OpenRaiser/Intern-Atlas huggingface dataset:
Edge-preserving Near-light Photometric Stereo with Neural Surfaces
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction. Unlike previous methods that rely on finite differentiation for approximating depth partial derivatives and surface normals, we introduce an analytically differentiable neural surface in near-light photometric stereo for avoiding differentiation errors at sharp depth edges, where the depth is represented as a neural function of the image coordinates. By further formulating the Lambertian albedo as a dependent variable resulting from the surface normal and depth, our method is insusceptible to inaccurate depth initialization. Experiments on both synthetic and real-world scenes demonstrate the effectiveness of our method for detailed shape recovery with edge preservation.
Learning Large-scale Universal User Representation with Sparse Mixture of Experts
Venue: arXiv cs.LG 2022
Year: 2022

Learning user sequence behaviour embedding is very sophisticated and challenging due to the complicated feature interactions over time and high dimensions of user features. Recent emerging foundation models, e.g., BERT and its variants, encourage a large body of researchers to investigate in this field. However, unlike natural language processing (NLP) tasks, the parameters of user behaviour model come mostly from user embedding layer, which makes most existing works fail in training a universal user embedding of large scale. Furthermore, user representations are learned from multiple downstream tasks, and the past research work do not address the seesaw phenomenon. In this paper, we propose SUPERMOE, a generic framework to obtain high quality user representation from multiple tasks. Specifically, the user behaviour sequences are encoded by MoE transformer, and we can thus increase the model capacity to billions of parameters, or even to trillions of parameters. In order to deal with seesaw phenomenon when learning across multiple tasks, we design a new loss function with task indicators. We perform extensive offline experiments on public datasets and online experiments on private real-world business scenarios. Our approach achieves the best performance over state-of-the-art models, and the results demonstrate the effectiveness of our framework.
Topic-Grained Text Representation-based Model for Document Retrieval
Venue: arXiv cs.IR 2022
Year: 2022

Document retrieval enables users to find their required documents accurately and quickly. To satisfy the requirement of retrieval efficiency, prevalent deep neural methods adopt a representation-based matching paradigm, which saves online matching time by pre-storing document representations offline. However, the above paradigm consumes vast local storage space, especially when storing the document as word-grained representations. To tackle this, we present TGTR, a Topic-Grained Text Representation-based Model for document retrieval. Following the representation-based matching paradigm, TGTR stores the document representations offline to ensure retrieval efficiency, whereas it significantly reduces the storage requirements by using novel topicgrained representations rather than traditional word-grained. Experimental results demonstrate that compared to word-grained baselines, TGTR is consistently competitive with them on TREC CAR and MS MARCO in terms of retrieval accuracy, but it requires less than 1/10 of the storage space required by them. Moreover, TGTR overwhelmingly surpasses global-grained baselines in terms of retrieval accuracy.
Automatic Quantization for Physics-Based Simulation
Venue: arXiv cs.GR 2022
Year: 2022

Quantization has proven effective in high-resolution and large-scale simulations, which benefit from bit-level memory saving. However, identifying a quantization scheme that meets the requirement of both precision and memory efficiency requires trial and error. In this paper, we propose a novel framework to allow users to obtain a quantization scheme by simply specifying either an error bound or a memory compression rate. Based on the error propagation theory, our method takes advantage of auto-diff to estimate the contributions of each quantization operation to the total error. We formulate the task as a constrained optimization problem, which can be efficiently solved with analytical formulas derived for the linearized objective function. Our workflow extends the Taichi compiler and introduces dithering to improve the precision of quantized simulations. We demonstrate the generality and efficiency of our method via several challenging examples of physics-based simulation, which achieves up to 2.5x memory compression without noticeable degradation of visual quality in the results. Our code and data are available at https://github.com/Hanke98/AutoQuantizer.
SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder
Venue: arXiv cs.CL 2022
Year: 2022

