Reviewerly/exHarmony huggingface dataset:
No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features

During acquisition, storage, and transmission, the quality of digital videos degrades significantly. Low-quality videos lead to the failure of many computer vision applications, such as object tracking or detection, intelligent surveillance, etc. Over the years, many different features have been developed to resolve the problem of no-reference video quality assessment (NR-VQA). In this paper, we propose a novel NR-VQA algorithm that integrates the fusion of temporal statistics of local and global image features with an ensemble learning framework in a single architecture. Namely, the temporal statistics of global features reflect all parts of the video frames, while the temporal statistics of local features reflect the details. Specifically, we apply a broad spectrum of statistics of local and global features to characterize the variety of possible video distortions. In order to study the effectiveness of the method introduced in this paper, we conducted experiments on two large benchmark databases, i.e., KoNViD-1k and LIVE VQC, which contain authentic distortions, and we compared it to 14 other well-known NR-VQA algorithms. The experimental results show that the proposed method is able to achieve greatly improved results on the considered benchmark datasets. Namely, the proposed method exhibits significant progress in performance over other recent NR-VQA approaches.
3D Quantitative Metallogenic Prediction of Indium-Rich Ore Bodies in the Dulong Sn-Zn Polymetallic Deposit, Yunnan Province, SW China

The southwestern South China Block is one of the most important Sn polymetallic ore districts in the world, of which the Dulong Sn-Zn polymetallic deposit, closely related to Late Cretaceous granitic magmatism, contains 0.4 Mt Sn, 5.0 Mt Zn, 0.2 Mt Pb, and 7 Kt In, and is one of the largest Sn-Zn polymetallic deposits in this region. In this paper, on the basis of a 3D model of ore bodies established by the cut-off grade of the main ore-forming elements, the In grades were estimated by the ordinary Kriging method and the In-rich cells were extracted. The 3D models of strata, faults, granites, and granite porphyries in the mining area were established and assigned the attributes to the cells, which built buffer zones representing the influence space of the geological factors. The weight of evidence and artificial neural network methods were used to quantitatively evaluate the contribution of each geological factor to mineralization. The results show that the Neoproterozoic Xinzhai Formation (Pt3x), fault (F1), and Silurian granites (S3L) have considerable control effects on the occurrence of In-rich ore bodies. The metallogenic predictions according to the spatial coupling relationship of each geological factor in 3D space were carried out, and then the 3D-space-prospecting target areas of In-rich ore bodies were delineated. In addition, the early geological maps and data information of the mining area were comprehensively integrated in 3D space. The feasibility of 3D quantitative metallogenic prediction based on the deposit model was explored by comparing the two methods, and then, the 3D-space prospecting target area was delineated. The ROC curve evaluation shows that the results of two methods have indicative value for prospecting. The modeling results may support its use for future deep prospecting and exploitation of the Dulong and other similar deposits.
Compact Abstract Graphs for Detecting Code Vulnerability with GNN Models

Source code representation is critical to the machine-learning-based approach to detecting code vulnerability. This paper proposes Compact Abstract Graphs (CAGs) of source code in different programming languages for predicting a broad range of code vulnerabilities with Graph Neural Network (GNN) models. CAGs make the source code representation aligned with the task of vulnerability classification and reduce the graph size to accelerate model training with minimum impact on the prediction performance. We have applied CAGs to six GNN models and large Java/C datasets with 114 vulnerability types in Java programs and 106 vulnerability types in C programs. The experiment results show that the GNN models have performed well, with accuracy ranging from 94.7% to 96.3% on the Java dataset and from 91.6% to 93.2% on the C dataset. The resultant GNN models have achieved promising performance when applied to more than 2,500 vulnerabilities collected from real-world software projects. The results also show that using CAGs for GNN models is significantly better than ASTs, CFGs (Control Flow Graphs), and PDGs (Program Dependence Graphs). A comparative study has demonstrated that the CAG-based GNN models can outperform the existing methods for machine learning-based vulnerability detection.
Heimdallr: Fingerprinting SD-WAN Control-Plane Architecture via Encrypted Control Traffic

