OpenRaiser/Intern-Atlas huggingface dataset:
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning
Venue: arXiv cs.LG 2022
Year: 2022

Multi-task learning (MTL) is a novel framework to learn several tasks simultaneously with a single shared network where each task has its distinct personalized header network for fine-tuning. MTL can be implemented in federated learning settings as well, in which tasks are distributed across clients. In federated settings, the statistical heterogeneity due to different task complexities and data heterogeneity due to non-iid nature of local datasets can both degrade the learning performance of the system. In addition, tasks can negatively affect each other's learning performance due to negative transference effects. To cope with these challenges, we propose FedGradNorm which uses a dynamic-weighting method to normalize gradient norms in order to balance learning speeds among different tasks. FedGradNorm improves the overall learning performance in a personalized federated learning setting. We provide convergence analysis for FedGradNorm by showing that it has an exponential convergence rate. We also conduct experiments on multi-task facial landmark (MTFL) and wireless communication system dataset (RadComDynamic). The experimental results show that our framework can achieve faster training performance compared to equal-weighting strategy. In addition to improving training speed, FedGradNorm also compensates for the imbalanced datasets among clients.
LQoCo: Learning to Optimize Cache Capacity Overloading in Storage Systems
Venue: arXiv cs.AR 2022
Year: 2022

Cache plays an important role to maintain high and stable performance (i.e. high throughput, low tail latency and throughput jitter) in storage systems. Existing rule-based cache management methods, coupled with engineers' manual configurations, cannot meet ever-growing requirements of both time-varying workloads and complex storage systems, leading to frequent cache overloading. In this paper, we for the first time propose a light-weight learning-based cache bandwidth control technique, called \LQoCo which can adaptively control the cache bandwidth so as to effectively prevent cache overloading in storage systems. Extensive experiments with various workloads on real systems show that LQoCo, with its strong adaptability and fast learning ability, can adapt to various workloads to effectively control cache bandwidth, thereby significantly improving the storage performance (e.g. increasing the throughput by 10\%-20\% and reducing the throughput jitter and tail latency by 2X-6X and 1.5X-4X, respectively, compared with two representative rule-based methods).
PDE-based Dynamic Control and Estimation of Soft Robotic Arms
Venue: arXiv cs.RO 2022
Year: 2022

Compared with traditional rigid-body robots, soft robots not only exhibit unprecedented adaptation and flexibility but also present novel challenges in their modeling and control because of their infinite degrees of freedom. Most of the existing approaches have mainly relied on approximated models so that the well-developed finite-dimensional control theory can be exploited. However, this may bring in modeling uncertainty and performance degradation. Hence, we propose to exploit infinite-dimensional analysis for soft robotic systems. Our control design is based on the increasingly adopted Cosserat rod model, which describes the kinematics and dynamics of soft robotic arms using nonlinear partial differential equations (PDE). We design infinite-dimensional state feedback control laws for the Cosserat PDE model to achieve trajectory tracking (consisting of position, rotation, linear and angular velocities) and prove their uniform tracking convergence. We also design an infinite-dimensional extended Kalman filter on Lie groups for the PDE system to estimate all the state variables (including position, rotation, strains, curvature, linear and angular velocities) using only position measurements. The proposed algorithms are evaluated using simulations.
Analysis of the use of color and its emotional relationship in visual creations based on experiences during the context of the COVID-19 pandemic
Venue: arXiv cs.CV 2022
Year: 2022

Color is a complex communicative element that helps us understand and evaluate our environment. At the level of artistic creation, this component influences both the formal aspects of the composition and the symbolic weight, directly affecting the construction and transmission of the message that you want to communicate, creating a specific emotional reaction. During the COVID-19 pandemic, people generated countless images transmitting this event's subjective experiences. Using the repository of images created in the Instagram account CAM (The COVID Art Museum), we propose a methodology to understand the use of color and its emotional relationship in this context. The process considers two stages in parallel that are then combined. First, emotions are extracted and classified from the CAM dataset images through a convolutional neural network. Second, we extract the colors and their harmonies through a clustering process. Once both processes are completed, we combine the results generating an expanded discussion on the usage of color, harmonies, and emotion. The results indicate that warm colors are prevalent in the sample, with a preference for analog compositions over complementary ones. The relationship between emotions and these compositions shows a trend in positive emotions, reinforced by the results of the algorithm a priori and the emotional relationship analysis of the attributes of color (hue, chroma, and lighting).
$ω$-Forest Algebras and Temporal Logics
Venue: arXiv cs.LO 2022
Year: 2022