Text summarization models are often trained to produce summaries that meet human quality requirements. However, the existing evaluation metrics for summary text are only rough proxies for summary quality, suffering from low correlation with human scoring and inhibition of summary diversity. To solve these problems, we propose SummScore, a comprehensive metric for summary quality evaluation based on CrossEncoder. Firstly, by adopting the original-summary measurement mode and comparing the semantics of the original text, SummScore gets rid of the inhibition of summary diversity. With the help of the text-matching pre-training Cross-Encoder, SummScore can effectively capture the subtle differences between the semantics of summaries. Secondly, to improve the comprehensiveness and interpretability, SummScore consists of four fine-grained submodels, which measure Coherence, Consistency, Fluency, and Relevance separately. We use semi-supervised multi-rounds of training to improve the performance of our model on extremely limited annotated data. Extensive experiments show that SummScore significantly outperforms existing evaluation metrics in the above four dimensions in correlation with human scoring. We also provide the quality evaluation results of SummScore on 16 mainstream summarization models for later research.
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Venue: arXiv cs.RO 2022
Year: 2022

Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. Moreover, robotic policies learned with RL often fail when deployed beyond the carefully controlled setting in which they were learned. In this work, we study how these challenges can all be tackled by effective utilization of diverse offline datasets collected from previously seen tasks. When faced with a new task, our system adapts previously learned skills to quickly learn to both perform the new task and return the environment to an initial state, effectively performing its own environment reset. Our empirical results demonstrate that incorporating prior data into robotic reinforcement learning enables autonomous learning, substantially improves sample-efficiency of learning, and enables better generalization. Project website: https://sites.google.com/view/ariel-berkeley/
Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry
Venue: arXiv cs.CV 2022
Year: 2022

Observation is an essential tool for understanding and studying human behavior and mental states. However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk. Machine learning (ML) methods offer ways to improve reliability, decrease cost, and scale up behavioral coding for application in clinical and research settings. Here, we use computer vision to derive behavioral codes or concepts of a gold standard behavioral rating system, offering familiar interpretation for mental health professionals. Features were extracted from videos of clinical diagnostic interviews of children and adolescents with and without obsessive-compulsive disorder. Our computationally-derived ratings were comparable to human expert ratings for negative emotions, activity-level/arousal and anxiety. For the attention and positive affect concepts, our ML ratings performed reasonably. However, results for gaze and vocalization indicate a need for improved data quality or additional data modalities.
Collaborative Load Management in Smart Home Area Network
Venue: arXiv cs.DC 2022
Year: 2022

An efficient Home Area Network (HAN) acts as a base of an Advanced Metering Infrastructure (AMI). A HAN not only facilitates AMI with efficient real-time monitoring of the electricity consumption but also manages the load profile of the whole system. However, the existing works on implementing HAN are mostly centralized and suffer from well-known problems. In this work, we propose an IoT-based efficient decentralized strategy using synchronous transmission to practically realize HAN. An inter-device coordination strategy is proposed to minimize the peak load as well as reduce the sudden changes in the overall system without compromising the users requirements. Through experiments over IoT-testbeds, we demonstrate that the proposed strategy can reduce the peak load upto 50% and reduce the load variations upto 58% for even a high and random rate of requests for execution of power-hungry house appliances.
Robust Beamforming Design for IRS-Aided URLLC in D2D Networks
Venue: arXiv cs.IT 2022
Year: 2022

Intelligent reflecting surface (IRS) and device-to-device (D2D) communication are two promising technologies for improving transmission reliability between transceivers in communication systems. In this paper, we consider the design of reliable communication between the access point (AP) and actuators for a downlink multiuser multiple-input single-output (MISO) system in the industrial IoT (IIoT) scenario. We propose a two-stage protocol combining IRS with D2D communication so that all actuators can successfully receive the message from AP within a given delay. The superiority of the protocol is that the communication reliability between AP and actuators is doubly augmented by the IRS-aided first-stage transmission and the second-stage D2D transmission. A joint optimization problem of active and passive beamforming is formulated, which aims to maximize the number of actuators with successful decoding. We study the joint beamforming problem for cases where the channel state information (CSI) is perfect and imperfect. For each case, we develop efficient algorithms that include convergence and complexity analysis. Simulation results demonstrate the necessity and role of IRS with a well-optimized reflection matrix, and the D2D network in promoting reliable communication. Moreover, the proposed protocol can enable reliable communication even in the presence of stringent latency requirements and CSI estimation errors.
Snow Mask Guided Adaptive Residual Network for Image Snow Removal
Venue: arXiv cs.CV 2022
Year: 2022

Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely common atmospheric phenomenon that will seriously affect the performance of high-level computer vision tasks, such as object detection and semantic segmentation. Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object. However, the distribution of snow location and shape is complex. Therefore, failure to detect snowflakes / snow streak effectively will affect snow removing and limit the model performance. To solve these issues, we propose a Snow Mask Guided Adaptive Residual Network (SMGARN). Specifically, SMGARN consists of three parts, Mask-Net, Guidance-Fusion Network (GF-Net), and Reconstruct-Net. Firstly, we build a Mask-Net with Self-pixel Attention (SA) and Cross-pixel Attention (CA) to capture the features of snowflakes and accurately localized the location of the snow, thus predicting an accurate snow mask. Secondly, the predicted snow mask is sent into the specially designed GF-Net to adaptively guide the model to remove snow. Finally, an efficient Reconstruct-Net is used to remove the veiling effect and correct the image to reconstruct the final snow-free image. Extensive experiments show that our SMGARN numerically outperforms all existing snow removal methods, and the reconstructed images are clearer in visual contrast. All codes will be available.
Whois? Deep Author Name Disambiguation using Bibliographic Data
Venue: arXiv cs.DL 2022
Year: 2022

As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally. This makes it challenging to assign newly published papers to their adequate authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use a collection from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, which is represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification
Venue: arXiv cs.CV 2022
Year: 2022

Imbalanced training data is a significant challenge for medical image classification. In this study, we propose a novel Progressive Class-Center Triplet (PCCT) framework to alleviate the class imbalance issue particularly for diagnosis of rare diseases, mainly by carefully designing the triplet sampling strategy and the triplet loss formation. Specifically, the PCCT framework includes two successive stages. In the first stage, PCCT trains the diagnosis system via a class-balanced triplet loss to coarsely separate distributions of different classes. In the second stage, the PCCT framework further improves the diagnosis system via a class-center involved triplet loss to cause a more compact distribution for each class. For the class-balanced triplet loss, triplets are sampled equally for each class at each training iteration, thus alleviating the imbalanced data issue. For the class-center involved triplet loss, the positive and negative samples in each triplet are replaced by their corresponding class centers, which enforces data representations of the same class closer to the class center. Furthermore, the class-center involved triplet loss is extended to the pair-wise ranking loss and the quadruplet loss, which demonstrates the generalization of the proposed framework. Extensive experiments support that the PCCT framework works effectively for medical image classification with imbalanced training images. On two skin image datasets and one chest X-ray dataset, the proposed approach respectively obtains the mean F1 score 86.2, 65.2, and 90.66 over all classes and 81.4, 63.87, and 81.92 for rare classes, achieving state-of-the-art performance and outperforming the widely used methods for the class imbalance issue.
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems
Venue: arXiv cs.CR 2022
Year: 2022

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models created this way are still vulnerable to evasion, poisoning, and exploratory attacks using adversarial examples. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We proposed integrating a statistical detector to detect and extract unknown adversarial samples. By including the unknown detected samples into the dataset of the detector, we augment the BFF-IDS with an additional model to detect original known attacks and the new adversarial inputs. The statistical adversarial detector confidently detected adversarial examples at the sample size of 50 and 100 input samples. Furthermore, the augmented BFF-IDS (BFF-IDS(AUG)) successfully mitigates the adversarial examples with more than 96% accuracy. With this approach, the model will continue to be augmented in a sandbox whenever an adversarial sample is detected and subsequently adopt the BFF-IDS(AUG) as the active security model. Consequently, the proposed integration of the statistical adversarial detector and the subsequent augmentation of the BFF-IDS with detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks.
Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters: A Case Study Implementation
Venue: arXiv cs.NE 2022
Year: 2022

