OpenRaiser/Intern-Atlas huggingface dataset:
Decomposition Theory Meets Reliability Analysis: Processing of Computation-Intensive Dependent Tasks over Vehicular Clouds with Dynamic Resources
Venue: arXiv cs.DC 2023
Year: 2023

Vehicular cloud (VC) is a promising technology for processing computation-intensive applications (CI-Apps) on smart vehicles. Implementing VCs over the network edge faces two key challenges: (C1) On-board computing resources of a single vehicle are often insufficient to process a CI-App; (C2) The dynamics of available resources, caused by vehicles' mobility, hinder reliable CI-App processing. This work is among the first to jointly address (C1) and (C2), while considering two common CI-App graph representations, directed acyclic graph (DAG) and undirected graph (UG). To address (C1), we consider partitioning a CI-App with $m$ dependent (sub-)tasks into $k\le m$ groups, which are dispersed across vehicles. To address (C2), we introduce a generalized reliability metric called conditional mean time to failure (C-MTTF). Subsequently, we increase the C-MTTF of dependent sub-tasks processing via introducing a general framework of redundancy-based processing of dependent sub-tasks over semi-dynamic VCs (RP-VC). We demonstrate that RP-VC can be modeled as a non-trivial semi-Markov process (SMP). To analyze this SMP model and its reliability, we develop a novel mathematical framework, called event stochastic algebra ($\langle e\rangle$-algebra). Based on $\langle e\rangle$-algebra, we propose decomposition theorem (DT) to transform the presented SMP to a decomposed SMP (D-SMP). We subsequently calculate the C-MTTF of our methodology. We demonstrate that $\langle e\rangle$-algebra and DT are general mathematical tools that can be used to analyze other cloud-based networks. Simulation results reveal the exactness of our analytical results and the efficiency of our methodology in terms of acceptance and success rates of CI-App processing.
Cognitive-Driven Development Helps Software Teams to Keep Code Units Under the Limit!
Venue: arXiv cs.SE 2023
Year: 2023

Software design techniques are undoubtedly crucial in the process of designing good software. Over the years, a large number of design techniques have been proposed by both researchers and practitioners. Unfortunately, despite their uniqueness, it is not uncommon to find software products that make subpar design decisions, leading to design degradation challenges. One potential reason for this behavior is that developers do not have a clear vision of how much a code unit could grow; without this vision, a code unit can grow endlessly, even when developers are equipped with an arsenal of design practices. Different than other design techniques, Cognitive Driven Development (CDD for short) focuses on 1) defining and 2) limiting the number of coding elements that developers could use at a given code unit. In this paper, we report on the experiences of a software development team in using CDD for building from scratch a learning management tool at Zup Innovation, a Brazilian tech company. By curating commit traces left in the repositories, combined with the developers' perception, we organized a set of findings and lessons that could be useful for those interested in adopting CDD. For instance, we noticed that by using CDD, despite the evolution of the product, developers were able to keep the code units under a small amount of size (in terms of size). Furthermore, although limiting the complexity is at the heart of CDD, we also discovered that developers tend to relax this notion of limit so that they can cope with the different complexities of the software. Still, we noticed that CDD could also influence testing practices; limiting the code units' size makes testing easier to perform.
Is It Worth the (Environmental) Cost? Limited Evidence for Temporal Adaptation via Continuous Training
Venue: arXiv cs.CL 2023
Year: 2023

Language is constantly changing and evolving, leaving language models to become quickly outdated. Consequently, we should continuously update our models with new data to expose them to new events and facts. However, that requires additional computing, which means new carbon emissions. Do any measurable benefits justify this cost? This paper looks for empirical evidence to support continuous training. We reproduce existing benchmarks and extend them to include additional time periods, models, and tasks. Our results show that the downstream task performance of temporally adapted English models for social media data do not improve over time. Pretrained models without temporal adaptation are actually significantly more effective and efficient. However, we also note a lack of suitable temporal benchmarks. Our findings invite a critical reflection on when and how to temporally adapt language models, accounting for sustainability.
Query Rewriting for Effective Misinformation Discovery
Venue: arXiv cs.CL 2023
Year: 2023

