OpenRaiser/Intern-Atlas huggingface dataset:
Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration
Venue: arXiv cs.RO 2022
Year: 2022

In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: (1) the user provides a task goal in the planning language PDDL, (2) a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified. An operator then chooses (3) reward functions and hyperparameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators (KPIs) such as safety and task performance since they can often affect each other. We adopt a multi-objective Bayesian optimization approach and learn entirely in simulation. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks. We show their successful execution on a real 7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human robot operators.
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference
Venue: arXiv cs.LG 2022
Year: 2022

Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting. While the dual-graph regularization contributes a major part of the success, computational costly hyper-parameter tunning is usually involved. To circumvent such a drawback and improve the completion performance, we propose a novel Bayesian learning algorithm that automatically learns the hyper-parameters associated with dual-graph regularization, and at the same time, guarantees the low-rankness of matrix completion. Notably, a novel prior is devised to promote the low-rankness of the matrix and encode the dual-graph information simultaneously, which is more challenging than the single-graph counterpart. A nontrivial conditional conjugacy between the proposed priors and likelihood function is then explored such that an efficient algorithm is derived under variational inference framework. Extensive experiments using synthetic and real-world datasets demonstrate the state-of-the-art performance of the proposed learning algorithm for various data analysis tasks.
Subreddit Links Drive Community Creation and User Engagement on Reddit
Venue: arXiv cs.SI 2022
Year: 2022

On Reddit, individual subreddits are used to organize content and connect users. One mode of interaction is the subreddit link, which occurs when a user makes a direct reference to a subreddit in another community. Based on the ubiquity of these references, we have undertaken a study on subreddit links on Reddit, with the goal of understanding their impact on both the referenced subreddit, and on the subreddit landscape as a whole. By way of an extensive observational study along with several natural experiments using the entire history of Reddit, we were able to determine that (1) subreddit links are a significant driver of new suberddit creation; (2) subreddit links (2a) substantially drive activity in the referenced subreddit, and (2b) are frequently created in response to high levels of activity in the referenced subreddit; and (3) the graph of subreddit links has become less dense and more treelike over time. We conclude with a discussion of how these results confirm, add to, and in some cases conflict with existing theories on information-seeking behavior and self-organizing behavior in online social systems.
Botnets Breaking Transformers: Localization of Power Botnet Attacks Against the Distribution Grid
Venue: arXiv cs.CR 2022
Year: 2022

Traditional botnet attacks leverage large and distributed numbers of compromised internet-connected devices to target and overwhelm other devices with internet packets. With increasing consumer adoption of high-wattage internet-facing "smart devices", a new "power botnet" attack emerges, where such devices are used to target and overwhelm power grid devices with unusual load demand. We introduce a variant of this attack, the power-botnet weardown-attack, which does not intend to cause blackouts or short-term acute instability, but instead forces expensive mechanical components to activate more frequently, necessitating costly replacements / repairs. Specifically, we target the on-load tap-changer (OLTC) transformer, which uses a mechanical switch that responds to change in load demand. In our analysis and simulations, these attacks can halve the lifespan of an OLTC, or in the most extreme cases, reduce it to $2.5\%$ of its original lifespan. Notably, these power botnets are composed of devices not connected to the internal SCADA systems used to control power grids. This represents a new internet-based cyberattack that targets the power grid from the outside. To help the power system to mitigate these types of botnet attacks, we develop attack-localization strategies. We formulate the problem as a supervised machine learning task to locate the source of power botnet attacks. Within a simulated environment, we generate the training and testing dataset to evaluate several machine learning algorithm based localization methods, including SVM, neural network and decision tree. We show that decision-tree based classification successfully identifies power botnet attacks and locates compromised devices with at least $94\%$ improvement of accuracy over a baseline "most-frequent" classifier.
Failing gracefully: Decryption failures and the Fujisaki-Okamoto transform
Venue: arXiv cs.CR 2022
Year: 2022