This discusses a case study on Fitness Dependent Optimizer or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare. The reproductive way is sparked by the bee swarm and the collaborative decision-making of FDO. As opposed to the honey bee or artificial bee colony algorithms, this algorithm has no connection to them. In FDO, the search agent's position is updated using speed or velocity, but it's done differently. It creates weights based on the fitness function value of the problem, which assists lead the agents through the exploration and exploitation processes. Other algorithms are evaluated and compared to FDO as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the original work. The key current algorithms:The Salp-Swarm Algorithms (SSA), Dragonfly Algorithm (DA), and Whale Optimization Algorithm (WOA) have been evaluated against FDO in terms of their results. Using these FDO experimental findings, we may conclude that FDO outperforms the other techniques stated. There are two primary goals for this chapter: first, the implementation of FDO will be shown step-by-step so that readers can better comprehend the algorithm method and apply FDO to solve real-world applications quickly. The second issue deals with how to tweak the FDO settings to make the meta-heuristic evolutionary algorithm better in the IoT health service system at evaluating big quantities of information. Ultimately, the target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems.
CougaR: Fast and Eclipse-Resilient Dissemination for Blockchain Networks
Venue: arXiv cs.DC 2022
Year: 2022

Despite their development for over a decade, a key problem blockchains are still facing is scalability in terms of throughput, typically limited to a few transactions per second. A fundamental factor limiting this metric is the propagation latency of blocks through the underlying peer-to-peer network, which is typically constructed by means of random connectivity. Disseminating blocks fast improves not only the transaction throughput, but also the security of the system as it reduces the probability of forks. In this paper we present CougaR: a simple yet efficient, eclipse-resistant, decentralized protocol that substantially reduces the block dissemination time in blockchain networks. CougaR's key advantages stem from its link selection policy, which combines a network latency criterion with randomness to offer fast and reliable block dissemination to the entire network. Moreover, CougaR is eclipse-resistant by design, as nodes are protected from having all their links directly or indirectly imposed on them by others, which is the typical vulnerability exploited to deploy eclipse attacks. We rigorously evaluate CougaR by an extensive set of experiments, both against a wide spectrum of parameter settings, and in comparison to the current state of the art.
Understanding the Role of External Pull Requests in the NPM Ecosystem
Venue: arXiv cs.SE 2022
Year: 2022

The risk to using third-party libraries in a software application is that much needed maintenance is solely carried out by library maintainers. These libraries may rely on a core team of maintainers (who might be a single maintainer that is unpaid and overworked) to serve a massive client user-base. On the other hand, being open source has the benefit of receiving contributions (in the form of External PRs) to help fix bugs and add new features. In this paper, we investigate the role by which External PRs (contributions from outside the core team of maintainers) contribute to a library. Through a preliminary analysis, we find that External PRs are prevalent, and just as likely to be accepted as maintainer PRs. We find that 26.75% of External PRs submitted fix existing issues. Moreover, fixes also belong to labels such as breaking changes, urgent, and on-hold. Differently from Internal PRs, External PRs cover documentation changes (44 out of 384 PRs), while not having as much refactoring (34 out of 384 PRs). On the other hand, External PRs also cover new features (380 out of 384 PRs) and bugs (120 out of 384). Our results lay the groundwork for understanding how maintainers decide which external contributions they select to evolve their libraries and what role they play in reducing the workload.
Sparsifying Binary Networks
Venue: arXiv cs.LG 2022
Year: 2022

Binary neural networks (BNNs) have demonstrated their ability to solve complex tasks with comparable accuracy as full-precision deep neural networks (DNNs), while also reducing computational power and storage requirements and increasing the processing speed. These properties make them an attractive alternative for the development and deployment of DNN-based applications in Internet-of-Things (IoT) devices. Despite the recent improvements, they suffer from a fixed and limited compression factor that may result insufficient for certain devices with very limited resources. In this work, we propose sparse binary neural networks (SBNNs), a novel model and training scheme which introduces sparsity in BNNs and a new quantization function for binarizing the network's weights. The proposed SBNN is able to achieve high compression factors and it reduces the number of operations and parameters at inference time. We also provide tools to assist the SBNN design, while respecting hardware resource constraints. We study the generalization properties of our method for different compression factors through a set of experiments on linear and convolutional networks on three datasets. Our experiments confirm that SBNNs can achieve high compression rates, without compromising generalization, while further reducing the operations of BNNs, making SBNNs a viable option for deploying DNNs in cheap, low-cost, limited-resources IoT devices and sensors.