataset:
Development, Deployment, and Evaluation of DyMand -- An Open-Source Smartwatch and Smartphone System for Capturing Couples' Dyadic Interactions in Chronic Disease Management in Daily Life
Venue: arXiv cs.HC 2022
Year: 2022

Dyadic interactions of couples are of interest as they provide insight into relationship quality and chronic disease management. Currently, ambulatory assessment of couples' interactions entails collecting data at random or scheduled times which could miss significant couples' interaction/conversation moments. In this work, we developed, deployed and evaluated DyMand, a novel open-source smartwatch and smartphone system for collecting self-report and sensor data from couples based on partners' interaction moments. Our smartwatch-based algorithm uses the Bluetooth signal strength between two smartwatches each worn by one partner, and a voice activity detection machine-learning algorithm to infer that the partners are interacting, and then to trigger data collection. We deployed the DyMand system in a 7-day field study and collected data about social support, emotional well-being, and health behavior from 13 (N=26) Swiss-based heterosexual couples managing diabetes mellitus type 2 of one partner. Our system triggered 99.1% of the expected number of sensor and self-report data when the app was running, and 77.6% of algorithm-triggered recordings contained partners' conversation moments compared to 43.8% for scheduled triggers. The usability evaluation showed that DyMand was easy to use. DyMand can be used by social, clinical, or health psychology researchers to understand the social dynamics of couples in everyday life, and for developing and delivering behavioral interventions for couples who are managing chronic diseases.
Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems
Venue: arXiv cs.RO 2022
Year: 2022

Robust motion planning entails computing a global motion plan that is safe under all possible uncertainty realizations, be it in the system dynamics, the robot's initial position, or with respect to external disturbances. Current approaches for robust motion planning either lack theoretical guarantees, or make restrictive assumptions on the system dynamics and uncertainty distributions. In this paper, we address these limitations by proposing the robust rapidly-exploring random-tree (Robust-RRT) algorithm, which integrates forward reachability analysis directly into sampling-based control trajectory synthesis. We prove that Robust-RRT is probabilistically complete (PC) for nonlinear Lipschitz continuous dynamical systems with bounded uncertainty. In other words, Robust-RRT eventually finds a robust motion plan that is feasible under all possible uncertainty realizations assuming such a plan exists. Our analysis applies even to unstable systems that admit only short-horizon feasible plans; this is because we explicitly consider the time evolution of reachable sets along control trajectories. Thanks to the explicit consideration of time dependency in our analysis, PC applies to unstabilizable systems. To the best of our knowledge, this is the most general PC proof for robust sampling-based motion planning, in terms of the types of uncertainties and dynamical systems it can handle. Considering that an exact computation of reachable sets can be computationally expensive for some dynamical systems, we incorporate sampling-based reachability analysis into Robust-RRT and demonstrate our robust planner on nonlinear, underactuated, and hybrid systems.
Efficient Algorithms for Planning with Participation Constraints
Venue: arXiv cs.GT 2022
Year: 2022

We consider the problem of planning with participation constraints introduced in [Zhang et al., 2022]. In this problem, a principal chooses actions in a Markov decision process, resulting in separate utilities for the principal and the agent. However, the agent can and will choose to end the process whenever his expected onward utility becomes negative. The principal seeks to compute and commit to a policy that maximizes her expected utility, under the constraint that the agent should always want to continue participating. We provide the first polynomial-time exact algorithm for this problem for finite-horizon settings, where previously only an additive $\varepsilon$-approximation algorithm was known. Our approach can also be extended to the (discounted) infinite-horizon case, for which we give an algorithm that runs in time polynomial in the size of the input and $\log(1/\varepsilon)$, and returns a policy that is optimal up to an additive error of $\varepsilon$.
Design and Evaluation of an Invariant Extended Kalman Filter for Trunk Motion Estimation with Sensor Misalignment
Venue: arXiv cs.RO 2022
Year: 2022

