Reviewerly/exHarmony huggingface dataset:
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection

In this paper, we propose PhantomSound, a query-efficient black-box attack toward voice assistants. Existing black-box adversarial attacks on voice assistants either apply substitution models or leverage the intermediate model output to estimate the gradients for crafting adversarial audio samples. However, these attack approaches require a significant amount of queries with a lengthy training stage. PhantomSound leverages the decision-based attack to produce effective adversarial audios, and reduces the number of queries by optimizing the gradient estimation. In the experiments, we perform our attack against 4 different speech-to-text APIs under 3 real-world scenarios to demonstrate the real-time attack impact. The results show that PhantomSound is practical and robust in attacking 5 popular commercial voice controllable devices over the air, and is able to bypass 3 liveness detection mechanisms with success rate. The benchmark result shows that PhantomSound can generate adversarial examples and launch the attack in a few minutes. We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93.1% and 65.5% compared with the state-of-the-art black-box attacks, using merely ∼ 300 queries (∼ 5 minutes) and ∼ 1,500 queries (∼ 25 minutes), respectively.
All Use-After-Free Vulnerabilities Are Not Created Equal: An Empirical Study on Their Characteristics and Detectability

Over the past decade, use-after-free (UaF) has become one of the most exploited types of vulnerabilities. To address this increasing threat, we need to advance the defense in multiple directions, such as UaF vulnerability detection, UaF exploit defense, and UaF bug fix. Unfortunately, the intricacy rooted in the temporal nature of UaF vulnerabilities makes it quite challenging to develop effective and efficient defenses in these directions. This calls for an in-depth understanding of real-world UaF characteristics. This paper presents the first comprehensive empirical study of UaF vulnerabilities, with 150 cases randomly sampled from multiple representative software suites, such as Linux kernel, Python, and Mozilla Firefox. We aim to identify the commonalities, root causes, and patterns from real-world UaF bugs, so that the empirical results can provide operational guidance to avoid, detect, deter, and fix UaF vulnerabilities. Our main finding is that the root causes of UaF bugs are diverse, and they are not evenly or equally distributed among different software. This implies that a generic UaF detector/fuzzer is probably not an optimal solution. We further categorize the root causes into 11 patterns, several of which can be translated into simple static detection rules to cover a large portion of the 150 UaF vulnerabilities with high accuracy. Motivated by our findings, we implement 11 checkers in a static bug detector called Palfrey. Running Palfrey on the code of popular open source software, we detect 9 new UaF vulnerabilities. Compared with state-of-the-art static bug detectors, Palfrey outperforms in coverage and accuracy for UaF detection, as well as time and memory overhead.
How (Not) to Build Threshold EdDSA

Edwards-curve digital signature algorithm (EdDSA) is a highly efficient scheme with a short key size. It is derived from the threshold-friendly Schnorr signatures and is covered by the NIST standardization efforts of threshold cryptographic primitives. Nevertheless, extending its deterministic nonce generation to the threshold setting requires heavyweight cryptographic techniques, even when the hash function is replaced with one optimized for secure multi-party computation. Indeed, an efficient extension to the threshold setting is considered a major challenge by NIST and academia.
Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability

Evasion attacks are a threat to machine learning models, where adversaries attempt to affect classifiers by injecting malicious samples. An alarming side-effect of evasion attacks is their ability to transfer among different models: this property is called transferability. Therefore, an attacker can produce adversarial samples on a custom model (surrogate) to conduct the attack on a victim’s organization later. Although literature widely discusses how adversaries can transfer their attacks, their experimental settings are limited and far from reality. For instance, many experiments consider both attacker and defender sharing the same dataset, balance level (i.e., how the ground truth is distributed), and model architecture.
SCVMON: Data-oriented attack recovery for RVs based on safety-critical variable monitoring

There are many various data-oriented attacks on robotic vehicles (RVs) that change the inputs of an RV control program. While much research has been dedicated to detecting the attacks, the recovery mechanism has received relatively less attention. Without recovery after detection, an RV cannot continue with its assigned missions. Unfortunately, the existing recovery mechanisms have limitations that make it difficult to deploy these in real RVs, such that they require additional hardware/software or can only recover from the limited types of data-oriented attacks. To overcome these limitations, we propose a framework called SCVMON that detects and helps RVs recover from various data-oriented attacks that generate inappropriate control commands. Based on the observation that data-oriented attacks inevitably change the values of some variables in RV control programs, SCVMON systematically identifies the safety-critical variables (SCVs) that can affect the safety of RVs. For efficient recovery, we extract from SCVs a set of monitored safety-critical variables (mSCVs) that can reflect all input changes, and monitor them to detect and recover from various data-oriented attacks. SCVMON does not depend on the physical nature of a specific sensor or hardware, which is a significant benefit, and it can be applied through a simple software update. Our evaluation shows that SCVMON can quickly detect and recover from 20 types of data-oriented attacks. Also, SCVMON incurs only 0.3% storage overhead and up to 5.1% runtime overhead, proving that it is suitable for RVs.
EBugDec: Detecting Inconsistency Bugs caused by RFC Evolution in Protocol Implementations