In known security reductions for the Fujisaki-Okamoto transformation, decryption failures are handled via a reduction solving the rather unnatural task of finding failing plaintexts given the private key, resulting in a Grover search bound. Moreover, they require an implicit rejection mechanism for invalid ciphertexts to achieve a reasonable security bound in the QROM. We present a reduction that has neither of these deficiencies: We introduce two security games related to finding decryption failures, one capturing the computationally hard task of using the public key to find a decryption failure, and one capturing the statistically hard task of searching the random oracle for key-independent failures like, e.g., large randomness. As a result, our security bounds in the QROM are tighter than previous ones with respect to the generic random oracle search attacks: The attacker can only partially compute the search predicate, namely for said key-independent failures. In addition, our entire reduction works for the explicit-reject variant of the transformation and improves significantly over all of its known reductions. Besides being the more natural variant of the transformation, security of the explicit reject mechanism is also relevant for side channel attack resilience of the implicit-rejection variant. Along the way, we prove several technical results characterizing preimage extraction and certain search tasks in the QROM that might be of independent interest.
Trackers Bounce Back: Measuring Evasion of Partitioned Storage in the Wild
Venue: arXiv cs.CR 2022
Year: 2022

This work presents a systematic study of navigational tracking, the latest development in the cat-and-mouse game between browsers and online trackers. Navigational tracking allows trackers to 'aggregate users' activities and behaviors across sites by modifying their navigation requests. This technique is particularly important because it circumvents the increasing efforts by browsers to partition or block third-party storage, which was previously necessary for most cross-website tracking. While previous work has studied specific navigational tracking techniques (i.e. "bounce tracking"), our work is the first effort to systematically study and measure the entire category of navigational tracking techniques. We describe and measure the frequency of two different navigational tracking techniques on the Web, and find that navigational tracking is present on slightly more than ten percent of all navigations that we made. Our contributions include identifying 214 domains belonging to at least 104 organizations tracking users across sites through link decoration techniques using direct or indirect navigation flows. We identify a further 23 domains belonging to at least 16 organizations tracking users through bounce tracking (i.e. bouncing users through unrelated third parties to generate user profiles). We also improve on prior techniques for differenting user identifiers from non-sensitive information, which is necessary to detect one class of navigational tracking. We discuss how our findings can used to protect users from navigational tracking, and commit to releasing both our complete dataset and our measurement pipeline
Cost Function Learning in Memorized Social Networks with Cognitive Behavioral Asymmetry
Venue: arXiv cs.SI 2022
Year: 2022

This paper investigates the cost function learning in social information networks, wherein the influence of humans' memory on information consumption is explicitly taken into account. We first propose a model for social information-diffusion dynamics with a focus on systematic modeling of asymmetric cognitive bias, represented by confirmation bias and novelty bias. Building on the proposed social model, we then propose the M$^{3}$IRL: a model and maximum-entropy based inverse reinforcement learning framework for learning the cost functions of target individuals in the memorized social networks. Compared with the existing Bayesian IRL, maximum entropy IRL, relative entropy IRL and maximum causal entropy IRL, the characteristics of M$^{3}$IRL are significantly different here: no dependency on the Markov Decision Process principle, the need of only a single finite-time trajectory sample, and bounded decision variables. Finally, the effectiveness of the proposed social information-diffusion model and the M$^{3}$IRL algorithm are validated by the online social media data.
Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards
Venue: arXiv cs.RO 2022
Year: 2022

The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this paper, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (RVO) and the scheme of deep reinforcement learning (DRL) to solve the reciprocal collision avoidance problem under limited information. The novelty of this work is threefold: (1) using a set of sequential VO and RVO vectors to represent the interactive environmental states of static and dynamic obstacles, respectively; (2) developing a bidirectional recurrent module based neural network, which maps the states of a varying number of surrounding obstacles to the actions directly; (3) developing a RVO area and expected collision time based reward function to encourage reciprocal collision avoidance behaviors and trade off between collision risk and travel time. The proposed policy is trained through simulated scenarios and updated by the actor-critic based DRL algorithm. We validate the policy in complex environments with various numbers of differential drive robots and obstacles. The experiment results demonstrate that our approach outperforms the state-of-art methods and other learning based approaches in terms of the success rate, travel time, and average speed. Source code of this approach is available at https://github.com/hanruihua/rl_rvo_nav.
An Efficient Approach to Move Elements in a Distributed Geo-Replicated Tree
Venue: arXiv cs.DC 2022
Year: 2022

