OpenRaiser/Intern-Atlas huggingface dataset:
Taxonomy of Attacks on Open-Source Software Supply Chains
Venue: arXiv cs.CR 2022
Year: 2022

The widespread dependency on open-source software makes it a fruitful target for malicious actors, as demonstrated by recurring attacks. The complexity of today's open-source supply chains results in a significant attack surface, giving attackers numerous opportunities to reach the goal of injecting malicious code into open-source artifacts that is then downloaded and executed by victims. This work proposes a general taxonomy for attacks on open-source supply chains, independent of specific programming languages or ecosystems, and covering all supply chain stages from code contributions to package distribution. Taking the form of an attack tree, it covers 107 unique vectors, linked to 94 real-world incidents, and mapped to 33 mitigating safeguards. User surveys conducted with 17 domain experts and 134 software developers positively validated the correctness, comprehensiveness and comprehensibility of the taxonomy, as well as its suitability for various use-cases. Survey participants also assessed the utility and costs of the identified safeguards, and whether they are used.
Confidence Score for Unsupervised Foreground Background Separation of Document Images
Venue: arXiv cs.CV 2022
Year: 2022

Foreground-background separation is an important problem in document image analysis. Popular unsupervised binarization methods (such as the Sauvola's algorithm) employ adaptive thresholding to classify pixels as foreground or background. In this work, we propose a novel approach for computing confidence scores of the classification in such algorithms. This score provides an insight of the confidence level of the prediction. The computational complexity of the proposed approach is the same as the underlying binarization algorithm. Our experiments illustrate the utility of the proposed scores in various applications like document binarization, document image cleanup, and texture addition.
Reliable Wireless Networking via Soft-Source Information Combining
Venue: arXiv cs.NI 2022
Year: 2022

This paper puts forth a multi-stream networking paradigm, referred to as soft-source-information-combining (SSIC), to support wireless Internet of Things (IoT) applications with ultra-reliability requirements. For SSIC networking, an SSIC dispatcher at the source dispatches duplicates of packets over multiple streams, which may be established over different physical wireless networks. If a packet on a stream cannot be decoded due to wireless interference or noise, the decoder makes available the packet's soft information. An aggregator then combines the soft information of the duplicates to boost reliability. Of importance are two challenges: i) how to descramble the scrambled soft information from different streams to enable correct SSIC; ii) the construct of an SSIC dispatching and aggregation framework compatible with commercial network interface cards (NICs) and TCP/IP networks. To address the challenges, we put forth: i) a soft descrambling (SD) method to minimize the bit-error rate (BER) and packet-error rate (PER) at the SSIC's output; ii) an SSIC networking architecture readily deployable over today's TCP/IP networks without specialized NICs. For concept proving and experimentation, we realized an SSIC system over two Wi-Fi's physical paths in such a way that all legacy TCP/IP applications can enjoy the reliability brought forth by SSIC without modification. Experiments over our testbed corroborate the effectiveness of SSIC in lowering the packet delivery failure rate and the possibility of SSIC in providing 99.99% reliable packet delivery for short-range communication.
Finding a Winning Strategy for Wordle is NP-complete
Venue: arXiv cs.CC 2022
Year: 2022

In this paper, we give a formal definition of the popular word-guessing game Wordle. We show that, in general, determining if a given Wordle instance admits a winning strategy is NP-complete. We also show that given a Wordle instance of size $N$, a winning strategy that uses $g$ guesses in the worst case (if any) can be found in time $N^{O(g)}$.
An XES Extension for Uncertain Event Data
Venue: arXiv cs.DB 2022
Year: 2022

Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are often tainted by noise, errors, and missing data. Recently, a novel body of research has emerged, with the aim to address and analyze a class of anomalies known as uncertainty-imprecisions quantified with meta-information in the event log. This paper illustrates an extension of the XES data standard capable of representing uncertain event data. Such an extension enables input, output, and manipulation of uncertain data, as well as analysis through the process discovery and conformance checking approaches available in literature.
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector
Venue: arXiv cs.CR 2022
Year: 2022

