OpenRaiser/Intern-Atlas huggingface dataset:
Load is not what you should balance: Introducing Prequal
Venue: arXiv cs.DC 2023
Year: 2023

We present Prequal (Probing to Reduce Queuing and Latency), a load balancer for distributed multi-tenant systems. Prequal aims to minimize real-time request latency in the presence of heterogeneous server capacities and non-uniform, time-varying antagonist load. It actively probes server load to leverage the power-of-d-choices paradigm, extending it with asynchronous and reusable probes. Cutting against received wisdom, Prequal does not balance CPU load, but instead selects servers according to estimated latency and active requests-in-flight (RIF). We explore its major design features on a testbed system and evaluate it on YouTube, where it has been deployed for more than two years. Prequal has dramatically decreased tail latency, error rates, and resource use, enabling YouTube and other production systems at Google to run at much higher utilization.
A Remark on Concept Drift for Dependent Data
Venue: arXiv cs.LG 2023
Year: 2023

Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Several works address the phenomenon of concept drift in the streaming context usually assuming that consecutive data points are independent of each other. To generalize to dependent data, many authors link the notion of concept drift to time series. In this work, we show that the temporal dependencies are strongly influencing the sampling process. Thus, the used definitions need major modifications. In particular, we show that the notion of stationarity is not suited for this setup and discuss alternatives. We demonstrate that these alternative formal notions describe the observable learning behavior in numerical experiments.
Implicit Modeling of Non-rigid Objects with Cross-Category Signals
Venue: arXiv cs.CV 2023
Year: 2023

Deep implicit functions (DIFs) have emerged as a potent and articulate means of representing 3D shapes. However, methods modeling object categories or non-rigid entities have mainly focused on single-object scenarios. In this work, we propose MODIF, a multi-object deep implicit function that jointly learns the deformation fields and instance-specific latent codes for multiple objects at once. Our emphasis is on non-rigid, non-interpenetrating entities such as organs. To effectively capture the interrelation between these entities and ensure precise, collision-free representations, our approach facilitates signaling between category-specific fields to adequately rectify shapes. We also introduce novel inter-object supervision: an attraction-repulsion loss is formulated to refine contact regions between objects. Our approach is demonstrated on various medical benchmarks, involving modeling different groups of intricate anatomical entities. Experimental results illustrate that our model can proficiently learn the shape representation of each organ and their relations to others, to the point that shapes missing from unseen instances can be consistently recovered by our method. Finally, MODIF can also propagate semantic information throughout the population via accurate point correspondences
Secure and Accurate Summation of Many Floating-Point Numbers
Venue: arXiv cs.CR 2023
Year: 2023

Motivated by the importance of floating-point computations, we study the problem of securely and accurately summing many floating-point numbers. Prior work has focused on security absent accuracy or accuracy absent security, whereas our approach achieves both of them. Specifically, we show how to implement floating-point superaccumulators using secure multi-party computation techniques, so that a number of participants holding secret shares of floating-point numbers can accurately compute their sum while keeping the individual values private.
Technical Report: Unresolved Challenges and Potential Features in EATXT
Venue: arXiv cs.SE 2023
Year: 2023

We developed a textual concrete syntax and a textual editor that supports it for the domain-specific language EAST-ADL, which we named EATXT. This document is a technical report that describes potential advanced features that could be added to EATXT that have not yet been implemented. The purpose of this report is to share our understanding of the relevant technical challenges and to assist potentially interested peers.
K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning
Venue: arXiv cs.CL 2023
Year: 2023

Automatic psychological counseling requires mass of professional knowledge that can be found in online counseling forums. Motivated by this, we propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system, transferring forum knowledge to response generation. We evaluate our model on an emotional support dataset ESConv, where the model retrieves and incorporates knowledge from external professional emotional Q\&A forum. Experiment results show that the proposed method outperforms existing baselines on both automatic evaluation and human evaluation, which shows that our approach significantly improves the correlation and diversity of responses and provides more comfort and better suggestion for the seeker.
RoboPainter -- a conceptual towards robotized interior finishes
Venue: arXiv cs.RO 2023
Year: 2023