Software-defined wide area network (SD-WAN) has emerged as a new paradigm for steering a large-scale network flexibly by adopting distributed software-defined network (SDN) controllers. The key to building a logically centralized but physically distributed control-plane is running diverse cluster management protocols to achieve consistency through an exchange of control traffic. Meanwhile, we observe that the control traffic exposes unique time-series patterns and directional relationships due to the operational structure even though the traffic is encrypted, and this pattern can disclose confidential information such as control-plane topology and protocol dependencies, which can be exploited for severe attacks. With this insight, we propose a new SD-WAN fingerprinting system, called Heimdallr. It analyzes periodical and operational patterns of SD-WAN cluster management protocols and the context of flow directions from the collected control traffic utilizing a deep learning-based approach, so that it can classify the cluster management protocols automatically from miscellaneous control traffic datasets. Our evaluation, which is performed in a realistic SD-WAN environment consisting of geographically distant three campus networks and one enterprise network shows that Heimdallr can classify SD-WAN control traffic with ≥ 93%, identify individual protocols with ≥ 80% macro F-1 scores, and finally can infer control-plane topology with ≥ 70% similarity.
CoCoTPM: Trusted Platform Modules for Virtual Machines in Confidential Computing Environments

Cloud computing has gained popularity and is increasingly used to process sensitive and valuable data. This development necessitates the protection of data from the cloud provider and results in a trend towards confidential computing. Hardware-based technologies by AMD, Intel and Arm address this and allow the protection of virtual machines and the data processed in them. Unfortunately, these hardware-based technologies do not offer a unified interface for necessary tasks like secure key generation and usage or secure storage of integrity measurements. Moreover, these technologies are oftentimes limited in functionality especially regarding remote attestation. On the other hand, a unified interface is widely used in the area of bare-metal systems to provide these functionalities: the Trusted Platform Module (TPM).
User Perceptions of the Privacy and Usability of Smart DNS

Smart DNS (SDNS) services enable their users to avoid geographic restrictions to content (i.e., geoblocking) with minimal internet quality of service overhead. While previous research has shown that usage of SDNS has numerous associated privacy risks, the security and privacy perceptions of users of SDNS are unexplored. In this paper, we perform a survey of n = 63 SDNS users, finding that many have limited understandings both of how these systems work and their overall security/privacy properties. As a result, many users put undue trust in purveyors of SDNS services and in the security they provide.
Parallel Small Polynomial Multiplication for Dilithium: A Faster Design and Implementation

The lattice-based signature scheme CRYSTALS-Dilithium is one of the two signature finalists in the third round NIST post-quantum cryptography (PQC) standardization project. For applications of low-power Internet-of-Things (IoT) devices, recent research efforts have been focusing on the performance optimization of PQC algorithms on embedded systems. In particular, performance optimization is more demanding for PQC signature algorithms that are usually significantly more time-consuming than PQC public-key encryption counterparts. For most cryptographic algorithms based on algebraic lattices including Dilithium, the fundamental and most time-consuming operation is polynomial multiplication over rings. For this computational task, number theoretic transform (NTT) is the most efficient multiplication method for NTT-friendly rings, and is now the typical technique for performing fast polynomial multiplications when implementing lattice-based PQC algorithms.
DF-SCA: Dynamic Frequency Side Channel Attacks are Practical

The arm race between hardware security engineers and side-channel researchers has become more competitive with more sophisticated attacks and defenses in the last decade. While modern hardware features improve the system performance significantly, they may create new attack surfaces for malicious people to extract sensitive information about users without physical access to the victim device. Although many previously exploited hardware and OS features were patched by OS developers and chip vendors, any feature that is accessible from userspace applications can be exploited to perform software-based side-channel attacks.
Accept All Exploits: Exploring the Security Impact of Cookie Banners

The General Data Protection Regulation (GDPR) and related regulations have had a profound impact on most aspects related to privacy on the Internet. By requiring the user’s consent for e.g., tracking, an affirmative action has to take place before such data collection is lawful, leading to spread of so-called cookie banners across the Web. While the privacy impact and how well companies adhere to those regulations have been studied in detail, an open question is what effect these banners have on the security of netizens.
MoLE: Mitigation of Side-channel Attacks against SGX via Dynamic Data Location Escape

Numerous works have experimentally shown that Intel Software Guard eXtensions (SGX) is vulnerable to side-channel attacks (SCAs) and related threats, including transient execution attacks. These threats compromise the security of SGX-protected apps. Obfuscating data access patterns is a realistic way to guard against these threats. However, existing defenses impose either too much performance overhead or additional usage restrictions (such as multi-threading). Furthermore, these obfuscation schemes may no longer work if the attacker has the capacity to single-step the target application.
If You Can’t Beat Them, Pay Them: Bitcoin Protection Racket is Profitable