Due to the importance of Critical Infrastructure (CI) in a nation's economy, they have been lucrative targets for cyber attackers. These critical infrastructures are usually Cyber-Physical Systems (CPS) such as power grids, water, and sewage treatment facilities, oil and gas pipelines, etc. In recent times, these systems have suffered from cyber attacks numerous times. Researchers have been developing cyber security solutions for CIs to avoid lasting damages. According to standard frameworks, cyber security based on identification, protection, detection, response, and recovery are at the core of these research. Detection of an ongoing attack that escapes standard protection such as firewall, anti-virus, and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the system. Therefore, anomaly detection in physical dynamics proves an effective means to implement defense-in-depth. PASAD is one example of anomaly detection in the sensor/actuator data, representing such systems' physical dynamics. We present EPASAD, which improves the detection technique used in PASAD to detect these micro-stealthy attacks, as our experiments show that PASAD's spherical boundary-based detection fails to detect. Our method EPASAD overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries in various dimensions, whereas a spherical boundary treats all dimensions equally. We validate EPASAD using the dataset produced by the TE-process simulator and the C-town datasets. The results show that EPASAD improves PASAD's average recall by 5.8% and 9.5% for the two datasets, respectively.
Leverage the Average: Averaged Sampling in Pre-Silicon Side-Channel Leakage Assessment
Venue: arXiv cs.AR 2022
Year: 2022

Pre-silicon side-channel leakage assessment is a useful tool to identify hardware vulnerabilities at design time, but it requires many high-resolution power traces and increases the power simulation cost of the design. By downsampling and averaging these high-resolution traces, we show that the power simulation cost can be considerably reduced without significant loss of side-channel leakage assessment quality. We introduce a theoretical basis for our claims. Our results demonstrate up to 6.5-fold power-simulation speed improvement on a gate-level side-channel leakage assessment of a RISC-V SoC. Furthermore, we clarify the conditions under which the averaged sampling technique can be successfully used.
Ranking with submodular functions on a budget
Venue: arXiv cs.DS 2022
Year: 2022

Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items. On the other hand, many real-world applications require a solution that is a ranking over a set of items. The problem of ranking in the context of submodular function maximization has been considered before, but to a much lesser extent than item-selection formulations. In this paper, we explore a novel formulation for ranking items with submodular valuations and budget constraints. We refer to this problem as max-submodular ranking (MSR). In more detail, given a set of items and a set of non-decreasing submodular functions, where each function is associated with a budget, we aim to find a ranking of the set of items that maximizes the sum of values achieved by all functions under the budget constraints. For the MSR problem with cardinality- and knapsack-type budget constraints we propose practical algorithms with approximation guarantees. In addition, we perform an empirical evaluation, which demonstrates the superior performance of the proposed algorithms against strong baselines.
Improving VANET's Performance by Incorporated Fog-Cloud Layer (FCL)
Venue: arXiv cs.NI 2022
Year: 2022

Because of its usefulness in various fields including as safety applications, traffic control applications, and entertainment applications, VANET is an essential topic that is now being investigated intensively. VANET confronts numerous challenges in terms of reaction time, storage capacity, and reliability, particularly in real-time applications. As a result, merging cloud computing and cloud computing has recently been researched. The goal of this study is to develop a system that merges the fog and cloud layers into a single layer known as the included fog-cloud layer. To lower the time it takes for real-time applications on VANETs to respond while also improving data flow management over the Internet and achieving an efficient perception service while avoiding the high cost of cloud connectivity.
Metasurface-enhanced Light Detection and Ranging Technology
Venue: arXiv cs.RO 2022
Year: 2022

Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Low-resolution peripheral field provides coarse scene exploration to direct the eye to focus to a highly resolved fovea region for sharp imaging. Among 3D computer vision techniques, Light Detection and Ranging (LiDAR) is currently considered at the industrial level for robotic vision. LiDAR is an imaging technique that monitors pulses of light at optical frequencies to sense the space and to recover three-dimensional ranging information. Notwithstanding the efforts on LiDAR integration and optimization, commercially available devices have slow frame rate and low image resolution, notably limited by the performance of mechanical or slow solid-state deflection systems. Metasurfaces (MS) are versatile optical components that can distribute the optical power in desired regions of space. Here, we report on an advanced LiDAR technology that uses ultrafast low FoV deflectors cascaded with large area metasurfaces to achieve large FoV and simultaneous peripheral and central imaging zones. This technology achieves MHz frame rate for 2D imaging, and up to KHz for 3D imaging, with extremely large FoV (up to 150°deg. on both vertical and horizontal scanning axes). The use of this disruptive LiDAR technology with advanced learning algorithms offers perspectives to improve further the perception capabilities and decision-making process of autonomous vehicles and robotic systems.
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
Venue: arXiv cs.LG 2022
Year: 2022