High demand for painters is required nowadays and foreseen in the near future for both developed and developing countries. To satisfy such demand, this paper presents the detailed computer aided design (CAD) model of a fully functional wall painting robot for interior finishes. The RoboPainter is capable of performing full scale wall-ceil painting in addition to decorative wall drawings. The 8 degrees of freedom (DOF) mobile robot structure consists of a 6DOF spray painting arm mounted on a 2DOF differentially driven mobile base. The design presented endows several achievements in terms of total robot mass and painting rate as compared to existing literature. Detailed dynamic model parameters are presented to allow for further enhancement in terms of robot motion control.
Statistical Analysis of Inter Coding in VVC Test Model (VTM)
Venue: arXiv cs.MM 2023
Year: 2023

The promising improvement in compression efficiency of Versatile Video Coding (VVC) compared to High Efficiency Video Coding (HEVC) comes at the cost of a non-negligible encoder side complexity. The largely increased complexity overhead is a possible obstacle towards its industrial implementation. Many papers have proposed acceleration methods for VVC. Still, a better understanding of VVC complexity, especially related to new partitions and coding tools, is desirable to help the design of new and better acceleration methods. For this purpose, statistical analyses have been conducted, with a focus on Coding Unit (CU) sizes and inter coding modes.
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
Venue: arXiv cs.LG 2023
Year: 2023

Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timeliness of computational-intensive updates, measured by Age-ofInformation (AoI), and study how to jointly optimize the task updating and offloading policies for AoI with fractional form. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. The uncertain edge load dynamics, the nature of the fractional objective, and hybrid continuous-discrete action space (due to the joint optimization) make this problem challenging and existing approaches not directly applicable. To this end, we propose a fractional reinforcement learning(RL) framework and prove its convergence. We further design a model-free fractional deep RL (DRL) algorithm, where each device makes scheduling decisions with the hybrid action space without knowing the system dynamics and decisions of other devices. Experimental results show that our proposed algorithms reduce the average AoI by up to 57.6% compared with several non-fractional benchmarks.
A Survey on Robotic Manipulation of Deformable Objects: Recent Advances, Open Challenges and New Frontiers
Venue: arXiv cs.RO 2023
Year: 2023

Deformable object manipulation (DOM) for robots has a wide range of applications in various fields such as industrial, service and health care sectors. However, compared to manipulation of rigid objects, DOM poses significant challenges for robotic perception, modeling and manipulation, due to the infinite dimensionality of the state space of deformable objects (DOs) and the complexity of their dynamics. The development of computer graphics and machine learning has enabled novel techniques for DOM. These techniques, based on data-driven paradigms, can address some of the challenges that analytical approaches of DOM face. However, some existing reviews do not include all aspects of DOM, and some previous reviews do not summarize data-driven approaches adequately. In this article, we survey more than 150 relevant studies (data-driven approaches mainly) and summarize recent advances, open challenges, and new frontiers for aspects of perception, modeling and manipulation for DOs. Particularly, we summarize initial progress made by Large Language Models (LLMs) in robotic manipulation, and indicates some valuable directions for further research. We believe that integrating data-driven approaches and analytical approaches can provide viable solutions to open challenges of DOM.
A Survey on Post-Quantum Cryptography: State-of-the-Art and Challenges
Venue: arXiv cs.CR 2023
Year: 2023

The paper explains that post-quantum cryptography is necessary due to the introduction of quantum computing causing certain algorithms to be broken. We analyze the different types of post-quantum cryptography, quantum cryptography and quantum-resistant cryptography, to provide a thorough understanding of the current solutions to the problems and their limitations. We explain the current state of quantum computing and how it has changed over time while discussing possible attacks on both types of post-quantum cryptography. Next, current post-quantum algorithms are discussed, and implementations are demonstrated. Lastly, we conclude that due to quantum cryptography's present limitations it is not a viable solution like it is often presented to be and that it is currently better to use quantum-resistant cryptography.
From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping
Venue: arXiv cs.CL 2023
Year: 2023

