OpenRaiser/Intern-Atlas huggingface dataset:
RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization
Venue: arXiv cs.CV 2022
Year: 2022

This paper explores training efficient VGG-style super-resolution (SR) networks with the structural re-parameterization technique. The general pipeline of re-parameterization is to train networks with multi-branch topology first, and then merge them into standard 3x3 convolutions for efficient inference. In this work, we revisit those primary designs and investigate essential components for re-parameterizing SR networks. First of all, we find that batch normalization (BN) is important to bring training non-linearity and improve the final performance. However, BN is typically ignored in SR, as it usually degrades the performance and introduces unpleasant artifacts. We carefully analyze the cause of BN issue and then propose a straightforward yet effective solution. In particular, we first train SR networks with mini-batch statistics as usual, and then switch to using population statistics at the later training period. While we have successfully re-introduced BN into SR, we further design a new re-parameterizable block tailored for SR, namely RepSR. It consists of a clean residual path and two expand-and-squeeze convolution paths with the modified BN. Extensive experiments demonstrate that our simple RepSR is capable of achieving superior performance to previous SR re-parameterization methods among different model sizes. In addition, our RepSR can achieve a better trade-off between performance and actual running time (throughput) than previous SR methods. Codes will be available at https://github.com/TencentARC/RepSR.
Trusted Container Extensions for Container-based Confidential Computing
Venue: arXiv cs.CR 2022
Year: 2022

Cloud computing has emerged as a corner stone of today's computing landscape. More and more customers who outsource their infrastructure benefit from the manageability, scalability and cost saving that come with cloud computing. Those benefits get amplified by the trend towards microservices. Instead of renting and maintaining full VMs, customers increasingly leverage container technologies, which come with a much more lightweight resource footprint while also removing the need to emulate complete systems and their devices. However, privacy concerns hamper many customers from moving to the cloud and leveraging its benefits. Furthermore, regulatory requirements prevent the adaption of cloud computing in many industries, such as health care or finance. Standard software isolation mechanisms have been proven to be insufficient if the host system is not fully trusted, e.g., when the cloud infrastructure gets compromised by malicious third-party actors. Consequently, confidential computing is gaining increasing relevance in the cloud computing field. We present Trusted Container Extensions (TCX), a novel container security architecture, which combines the manageability and agility of standard containers with the strong protection guarantees of hardware-enforced Trusted Execution Environments (TEEs) to enable confidential computing for container workloads. TCX provides significant performance advantages compared to existing approaches while protecting container workloads and the data processed by them. Our implementation, based on AMD Secure Encrypted Virtualization (SEV), ensures integrity and confidentiality of data and services during deployment, and allows secure interaction between protected containers as well as to external entities. Our evaluation shows that our implementation induces a low performance overhead of 5.77% on the standard SPEC2017 benchmark suite.
Representation Learning for Context-Dependent Decision-Making
Venue: arXiv cs.LG 2022
Year: 2022

Humans are capable of adjusting to changing environments flexibly and quickly. Empirical evidence has revealed that representation learning plays a crucial role in endowing humans with such a capability. Inspired by this observation, we study representation learning in the sequential decision-making scenario with contextual changes. We propose an online algorithm that is able to learn and transfer context-dependent representations and show that it significantly outperforms the existing ones that do not learn representations adaptively. As a case study, we apply our algorithm to the Wisconsin Card Sorting Task, a well-established test for the mental flexibility of humans in sequential decision-making. By comparing our algorithm with the standard Q-learning and Deep-Q learning algorithms, we demonstrate the benefits of adaptive representation learning.
Emotion-Centric Requirements Change Handling in Software Engineering
Venue: arXiv cs.SE 2022
Year: 2022