Understanding human motion is of critical importance for health monitoring and control of assistive robots, yet many human kinematic variables cannot be directly or accurately measured by wearable sensors. In recent years, invariant extended Kalman filtering (InEKF) has shown a great potential in nonlinear state estimation, but its applications to human poses new challenges, including imperfect placement of wearable sensors and inaccurate measurement models. To address these challenges, this paper proposes an augmented InEKF design which considers the misalignment of the inertial sensor at the trunk as part of the states and preserves the group affine property for the process model. Personalized lower-extremity forward kinematic models are built and employed as the measurement model for the augmented InEKF. Observability analysis for the new InEKF design is presented. The filter is evaluated with three subjects in squatting, rolling-foot walking, and ladder-climbing motions. Experimental results validate the superior performance of the proposed InEKF over the state-of-the-art InEKF. Improved accuracy and faster convergence in estimating the velocity and orientation of human, in all three motions, are achieved despite the significant initial estimation errors and the uncertainties associated with the forward kinematic measurement model.
RAN Slicing: Towards Multi-Tenancy IN 5G Radio Access Networks
Venue: arXiv cs.NI 2022
Year: 2022

A significant purpose of 5G networks is allowing sharing resources among different network tenants such as service providers and Mobile Virtual network Operators. Numerous domains are taken in account regarding resource sharing containing different infrastructure (storage, compute and networking), Radio Access Network (RAN) and Radio Frequency (RF) spectrum. RAN and spectrum, transport. Spectrum sharing and RAN are anticipated as the fundamental part in multi-tenant 5G network. Nevertheless, there is a shortage of evaluation platforms to determine the number of benefits that can be acquired from multilevel spectrum sharing rather than single-level spectrum sharing. The work presented in this paper intend to address this issue by presenting a modified SimuLTE model is used for evaluating active RAN based on multi-tenant 5G networks. The result shows an understanding into the actual advantages of RAN slicing for multi-tenants in 5G networks.
Benefits and Drawbacks of a Graduate Course: An Experience Teaching Systematic Literature Review
Venue: arXiv cs.SE 2022
Year: 2022

Graduate courses can provide specialized knowledge for Ph.D. and Master's students and contribute to develop their hard and soft skills. At the same time, Systematic Literature Review (SLR) has been increasingly adopted in the computing area as a valuable technique to synthesize the state of the art of a given research topic. However, there is still a poor understanding of the real benefits and drawbacks of offering the SLR course for graduate students. This paper reports an experience that examines such benefits and drawbacks, the difficulties for professors (i.e., educators), and the essential SLR topics to be taught as well as a way to better teach them. We also surveyed computer science graduate students who attended the SLR course, which we have offered for almost ten years for Ph.D. and Master's students in our institution. We found the attendance to the SLR course is a valuable opportunity for graduate students to conduct the required deep literature review of their research topic, improve their research skills, and increase their formation. Hence, we recommend that Ph.D. and Masters' programs offer the SLR course to contribute to their academic achievement.
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
Venue: arXiv cs.LG 2022
Year: 2022

The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.
Impact of Learning Rate on Noise Resistant Property of Deep Learning Models
Venue: arXiv cs.LG 2022
Year: 2022

The interest in analog computation has grown tremendously in recent years due to its fast computation speed and excellent energy efficiency, which is very important for edge and IoT devices in the sub-watt power envelope for deep learning inferencing. However, significant performance degradation suffered by deep learning models due to the inherent noise present in the analog computation can limit their use in mission-critical applications. Hence, there is a need to understand the impact of critical model hyperparameters choice on the resulting model noise-resistant property. This need is critical as the insight obtained can be used to design deep learning models that are robust to analog noise. In this paper, the impact of the learning rate, a critical design choice, on the noise-resistant property is investigated. The study is achieved by first training deep learning models using different learning rates. Thereafter, the models are injected with analog noise and the noise-resistant property of the resulting models is examined by measuring the performance degradation due to the analog noise. The results showed there exists a sweet spot of learning rate values that achieves a good balance between model prediction performance and model noise-resistant property. Furthermore, the theoretical justification of the observed phenomenon is provided.
Simple Contrastive Graph Clustering
Venue: arXiv cs.LG 2022
Year: 2022

Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming graph convolutional operation undermine the efficiency of these methods. To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function. As to the architecture, our network includes two main parts, i.e., pre-processing and network backbone. A simple low-pass denoising operation conducts neighbor information aggregation as an independent pre-processing, and only two multilayer perceptrons (MLPs) are included as the backbone. For data augmentation, instead of introducing complex operations over graphs, we construct two augmented views of the same vertex by designing parameter un-shared siamese encoders and corrupting the node embeddings directly. Finally, as to the objective function, to further improve the clustering performance, a novel cross-view structural consistency objective function is designed to enhance the discriminative capability of the learned network. Extensive experimental results on seven benchmark datasets validate our proposed algorithm's effectiveness and superiority. Significantly, our algorithm outperforms the recent contrastive deep clustering competitors with at least seven times speedup on average.
Developing patient-driven artificial intelligence based on personal rankings of care decision making steps
Venue: arXiv cs.HC 2022
Year: 2022

We propose and experimentally motivate a new methodology to support decision-making processes in healthcare with artificial intelligence based on personal rankings of care decision making steps that can be identified with our methodology, questionnaire data and its statistical patterns. Our longitudinal quantitative cross-sectional three-stage study gathered self-ratings for 437 expression statements concerning healthcare situations on Likert scales in respect to "the need for help", "the advancement of health", "the hopefulness", "the indication of compassion" and "the health condition", and 45 answers about the person's demographics, health and wellbeing, also the duration of giving answers. Online respondents between 1 June 2020 and 29 June 2021 were recruited from Finnish patient and disabled people's organizations, other health-related organizations and professionals, and educational institutions (n=1075). With Kruskal-Wallis test, Wilcoxon rank-sum test (i.e., Mann-Whitney U test), Wilcoxon rank-sum pairwise test, Welch's t test and one-way analysis of variance (ANOVA) between groups test we identified statistically significant differences of ratings and their durations for each expression statement in respect to respondent groupings based on the answer values of each background question. Frequencies of the later reordering of rating rankings showed dependencies with ratings given earlier in respect to various interpretation task entities, interpretation dimensions and respondent groupings. Our methodology, questionnaire data and its statistical patterns enable analyzing with self-rated expression statements the representations of decision making steps in healthcare situations and their chaining, agglomeration and branching in knowledge entities of personalized care paths. Our results support building artificial intelligence solutions to address the patient's needs concerning care.
Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation
Venue: arXiv cs.LG 2022
Year: 2022

A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system (GNSS) real time kinematic (RTK) positioning device and a synchronized IMU. Ground truth labels for the car speed were calculated using the position measurements obtained at the high rate of 50 Hz. A DNN architecture with long short-term memory layers is proposed to enable high-frequency speed estimation that accounts for previous inputs history and the nonlinear relation between speed, acceleration and angular velocity. A simplified aided dead reckoning localization scheme is formulated to assess the trained model which provides the speed pseudo-measurement. The trained model is shown to substantially improve the position accuracy during a 4 minutes drive without the use of GNSS position updates.
The Diversity of Argument-Making in the Wild: from Assumptions and Definitions to Causation and Anecdote in Reddit's "Change My View"
Venue: arXiv cs.CL 2022
Year: 2022

What kinds of arguments do people make, and what effect do they have on others? Normative constraints on argument-making are as old as philosophy itself, but little is known about the diversity of arguments made in practice. We use NLP tools to extract patterns of argument-making from the Reddit site "Change My View" (r/CMV). This reveals six distinct argument patterns: not just the familiar deductive and inductive forms, but also arguments about definitions, relevance, possibility and cause, and personal experience. Data from r/CMV also reveal differences in efficacy: personal experience and, to a lesser extent, arguments about causation and examples, are most likely to shift a person's view, while arguments about relevance are the least. Finally, our methods reveal a gradient of argument-making preferences among users: a two-axis model, of "personal--impersonal" and "concrete--abstract", can account for nearly 80% of the strategy variance between individuals.
Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification
Venue: arXiv cs.CL 2022
Year: 2022