The automatic transformation of verbose, natural language descriptions into structured process models remains a challenge of significant complexity - This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER) for extracting key elements from textual descriptions. Additionally, we utilize Subject-Verb-Object (SVO) constructs for identifying action relationships and integrate semantic analysis tools, including WordNet, for enriched contextual understanding. A novel aspect of our system is the application of neural coreference resolution, integrated with the SpaCy framework, enhancing the precision of entity linkage and anaphoric references. Furthermore, the system adeptly handles data transformation and visualization, converting extracted information into BPMN (Business Process Model and Notation) diagrams. This methodology not only streamlines the process of capturing and representing business workflows but also significantly reduces the manual effort and potential for error inherent in traditional modeling approaches.
A new method color MS-BSIF Features learning for the robust kinship verification
Venue: arXiv cs.CV 2023
Year: 2023

the paper presents a new method color MS-BSIF learning and MS-LBP for the kinship verification is the machine's ability to identify the genetic and blood the relationship and its degree between the facial images of humans. Facial verification of kinship refers to the task of training a machine to recognize the blood relationship between a pair of faces parent and non-parent (verification) based on features extracted from facial images, and determining the exact type or degree of this genetic relationship. We use the LBP and color BSIF learning features for the comparison and the TXQDA method for dimensionality reduction and data classification. We let's test the kinship facial verification application is namely the kinface Cornell database. This system improves the robustness of learning while controlling efficiency. The experimental results obtained and compared to other methods have proven the reliability of our framework and surpass the performance of other state-of-the-art techniques.
Time-Constrained Continuous Subgraph Matching Using Temporal Information for Filtering and Backtracking
Venue: arXiv cs.DB 2023
Year: 2023

Real-time analysis of graphs containing temporal information, such as social media streams, Q&A networks, and cyber data sources, plays an important role in various applications. Among them, detecting patterns is one of the fundamental graph analysis problems. In this paper, we study time-constrained continuous subgraph matching, which detects a pattern with a strict partial order on the edge set in real-time whenever a temporal data graph changes over time. We propose a new algorithm based on two novel techniques. First, we introduce a filtering technique called time-constrained matchable edge that uses temporal information for filtering with polynomial space. Second, we develop time-constrained pruning techniques that reduce the search space by pruning some of the parallel edges in backtracking, utilizing temporal information. Extensive experiments on real and synthetic datasets show that our approach outperforms the state-of-the-art algorithm by up to two orders of magnitude in terms of query processing time.
Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations
Venue: arXiv cs.IT 2023
Year: 2023

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to provide wireless connectivity for ground users (GUs) in various emergency scenarios. However, it is a NP-hard problem with exponential complexity in $M$ and $N$, in order to maximize the coverage rate of $M$ GUs by jointly placing $N$ ABSs with limited coverage range. The problem is further complicated when the coverage range becomes irregular due to site-specific blockages (e.g., buildings) on the air-ground channel, and/or when the GUs are moving. To address the above challenges, we study a multi-ABS movement optimization problem to maximize the average coverage rate of mobile GUs in a site-specific environment. The Spatial Deep Learning with Multi-dimensional Archive of Phenotypic Elites (SDL-ME) algorithm is proposed to tackle this challenging problem by 1) partitioning the complicated ABS movement problem into ABS placement sub-problems each spanning finite time horizon; 2) using an encoder-decoder deep neural network (DNN) as the emulator to capture the spatial correlation of ABSs/GUs and thereby reducing the cost of interaction with the actual environment; 3) employing the emulator to speed up a quality-diversity search for the optimal placement solution; and 4) proposing a planning-exploration-serving scheme for multi-ABS movement coordination. Numerical results demonstrate that the proposed approach significantly outperforms the benchmark Deep Reinforcement Learning (DRL)-based method and other two baselines in terms of average coverage rate, training time and/or sample efficiency. Moreover, with one-time training, our proposed method can be applied in scenarios where the number of ABSs/GUs dynamically changes on site and/or with different/varying GU speeds, which is thus more robust and flexible compared with conventional DRL-based methods.
Value of Information and Timing-aware Scheduling for Federated Learning
Venue: arXiv cs.NI 2023
Year: 2023

Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.
Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiries
Venue: arXiv cs.SE 2023
Year: 2023