The implementation of network protocol must comply with respective Request for Comments (RFC) and updated as RFCs evolve. However, due to the richness of RFCs and the complex relationships between them, systematically discovering the evolution of RFC requirements is non-trivial, which consequently brings in inconsistency bugs when modifying code to support new RFC documents. This can lead to inconsistency bugs when modifying code to support new RFC documents, known as RFC-evolutionary bugs or ebugs. Recent approaches have used natural language processing techniques to extract RFC rules and employed differential testing or static analysis to discover inconsistency bugs in protocol implementations. However, they seldom consider the evolution of RFC requirements nor their related bugs.
Development of Indoor Positioning Engine Application at Workshop PT Garuda Maintenance Facilities Aero Asia Tbk

Information systems are part of the development of information technology. The role of information systems in an organization cannot be doubted and can even have an impact on rapid changes in various areas of the organization. The purpose of this study is to develop an information system that can facilitate companies in monitoring and controlling the location of aircraft engines. This is because the monitoring and control work is done manually using a spreadsheet which prevents updating information on the presence of aircraft engines. The methodology used in this study is a waterfall approach where the stages are carried out in a structured manner, starting from planning, analysis, design, to implementation. The result of the design produces a web-based information system that can make it easier for companies to monitor and control the location of aircraft engines.
Autonomous Team Role Selection On Flexible Projects

In this study, a matrix-based project planning model that can handle flexible projects, employee synergies, and the distinction between hard and soft skills is proposed. Traditional scheduling methods cannot capture the importance of flexible policies such as those found in agile projects. The proposed method includes autonomous team role selection and is evaluated through a real-life case study. The findings show that positive synergies between employees can decrease project makespan, and the best solution can be achieved through autonomous team role selection. This research expands on the synergy-based software project scheduling problem (SPSP) to show how the proposed model can effectively manage team roles and the synergies among employees. The results suggest that the proposed method outperforms the existing nonsynergy-based project scheduling methods, and the use of this approach can help project managers to better understand the importance of such policies and optimize their project schedules accordingly.
Fine-tuned deep convolutional neural network for hand segmentation in egocentric videos

Semantic segmentation is a high-level task in computer vision that associates each pixel of an image with a semantic(class) label. Fine-semantic segmentation is a pixel-level task that provides detailed information necessary to easily identify the region of the object of interest. Hands are one of the main channels for communication, enhancing human-object and human-environment interaction, and in egocentric videos, they appear to be ubiquitous and at the center of vision and activities, hence our interest in hand segmentation. Fine-semantic segmentation of hands locates, identifies, and groups together pixels associated with the hands, with a hand semantic label. We performed fine semantic segmentation of hands, by improving the architecture of the state-of-the-art deep convolutional neural network (RefineNet). We achieve a finer and more accurate result by amending the process of obtaining and combining high and low-level features, and the pixel grouping for pixel-level classification. We performed this task on a public egocentric video dataset (EgoHands). We evaluate our model (RefineNet-Pix) performance by adopting the existing pixel-level metric, mean precision (mPrecision). Comparing our result with the baseline reported in Urooj’s work, we obtain accuracy higher than 87.9% of the benchmark. Our finer and more accurate semantic segmentation result guarantees good performance under various lighting conditions and complex backgrounds, making it suitable for use in both indoor and outdoor environments. Fine-hand semantic segmentation can be applied in image analysis, medical systems (with a focus on understanding hand motion for prediction, diagnosis, and monitoring), hand gesture recognition (human-computer interaction and understanding action), and robotics(grasp and manipulation of objects).
Full-aperture wavefront reconstruction method in quadri-wave lateral shearing interferometry (QWLSI)