In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared task and demonstrate significant improvements over reported baselines for its three subtasks. The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories. Our final solution is an ensemble of models that employs multitask learning and a self-consistency correction method yielding 82.7% on the hate speech subtask -- reflecting a 3.4% relative improvement compared to previous work.
SciLander: Mapping the Scientific News Landscape
Venue: arXiv cs.CY 2022
Year: 2022

The COVID-19 pandemic has fueled the spread of misinformation on social media and the Web as a whole. The phenomenon dubbed `infodemic' has taken the challenges of information veracity and trust to new heights by massively introducing seemingly scientific and technical elements into misleading content. Despite the existing body of work on modeling and predicting misinformation, the coverage of very complex scientific topics with inherent uncertainty and an evolving set of findings, such as COVID-19, provides many new challenges that are not easily solved by existing tools. To address these issues, we introduce SciLander, a method for learning representations of news sources reporting on science-based topics. SciLander extracts four heterogeneous indicators for the news sources; two generic indicators that capture (1) the copying of news stories between sources, and (2) the use of the same terms to mean different things (i.e., the semantic shift of terms), and two scientific indicators that capture (1) the usage of jargon and (2) the stance towards specific citations. We use these indicators as signals of source agreement, sampling pairs of positive (similar) and negative (dissimilar) samples, and combine them in a unified framework to train unsupervised news source embeddings with a triplet margin loss objective. We evaluate our method on a novel COVID-19 dataset containing nearly 1M news articles from 500 sources spanning a period of 18 months since the beginning of the pandemic in 2020. Our results show that the features learned by our model outperform state-of-the-art baseline methods on the task of news veracity classification. Furthermore, a clustering analysis suggests that the learned representations encode information about the reliability, political leaning, and partisanship bias of these sources.
TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-based FPGAs
Venue: arXiv cs.AR 2022
Year: 2022

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available off-chip memory bandwidth. However, it is nontrivial for designers to fully utilize this immense bandwidth. First, the existing sorter designs cannot be directly scaled at the increasing rate of available off-chip bandwidth, as the required on-chip resource usage grows at a much faster rate and would bound the sorting performance in turn. Second, designers need an in-depth understanding of HBM characteristics to effectively utilize the HBM bandwidth. To tackle these challenges, we present TopSort, a novel two-phase sorting solution optimized for HBM-based FPGAs. In the first phase, 16 merge trees work in parallel to fully utilize 32 HBM channels. In the second phase, TopSort reuses the logic from phase one to form a wider merge tree to merge the partially sorted results from phase one. TopSort also adopts HBM-specific optimizations to reduce resource overhead and improve bandwidth utilization. TopSort can sort up to 4 GB data using all 32 HBM channels, with an overall sorting performance of 15.6 GB/s. TopSort is 6.7x and 2.2x faster than state-of-the-art CPU and FPGA sorters.
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes
Venue: arXiv cs.CR 2022
Year: 2022

In recent years cybersecurity has become a major concern in adaptation of smart applications. Specially, in smart homes where a large number of IoT devices are used having a secure and trusted mechanisms can provide peace of mind for users. Accurate detection of cyber attacks is crucial, however precise identification of the type of attacks plays a huge role if devising the countermeasure for protecting the system. Artificial Neural Networks (ANN) have provided promising results for detecting any security attacks for smart applications. However, due to complex nature of the model used for this technique it is not easy for normal users to trust ANN based security solutions. Also, selection of right hyperparameters for ANN architecture plays a crucial role in the accurate detection of security attacks, especially when it come to identifying the subcategories of attacks. In this paper, we propose a model that considers both the issues of explainability of ANN model and the hyperparameter selection for this approach to be easily trusted and adapted by users of smart home applications. Also, our approach considers a subset of the dataset for optimal selection of hyperparamters to reduce the overhead of the process of ANN architecture design. Distinctively this paper focuses on configuration, performance and evaluation of ANN architecture for identification of five categorical attacks and nine subcategorical attacks. Using a very recent IoT dataset our approach showed high performance for intrusion detection with 99.9%, 99.7%, and 97.7% accuracy for Binary, Category, and Subcategory level classification of attacks.