OpenRaiser/Intern-Atlas huggingface dataset:
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but also by other factors, such as duration bias and noisy watching. Duration bias refers to the tendency for users to spend more time on videos with longer durations, regardless of their actual interest level. Noisy watching, on the other hand, describes users taking time to determine whether they like a video or not, which can result in users spending time watching videos they do not like. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for indicating user interest. Furthermore, current methods primarily address duration bias and ignore the impact of noisy watching, which may limit their effectiveness in uncovering user interest from watch time. In this study, we first analyze the generation mechanism of users' watch time from a unified causal viewpoint. Specifically, we considered the watch time as a mixture of the user's actual interest level, the duration-biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D$^2$Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets and online A/B testing indicate the effectiveness of the proposed method.
Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals
Venue: arXiv cs.SD 2023
Year: 2023

Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, considering the practicality in real scenarios, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. In this paper, we propose Radio2Text, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13,000 words. Radio2Text is based on a tailored streaming Transformer that is capable of effectively learning representations of speech-related features, paving the way for streaming ASR with a large vocabulary. To alleviate the deficiency of streaming networks unable to access entire future inputs, we propose the Guidance Initialization that facilitates the transfer of feature knowledge related to the global context from the non-streaming Transformer to the tailored streaming Transformer through weight inheritance. Further, we propose a cross-modal structure based on knowledge distillation (KD), named cross-modal KD, to mitigate the negative effect of low quality mmWave signals on recognition performance. In the cross-modal KD, the audio streaming Transformer provides feature and response guidance that inherit fruitful and accurate speech information to supervise the training of the tailored radio streaming Transformer. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13,000 words.
LeakPair: Proactive Repairing of Memory Leaks in Single Page Web Applications
Venue: arXiv cs.SE 2023
Year: 2023

Modern web applications often resort to application development frameworks such as React, Vue.js, and Angular. While the frameworks facilitate the development of web applications with several useful components, they are inevitably vulnerable to unmanaged memory consumption since the frameworks often produce Single Page Applications (SPAs). Web applications can be alive for hours and days with behavior loops, in such cases, even a single memory leak in a SPA app can cause performance degradation on the client side. However, recent debugging techniques for web applications still focus on memory leak detection, which requires manual tasks and produces imprecise results. We propose LeakPair, a technique to repair memory leaks in single page applications. Given the insight that memory leaks are mostly non-functional bugs and fixing them might not change the behavior of an application, the technique is designed to proactively generate patches to fix memory leaks, without leak detection, which is often heavy and tedious. To generate effective patches, LeakPair follows the idea of pattern-based program repair since the automated repair strategy shows successful results in many recent studies. We evaluate the technique on more than 20 open-source projects without using explicit leak detection. The patches generated by our technique are also submitted to the projects as pull requests. The results show that LeakPair can generate effective patches to reduce memory consumption that are acceptable to developers. In addition, we execute the test suites given by the projects after applying the patches, and it turns out that the patches do not cause any functionality breakage; this might imply that LeakPair can generate non-intrusive patches for memory leaks.
Resolution of Near-Field Beamforming and Its Impact on NOMA
Venue: arXiv cs.IT 2023
Year: 2023

The resolution of near-field beamforming is an important metric to measure how effectively users with different locations can be located. This letter identifies the condition under which the resolution of near-field beamforming is not perfect. This imperfect resolution means that one user's near-field beam can be still useful to other users, which motivates the application of non-orthogonal multiple access (NOMA). Both the analytical and simulation results are developed to demonstrate that those near-field beams preconfigured for legacy users can indeed be used to effectively serve additional NOMA users, which improves the overall connectivity and system throughput.
Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System
Venue: arXiv cs.CL 2023
Year: 2023

End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capabilities of pre-trained models. This work enables the TOD systems with more flexibility through a simple cache. The cache provides the flexibility to dynamically update the TOD systems and handle both existing and unseen dialogue scenarios. Towards this end, we first fine-tune a retrieval module to effectively retrieve the most relevant information entries from the cache. We then train end-to-end TOD models that can refer to and ground on both dialogue history and retrieved information during TOD generation. The cache is straightforward to construct, and the backbone models of TOD systems are compatible with existing pre-trained generative models. Extensive experiments demonstrate the superior performance of our framework, with a notable improvement in non-empty joint goal accuracy by 6.7% compared to strong baselines.
Automatic Vision-Based Parking Slot Detection and Occupancy Classification
Venue: arXiv cs.CV 2023
Year: 2023