We use the algebraic framework for languages of infinite trees introduced in [4] to derive effective characterisations of various temporal logics, in particular the logic EF (a fragment of CTL) and its counting variant cEF.
Manipulating Twitter Through Deletions
Venue: arXiv cs.SI 2022
Year: 2022

Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. These APIs provide access to public tweets that have not been deleted. However, bad actors can delete content strategically to manipulate the system. Unfortunately, estimates based on publicly available Twitter data underestimate the true deletion volume. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. We find that a small fraction of accounts delete a large number of tweets daily. We also uncover two abusive behaviors that exploit deletions. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms. These kinds of abuse can be exploited to amplify content and inflate popularity, while evading detection. Our study provides platforms and researchers with new methods for identifying social media abuse.
Balanced Allocations in Batches: Simplified and Generalized
Venue: arXiv cs.DM 2022
Year: 2022

We consider the allocation of $m$ balls (jobs) into $n$ bins (servers). In the Two-Choice process, for each of $m$ sequentially arriving balls, two randomly chosen bins are sampled and the ball is placed in the least loaded bin. It is well-known that the maximum load is $m/n+\log_2 \log n + O(1)$ w.h.p. Berenbrink, Czumaj, Englert, Friedetzky and Nagel (2012) introduced a parallel version of this process, where $m$ balls arrive in consecutive batches of size $b=n$ each. Balls within the same batch are allocated in parallel, using the load information of the bins at the beginning of the batch. They proved that the gap of this process is $O(\log n)$ with high probability. In this work, we present a new analysis of this setting, which is based on exponential potential functions. This allows us to both simplify and generalize the analysis of [BCE12] in different ways: $\quad 1.$ Our analysis covers a broad class of processes. This includes not only Two-Choice, but also processes with fewer bin samples like $(1+β)$, processes which can only receive one bit of information from each bin sample and graphical allocation, where bins correspond to vertices in a graph. $\quad 2.$ Balls may be of different weights, as long as their weights are independent samples from a distribution satisfying a technical condition on its moment generating function. $\quad 3.$ For arbitrary batch sizes $b \geq n$, we prove a gap of $O(b/n \cdot \log n)$. For any $b \in [n , n^3]$, we improve this to $O(b/n + \log n)$ and show that it is tight for a family of processes. This implies the unexpected result that for e.g. $(1+β)$ with constant $β\in (0, 1]$, the gap is $Θ(\log n)$ for all $b \in [n,n \log n]$. We also conduct experiments which support our theoretical results, and even hint at a superiority of less powerful processes like $(1+β)$ for large batch sizes.
Using Virtual Reality to Design and Evaluate a Lunar Lander: The EL3 Case Study
Venue: arXiv cs.HC 2022
Year: 2022

The European Large Logistics Lander (EL3) is being designed to carry out cargo delivery missions in support of future lunar ground crews. The capacity of virtual reality (VR) to visualize and interactively simulate the unique lunar environment makes it a potentially powerful design tool during the early development stages of such solutions. Based on input from the EL3 development team, we have produced a VR-based operational scenario featuring a hypothetical configuration of the lander. Relying on HCI research methods, we have subsequently evaluated this scenario with relevant experts (n=10). Qualitative findings from this initial pilot study have demonstrated the usefulness of VR as a design tool in this context, but likewise surfaced a number of limitations in the form of potentially impaired validity and generalizability. We conclude by outlining our future research plan and reflect on the potential use of physical stimuli to improve the validity of VR-based simulations in forthcoming design activities.
Fusing Global and Local Features for Generalized AI-Synthesized Image Detection
Venue: arXiv cs.CV 2022
Year: 2022

