Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Self-Supervised Depth and Uncertainty Predictions for Monocular Visual Odometry

This paper presents a robust incorporating deep learning method for monocular visual odometry (VO). The learning-based depth predictions can be aided to select the appropriate points and then reconstruct their 3D structure. And a motion decoupling strategy is first designed to estimate the pose in two parts, which reduces the rotation errors caused by the predictions. The rotation is estimated by using a classic monocular method and the translation is solved by optimizing a minimizing reprojection problem based on the estimated rotation. Particularly, a novel self-supervised teacher-student framework is proposed to output the predicted per-pixel depth and its uncertainty, while avoiding the scale inconsistency issue and capturing illumination changes. Simulation results show that the proposed framework is state-of-the-art in terms of Kitti dataset depth estimation. The effectiveness and superiority of the proposed approach in terms of Kitti odometry estimation is also demonstrated.
Fireworks: a fast, efficient, and safe serverless framework using VM-level post-JIT snapshot

Serverless computing is a new paradigm that is rapidly gaining popularity in Cloud computing. One unique property in serverless computing is that the unit of deployment and execution is a serverless function, which is much smaller than a typical server program. Serverless computing introduces a new pay-as-you-go billing model and provides a high economic benefit from highly elastic resource provisioning. However, serverless computing also brings new challenges such as (1) long start-up times compared to relatively short function execution times, (2) security risks from a highly consolidated environment, and (3) memory efficiency problems from unpredictable function invocations. These problems not only degrade performance but also lower the economic benefits of Cloud providers. To address these challenges without any compromises, we propose a novel VM-level post-JIT snapshot approach and develop a new serverless framework, Fireworks. Our key idea is to synergistically leverage a virtual machine (VM)-level snapshot with a language runtime-level just-in-time (JIT) compilation in tandem. Fireworks leverages JITted serverless function code to reduce both start-up time and execution time of functions and improves memory efficiency by sharing the JITted code. Also, Fireworks can provide a high level of isolation by using a VM as a sandbox to execute a serverless function. Our evaluation results show that Fireworks outperforms state-of-art serverless platforms by 20.6× and provides higher memory efficiency of up to 7.3×.
Where the sidewalk ends: privacy of opportunistic backhaul

We explore the challenges of implementing a privacy-preserving opportunistic data collection network for resource-constrained edge IoT devices. Opportunistic networks require no fixed infrastructure and allow edge devices to piggy-back messages through stationary or mobile gateways. Research interest in such networks has waxed and waned over the years, but commercial deployments have not taken off until recently. Over the past year, we have witnessed a resurgence of interest fueled by wide-scale commercial deployments, most notably Amazon's Sidewalk network, but also Apple's Find My and the Tile network. As these networks become more prevalent, maintaining the privacy of the individuals who participate in them will become increasingly important. In this paper, we demonstrate that current opportunistic networks leak access patterns to the network operator itself through communication metadata, which can be used to reconstruct location traces. We argue that opportunistic networks and privacy are not mutually exclusive, and suggest some potential research directions to strengthen the privacy properties of these networks. Since opportunistic networks are now being deployed at massive scale, we argue that the time is ripe to make them privacy-preserving before it is too late.
Dealing with Security Alert Flooding: Using Machine Learning for Domain-independent Alert Aggregation

Intrusion Detection Systems (IDS) secure all kinds of IT infrastructures through automatic detection of malicious activities. Unfortunately, they are known to produce large numbers of alerts that often become overwhelming for manual analysis. Therefore, aggregation methods have been developed for filtering, grouping, and correlating alerts. However, existing techniques either rely on manually defined attack scenarios or require specific alert formats, such as IDMEF that include IP addresses. This makes the application of existing aggregation methods infeasible for alerts from host-based or anomaly-based IDSs that frequently lack such network-related data. In this paper, we therefore present a domain-independent alert aggregation technique. We introduce similarity measures and merging strategies for arbitrary semi-structured alerts and alert groups. Based on these metrics and techniques we propose an incremental procedure for the generation of abstract alert patterns that enable continuous classification of incoming alerts. Evaluations show that our approach is capable of reducing the number of alert groups for human review by around \( 80\% \) and assigning attack classifiers to the groups with true positive rates of \( 80\% \) and false positive rates lower than \( 5\% \) .
Towards securely migrating webassembly enclaves

To run services in Trusted Execution Environments and Secure Enclaves is an established approach to protect privacy-sensitive data or payment processing. The increased importance of service elasticity in e.g. cloud computing, but also between edge and cloud, highlights the need of (hardware) architecture agnostic secure migration of such processing. We present an enclave software design, based on a WebAssembly (WASM) runtime, that allows for secure migration of enclave services with integrity and confidentiality guarantees for both enclave code and software state (data). We provide security analysis for our migration approach, and architecture benchmarking for a wide variety of existing (and future) secure enclave hardware as proof that elasticity in computation---without compromising security---is an achievable goal.
Building an efficient key-value store in a flexible address space