Parking guidance information (PGI) systems are used to provide information to drivers about the nearest parking lots and the number of vacant parking slots. Recently, vision-based solutions started to appear as a cost-effective alternative to standard PGI systems based on hardware sensors mounted on each parking slot. Vision-based systems provide information about parking occupancy based on images taken by a camera that is recording a parking lot. However, such systems are challenging to develop due to various possible viewpoints, weather conditions, and object occlusions. Most notably, they require manual labeling of parking slot locations in the input image which is sensitive to camera angle change, replacement, or maintenance. In this paper, the algorithm that performs Automatic Parking Slot Detection and Occupancy Classification (APSD-OC) solely on input images is proposed. Automatic parking slot detection is based on vehicle detections in a series of parking lot images upon which clustering is applied in bird's eye view to detect parking slots. Once the parking slots positions are determined in the input image, each detected parking slot is classified as occupied or vacant using a specifically trained ResNet34 deep classifier. The proposed approach is extensively evaluated on well-known publicly available datasets (PKLot and CNRPark+EXT), showing high efficiency in parking slot detection and robustness to the presence of illegal parking or passing vehicles. Trained classifier achieves high accuracy in parking slot occupancy classification.
A Hybrid Wireless Image Transmission Scheme with Diffusion
Venue: arXiv cs.IT 2023
Year: 2023

We propose a hybrid joint source-channel coding (JSCC) scheme, in which the conventional digital communication scheme is complemented with a generative refinement component to improve the perceptual quality of the reconstruction. The input image is decomposed into two components: the first is a coarse compressed version, and is transmitted following the conventional separation based approach. An additional component is obtained through the diffusion process by adding independent Gaussian noise to the input image, and is transmitted using DeepJSCC. The decoder combines the two signals to produce a high quality reconstruction of the source. Experimental results show that the hybrid design provides bandwidth savings and enables graceful performance improvement as the channel quality improves.
Description Logics Go Second-Order -- Extending EL with Universally Quantified Concepts
Venue: arXiv cs.LO 2023
Year: 2023

The study of Description Logics have been historically mostly focused on features that can be translated to decidable fragments of first-order logic. In this paper, we leave this restriction behind and look for useful and decidable extensions outside first-order logic. We introduce universally quantified concepts, which take the form of variables that can be replaced with arbitrary concepts, and define two semantics of this extension. A schema semantics allows replacements of concept variables only by concepts from a particular language, giving us axiom schemata similar to modal logics. A second-order semantics allows replacement of concept variables with arbitrary subsets of the domain, which is similar to quantified predicates in second-order logic. To study the proposed semantics, we focus on the extension of the description logic $\mathcal{EL}$. We show that for a useful fragment of the extension, the conclusions entailed by the different semantics coincide, allowing us to use classical $\mathcal{EL}$ reasoning algorithms even for the second-order semantics. For a slightly smaller, but still useful, fragment, we were also able to show polynomial decidability of the extension. This fragment, in particular, can express a generalized form of role chain axioms, positive self restrictions, and some forms of (local) role-value-maps from KL-ONE, without requiring any additional constructors.
MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
Venue: arXiv cs.CV 2023
Year: 2023

Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact with- and support humans in social interactions. In MultiMediate'23, we address two key human social behaviour analysis tasks for the first time in a controlled challenge: engagement estimation and bodily behaviour recognition in social interactions. This paper describes the MultiMediate'23 challenge and presents novel sets of annotations for both tasks. For engagement estimation we collected novel annotations on the NOvice eXpert Interaction (NOXI) database. For bodily behaviour recognition, we annotated test recordings of the MPIIGroupInteraction corpus with the BBSI annotation scheme. In addition, we present baseline results for both challenge tasks.
Improving Audio-Visual Segmentation with Bidirectional Generation
Venue: arXiv cs.CV 2023
Year: 2023

The aim of audio-visual segmentation (AVS) is to precisely differentiate audible objects within videos down to the pixel level. Traditional approaches often tackle this challenge by combining information from various modalities, where the contribution of each modality is implicitly or explicitly modeled. Nevertheless, the interconnections between different modalities tend to be overlooked in audio-visual modeling. In this paper, inspired by the human ability to mentally simulate the sound of an object and its visual appearance, we introduce a bidirectional generation framework. This framework establishes robust correlations between an object's visual characteristics and its associated sound, thereby enhancing the performance of AVS. To achieve this, we employ a visual-to-audio projection component that reconstructs audio features from object segmentation masks and minimizes reconstruction errors. Moreover, recognizing that many sounds are linked to object movements, we introduce an implicit volumetric motion estimation module to handle temporal dynamics that may be challenging to capture using conventional optical flow methods. To showcase the effectiveness of our approach, we conduct comprehensive experiments and analyses on the widely recognized AVSBench benchmark. As a result, we establish a new state-of-the-art performance level in the AVS benchmark, particularly excelling in the challenging MS3 subset which involves segmenting multiple sound sources. To facilitate reproducibility, we plan to release both the source code and the pre-trained model.
Techniques for Improving the Energy Efficiency of Mobile Apps: A Taxonomy and Systematic Literature Review
Venue: arXiv cs.SE 2023
Year: 2023

Building energy efficient software is an increasingly important task for mobile developers. However, a cumulative body of knowledge of techniques that support this goal does not exist. We conduct a systematic literature review to gather information on existing techniques that allow developers to increase energy efficiency in mobile apps. Based on a synthesis of the 91 included primary studies, we propose a taxonomy of techniques for improving the energy efficiency in mobile apps. The taxonomy includes seven main categories of techniques and serves as a collection of available methods for developers and as a reference guide for software testers when performing energy efficiency testing by the means of benchmark tests.
Soft-Information Post-Processing for Chase-Pyndiah Decoding Based on Generalized Mutual Information
Venue: arXiv cs.IT 2023
Year: 2023