With the development of the Generative Adversarial Networks (GANs) and DeepFakes, AI-synthesized images are now of such high quality that humans can hardly distinguish them from real images. It is imperative for media forensics to develop detectors to expose them accurately. Existing detection methods have shown high performance in generated images detection, but they tend to generalize poorly in the real-world scenarios, where the synthetic images are usually generated with unseen models using unknown source data. In this work, we emphasize the importance of combining information from the whole image and informative patches in improving the generalization ability of AI-synthesized image detection. Specifically, we design a two-branch model to combine global spatial information from the whole image and local informative features from multiple patches selected by a novel patch selection module. Multi-head attention mechanism is further utilized to fuse the global and local features. We collect a highly diverse dataset synthesized by 19 models with various objects and resolutions to evaluate our model. Experimental results demonstrate the high accuracy and good generalization ability of our method in detecting generated images. Our code is available at https://github.com/littlejuyan/FusingGlobalandLocal.
Preventing Outages under Coordinated Cyber-Physical Attack with Secured PMUs
Venue: arXiv cs.PF 2022
Year: 2022

Due to the potentially severe consequences of coordinated cyber-physical attacks (CCPA), the design of defenses has gained significant attention. A popular approach is to eliminate the existence of attacks by either securing existing sensors or deploying secured PMUs. In this work, we improve this approach by lowering the defense target from eliminating attacks to preventing outages and reducing the required number of PMUs. To this end, we formulate the problem of PMU Placement for Outage Prevention (PPOP) under DC power flow model as a tri-level non-linear optimization problem and transform it into a bi-level mixed-integer linear programming (MILP) problem. Then, we propose an alternating optimization framework to solve PPOP by iteratively adding constraints, for which we develop two constraint generation algorithms. In addition, for large-scale grids, we propose a polynomial-time heuristic algorithm to obtain suboptimal solutions. Next, we extend our solution to achieve the defense goal under AC power flow model. Finally, we evaluate our algorithm on IEEE 30-bus, 57-bus, 118-bus, and 300-bus systems, which demonstrates the potential of the proposed approach in greatly reducing the required number of PMUs.
Driver Side and Traffic Based Evaluation Model for On-Street Parking Solutions
Venue: arXiv cs.CY 2022
Year: 2022

Parking has been a painful problem for urban drivers. The parking pain exacerbates as more people tend to live in cities in the context of global urbanization. Thus, it is demanding to find a solution to mitigate d rivers' parking headaches. Many solutions tried to resolve the parking issue by predicting parking occupancy. Their focuses were on the accuracy of the theoretical side but lacked a standardized model to evaluate these proposals in practice. This paper develops a Driver Side and Traffic Based Evaluation Model (DSTBM), which provides a general evaluation scheme for different parking solutions. Two common parking detection methods, fixed sensing and mobile sensing are analyzed using DSTBM. The results indicate first, DSTBM examines different solutions from the driver's perspective and has no conflicts with other evaluation schemes; second, DSTBM confirms that fixed sensing performs better than mobile sensing in terms of prediction accuracy.
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data
Venue: arXiv cs.IR 2022
Year: 2022

Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other similar customers. Data Sparsity is one of the major drawbacks with collaborative filtering technique arising due to the less number of transactions and feedback data. In order to reduce the sparsity problem, techniques called transfer learning/cross-domain recommendation has emerged. In transfer learning methods, the data from other dense domain(s) (source) is considered in order to predict the missing ratings in the sparse domain (target). In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique. Thereafter we apply the Maximum Margin Matrix factorization (MMMF) technique on the codebook in order to learn the user and item latent features of codebook. Prediction of the target rating matrix is achieved by introducing these latent features in a novel way into the optimisation function. In the experiments we demonstrate that our model improves the prediction accuracy of the target matrix on benchmark datasets.
Learn to Adapt for Monocular Depth Estimation
Venue: arXiv cs.CV 2022
Year: 2022

Monocular depth estimation is one of the fundamental tasks in environmental perception and has achieved tremendous progress in virtue of deep learning. However, the performance of trained models tends to degrade or deteriorate when employed on other new datasets due to the gap between different datasets. Though some methods utilize domain adaptation technologies to jointly train different domains and narrow the gap between them, the trained models cannot generalize to new domains that are not involved in training. To boost the transferability of depth estimation models, we propose an adversarial depth estimation task and train the model in the pipeline of meta-learning. Our proposed adversarial task mitigates the issue of meta-overfitting, since the network is trained in an adversarial manner and aims to extract domain invariant representations. In addition, we propose a constraint to impose upon cross-task depth consistency to compel the depth estimation to be identical in different adversarial tasks, which improves the performance of our method and smoothens the training process. Experiments demonstrate that our method adapts well to new datasets after few training steps during the test procedure.
Data Augmentation Strategies for Improving Sequential Recommender Systems
Venue: arXiv cs.IR 2022
Year: 2022

