Reviewerly/exHarmony huggingface dataset:
Reconstructing Hand-Held Objects from Monocular Video

This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach. The dataset and code will be released at https://dihuangdh.github.io/hhor.
GCNScheduler

We provide a highly-efficient solution to the classical problem of scheduling task graphs corresponding to complex applications on distributed computing systems. A number of heuristics have been previously proposed to optimize task scheduling with respect to different metrics (e.g. makespan and throughput). However, they tend to be slow to run, particularly for larger problem instances, limiting their applicability in more dynamic systems. Motivated by the goal of solving these problems more rapidly, we propose, for the first time, a graph convolutional network-based scheduler (GCNScheduler). By carefully integrating the inter-task data dependency structure and the computational network into a single input graph, the GCNScheduler can efficiently schedule tasks of complex applications for a given objective. We use simulations to illustrate that not only can our scheme quickly and efficiently learn from existing scheduling schemes, but also it can easily be applied to large-scale settings that current scheduling schemes fail to handle. We demonstrate the generalization of GCNScheduler to unseen real-world applications and show that it achieves almost the same makespan and throughput as benchmarks, while providing several orders of magnitude faster scheduling times.
k-anonymization of social network data using Neural Network and SVM

With the coming up of social network sites like Twitter, Facebook, LinkedIn, etc information sharing has seen tremendous growth in the past few years. Although people wish to interact with like-minded persons through such platforms, there is a lot of personal information about individuals that need to be secured. To maintain the privacy of sensitive information in a social network, efficient privacy-preserving techniques like k-anonymization, randomization, generalization, etc., are used. The majority of the proposed techniques apply edge editing and clustering based methods for creating k-anonymized social network graphs. Since these techniques distort the original properties of the graph to a great extent, various enhancements have been proposed which consider the addition of noisy nodes as one of the alternatives. In this paper, an improved version of k-degree-anonymization on social network graph has been provided which uses the hybridization of Neural Network and SVM, called NeuroSVM where average path length of the graph is preserved and addition of noisy nodes and noisy edges is reduced significantly. The proposed technique is evaluated on various parameters which include Average path length, Precision, Recall, F-measure, and Information loss. It has been experimentally proved that the proposed technique has less distortion in average path length as compared to existing techniques. The accuracy of the proposed technique is more than 75% and information loss is also reduced.
ASAT: Adaptively scaled adversarial training in time series

Adversarial training is a method for enhancing neural networks to improve the robustness against adversarial examples. Besides the security concerns of potential adversarial examples, adversarial training can also improve the generalization ability of neural networks, train robust neural networks, and provide interpretability for neural networks. In this work, we introduce adversarial training in time series analysis to enhance the neural networks for better generalization ability by taking the finance field as an example. Rethinking existing research on adversarial training, we propose the adaptively scaled adversarial training (ASAT) in time series analysis, by rescaling data at different time slots with adaptive scales. Experimental results show that the proposed ASAT can improve both the generalization ability and the adversarial robustness of neural networks compared to the baselines. Compared to the traditional adversarial training algorithm, ASAT can achieve better generalization ability and similar adversarial robustness.
Fully reusing clause deduction algorithm based on standard contradiction separation rule

An automated theorem proving (ATP) system's capacity for reasoning is significantly influenced by the inference rules it uses. The recently introduced standard contradiction separation (S-CS) inference rule extends binary resolution to a multi-clause, dynamic, contradiction separation inference mechanism. The S-CS rule is used in the present work to provide a framework for fully clause reusing deductions. Accordingly, a fully reusing clause deduction algorithm (called the FRC algorithm) is built. The FRC algorithm is then incorporated as an algorithm module into the architecture of a top ATP, Vampire, creating a single integrated ATP system dubbed V_FRC. The objective of this integration is to enhance Vampire's performance while assessing the FRC algorithm's capacity for reasoning. According to experimental findings, V_FRC not only outperforms Vampire in a variety of aspects, but also solves 46 problems in the TPTP benchmark database that have a rating of 1, meaning that none of the existing ATP systems are able to resolve them.
Looking deep in the eyes: Investigating interpretation methods for neural models on reading tasks using human eye-movement behaviour