This study presents a method for reconstructing the full-aperture wavefront, particularly at the edge-side wavefront in Quadri-Wave Lateral Shearing Interferometry (QWLSI). While QWLSI is a powerful method for measuring wavefront error in an optical system, it faces the challenge of obtaining wavefront information on the non-interfering region on the edge side of the input beam by lateral shearing. This challenge becomes more significant for larger apertures. To address this, the study proposes a modified method for reconstructing the full-aperture wavefront using the basic principle of partial slope detection in lateral shearing interferometry (LSI). QWLSI provides four directional two-point slope functions by wavefront shearing. The inverse LSI slope can be deduced using the spatial relation between the reconstructed center wavefront and the measured slope information. The partial edge-side wavefront can then be reconstructed using this inverse equation, following the four-shearing direction. Finally, the partial edge-side wavefront maps can be combined using a phase stitching algorithm to obtain a full-aperture wavefront. The effectiveness of this method was demonstrated through numerical simulation and experimental results, which showed that the proposed method accurately reconstructed the noninterfering edge-side wavefront in QWLSI, improving the spatial range of wavefront measurement. This technique has potential applications in optical testing, particularly in improving the efficiency of large aperture mirror testing by reducing the margin diameter of the mirror blank preparation. The study introduces the reconstruction method in detail and presents some testing results for verification.
Deflectometry for ophthalmic optics testing and the impact of test surface orientation

The effects of the misalignment of a test surface in a deflectometry system were explored. A Stewart Platform Hexapod stage was used to create the intentional misalignment, rotate the test surface position around the X and Y axes as well as displace their Z location. The measured surface maps were analyzed in coefficients of a fit of the surface map to the Zernike coefficients. The Zernike term results were adopted to show the relationship between the induced pose changes and the reconstructed surface map. Such an understanding of the errors in location and orientation and deflectometry measurement results would be beneficial in the future measurement of ophthalmic optics with a deflectometry system.
Image recoloring using generative adversarial neural networks

This paper presents an in-depth exploration of a Neural Network designed to recolor grayscale images with minimal input requirements. The paper delves into the intricate process of training the network, which involves carefully selecting a fitness function and creating an effective adversarial network. Throughout the paper, various alternatives are considered and evaluated until a suitable approach is identified for further training. Notably, the implementation adopts a random batch sampling approach to gather images in each batch selection, allowing for diverse and comprehensive training. Moreover, several techniques, including Batch Normalization, Leaky ReLU, and Label Smoothing, are strategically employed to tackle challenges related to generalization and achieve a balanced interplay between the generator and discriminator. The experimental results are thoroughly discussed, showcasing the substantial progress achieved in addressing the problem at hand. Remarkably, the Neural Network attains a Structural Similarity Index (SSIM) of -0.5944 on the test set and -0.5922 on the training set, signifying its proficiency in accurately recoloring grayscale images. This paper contributes valuable insights into the realm of image recoloring using neural networks and demonstrates the effectiveness of the proposed methodology in achieving good results.
Grating-embedded mirrors for heliostat tracking error metrology

Concentrated solar power (CSP) plants need to monitor the surface slope error of thousands of heliostats with sub-milliradian accuracy. We present grating embedded mirrors (GEMs) and the accompanying Diffractive AutoStigmatic Hartmann Camera (DASHCam) metrology system for measuring heliostat surface slopes. GEMs have multiple phase diffraction gratings written within a glass mirror substrate using an ultrafast laser. The gratings direct a small fraction of incident light to non-specular directions, which the DASHCam senses from a virtual center of curvature to measure the facet slopes at each grating location. Our results show, in a laboratory environment, 24 &mu;rad RMS measurement repeatability and 47 µrad RMS accuracy, with single-shot image capture. GEMs and DASHCam are a compact, accurate, and high-speed heliostat slope error metrology system that is robust to the harsh environmental conditions at CSP plants.
The encrypted image transmission system using the MPSoC platform with Python

In today's digital world, the secure transmission of sensitive information such as images is of paramount importance. Image data often contains private and sensitive information, so protecting it from unauthorized access and interception becomes a critical challenge. This article shows an encrypted image transmission scheme based on a chaotic dynamic configuration of multiple displacements supported by the saturated nonlinear function (SNLF) and implemented on the multipurpose system-on-chip (MPSoC) platform using Python. The main activity was the realization of a chaotic SNLF space system on the Xilinx FPGA PYNQ-Z1 (MPSoC) board by programming by interactive Python on Jupyter Notebook, with the purpose of implementing a secure communication system. The main contribution is the successful synchronization of a system with several chaotic attractors in a master-slave topology. Another important contribution is the rapid implementation on the PYNQ-Z1 FPGA of the robust secure communication system capable of resisting comprehensive attacks based on chaotic space attractor SNLF. Among the results obtained is to encrypt an image, in grayscale and RGB, with chaos and transmission key in the transmission system, send the encrypted image through its state variables and from them reconstruct the encrypted image by which the receiving system recovers the sent image without loss of information. By achieving this digital image processing architecture in MPSoC, it be possible to program and synthesize other types of algorithms for digital processing and real-time video application.
On the performance of video super-resolution algorithms for HTTP-based adaptive streaming applications