Background: Requirements Changes (RCs) -- the additions/modifications/deletions of functional/non-functional requirements in software products -- are challenging for software practitioners to handle. Handling some changes may significantly impact the emotions of the practitioners. Objective: We wanted to know the key challenges that make RC handling difficult, how these impact the emotions of software practitioners, what influences their RC handling, and how RC handling can be made less emotionally challenging. Method: We followed a mixed-methods approach. We conducted two survey studies, with 40 participants and 201 participants respectively. The presentation of key quantitative data was followed by descriptive statistical analysis, and the qualitative data was analysed using Strauss-Corbinian Grounded Theory, and Socio-Technical Grounded Theory analysis techniques. Findings:We found (1) several key factors that make RC handling an emotional challenge, (2) varying emotions that practitioners feel when it is challenging to handle RCs, (3) how stakeholders, including practitioners themselves, peers, managers and customers, influence the RC handling and how practitioners feel due to the stakeholder influence, and (4) practices that can be used to better handle RCs. Conclusion: Some challenges are technical and some are social which also belong to aspects of agile practice, emotional intelligence, and cognitive intelligence. Therefore, to better handle RCs with positive emotions in socio-technical environments, agility, emotional intelligence, and cognitive intelligence need to cooperate with each other.
Supplementary Material: Implementation and Experiments for GAU-based Model
Venue: arXiv cs.CL 2022
Year: 2022

In February this year Google proposed a new Transformer variant called FLASH, which has a faster speed, lower VRAM footprint and better performance. This is achieved by designing a performant layer named GAU (Gated Attention Unit), which combines the Attention layer and FFN. In this paper, some implementation details are re-analyzed both theoretically and practically. We then propose a novel GAU-based model and pre-train it on a Chinese corpus. Results of the CLUE benchmark show that our model achieves a dev average score of 75.02, 1% higher than RoFormerV1 and being 45% faster, which is also competitive with RoFormerV2.
Distinction Maximization Loss: Efficiently Improving Out-of-Distribution Detection and Uncertainty Estimation by Replacing the Loss and Calibrating
Venue: arXiv cs.LG 2022
Year: 2022

Building robust deterministic neural networks remains a challenge. On the one hand, some approaches improve out-of-distribution detection at the cost of reducing classification accuracy in some situations. On the other hand, some methods simultaneously increase classification accuracy, uncertainty estimation, and out-of-distribution detection at the expense of reducing the inference efficiency. In this paper, we propose training deterministic neural networks using our DisMax loss, which works as a drop-in replacement for the usual SoftMax loss (i.e., the combination of the linear output layer, the SoftMax activation, and the cross-entropy loss). Starting from the IsoMax+ loss, we create each logit based on the distances to all prototypes, rather than just the one associated with the correct class. We also introduce a mechanism to combine images to construct what we call fractional probability regularization. Moreover, we present a fast way to calibrate the network after training. Finally, we propose a composite score to perform out-of-distribution detection. Our experiments show that DisMax usually outperforms current approaches simultaneously in classification accuracy, uncertainty estimation, and out-of-distribution detection while maintaining deterministic neural network inference efficiency. The code to reproduce the results is available at https://github.com/dlmacedo/distinction-maximization-loss.
A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems
Venue: arXiv cs.CL 2022
Year: 2022

The goal of building intelligent dialogue systems has largely been separately pursued under two motives: task-oriented dialogue (TOD) systems, and open-domain systems for chit-chat (CC). Although previous TOD dialogue systems work well in the testing sets of benchmarks, they would lead to undesirable failure when being exposed to natural scenarios in practice, where user utterances can be of high motive-diversity that fusing both TOD and CC in multi-turn interaction. Since an industrial TOD system should be able to converse with the user between TOD and CC motives, constructing a fuse-motive dialogue dataset that contains both TOD or CC is important. Most prior work relies on crowd workers to collect and annotate large scale dataset and is restricted to English language setting. Our work, on the contrary, addresses this problem in a more effective way and releases a multi-turn dialogues dataset called CCET (Chinese Chat-Enhanced-Task). Meanwhile, we also propose a line of fuse-motive dialogues formalization approach, along with several evaluation metrics for TOD sessions that are integrated by CC utterances.
Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets
Venue: arXiv cs.LG 2022
Year: 2022

Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) black-box models a workflow based on Shapley values was developed. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated for an independent ADNI test set, as well as the external Australian Imaging and Lifestyle flagship study of Ageing (AIBL), and Open Access Series of Imaging Studies (OASIS) datasets. Shapley values were compared to intuitively interpretable Decision Trees (DTs), and Logistic Regression (LR), as well as natural and permutation feature importances. To avoid the reduction of the explanation validity caused by correlated features, forward selection and aspect consolidation were implemented. Results: Some black-box models outperformed DTs and LR. The forward-selected features correspond to brain areas previously associated with AD. Shapley values identified biologically plausible associations with moderate to strong correlations with feature importances. The most important RF features to predict AD conversion were the volume of the amygdalae, and a cognitive test score. Good cognitive test performances and large brain volumes decreased the AD risk. The models trained using cognitive test scores significantly outperformed brain volumetric models ($p<0.05$). Cognitive Normal (CN) vs. AD models were successfully transferred to external datasets. Conclusion: In comparison to previous work, improved performances for ADNI and AIBL were achieved for CN vs. Mild Cognitive Impairment (MCI) classification using brain volumes. The Shapley values and the feature importances showed moderate to strong correlations.
Query Complexity Based Optimal Processing of Raw Data
Venue: arXiv cs.DB 2022
Year: 2022

The paper aims to find an efficient way for processing large datasets having different types of workload queries with minimal replication. The work first identifies the complexity of queries best suited for the given data processing tool . The paper proposes Query Complexity Aware partitioning technique QCA with a lightweight query identification and partitioning algorithm. Different replication approaches have been studied to cover more use-cases for different application workloads. The technique is demonstrated using a scientific dataset known as Sloan Digital Sky Survey SDSS. The results show workload execution time WET reduced by 94.6% using only 6.7% of the dataset in loaded format compared to the original dataset. The QCA technique also reduced multi-node replication by 5.8x times compared to state-of-the-art workload aware WA techniques. The multi-node and multi-core execution of workload using QCA proposed partitions reduced WET by 42.66% and 25.46% compared to WA.
CorAl: Introspection for Robust Radar and Lidar Perception in Diverse Environments Using Differential Entropy
Venue: arXiv cs.RO 2022
Year: 2022

Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and preprocessing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-stage filtering technique that produces high-quality point clouds from noisy radar scans. Thus we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.
Vectorized and performance-portable Quicksort
Venue: arXiv cs.IR 2022
Year: 2022

Recent works showed that implementations of Quicksort using vector CPU instructions can outperform the non-vectorized algorithms in widespread use. However, these implementations are typically single-threaded, implemented for a particular instruction set, and restricted to a small set of key types. We lift these three restrictions: our proposed 'vqsort' algorithm integrates into the state-of-the-art parallel sorter 'ips4o', with a geometric mean speedup of 1.59. The same implementation works on seven instruction sets (including SVE and RISC-V V) across four platforms. It also supports floating-point and 16-128 bit integer keys. To the best of our knowledge, this is the fastest sort for non-tuple keys on CPUs, up to 20 times as fast as the sorting algorithms implemented in standard libraries. This paper focuses on the practical engineering aspects enabling the speed and portability, which we have not yet seen demonstrated for a Quicksort implementation. Furthermore, we introduce compact and transpose-free sorting networks for in-register sorting of small arrays, and a vector-friendly pivot sampling strategy that is robust against adversarial input.
Synergia: Hardening High-Assurance Security Systems with Confidential and Trusted Computing
Venue: arXiv cs.CR 2022
Year: 2022