This paper provides the first broad overview of the relation between different interpretation methods and human eye-movement behaviour across different tasks and architectures. The interpretation methods of neural networks provide the information the machine considers important, while the human eye-gaze has been believed to be a proxy of the human cognitive process. Thus, comparing them explains machine behaviour in terms of human behaviour, leading to improvement in machine performance through minimising their difference. We consider three types of natural language processing (NLP) tasks: sentiment analysis, relation classification and question answering, and four interpretation methods based on: simple gradient, integrated gradient, input-perturbation and attention, and three architectures: LSTM, CNN and Transformer. We leverage two corpora annotated with eye-gaze information: the Zuco dataset and the MQA-RC dataset. This research sets up two research questions. First, we investigate whether the saliency (importance) of input-words conform with those from human eye-gaze features. To this end, we compute a saliency distance (SD) between input words (by an interpretation method) and an eye-gaze feature. SD is defined as the KL-divergence between the saliency distribution over input words and an eye-gaze feature. We found that the SD scores vary depending on the combinations of tasks, interpretation methods and architectures. Second, we investigate whether the models with good saliency conformity to human eye-gaze behaviour have better prediction performances. To this end, we propose a novel evaluation device called “SD-performance curve” (SDPC) which represents the cumulative model performance against the SD scores. SDPC enables us to analyse the underlying phenomena that were overlooked using only the macroscopic metrics, such as average SD scores and rank correlations, that are typically used in the past studies. We observe that the impact of good saliency conformity between humans and machines on task performance varies among the combinations of tasks, interpretation methods and architectures. Our findings should be considered when introducing eye-gaze information for model training to improve the model performance.
MMSRNet: Pathological image super-resolution by multi-task and multi-scale learning

Pathological diagnosis is the gold standard for disease assessment in clinical practice. It is conducted by inspecting the specimen at the microscopical level. Therefore, a very high-resolution pathological image that precisely describes the submicron-scale appearance is essential in the era of digital pathology, which is not easily obtained. Recently, pathological image super-resolution (SR) has shown promising prospects in bridging this gap. However, existing studies have not fully explored the peculiarity of pathological data, which contains several gradually enlarged images describing the specimen at different magnifications. In this paper, we propose a novel MMSRNet that formulates the pathological image SR in a multi-task learning way. It adds an image magnification classification branch on top of the CNN-based SR network, e.g., RCAN. Therefore, the learning objective is transformed into performing the SR while classifying the magnification as accurately as possible. The incorporated classification label guides the network to learn a more powerful feature representation. Meanwhile, the multi-task learning paradigm also encourages the joint learning of multi-scale mapping functions corresponding to multiple magnifications. It thus enables the learned model to adaptively accommodate the magnification variants, overcoming the problem that performing SR from different magnifications is treated as independent tasks in existing studies. Extensive experiments are conducted to validate the effectiveness of MMSRNet. It not only gains better performance in performing SR across magnifications and scaling factors, but also exhibits attractive plug-and-play nature when RCAN is substituted by other SR networks. The generated images are also supposed to be helpful in clinical diagnosis.
CM-NET: Cross-Modal Learning Network for CSI-Based Indoor People Counting in Internet of Things

In recent years, multiple IoT solutions have used computational intelligence technologies to identify people and count them. WIFI Channel State Information (CSI) has recently been applied to counting people with multiple benefits, such as being cost-effective, easily accessible, free of privacy concerns, etc. However, most current CSI-based work is limited to human location-fixed environments since human location-random environments are more complicated. Aiming to fix the problem of counting people in human location-random environments, we propose a solution using deep learning CM-NET, an end-to-end cross-modal learning network. Since it is difficult to count people with CSI straightforwardly, CM-NET approaches this problem using deep learning, utilizing a multi-layer transformer model to automatically extract the correlations between channels and the number of people. Owing to the complexity of human location-random environments, the transformer model cannot extract characteristics describing the number of people. To enhance the feature learning capability of the transformer model, CM-NET takes the feature knowledge learned by the image-based people counting model to supervise the learning process. In particular, CM-NET works with CSI alone during the testing phase without any image information, and ultimately achieves sound results with an average accuracy of 86%. Meanwhile, the superiority of CM-NET has been verified by comparison with the latest available related methods.
THE CONCEPT OF HASH VALUE AND THE COLLISION OF HASH VALUE AS THE WAY TO PROVIDE UNASSAILABILITY OF DIGITAL EVIDENCE

The evidence used to prove the material fact which is subject to the criminal procedure should not be changed until it is brought before the court. It is relatively easy to ensure the accuracy of physical evidences by taking them into judicial custody. However, it is difficult to ensure the accuracy of the digital evidence since it is easy to copy and modify it. Digital evidences are used to provide the technological means to ensure accuracy. The hash value, which provides a one-way summarization value, can be applied to digital evidence such as a fingerprint and helps to ensure the accuracy of the numerical evidence. However, a collision of hash value, known as the hash value conflict, which occurs when two different digital data have the same hash value, is a problematic area in ensuring the accuracy of numerical evidence. In this study, the concept of summary value as a means of ensuring the accuracy of numerical evidence will be explained and the possible effects of the hash value collision will be discussed
Squeezing More Utility via Adaptive Clipping on Differentially Private Gradients in Federated Meta-Learning