Pooled mining has become the most popular mining approach in the Bitcoin system, which can effectively reduce the variance of the block generation reward of participants. The security of pooled mining depends on whether it is incentive compatible, that is, an honest participant will get a reward proportional to his work. Recent attacks on mining pools, for example, Block Withholding, Fork After Withholding, and Power Adjusting Withholding (PAW) attacks, show that malicious participants may undermine the revenue of the honest pools and receive an unfair share of the mining reward. This paper shows that the security of Bitcoin is even worse than what the recent attacks demonstrated. We describe an attack called Fork Withholding Attack under a Protection Racket (FWAP), in which the mining pool pays the attacker for withholding a fork. Our insight is that the mining pools under forking attacks have incentives to pay in exchange for not being forked. The attacker and the paying pool negotiate how much to be paid, and we show that it is possible for both the attacker and the paying pool to earn higher rewards at the expense of the other pools. In particular, our formal analysis and simulation demonstrate that the payer and the FWAP attacker can get up to 1.8 × and 3.8 × of extra reward as in PAW, respectively. Furthermore, FWAP can escape from the “miners’ dilemma’’ when two FWAP attackers attack each other under some circumstances. We also propose simple approaches that serve as the first step towards preventing the FWAP attack.
Label Guided Discrete Hashing for Cross-Modal Retrieval

Due to their low storage capacity and fast retrieval speed, hashing techniques have received much attention in cross-modal retrieval. However, there are some issues that need to be further explored. First, some existing hashing methods use the labels to construct the semantic similarity matrix between pairwise data, ignoring the potential manifold structure between heterogeneous data. Second, some existing methods underestimate the importance of multi-label and the gaps between different class labels, making the learned hash codes less discriminative. Third, few of them embed both manifold and balanced structures within the same model, and the relaxation of discrete constraints will lead to an increasing quantization error. To mitigate these problems, this paper proposes a novel supervised hashing method, termed Label Guided Discrete Hashing (LGDH), which simultaneously preserves the comprehensive manifold structure and discriminative balanced codes that are both constructed by label information into Hamming space. We develop a local category distribution of the nearest neighbors, to excavate the underlying manifold structure of heterogeneous data. To maximize the gaps of different categories, a balanced matrix is constructed by labels to generate hash codes with balanced bits. For multi-label data, we also design a novel multi-label manifold and balanced structure matrix to adapt the real-world scenarios. An effective discrete optimization method is used to optimize our proposed objective function instead of the relaxation one. Extensive experiments on three benchmark datasets verify the effectiveness of LGDH. The comparison results demonstrat that LGDH achieves about 2% and 3% improved to different cross-modal tasks on average.
Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?

Medical professionals have been burdened by clerical work, and artificial intelligence may efficiently support physicians by generating clinical summaries. However, whether hospital discharge summaries can be generated automatically from inpatient records stored in electronic health records remains unclear. Therefore, this study investigated the sources of information in discharge summaries. First, the discharge summaries were automatically split into fine-grained segments, such as those representing medical expressions, using a machine learning model from a previous study. Second, these segments in the discharge summaries that did not originate from inpatient records were filtered out. This was performed by calculating the n-gram overlap between inpatient records and discharge summaries. The final source origin decision was made manually. Finally, to reveal the specific sources (e.g., referral documents, prescriptions, and physician's memory) from which the segments originated, they were manually classified by consulting medical professionals. For further and deeper analysis, this study designed and annotated clinical role labels that represent the subjectivity of the expressions and builds a machine learning model to assign them automatically. The analysis results revealed the following: First, 39% of the information in the discharge summary originated from external sources other than inpatient records. Second, patient's past clinical records constituted 43%, and patient referral documents constituted 18% of the expressions derived from external sources. Third, 11% of the missing information was not derived from any documents. These are possibly derived from physicians' memories or reasoning. According to these results, end-to-end summarization using machine learning is considered infeasible. Machine summarization with an assisted post-editing process is the best fit for this problem domain.
Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Cloud computing technology is favored by users because of its strong computing power and convenient services.At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality.Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours.Therefore, the scheduling problem has promising application prospects under the multi-cloud environment.This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks.This paper reviews the concepts, types, objectives, advantages, challenges, and research status of task scheduling in the multi-cloud environment.Task scheduling strategies proposed in the existing related references are analyzed, discussed, and summarized, including research motivation, optimization algorithm, and related objectives.Finally, the research status of the two kinds of task scheduling is compared, and several future important research directions of multi-cloud task scheduling are proposed.
Drone-Aided Detection of Weeds: Transfer Learning for Embedded Image Processing