Data-free quantization is a task that compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due to inaccurate activation clipping range and quantization error, especially for low bit-width. In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization. Accurate activation clipping (AAC) improves the model accuracy by exploiting accurate activation information from the full-precision model. Adaptive batch normalization firstly proposes to address the quantization error from distribution changes by updating the batch normalization layer adaptively. Extensive experiments demonstrate that the proposed data-free quantization method can yield surprisingly performance, achieving 64.33% top-1 accuracy of ResNet18 on ImageNet dataset, with 3.7% absolute improvement outperforming the existing state-of-the-art methods.
Clone Detection on Large Scala Codebases
Venue: arXiv cs.SE 2022
Year: 2022

Code clones are identical or similar code segments. The wide existence of code clones can increase the cost of maintenance and jeopardise the quality of software. The research community has developed many techniques to detect code clones, however, there is little evidence of how these techniques may perform in industrial use cases. In this paper, we aim to uncover the differences when such techniques are applied in industrial use cases. We conducted large scale experimental research on the performance of two state-of-the-art code clone detection techniques, SourcererCC and AutoenCODE, on both open source projects and an industrial project written in the Scala language. Our results reveal that both algorithms perform differently on the industrial project, with the largest drop in precision being 30.7\%, and the largest increase in recall being 32.4\%. By manually labelling samples of the industrial project by its developers, we discovered that there are substantially less Type-3 clones in the aforementioned project than that in the open source projects.
Interpretable AI for policy-making in pandemics
Venue: arXiv cs.LG 2022
Year: 2022

Since the first wave of the COVID-19 pandemic, governments have applied restrictions in order to slow down its spreading. However, creating such policies is hard, especially because the government needs to trade-off the spreading of the pandemic with the economic losses. For this reason, several works have applied machine learning techniques, often with the help of special-purpose simulators, to generate policies that were more effective than the ones obtained by governments. While the performance of such approaches are promising, they suffer from a fundamental issue: since such approaches are based on black-box machine learning, their real-world applicability is limited, because these policies cannot be analyzed, nor tested, and thus they are not trustable. In this work, we employ a recently developed hybrid approach, which combines reinforcement learning with evolutionary computation, for the generation of interpretable policies for containing the pandemic. These policies, trained on an existing simulator, aim to reduce the spreading of the pandemic while minimizing the economic losses. Our results show that our approach is able to find solutions that are extremely simple, yet very powerful. In fact, our approach has significantly better performance (in simulated scenarios) than both previous work and government policies.
The History of the Grid
Venue: arXiv cs.DC 2022
Year: 2022

With the widespread availability of high-speed networks, it becomes feasible to outsource computing to remote providers and to federate resources from many locations. Such observations motivated the development, from the mid-1990s onwards, of a range of innovative Grid technologies, applications, and infrastructures. We review the history, current status, and future prospects for Grid computing.
Fundamental Limits on Detection With a Dual-function Radar Communication System
Venue: arXiv cs.IT 2022
Year: 2022

This paper investigates the fundamental limits on the target detection performance with a dual-function multiple-input-multiple-output (MIMO) radar communication (RadCom) systems. By assuming the presence of a point-like target and a communication receiver, closed-form expressions for the maximum detection probability and the transmit waveforms achieving the optimal performance are derived. Results show that for the considered case, the dual-function system should transmit coherent waveforms to achieve the optimal detection performance. Moreover, the angle separation between the target and communication receiver has a great impact on the achievable detection performance.
An Improved Integer Modular Multiplicative Inverse (modulo $2^w$)
Venue: arXiv cs.DS 2022
Year: 2022

This paper presents an algorithm for the integer multiplicative inverse (mod $2^w$) which completes in the fewest cycles known for modern microprocessors, when using the native bit width $w$ for the modulus $2^w$. The algorithm is a modification of a method by Dumas, and for computers it slightly increases generality and efficiency. A proof is given, and the algorithm is shown to be closely related to the better known Newton's method algorithm for the inverse. Simple direct formulas, which are needed by this algorithm and by Newton's method, are reviewed and proven for the integer inverse modulo $2^k$ with $k$ = 1, 2, 3, 4, or 5, providing the first proof of the preferred formula with $k$=4 or 5.
Data Augmentation for Electrocardiograms
Venue: arXiv cs.LG 2022
Year: 2022

Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to be trained with large, labelled datasets, which are not available for many predictive tasks of interest. In this work, we perform an empirical study examining whether training time data augmentation methods can be used to improve performance on such data-scarce ECG prediction problems. We investigate how data augmentation strategies impact model performance when detecting cardiac abnormalities from the ECG. Motivated by our finding that the effectiveness of existing augmentation strategies is highly task-dependent, we introduce a new method, TaskAug, which defines a flexible augmentation policy that is optimized on a per-task basis. We outline an efficient learning algorithm to do so that leverages recent work in nested optimization and implicit differentiation. In experiments, considering three datasets and eight predictive tasks, we find that TaskAug is competitive with or improves on prior work, and the learned policies shed light on what transformations are most effective for different tasks. We distill key insights from our experimental evaluation, generating a set of best practices for applying data augmentation to ECG prediction problems.
Application of machine learning for predicting the spread of COVID-19
Venue: arXiv cs.LG 2022
Year: 2022

The spread of diseases has been studied for many years, but it receives a particular focus recently due to the outbreak and spread of COVID-19. Studies show that the spread of COVID-19 can be characterized by the Susceptible-Infectious-Recovered-Deceased (SIRD) model with containment coefficients (due to quarantine and keeping social distance). This project aims to apply the machine learning technique to predict the severity of COVID-19 and the effect of quarantine, keeping social distance, working from home, and wearing masks on the transmission of the disease. This work deepens our understanding of disease transmission and reveals the importance of following policies.
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method
Venue: arXiv cs.LG 2022
Year: 2022

We propose an adaptive projection-gradient descent-shrinkage-splitting method (APGDSSM) to integrate penalty based channel pruning into quantization-aware training (QAT). APGDSSM concurrently searches weights in both the quantized subspace and the sparse subspace. APGDSSM uses shrinkage operator and a splitting technique to create sparse weights, as well as the Group Lasso penalty to push the weight sparsity into channel sparsity. In addition, we propose a novel complementary transformed l1 penalty to stabilize the training for extreme compression.
E^2TAD: An Energy-Efficient Tracking-based Action Detector
Venue: arXiv cs.CV 2022
Year: 2022

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC).
Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach
Venue: arXiv cs.CV 2022
Year: 2022

One billion people worldwide are estimated to be living in slums, and documenting and analyzing these regions is a challenging task. As compared to regular slums; the small, scattered and temporary nature of temporary slums makes data collection and labeling tedious and time-consuming. To tackle this challenging problem of temporary slums detection, we present a semi-supervised deep learning segmentation-based approach; with the strategy to detect initial seed images in the zero-labeled data settings. A small set of seed samples (32 in our case) are automatically discovered by analyzing the temporal changes, which are manually labeled to train a segmentation and representation learning module. The segmentation module gathers high dimensional image representations, and the representation learning module transforms image representations into embedding vectors. After that, a scoring module uses the embedding vectors to sample images from a large pool of unlabeled images and generates pseudo-labels for the sampled images. These sampled images with their pseudo-labels are added to the training set to update the segmentation and representation learning modules iteratively. To analyze the effectiveness of our technique, we construct a large geographically marked dataset of temporary slums. This dataset constitutes more than 200 potential temporary slum locations (2.28 square kilometers) found by sieving sixty-eight thousand images from 12 metropolitan cities of Pakistan covering 8000 square kilometers. Furthermore, our proposed method outperforms several competitive semi-supervised semantic segmentation baselines on a similar setting. The code and the dataset will be made publicly available.
Reduction ratio of the IS-algorithm: worst and random cases
Venue: arXiv cs.DS 2022
Year: 2022

We study the IS-algorithm, a well-known linear-time algorithm for computing the suffix array of a word. This algorithm relies on transforming the input word $w$ into another word, called the reduced word of $w$, that will be at least twice shorter; then, the algorithm recursively computes the suffix array of the reduced word. In this article, we study the reduction ratio of the IS-algorithm, i.e., the ratio between the lengths of the input word and the word obtained after reducing $k$ times the input word. We investigate both worst cases, in which we find precise results, and random cases, where we prove some strong convergence phenomena. Finally, we prove that, if the input word is a randomly chosen word of length $n$, we should not expect much more than $\log(\log(n))$ recursive function calls.