Data management applications store their data using structured files in which data are usually sorted to serve indexing and queries. However, in-place insertions and removals of data are not naturally supported in a file's address space. To avoid repeatedly rewriting existing data in a sorted file to admit changes in place, applications usually employ extra layers of indirections, such as mapping tables and logs, to admit changes out of place. However, this approach leads to increased access cost and excessive complexity. This paper presents a novel storage abstraction that provides a flexible address space, where in-place updates of arbitrary-sized data, such as insertions and removals, can be performed efficiently. With these mechanisms, applications can manage sorted data in a linear address space with minimal complexity. Extensive evaluations show that a key-value store built on top of it can achieve high performance and efficiency with a simple implementation.
PKRU-safe: automatically locking down the heap between safe and unsafe languages

After more than twenty-five years of research, memory safety violations remain one of the major causes of security vulnerabilities in real-world software. Memory-safe languages, like Rust, have demonstrated that compiler technology can assist developers in writing efficient low-level code without the risk of memory corruption. However, many memory-safe languages still have to interface with unsafe code to some extent, which opens up the possibility for attackers to exploit memory-corruption vulnerabilities in the unsafe part of the system and subvert the safety guarantees provided by the memory-safe language. In this paper, we present PKRU-Safe, an automated method for enforcing the principle of least privilege on unsafe components in mixed-language environments. PKRU-Safe ensures that unsafe (external) code cannot corrupt or otherwise abuse memory used exclusively by the safe-language components. Our approach is automated using traditional compiler infrastructure to limit memory accesses for developer-designated components efficiently. PKRU-Safe does not require any modifications to the program's original data flows or execution model. It can be adopted by projects containing legacy code with minimal effort, requiring only a small number of changes to a project's build files and dependencies, and a few lines of annotations for each untrusted library. We apply PKRU-Safe to Servo, one of the largest Rust projects with approximately two million lines of Rust code (including dependencies) to automatically partition and protect the browser's heap from its JavaScript engine written in unsafe C/C++. Our detailed evaluation shows that PKRU-Safe is able to thwart real-world exploits, often without measurable overhead, and with a mean overhead under 11.55% in our most pessimistic benchmark suite. As the method is language agnostic and major prototype components operate directly on LLVM IR, applying our techniques to other languages is straightforward.
Analyzing The Effects of Business Strategy, Tax Avoidance, and Dividend Policy on Company Value: An Empirical Study

A company's value is an important aspect to be maintained in the company. There will be more possible investors for the company if its value is high. The goal of this research is to look at the effect of business strategy, tax avoidance, and dividend policy on company value. Business strategy is one of the variables that investors consider when determining if a company is performing well. Tax avoidance is a risky business practice; thus, investors will be skeptical about the company and might choose not to invest in it. Dividend policy is one indication that the company is profitable; investors will want to invest in a profitable company. As a result, maintaining the company's value from a stakeholder standpoint is critical. The samples used are 29 manufacturing companies for 5 periods (2016-2020), so the total samples were 145 samples. The data used derives from the company's financial statements, which are available on the company's official website or the IDX website. The data were analyzed using panel data regression analysis. This study found that dividend policy has a positive effect on company value, meaning that the more profitable management's dividend policy is for investors, the company's value will increase because dividend distribution is considered as a return on investment and make investors feel more profitable. Finally, implications are discussed based on the findings.
Parameterized Complexity of Elimination Distance to First-Order Logic Properties

The elimination distance to some target graph property P is a general graph modification parameter introduced by Bulian and Dawar. We initiate the study of elimination distances to graph properties expressible in first-order logic. We delimit the problem’s fixed-parameter tractability by identifying sufficient and necessary conditions on the structure of prefixes of first-order logic formulas. Our main result is the following meta-theorem: For every graph property P expressible by a first order-logic formula \( \varphi \in \Sigma _3 \) , that is, of the form \( \begin{equation*} \varphi =\exists x_1\exists x_2\cdots \exists x_r\ \ \forall y_{1}\forall y_{2}\cdots \forall y_{s}\ \ \exists z_1\exists z_2\cdots \exists z_t~~ \psi ,\end{equation*} \) where \( \psi \) is a quantifier-free first-order formula, checking whether the elimination distance of a graph to P does not exceed \( k \) , is fixed-parameter tractable parameterized by \( k \) . Properties of graphs expressible by formulas from \( \Sigma _3 \) include being of bounded degree, excluding a forbidden subgraph, or containing a bounded dominating set. We complement this theorem by showing that such a general statement does not hold for formulas with even slightly more expressive prefix structure: There are formulas \( \varphi \in \Pi _3 \) , for which computing elimination distance is \( {\sf W}[2] \) -hard.
The Importance of Procurement Organization, Policy, and Value on Supply Chain Performance