ChatGPT is currently the most popular large language model (LLM), with over 100 million users, making a significant impact on people's lives. However, due to the presence of jailbreak vulnerabilities, ChatGPT might have negative effects on people's lives, potentially even facilitating criminal activities. Testing whether ChatGPT can cause jailbreak is crucial because it can enhance ChatGPT's security, reliability, and social responsibility. Inspired by previous research revealing the varied performance of LLMs in different language translations, we suspected that wrapping prompts in multiple languages might lead to ChatGPT jailbreak. To investigate this, we designed a study with a fuzzing testing approach to analyzing ChatGPT's cross-linguistic proficiency. Our study includes three strategies by automatically posing different formats of malicious questions to ChatGPT: (1) each malicious question involving only one language, (2) multilingual malicious questions, (3) specifying that ChatGPT responds in a language different from the prompts. In addition, we also combine our strategies by utilizing prompt injection templates to wrap the three aforementioned types of questions. We examined a total of 7,892 Q&A data points, discovering that multilingual wrapping can indeed lead to ChatGPT's jailbreak, with different wrapping methods having varying effects on jailbreak probability. Prompt injection can amplify the probability of jailbreak caused by multilingual wrapping. This work provides insights for OpenAI developers to enhance ChatGPT's support for language diversity and inclusion.
Runtime Architecture and Task Plan Co-Adaptation for Autonomous Robots with Metaplan
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous robots need to be able to handle uncertainties when deployed in the real world. For the robot to be able to robustly work in such an environment, it needs to be able to adapt both its architecture as well as its task plan. Architecture adaptation and task plan adaptation are mutually dependent, and therefore require the system to apply runtime architecture and task plan co-adaptation. This work presents Metaplan, which makes use of models of the robot and its environment, together with a PDDL planner to apply runtime architecture and task plan co-adaptation. Metaplan is designed to be easily reusable across different domains. Metaplan is shown to successfully perform runtime architecture and task plan co-adaptation with a self-adaptive unmanned underwater vehicle exemplar, and its reusability is demonstrated by applying it to an unmanned ground vehicle.
Transformers in Unsupervised Structure-from-Motion
Venue: arXiv cs.CV 2023
Year: 2023

Transformers have revolutionized deep learning based computer vision with improved performance as well as robustness to natural corruptions and adversarial attacks. Transformers are used predominantly for 2D vision tasks, including image classification, semantic segmentation, and object detection. However, robots and advanced driver assistance systems also require 3D scene understanding for decision making by extracting structure-from-motion (SfM). We propose a robust transformer-based monocular SfM method that learns to predict monocular pixel-wise depth, ego vehicle's translation and rotation, as well as camera's focal length and principal point, simultaneously. With experiments on KITTI and DDAD datasets, we demonstrate how to adapt different vision transformers and compare them against contemporary CNN-based methods. Our study shows that transformer-based architecture, though lower in run-time efficiency, achieves comparable performance while being more robust against natural corruptions, as well as untargeted and targeted attacks.
A Weighted K-Center Algorithm for Data Subset Selection
Venue: arXiv cs.LG 2023
Year: 2023

The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data, which can then be used to produce similar models as the ones trained with full data. Two prior methods are shown to achieve impressive results: (1) margin sampling that focuses on selecting points with high uncertainty, and (2) core-sets or clustering methods such as k-center for informative and diverse subsets. We are not aware of any work that combines these methods in a principled manner. To this end, we develop a novel and efficient factor 3-approximation algorithm to compute subsets based on the weighted sum of both k-center and uncertainty sampling objective functions. To handle large datasets, we show a parallel algorithm to run on multiple machines with approximation guarantees. The proposed algorithm achieves similar or better performance compared to other strong baselines on vision datasets such as CIFAR-10, CIFAR-100, and ImageNet.
A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration
Venue: arXiv cs.CV 2023
Year: 2023

Multi-exposure image fusion aims to generate a single high-dynamic image by integrating images with different exposures. Existing deep learning-based multi-exposure image fusion methods primarily focus on spatial domain fusion, neglecting the global modeling ability of the frequency domain. To effectively leverage the global illumination modeling ability of the frequency domain, we propose a novelty perspective on multi-exposure image fusion via the Spatial-Frequency Integration Framework, named MEF-SFI. Initially, we revisit the properties of the Fourier transform on the 2D image, and verify the feasibility of multi-exposure image fusion on the frequency domain where the amplitude and phase component is able to guide the integration of the illumination information. Subsequently, we present the deep Fourier-based multi-exposure image fusion framework, which consists of a spatial path and frequency path for local and global modeling separately. Specifically, we introduce a Spatial-Frequency Fusion Block to facilitate efficient interaction between dual domains and capture complementary information from input images with different exposures. Finally, we combine a dual domain loss function to ensure the retention of complementary information in both the spatial and frequency domains. Extensive experiments on the PQA-MEF dataset demonstrate that our method achieves visual-appealing fusion results against state-of-the-art multi-exposure image fusion approaches. Our code is available at https://github.com/SSyangguang/MEF-freq.