We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms. We introduce an adaptable rewriting strategy, where editing actions for queries containing claims (e.g., swap a word with its synonym; change verb tense into present simple) are automatically learned through offline reinforcement learning. Our model uses a decision transformer to learn a sequence of editing actions that maximizes query retrieval metrics such as mean average precision. We conduct a series of experiments showing that our query rewriting system achieves a relative increase in the effectiveness of the queries of up to 42%, while producing editing action sequences that are human interpretable.
Watermarking Pre-trained Language Models with Backdooring
Venue: arXiv cs.CL 2023
Year: 2023

Large pre-trained language models (PLMs) have proven to be a crucial component of modern natural language processing systems. PLMs typically need to be fine-tuned on task-specific downstream datasets, which makes it hard to claim the ownership of PLMs and protect the developer's intellectual property due to the catastrophic forgetting phenomenon. We show that PLMs can be watermarked with a multi-task learning framework by embedding backdoors triggered by specific inputs defined by the owners, and those watermarks are hard to remove even though the watermarked PLMs are fine-tuned on multiple downstream tasks. In addition to using some rare words as triggers, we also show that the combination of common words can be used as backdoor triggers to avoid them being easily detected. Extensive experiments on multiple datasets demonstrate that the embedded watermarks can be robustly extracted with a high success rate and less influenced by the follow-up fine-tuning.
Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning
Venue: arXiv cs.CL 2023
Year: 2023

Prompt tuning is a parameter-efficient approach to adapting pre-trained language models to downstream tasks. Although prompt tuning has been shown to match the performance of full model tuning when training data is sufficient, it tends to struggle in few-shot learning settings. In this paper, we present Multi-task Pre-trained Modular Prompt (MP2) to boost prompt tuning for few-shot learning. MP2 is a set of combinable prompts pre-trained on 38 Chinese tasks. On downstream tasks, the pre-trained prompts are selectively activated and combined, leading to strong compositional generalization to unseen tasks. To bridge the gap between pre-training and fine-tuning, we formulate upstream and downstream tasks into a unified machine reading comprehension task. Extensive experiments under two learning paradigms, i.e., gradient descent and black-box tuning, show that MP2 significantly outperforms prompt tuning, full model tuning, and prior prompt pre-training methods in few-shot settings. In addition, we demonstrate that MP2 can achieve surprisingly fast and strong adaptation to downstream tasks by merely learning 8 parameters to combine the pre-trained modular prompts.
A Non-iterative Spatio-temporal Multi-task Assignments based Collision-free Trajectories for Music Playing Robots
Venue: arXiv cs.RO 2023
Year: 2023

In this paper, a non-iterative spatio-temporal multi-task assignment approach is used for playing piano music by a team of robots. This paper considers the piano playing problem, in which an algorithm needs to compute the trajectories for a dynamically sized team of robots who will play the musical notes by traveling through the specific locations associated with musical notes at their respective specific times. A two-step dynamic resource allocation based on a spatio-temporal multi-task assignment problem (DREAM), has been implemented to assign robots for playing the musical tune. The algorithm computes the required number of robots to play the music in the first step. In the second step, optimal assignments are computed for the updated team of robots, which minimizes the total distance traveled by the team. Even for the individual feasible trajectories, the multi-robot execution may fail if robots encounter a collision. As some time will be utilized for this conflict resolution, robots may not be able to reach the desired location on time. This paper analyses and proves that, if robots are operating in a convex region, the solution of the DREAM approach provides collision-free trajectories. The working of the DREAM approach has been illustrated with the help of the high fidelity simulations in Gazebo operated using ROS2. The result clearly shows that the DREAM approach computes the required number of robots and assigns multiple tasks to robots in at most two steps. The simulation of the robots playing music, using computed assignments, is demonstrated in the attached video. video link: \url{https://youtu.be/XToicNm-CO8}
Multi-Task Learning based Video Anomaly Detection with Attention
Venue: arXiv cs.CV 2023
Year: 2023