∗ The supply chain plays a critical and strategic role in government and the private sector. In terms of efficiency and effectiveness, a good supply chain performance is very important. The purpose of this research is to determine the significance of procurement organization, policy, and value in improving supply chain performance of an Indonesian non-ministerial government entity. This research is a cross-sectional survey by quantitative approach. In this study, a G-form questionnaire was used to collect data, which was then processed using the Pearson Product-Moment correlation test. Purposive sampling was also used in this study, with a sample size of 58 respondents. The results showed that the procurement organization, policy, and value all played an important role in the supply chain performance of the procurement department of an Indonesian non-ministerial government entity. In accordance with the findings, the study was provided by discussion, implications, and future studies advice.
Micro-expression Recognition based on Bimodal Contrastive Learning

Micro-expressions are brief, subtle and involuntary facial gestures, which usually hide real emotions of people and are difficult to capture. Considering that micro-expressions data samples contain distinctive features of specific categories and common features of different categories, a micro-expressions recognition network based on bimodal contrastive learning is proposed in this paper. The network mainly includes bimodal feature extraction module, bimodal contrastive learning fusion module and classification and recognition module. First, the micro-expressions sequence is divided into RGB sequence and optical flow sequence, and the loss between them is constructed by contrastive learning. The network extracts bimodal common features. Second, in order to extract distinctive features, bimodal features are fused and label data is used to optimize the network. The network extracts bimodal features, while extracting distinctive features of different categories. The results show the superiority of the proposed method over other state-of-the-art methods for 5 categories of micro-expressions on CASME II, SAMM and 6 categories of micro-expressions on MMEW.
Spatial Non-cooperative Target point Cloud Reconstruction

Due to the inherent defects of laser sensors, the original point cloud of non-cooperate targets are usually irregularly distributed, which brings great challenges to high-quality 3D surface reconstruction of non-cooperate targets. In this paper, we leverage a method based local hierarchical clustering (LHC) to improve the consistency of point distribution. Specifically, our method includes two main steps. The first one is the adaptive octree-based 3D spatial decomposition and the second one is hierarchical clustering. The main purpose of the former one is to reduce the complexity of the algorithm, and the later aims to convert the non-uniform point set to uniform one. We carried out experiments on three non-cooperative target models. The results of visualization and quantitative calculation verify the effectiveness of our method.
A Noise-robust Feature Fusion Model Combining Non-local Attention for Material Recognition

Material recognition, as an important task of computer vision, is hugely challenging, due to large intra-class variances and small inter-class variances between material images. To address those recognition problems, multi-scale feature fusion methods based on deep convolutional neural networks are presented, which has been widely studied in recent years. However, the past research works paid too much attention to the local features of the image, while ignoring the non-local features that are also crucial for fine image recognition tasks such as material recognition. In this paper, Non-local Attentional Feature Fusion Network (NLA-FFNet) is proposed that combines local and non-local feature of images to improve the feature representation capability. Firstly, we utilize the pre-trained deep convolutional neural network to extract the image feature. Secondly, a Multilayer Non-local Attention (MNLA) block is designed to generate a non-local attention map which represents the long-range dependencies between features of different positions. Therefore, it can achieve stronger noise-robustness of model and better ability to represent fine features. Finally, combined our Multilayer Non-local Attention block with bilinear pooling which has been proved to be effective for feature fusion, we propose a deep neural network framework, NLA-FFNet, with noise-robust multi-layer feature fusion. Experiment prove that our model can achieve a competitive classification accuracy in material image recognition, and has stronger noise-robustness at the same time.
SpikeFormer: Image Reconstruction from the Sequence of Spike Camera Based on Transformer

The recently invented retina-inspired spike camera produces asynchronous binary spike streams to record the dynamic light intensity variation process. This paper develops a novel image reconstruction method, called SpikeFormer, which reconstructs the dynamic scene from binary spike streams in a supervised learning strategy. We construct the training dataset which composes of spike streams and corresponding ground truth images by simulating the working mechanism of spike camera. Spike noises are also taken into consideration in the simulator. Firstly, the input spike stream is encoded as an enlarged binary image by interlacing temporal and spatial information. Then the binary image is inputted to the SpikeFormer to recover the dynamic scene. SpikeFormer adopts Transformer architecture which includes an encoder and a decoder. In particular, we propose a hierarchical architecture encoder to exploit multi-scale temporal and spatial features progressively. The decoder aggregates information from different stages to incorporate both local and global attention. Multi-task loss including reconstruction loss, perception loss, edge loss, and temporal consistency loss are combined to restrict the model. Extensive experimental results demonstrate that the proposed framework achieves encouraging results in details reconstruction and noise alleviation.
Tebis: index shipping for efficient replication in LSM key-value stores