In this article, we address the problem of hogweed detection using a drone equipped with red, green, blue (RGB) and multispectral cameras. We study two approaches: 1) offline detection running on the orthophoto of the area scanned within the mission and 2) real-time scanning from the frame stream directly on the edge device performing the flight mission. We show that by fusing the information from an additional multispectral camera installed on the drone, there is an opportunity to boost the detection quality, which can then be preserved even with a single RGB camera setup by the introduction of an additional convolution neural network trained with transfer learning to produce the fake multispectral images directly from the RGB stream. We show that this approach helps either eliminate the multispectral hardware from the drone or, if only the RGB camera is at hand, boost the segmentation performance by the cost of slight increase in computational budget. To support this claim, we have performed an extensive study of network performance in simulations of both the real-time and offline modes, where we achieve at least 1.1% increase in terms of the mean intersection over union metric when evaluated on the RGB stream from the camera and 1.4% when evaluated on orthophoto data. Our results show that the proper optimization guarantees a complete elimination of the multispectral camera from the flight mission by adding a preprocessing stage to the segmentation network without the loss of quality.
Curriculum-Style Fine-Grained Adaption for Unsupervised Cross-Lingual Dependency Transfer

Unsupervised cross-lingual transfer has been shown great potentials for dependency parsing of the low-resource languages when there is no annotated treebank available. Recently, the self-training method has received increasing interests because of its state-of-the-art performance in this scenario. In this work, we advance the method further by coupling it with curriculum learning, which guides the self-training in an easy-to-hard manner. Concretely, we present a novel metric to measure the instance difficulty of a dependency parser which is trained mainly on a Treebank from a resource-rich source language. By using the metric, we divide a low-resource target language into several fine-grained sub-languages by their difficulties, and then apply iterative-self-training progressively on these sub-languages. To fully explore the auto-parsed training corpus from sub-languages, we exploit an improved parameter generation network to model the sub-languages for better representation learning. Experimental results show that our final curriculum-style self-training can outperform a range of strong baselines, leading to new state-of-the-art results on unsupervised cross-lingual dependency parsing. We also conduct detailed experimental analyses to examine the proposed approach in depth for comprehensive understandings.
Minimising Biasing Word Errors for Contextual ASR With the Tree-Constrained Pointer Generator

Contextual knowledge is essential for reducing speech recognition errors on high-valued long-tail words. This paper proposes a novel tree-constrained pointer generator (TCPGen) component that enables end-to-end ASR models to bias towards a list of long-tail words obtained using external contextual information. With only a small overhead in memory use and computation cost, TCPGen can structure thousands of biasing words efficiently into a symbolic prefix-tree, and creates a neural shortcut between the tree and the final ASR output to facilitate the recognition of the biasing words. To enhance TCPGen, we further propose a novel minimum biasing word error (MBWE) loss that directly optimises biasing word errors during training, along with a biasing-word-driven language model discounting (BLMD) method during the test. All contextual ASR systems were evaluated on the public Librispeech audiobook corpus and the data from the dialogue state tracking challenges (DSTC) with the biasing lists extracted from the dialogue-system ontology. Consistent word error rate (WER) reductions were achieved with TCPGen, which were particularly significant on the biasing words with around 40% relative reductions in the recognition error rates. MBWE and BLMD further improved the effectiveness of TCPGen, and achieved more significant WER reductions on the biasing words. TCPGen also achieved zero-shot learning of words not in the audio training set with large WER reductions on the out-of-vocabulary words in the biasing list.
Analysis of Optimizers on AlexNet Architecture for Face Biometric Authentication System

Nowadays, biometric authentication is more important than a password or token-based authentication. There have been many techniques suggested for biometric authentication algorithms, however, it can be observed that the Deep Learning approach is significantly more effective and secure than other methods, specifically Convolutional Neural Networks (CNN) with AlexNet architecture for face recognition. However, an optimization technique is crucial in the Deep Learning models so, this paper will analyze the best optimizers for AlexNet architecture which are SGD, AdaGrad, RMSProp, AdaDelta, Adam, and AdaMax by using the proposed face dataset includes 7 celebrity classes, each with 35 images obtained from Google Images. To enhance the size of the dataset, data augmentation was employed before it was fed into the AlexNet model. The experiment shows AdaMax performs well when compared to the other optimizers on the proposed dataset.