Multi-task learning based video anomaly detection methods combine multiple proxy tasks in different branches to detect video anomalies in different situations. Most existing methods either do not combine complementary tasks to effectively cover all motion patterns, or the class of the objects is not explicitly considered. To address the aforementioned shortcomings, we propose a novel multi-task learning based method that combines complementary proxy tasks to better consider the motion and appearance features. We combine the semantic segmentation and future frame prediction tasks in a single branch to learn the object class and consistent motion patterns, and to detect respective anomalies simultaneously. In the second branch, we added several attention mechanisms to detect motion anomalies with attention to object parts, the direction of motion, and the distance of the objects from the camera. Our qualitative results show that the proposed method considers the object class effectively and learns motion with attention to the aforementioned important factors which results in a precise motion modeling and a better motion anomaly detection. Additionally, quantitative results show the superiority of our method compared with state-of-the-art methods.
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Venue: arXiv cs.LG 2023
Year: 2023

Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few active learning studies have attempted to deal with this open-set noise for sample selection by filtering out the noisy examples. However, because focusing on the purity of examples in a query set leads to overlooking the informativeness of the examples, the best balancing of purity and informativeness remains an important question. In this paper, to solve this purity-informativeness dilemma in open-set active learning, we propose a novel Meta-Query-Net,(MQ-Net) that adaptively finds the best balancing between the two factors. Specifically, by leveraging the multi-round property of active learning, we train MQ-Net using a query set without an additional validation set. Furthermore, a clear dominance relationship between unlabeled examples is effectively captured by MQ-Net through a novel skyline regularization. Extensive experiments on multiple open-set active learning scenarios demonstrate that the proposed MQ-Net achieves 20.14% improvement in terms of accuracy, compared with the state-of-the-art methods.
Intra-session Context-aware Feed Recommendation in Live Systems
Venue: arXiv cs.IR 2023
Year: 2023

Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios. Within a session, user's decision to continue browsing or not substantially affects occurrences of later clicks. However, such type of exposure bias is generally ignored or not explicitly modeled in most feed recommendation studies. In this paper, we model this effect as part of intra-session context, and propose a novel intra-session Context-aware Feed Recommendation (INSCAFER) framework to maximize the total views and total clicks simultaneously. User click and browsing decisions are jointly learned by a multi-task setting, and the intra-session context is encoded by the session-wise exposed item sequence. We deploy our model online with all key business benchmarks improved. Our method sheds some lights on feed recommendation studies which aim to optimize session-level click and view metrics.
Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services
Venue: arXiv cs.NI 2023
Year: 2023

In applications such as elderly care, dementia anti-wandering and pandemic control, it is important to ensure that people are within a predefined area for their safety and well-being. We propose GEM, a practical, semi-supervised Geofencing system with network EMbedding, which is based only on ambient radio frequency (RF) signals. GEM models measured RF signal records as a weighted bipartite graph. With access points on one side and signal records on the other, it is able to precisely capture the relationships between signal records. GEM then learns node embeddings from the graph via a novel bipartite network embedding algorithm called BiSAGE, based on a Bipartite graph neural network with a novel bi-level SAmple and aggreGatE mechanism and non-uniform neighborhood sampling. Using the learned embeddings, GEM finally builds a one-class classification model via an enhanced histogram-based algorithm for in-out detection, i.e., to detect whether the user is inside the area or not. This model also keeps on improving with newly collected signal records. We demonstrate through extensive experiments in diverse environments that GEM shows state-of-the-art performance with up to 34% improvement in F-score. BiSAGE in GEM leads to a 54% improvement in F-score, as compared to the one without BiSAGE.
Rate and Detection-Error Exponent Tradeoff for Joint Communication and Sensing of Fixed Channel States
Venue: arXiv cs.IT 2023
Year: 2023

We study the information-theoretic limits of joint communication and sensing when the sensing task is modeled as the estimation of a discrete channel state fixed during the transmission of an entire codeword. This setting captures scenarios in which the time scale over which sensing happens is significantly slower than the time scale over which symbol transmission occurs. The tradeoff between communication and sensing then takes the form of a tradeoff region between the rate of reliable communication and the state detection-error exponent. We investigate such tradeoffs for both mono-static and bi-static scenarios, in which the sensing task is performed at the transmitter or receiver, respectively. In the mono-static case, we develop an exact characterization of the tradeoff in open-loop, when the sensing is not used to assist the communication. We also show the strict improvement brought by a closed-loop operation, in which the sensing informs the communication. In the bi-static case, we develop an achievable tradeoff region that highlights the fundamentally different nature of the bi-static scenario. Specifically, the rate of communication plays a key role in the characterization of the tradeoff and we show how joint strategies, which simultaneously estimate message and state, outperform successive strategies, which only estimate the state after decoding the transmitted message.
Failure Analysis of Big Cloud Service Providers Prior to and During Covid-19 Period
Venue: arXiv cs.DC 2023
Year: 2023