Sequential recommender systems have recently achieved significant performance improvements with the exploitation of deep learning (DL) based methods. However, although various DL-based methods have been introduced, most of them only focus on the transformations of network structure, neglecting the importance of other influential factors including data augmentation. Obviously, DL-based models require a large amount of training data in order to estimate parameters well and achieve high performances, which leads to the early efforts to increase the training data through data augmentation in computer vision and speech domains. In this paper, we seek to figure out that various data augmentation strategies can improve the performance of sequential recommender systems, especially when the training dataset is not large enough. To this end, we propose a simple set of data augmentation strategies, all of which transform original item sequences in the way of direct corruption and describe how data augmentation changes the performance. Extensive experiments on the latest DL-based model show that applying data augmentation can help the model generalize better, and it can be significantly effective to boost model performances especially when the amount of training data is small. Furthermore, it is shown that our proposed strategies can improve performances to a better or competitive level to existing strategies suggested in the prior works.
Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning
Venue: arXiv cs.AI 2022
Year: 2022

A major challenge in todays power grid is to manage the increasing load from electric vehicle (EV) charging. Demand response (DR) solutions aim to exploit flexibility therein, i.e., the ability to shift EV charging in time and thus avoid excessive peaks or achieve better balancing. Whereas the majority of existing research works either focus on control strategies for a single EV charger, or use a multi-step approach (e.g., a first high level aggregate control decision step, followed by individual EV control decisions), we rather propose a single-step solution that jointly coordinates multiple charging points at once. In this paper, we further refine an initial proposal using reinforcement learning (RL), specifically addressing computational challenges that would limit its deployment in practice. More precisely, we design a new Markov decision process (MDP) formulation of the EV charging coordination process, exhibiting only linear space and time complexity (as opposed to the earlier quadratic space complexity). We thus improve upon earlier state-of-the-art, demonstrating 30% reduction of training time in our case study using real-world EV charging session data. Yet, we do not sacrifice the resulting performance in meeting the DR objectives: our new RL solutions still improve the performance of charging demand coordination by 40-50% compared to a business-as-usual policy (that charges EV fully upon arrival) and 20-30% compared to a heuristic policy (that uniformly spreads individual EV charging over time).
Distributed data analytics
Venue: arXiv cs.DC 2022
Year: 2022

Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are a key component of online service providers. The financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, risk-management, and compliance. Deep Learning is the technology behind voice-based personal assistants, etc. Deployment of ML technologies onto cloud computing infrastructures has benefited numerous aspects of our daily life. The advertising and associated online industries in particular have fuelled a rapid rise the in deployment of personal data collection and analytics tools. Traditionally, behavioural analytics relies on collecting vast amounts of data in centralised cloud infrastructure before using it to train machine learning models that allow user behaviour and preferences to be inferred. A contrasting approach, distributed data analytics, where code and models for training and inference are distributed to the places where data is collected, has been boosted by two recent, ongoing developments: increased processing power and memory capacity available in user devices at the edge of the network, such as smartphones and home assistants; and increased sensitivity to the highly intrusive nature of many of these devices and services and the attendant demands for improved privacy. Indeed, the potential for increased privacy is not the only benefit of distributing data analytics to the edges of the network: reducing the movement of large volumes of data can also improve energy efficiency, helping to ameliorate the ever increasing carbon footprint of our digital infrastructure, enabling much lower latency for service interactions than is possible when services are cloud-hosted. These approaches often introduce challenges in privacy, utility, and efficiency trade-offs, while having to ensure fruitful user engagement.
MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain
Venue: arXiv cs.CL 2022
Year: 2022

This work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model is trained on question descriptions and source codes in multiple programming languages. We design two new learning objectives to improve duplicate detection capabilities. The result of this work is a mature, fine-tuned Multimodal Question Duplicity Detection (MQDD) model, ready to be integrated into a Stack Overflow search system, where it can help users find answers for already answered questions. Alongside the MQDD model, we release two datasets related to the software engineering domain. The first Stack Overflow Dataset (SOD) represents a massive corpus of paired questions and answers. The second Stack Overflow Duplicity Dataset (SODD) contains data for training duplicate detection models.