Chase-Pyndiah decoding is widely used for decoding product codes. However, this method is suboptimal and requires scaling the soft information exchanged during the iterative processing. In this paper, we propose a framework for obtaining the scaling coefficients based on maximizing the generalized mutual information. Our approach yields gains up to 0.11 dB for product codes with two-error correcting extended BCH component codes over the binary-input additive white Gaussian noise channel compared to the original Chase-Pyndiah decoder with heuristically obtained coefficients. We also introduce an extrinsic version of the Chase-Pyndiah decoder and associate product codes with a turbo-like code ensemble to derive a Monte Carlo-based density evolution analysis. The resulting iterative decoding thresholds accurately predict the onset of the waterfall region.
SummHelper: Collaborative Human-Computer Summarization
Venue: arXiv cs.CL 2023
Year: 2023

Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process. In this paper, we introduce SummHelper, a 2-phase summarization assistant designed to foster human-machine collaboration. The initial phase involves content selection, where the system recommends potential content, allowing users to accept, modify, or introduce additional selections. The subsequent phase, content consolidation, involves SummHelper generating a coherent summary from these selections, which users can then refine using visual mappings between the summary and the source text. Small-scale user studies reveal the effectiveness of our application, with participants being especially appreciative of the balance between automated guidance and opportunities for personal input.
Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model
Venue: arXiv cs.CR 2023
Year: 2023

To enhance the security of text CAPTCHAs, various methods have been employed, such as adding the interference lines on the text, randomly distorting the characters, and overlapping multiple characters. These methods partly increase the difficulty of automated segmentation and recognition attacks. However, facing the rapid development of the end-to-end breaking algorithms, their security has been greatly weakened. The diffusion model is a novel image generation model that can generate the text images with deep fusion of characters and background images. In this paper, an image-click CAPTCHA scheme called Diff-CAPTCHA is proposed based on denoising diffusion models. The background image and characters of the CAPTCHA are treated as a whole to guide the generation process of a diffusion model, thus weakening the character features available for machine learning, enhancing the diversity of character features in the CAPTCHA, and increasing the difficulty of breaking algorithms. To evaluate the security of Diff-CAPTCHA, this paper develops several attack methods, including end-to-end attacks based on Faster R-CNN and two-stage attacks, and Diff-CAPTCHA is compared with three baseline schemes, including commercial CAPTCHA scheme and security-enhanced CAPTCHA scheme based on style transfer. The experimental results show that diffusion models can effectively enhance CAPTCHA security while maintaining good usability in human testing.
PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing
Venue: arXiv cs.AI 2023
Year: 2023

Procedural knowledge describes how to accomplish tasks and mitigate problems. Such knowledge is commonly held by domain experts, e.g. operators in manufacturing who adjust parameters to achieve quality targets. To the best of our knowledge, no real-world datasets containing process data and corresponding procedural knowledge are publicly available, possibly due to corporate apprehensions regarding the loss of knowledge advances. Therefore, we provide a framework to generate synthetic datasets that can be adapted to different domains. The design choices are inspired by two real-world datasets of procedural knowledge we have access to. Apart from containing representations of procedural knowledge in Resource Description Framework (RDF)-compliant knowledge graphs, the framework simulates parametrisation processes and provides consistent process data. We compare established embedding methods on the resulting knowledge graphs, detailing which out-of-the-box methods have the potential to represent procedural knowledge. This provides a baseline which can be used to increase the comparability of future work. Furthermore, we validate the overall characteristics of a synthesised dataset by comparing the results to those achievable on a real-world dataset. The framework and evaluation code, as well as the dataset used in the evaluation, are available open source.
A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks
Venue: arXiv cs.LG 2023
Year: 2023

We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. This dynamic selection is jointly learned with the task model in an end-to-end way, using the Gumbel-Softmax trick to allow the discrete decisions to be learned through standard backpropagation. We then show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit. We further improve performance by including a dynamic spatial filter that makes the task-DNN more robust against the fact that it now needs to be able to handle a multitude of possible node subsets. Finally, we explain how the selection of the optimal channels can be distributed across the different nodes in a WSN. We validate this method on a use case in the context of body-sensor networks, where we use real electroencephalography (EEG) sensor data to emulate an EEG sensor network. We analyze the resulting trade-offs between transmission load and task accuracy.
Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks.
A topological counterpart of well-founded trees in dependent type theory
Venue: arXiv cs.LO 2023
Year: 2023

Within dependent type theory, we provide a topological counterpart of well-founded trees (for short, W-types) by using a proof-relevant version of the notion of inductively generated suplattices introduced in the context of formal topology under the name of inductively generated basic covers. In more detail, we show, firstly, that in Homotopy Type Theory, W-types and proof relevant inductively generated basic covers are propositionally mutually encodable. Secondly, we prove they are definitionally mutually encodable in the Agda implementation of intensional Martin-Loef's type theory. Finally, we reframe the equivalence in the Minimalist Foundation framework by introducing well-founded predicates as the logical counterpart for predicates of dependent W-types. All the results have been checked in the Agda proof-assistant.