Recent years have seen tremendous growth and advancement in the field of super-resolution algorithms for both images and videos. Such algorithms are mainly based on deep learning technologies and are primarily used for upsampling lower resolution images and videos, often outperforming existing traditional upsampling algorithms. Using such advanced upscaling algorithms on the client side can result in significant bandwidth and storage savings as the client can simply request lower-resolution images/videos and then upscale them to the required (higher) display resolution. However, the performance analysis of such proposed algorithms has been limited to a few datasets which are not representative of modern-era adaptive bitrate video streaming applications. Also, many times they only consider scaling artefacts, and hence their performance when considering typical compression artefacts is not known. In this paper, we evaluate the performance of such AI-based upscaling algorithms on different datasets considering a typical adaptive streaming system. Different content types, video compression standards and renditions are considered. Our results indicate that the performance of video upsampling algorithms measured objectively in terms of PSNR and SSIM is insignificant compared to traditional upsampling algorithms. However, more detailed analysis in terms of other advanced quality metrics as well as subjective tests are required for a comprehensive evaluation.
On the assessment of high-quality images: advances on the JPEG AIC-3 activity

Recent advances in image compression made it possible and desirable for image quality to approach the visually lossless range. Nevertheless, the most commonly used subjective visual quality assessment protocols, e.g. the ones reported in ITU-T Rec. BT.500, were found ineffective in evaluating images with visual quality between high to nearly visually lossless. In this context, the JPEG Committee initiated a renewed activity on the Assessment of Image Coding, also referred to as JPEG AIC, aiming at standardizing new subjective and objective image quality assessment methodologies applicable in the quality range from high to nearly visually lossless. For this purpose, a Call for Contributions on Subjective Image Quality Assessment was released with deadline in April 2023, and a Call for Proposals on Objective Image Quality Assessment is expected to be issued in the near future. This paper aims at providing an overview of submissions to the Call for Contributions and presenting the recent advances in this activity, as well as future directions.
A fast, simple, and parallelizable deconvolution algorithm for real-time applications

We present a fast, simple, and parallelizable deconvolution algorithm for the real-time deblurring of one- or two- dimensional signals (i.e. images) degraded by defocus or bokeh-like blur. The proposed algorithm runs in linear- time and performs significantly faster than other popular deconvolution methods tested, bringing the deblurring time down to under 10ms for full-HD images. It has a simple software implementation, requiring no Fourier transforms or dynamic memory allocation. Its parallel design makes it especially suitable for GPU acceleration. For one-dimensional noise-free signals, the algorithm is proven to converge exactly to the original un-blurred signal.
A case for FPGA-based accelerators for energy-efficient motion picture video processing

In recent years, computing-based video processing workflows have become an integral part of the motion picture industry. These workloads are highly data- and compute-intensive, requiring capable hardware to achieve the required performance. Currently, general-purpose CPUs and GPUs are used to accelerate video processing functions in motion picture workflows. While such devices are highly suited to software-driven video processing workflows, they consume large amounts of energy in these tasks. In this work, we present a case for deploying an FPGA-based accelerator as an energy-efficient alternative to general-purpose hardware in high-resolution motion picture video processing, using an ingest module as a case study. We show that an FPGA-based accelerator for decoding 8K OpenEXR B44 video frames, designed using a commercial high-level synthesis workflow and executing on a PCIe-connected Alveo U50 device, outperforms a highly parallel, CPU-optimised inbuilt B44 decoder implementation in terms of energy consumption per frame decoded. In our experiments, the FPGA-accelerated B44 was able to decode 8K frames with 47.9 ms latency while consuming 0.98 J of energy per frame, compared to the 58.3 ms achieved by a high-end Intel 11700-K CPU while consuming 4.5 J per frame, when averaged over 1000 runs. We further show that this offload can be seamlessly integrated into state-of-the-art motion picture tools such as NUKE with minimal effort. With FPGAs becoming mainstream in cloud servers, we envision that this work paves the way for more efficient integration and utilisation of custom hardware and FPGAs in compute-intensive motion picture workflows.
Super-resolution video coding with additional residual data coding

Super-resolution video coding describes the process of coding video at lower resolution and upsampling the result. This process is included in the AV1 standard, which ensures the same super-resolution process is employed on all receiving devices. Regrettably, the design is limited to horizontal scaling with a maximum scale factor of two. In this paper, we analyze the benefit of enabling two-dimensional upsampling with larger scale factors. Additionally, we consider the value of sending residual information to correct the super-resolution output. Results show a 6.3% and 5.6% improvement in coding efficiency for UHD SDR and UHD HDR content.