High-assurance security systems require strong isolation from the untrusted world to protect the security-sensitive or privacy-sensitive data they process. Existing regulations impose that such systems must execute in a trustworthy operating system (OS) to ensure they are not collocated with untrusted software that might negatively impact their availability or security. However, the existing techniques to attest to the OS integrity fall short due to the cuckoo attack. In this paper, we first show a novel defense mechanism against the cuckoo attack, and we formally prove it. Then, we implement it as part of an integrity monitoring and enforcement framework that attests to the trustworthiness of the OS from 3.7x to 8.5x faster than the existing integrity monitoring systems. We demonstrate its practicality by protecting the execution of a real-world eHealth application, performing micro and macro-benchmarks, and assessing the security risk.
Evil Never Sleeps: When Wireless Malware Stays On After Turning Off iPhones
Venue: arXiv cs.CR 2022
Year: 2022

When an iPhone is turned off, most wireless chips stay on. For instance, upon user-initiated shutdown, the iPhone remains locatable via the Find My network. If the battery runs low, the iPhone shuts down automatically and enters a power reserve mode. Yet, users can still access credit cards, student passes, and other items in their Wallet. We analyze how Apple implements these standalone wireless features, working while iOS is not running, and determine their security boundaries. On recent iPhones, Bluetooth, Near Field Communication (NFC), and Ultra-wideband (UWB) keep running after power off, and all three wireless chips have direct access to the secure element. As a practical example what this means to security, we demonstrate the possibility to load malware onto a Bluetooth chip that is executed while the iPhone is off.
Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers
Venue: arXiv cs.LG 2022
Year: 2022

Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number and speed of room to room transitions, provides a proxy outcome which represents mobility and could be used as a digital biomarker to quantify how mobility changes as this disease progresses. We use data collected from 10 people with Parkinson's, and 10 controls, each of whom lived for five days in a smart home with various sensors. In order to more effectively localise them indoors, we propose a transformer-based approach utilizing two data modalities, Received Signal Strength Indicator (RSSI) and accelerometer data from wearable devices, which provide complementary views of movement. Our approach makes asymmetric and dynamic correlations by a) learning temporal correlations at different scales and levels, and b) utilizing various gating mechanisms to select relevant features within modality and suppress unnecessary modalities. On a dataset with real patients, we demonstrate that our proposed method gives an average accuracy of 89.9%, outperforming competitors. We also show that our model is able to better predict in-home mobility for people with Parkinson's with an average offset of 1.13 seconds to ground truth.
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Venue: arXiv cs.LG 2022
Year: 2022

Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require augmenting data with large amounts of noise, which leads to significant drops in accuracy. We propose a training-free, modified smoothing approach, Smooth-Reduce, that leverages patching and aggregation to provide improved classifier certificates. Our algorithm classifies overlapping patches extracted from an input image, and aggregates the predicted logits to certify a larger radius around the input. We study two aggregation schemes -- max and mean -- and show that both approaches provide better certificates in terms of certified accuracy, average certified radii and abstention rates as compared to concurrent approaches. We also provide theoretical guarantees for such certificates, and empirically show significant improvements over other randomized smoothing methods that require expensive retraining. Further, we extend our approach to videos and provide meaningful certificates for video classifiers. A project page can be found at https://nyu-dice-lab.github.io/SmoothReduce/
A Generalist Agent
Venue: arXiv cs.AI 2022
Year: 2022

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.
Local Motif Clustering via (Hyper)Graph Partitioning
Venue: arXiv cs.SI 2022
Year: 2022

A widely-used operation on graphs is local clustering, i.e., extracting a well-characterized community around a seed node without the need to process the whole graph. Recently local motif clustering has been proposed: it looks for a local cluster based on the distribution of motifs. Since this local clustering perspective is relatively new, most approaches proposed for it are extensions of statistical and numerical methods previously used for edge-based local clustering, while the available combinatorial approaches are still few and relatively simple. In this work, we build a hypergraph and a graph model which both represent the motif-distribution around the seed node. We solve these models using sophisticated combinatorial algorithms designed for (hyper)graph partitioning. In extensive experiments with the triangle motif, we observe that our algorithm computes communities with a motif conductance value being one third on average in comparison against the communities computed by the state-of-the-art tool MAPPR while being 6.3 times faster on average.