Replicated tree data structures are extensively used in collaborative applications and distributed file systems, where clients often perform move operations. Local move operations at different replicas may be safe. However, remote move operations may not be safe. When clients perform arbitrary move operations concurrently on different replicas, it could result in various bugs, making this operation challenging to implement. Previous work has revealed bugs such as data duplication and cycling in replicated trees. In this paper, we present an efficient algorithm to perform move operations on the distributed replicated tree while ensuring eventual consistency. The proposed technique is primarily concerned with resolving conflicts efficiently, requires no interaction between replicas, and works well with network partitions. We use the last write win semantics for conflict resolution based on globally unique timestamps of operations. The proposed solution requires only one compensation operation to avoid cycles being formed when move operations are applied. The proposed approach achieves an effective speedup of 68.19$\times$ over the state-of-the-art approach in a geo-replicated setting on Microsoft Azure standard instances at three different continents.
Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features
Venue: arXiv cs.CL 2022
Year: 2022

Because of the current COVID-19 pandemic with its increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected the developed countries but also developing countries such as Nepal. These complications can be understood from peoples' tweets/comments posted online after their proper analysis and sentiment classification. Nevertheless, owing to the limited number of tokens/words in each tweet, it is always crucial to capture multiple information associated with them for their better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag of words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel convolutional neural network (MCNN), which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification.
Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing
Venue: arXiv cs.LG 2022
Year: 2022

Dynamic demand prediction is a key issue in ride-hailing dispatching. Many methods have been developed to improve the demand prediction accuracy of an increase in demand-responsive, ride-hailing transport services. However, the uncertainties in predicting ride-hailing demands due to multiscale spatiotemporal granularity, as well as the resulting statistical errors, are seldom explored. This paper attempts to fill this gap and to examine the spatiotemporal granularity effects on ride-hailing demand prediction accuracy by using empirical data for Chengdu, China. A convolutional, long short-term memory model combined with a hexagonal convolution operation (H-ConvLSTM) is proposed to explore the complex spatial and temporal relations. Experimental analysis results show that the proposed approach outperforms conventional methods in terms of prediction accuracy. A comparison of 36 spatiotemporal granularities with both departure demands and arrival demands shows that the combination of a hexagonal spatial partition with an 800 m side length and a 30 min time interval achieves the best comprehensive prediction accuracy. However, the departure demands and arrival demands reveal different variation trends in the prediction errors for various spatiotemporal granularities.
Practical Recommendations for Replay-based Continual Learning Methods
Venue: arXiv cs.LG 2022
Year: 2022

Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge. Several approaches have been developed in the literature to tackle the Continual Learning challenge. Among them, Replay approaches have empirically proved to be the most effective ones. Replay operates by saving some samples in memory which are then used to rehearse knowledge during training in subsequent tasks. However, an extensive comparison and deeper understanding of different replay implementation subtleties is still missing in the literature. The aim of this work is to compare and analyze existing replay-based strategies and provide practical recommendations on developing efficient, effective and generally applicable replay-based strategies. In particular, we investigate the role of the memory size value, different weighting policies and discuss about the impact of data augmentation, which allows reaching better performance with lower memory sizes.
Differential Private Discrete Noise Adding Mechanism: Conditions, Properties and Optimization
Venue: arXiv cs.CR 2022
Year: 2022