Cloud services are important for societal function such as healthcare, commerce, entertainment and education. Cloud can provide a variety of features such as increased collaboration and inexpensive computing. Failures are unavoidable in cloud services due to the large size and complexity, resulting in decreased reliability and efficiency. For example, due to bugs, many high-severity failures have been occurring in cloud infrastructure of popular providers, causing outages of several hours and the unrecoverable loss of user data. There are prior studies about cloud failure analyses are limited and use sources such as news articles. However, a detailed cloud failure focused study is required that provides analyses for cloud failure data gathered directly from the vendors. Furthermore, the Covid-19 cloud failures should be studied as cloud services played a major role throughout the Covid-19 period, as individuals relied on cloud services for activities such as working from home. A program can be made for this task. As a result, we will be able to better understand and mitigate cloud failures to reduce the effect of cloud failures.
Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)
Venue: arXiv cs.RO 2023
Year: 2023

Approaches for stochastic nonlinear model predictive control (SNMPC) typically make restrictive assumptions about the system dynamics and rely on approximations to characterize the evolution of the underlying uncertainty distributions. For this reason, they are often unable to capture more complex distributions (e.g., non-Gaussian or multi-modal) and cannot provide accurate guarantees of performance. In this paper, we present a sampling-based SNMPC approach that leverages recently derived sample complexity bounds to certify the performance of a feedback policy without making assumptions about the system dynamics or underlying uncertainty distributions. By parallelizing our approach, we are able to demonstrate real-time receding-horizon SNMPC with statistical safety guarantees in simulation and on hardware using a 1/10th scale rally car and a 24-inch wingspan fixed-wing unmanned aerial vehicle (UAV).
TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation
Venue: arXiv cs.RO 2023
Year: 2023

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion planning and relative distance to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.
Generating Synthetic Speech from SpokenVocab for Speech Translation
Venue: arXiv cs.CL 2023
Year: 2023

Training end-to-end speech translation (ST) systems requires sufficiently large-scale data, which is unavailable for most language pairs and domains. One practical solution to the data scarcity issue is to convert machine translation data (MT) to ST data via text-to-speech (TTS) systems. Yet, using TTS systems can be tedious and slow, as the conversion needs to be done for each MT dataset. In this work, we propose a simple, scalable and effective data augmentation technique, i.e., SpokenVocab, to convert MT data to ST data on-the-fly. The idea is to retrieve and stitch audio snippets from a SpokenVocab bank according to words in an MT sequence. Our experiments on multiple language pairs from Must-C show that this method outperforms strong baselines by an average of 1.83 BLEU scores, and it performs equally well as TTS-generated speech. We also showcase how SpokenVocab can be applied in code-switching ST for which often no TTS systems exit. Our code is available at https://github.com/mingzi151/SpokenVocab
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Venue: arXiv cs.IT 2023
Year: 2023

We provide an exact characterization of the expected generalization error (gen-error) for semi-supervised learning (SSL) with pseudo-labeling via the Gibbs algorithm. The gen-error is expressed in terms of the symmetrized KL information between the output hypothesis, the pseudo-labeled dataset, and the labeled dataset. Distribution-free upper and lower bounds on the gen-error can also be obtained. Our findings offer new insights that the generalization performance of SSL with pseudo-labeling is affected not only by the information between the output hypothesis and input training data but also by the information {\em shared} between the {\em labeled} and {\em pseudo-labeled} data samples. This serves as a guideline to choose an appropriate pseudo-labeling method from a given family of methods. To deepen our understanding, we further explore two examples -- mean estimation and logistic regression. In particular, we analyze how the ratio of the number of unlabeled to labeled data $λ$ affects the gen-error under both scenarios. As $λ$ increases, the gen-error for mean estimation decreases and then saturates at a value larger than when all the samples are labeled, and the gap can be quantified {\em exactly} with our analysis, and is dependent on the \emph{cross-covariance} between the labeled and pseudo-labeled data samples. For logistic regression, the gen-error and the variance component of the excess risk also decrease as $λ$ increases.