Key-value (KV) stores based on LSM tree have become a foundational layer in the storage stack of datacenters and cloud services. Current approaches for achieving reliability and availability favor reducing network traffic and send to replicas only new KV pairs. As a result, they perform costly compactions to reorganize data in both the primary and backup nodes, which increases device I/O traffic and CPU overhead, and eventually hurts overall system performance. In this paper we describe Tebis, an efficient LSM-based KV store that reduces I/O amplification and CPU overhead for maintaining the replica index. We use a primary-backup replication scheme that performs compactions only on the primary nodes and sends pre-built indexes to backup nodes, avoiding all compactions in backup nodes. Our approach includes an efficient mechanism to deal with pointer translation across nodes in the pre-built region index. Our results show that Tebis reduces pressure on backup nodes compared to performing full compactions: Throughput is increased by 1.1 -- 1.48×, CPU efficiency is increased by 1.06 -- 1.54×, and I/O amplification is reduced by 1.13 -- 1.81×, without increasing server to server network traffic excessively (by up to 1.09 -- 1.82×).
Interference relation-guided SMT solving for multi-threaded program verification

Concurrent program verification is challenging due to a large number of thread interferences. A popular approach is to encode concurrent programs as SMT formulas and then rely on off-the-shelf SMT solvers to accomplish the verification. In most existing works, an SMT solver is simply treated as the backend. There is little research on improving SMT solving for concurrent program verification. In this paper, we recognize the characteristics of interference relation in multi-threaded programs and propose a novel approach for utilizing the interference relation in the SMT solving of multi-threaded program verification under various memory models. We show that the backend SMT solver can benefit a lot from the domain knowledge of concurrent programs. We implemented our approach in a prototype tool called Zpre. We compared it with the state-of-the-art Z3 tool on credible benchmarks from the ConcurrencySafety category of SV-COMP 2019. Experimental results show promising improvements attributed to our approach.
Optimizing consistency for partially replicated data stores

We present a framework that allows programmers to specify replicated data stores through application logic, data replication scheme, and high-level invariants that needs to be satisfied. From such specifications all the needed consistency requirements can be inferred from traces of executions of the potential data store, to determine the optimal data store coordination. The framework supports arbitrarily complex data store operations and partial data replication. This leads to expressiveness for a wide range of data stores, with significant run-time performance benefits.
LOTUS: locality optimizing triangle counting

Triangle Counting (TC) is a basic graph mining problem with numerous applications. However, the large size of real-world graphs has a severe effect on TC performance. This paper studies the TC algorithm from the perspective of memory utilization. We investigate the implications of the skewed degree distribution of real-world graphs on TC and make novel observations on how memory locality is negatively affected. Based on this, we introduce the LOTUS algorithm as a structure-aware TC that optimizes locality. The evaluation on 14 real-world graphs with up to 162 billion edges and on 3 different processor architectures of up to 128 cores shows that Lotus is 2.2--5.5X faster than previous works.
Hardening binaries against more memory errors

Memory errors, such as buffer overflows and use-after-free, remain the root cause of many security vulnerabilities in modern software. The use of closed source software further exacerbates the problem, as source-based memory error mitigation cannot be applied. While many memory error detection tools exist, most are based on a single error detection methodology with resulting known limitations, such as incomplete memory error detection (redzones) or false error detections (low-fat pointers). In this paper we introduce RedFat, a memory error hardening tool for stripped binaries that is fast, practical and scalable. The core idea behind RedFat is to combine complementary error detection methodologies---redzones and low-fat pointers---in order to detect more memory errors that can be detected by each individual methodology alone. However, complementary error detection also inherits the limitations of each approach, such as false error detections from low-fat pointers. To mitigate this, we introduce a profile-based analysis that automatically determines the strongest memory error protection possible without negative side effects. We implement RedFat on top of a scalable binary rewriting framework, and demonstrate low overheads compared to the current state-of-the-art. We show RedFat to be language agnostic on C/C++/Fortran binaries with minimal requirements, and works with stripped binaries for both position independent/dependent code. We also show that the RedFat instrumentation can scale to very large/complex binaries, such as Google Chrome.