Differential privacy is a standard framework to quantify the privacy loss in the data anonymization process. To preserve differential privacy, a random noise adding mechanism is widely adopted, where the trade-off between data privacy level and data utility is of great concern. The privacy and utility properties for the continuous noise adding mechanism have been well studied. However, the related works are insufficient for the discrete random mechanism on discretely distributed data, e.g., traffic data, health records. This paper focuses on the discrete random noise adding mechanisms. We study the basic differential privacy conditions and properties for the general discrete random mechanisms, as well as the trade-off between data privacy and data utility. Specifically, we derive a sufficient and necessary condition for discrete epsilon-differential privacy and a sufficient condition for discrete (epsilon, delta)-differential privacy, with the numerical estimation of differential privacy parameters. These conditions can be applied to analyze the differential privacy properties for the discrete noise adding mechanisms with various kinds of noises. Then, with the differential privacy guarantees, we propose an optimal discrete epsilon-differential private noise adding mechanism under the utility-maximization framework, where the utility is characterized by the similarity of the statistical properties between the mechanism's input and output. For this setup, we find that the class of the discrete noise probability distributions in the optimal mechanism is Staircase-shaped.
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense
Venue: arXiv cs.LG 2022
Year: 2022

We proposes a novel algorithm, ANTHRO, that inductively extracts over 600K human-written text perturbations in the wild and leverages them for realistic adversarial attack. Unlike existing character-based attacks which often deductively hypothesize a set of manipulation strategies, our work is grounded on actual observations from real-world texts. We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i.e. indistinguishable from human writings hence harder to be flagged as suspicious. Specifically, our attacks accomplished around 83% and 91% attack success rates on BERT and RoBERTa, respectively. Moreover, it outperformed the TextBugger baseline with an increase of 50% and 40% in terms of semantic preservation and stealthiness when evaluated by both layperson and professional human workers. ANTHRO can further enhance a BERT classifier's performance in understanding different variations of human-written toxic texts via adversarial training when compared to the Perspective API.
Investigating End-Users' Values in Agriculture Mobile Applications Development: An Empirical Study on Bangladeshi Female Farmers
Venue: arXiv cs.SE 2022
Year: 2022

The omnipresent nature of mobile applications (apps) in all aspects of daily lives raises the necessity of reflecting end-users values (e.g., fairness, honesty, etc.) in apps. However, there are limited considerations of end-users values in apps development. Value violations by apps have been reported in the media and are responsible for end-users dissatisfaction and negative socio-economic consequences. Value violations may bring more severe and lasting problems for marginalized and vulnerable end-users of apps, which have been explored less (if at all) in the software engineering community. However, understanding the values of the end-users of apps is the essential first step towards values-based apps development. This research aims to fill this gap by investigating the human values of Bangladeshi female farmers as a marginalized and vulnerable group of end-users of Bangladeshi agriculture apps. We conducted an empirical study that collected and analyzed data from a survey with 193 Bangladeshi female farmers to explore the underlying factor structure of the values of Bangladeshi female farmers and the significance of demographics on their values. The results identified three underlying factors of Bangladeshi female farmers. The first factor comprises of five values: benevolence, security, conformity, universalism, and tradition. The second factor consists of two values: self-direction and stimulation. The third factor includes three values: power, achievement, and hedonism. We also identified strong influences of demographics on some of the values of Bangladeshi female farmers. For example, area has significant impacts on three values: hedonism, achievement, and tradition. Similarly, there are also strong influences of household income on power and security.
Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural Networks
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we propose a new adapter network for adapting a pre-trained deep neural network to a target domain with minimal computation. The proposed model, unidirectional thin adapter (UDTA), helps the classifier adapt to new data by providing auxiliary features that complement the backbone network. UDTA takes outputs from multiple layers of the backbone as input features but does not transmit any feature to the backbone. As a result, UDTA can learn without computing the gradient of the backbone, which saves computation for training significantly. In addition, since UDTA learns the target task without modifying the backbone, a single backbone can adapt to multiple tasks by learning only UDTAs separately. In experiments on five fine-grained classification datasets consisting of a small number of samples, UDTA significantly reduced computation and training time required for backpropagation while showing comparable or even improved accuracy compared with conventional adapter models.