Federated meta-learning has emerged as a promising AI framework for today’s mobile computing scenes involving distributed clients. It enables collaborative model training using the data located at distributed mobile clients and accommodates clients that need fast model customization with limited new data. However, federated meta-learning solutions are susceptible to inference-based privacy attacks since the global model encoded with clients’ training data is open to all clients and the central server. Meanwhile, differential privacy (DP) has been widely used as a countermeasure against privacy inference attacks in federated learning. The adoption of DP in federated meta-learning is complicated by the model accuracy-privacy trade-off and the model hierarchy attributed to the meta-learning component. In this paper, we introduce DP-FedMeta, a new differentially private federated meta-learning architecture that addresses such data privacy challenges. DP-FedMeta features an adaptive gradient clipping method and a one-pass meta-training process to improve the model utility-privacy trade-off. At the core of DP-FedMeta are two DP mechanisms, namely DP-AGR and DP-AGRLR, to provide two notions of privacy protection for the hierarchical models. Extensive experiments in an emulated federated meta-learning scenario on well-known datasets (Omniglot, CIFAR-FS, and Mini-ImageNet) demonstrate that DP-FedMeta accomplishes better privacy protection while maintaining comparable model accuracy compared to the state-of-the-art solution that directly applies DP-based meta-learning to the federated setting.
BayesImposter: Bayesian Estimation Based.bss Imposter Attack on Industrial Control Systems

Over the last six years, several papers used memory deduplication to trigger various security issues, such as leaking heap-address and causing bit-flip in the physical memory. The most essential requirement for successful memory deduplication is to provide identical copies of a physical page. Recent works use a brute-force approach to create identical copies of a physical page that is an inaccurate and time-consuming primitive from the attacker’s perspective.
AFLGuard: Byzantine-robust Asynchronous Federated Learning

Federated learning (FL) is an emerging machine learning paradigm, in which clients jointly learn a model with the help of a cloud server. A fundamental challenge of FL is that the clients are often heterogeneous, e.g., they have different computing powers, and thus the clients may send model updates to the server with substantially different delays. Asynchronous FL aims to address this challenge by enabling the server to update the model once any client’s model update reaches it without waiting for other clients’ model updates. However, like synchronous FL, asynchronous FL is also vulnerable to poisoning attacks, in which malicious clients manipulate the model via poisoning their local data and/or model updates sent to the server. Byzantine-robust FL aims to defend against poisoning attacks. In particular, Byzantine-robust FL can learn an accurate model even if some clients are malicious and have Byzantine behaviors. However, most existing studies on Byzantine-robust FL focused on synchronous FL, leaving asynchronous FL largely unexplored. In this work, we bridge this gap by proposing AFLGuard, a Byzantine-robust asynchronous FL method. We show that, both theoretically and empirically, AFLGuard is robust against various existing and adaptive poisoning attacks (both untargeted and targeted). Moreover, AFLGuard outperforms existing Byzantine-robust asynchronous FL methods.
Investigating the user experience of IDN based virtual reality environments for solving complex issues

Interactive Digital Narratives (IDNs) have evolved as a medium to address complex societal challenges due to its integration in advance technologies such as VR. Recent research has shown that VR provides a suitable environment to implement IDNs allowing the end users to experience narratives in a systematic and participatory setting. However, research is needed to understand the perceived user experience of these narrative experiences. This research highlights the role of IDNs in VR designed for natural hazard risk mitigation and how it affects the perceived user experience. we present two case studies quantifying the user experience of an IDN-based emergency preparedness VR system for solving the complex issue of natural hazards. The results demonstrate how spatial presence, cognitive behaviour and engagement is positively influenced by incorporating IDNs in VR. The research is part of a project called World of Wild Waters (WoWW) and illustrates the importance of narrative representations in VREs showing that IDN can be considered an essential factor in shaping the positive experience of end-users.
Trebiz: Byzantine Fault Tolerance with Byzantine Merchants

The popularity of blockchain technology has revived interest in Byzantine Fault Tolerance (BFT) consensus protocols. However, existing protocols suffer from high latency, especially when the system is deployed in a worldwide manner. Taking Practical Byzantine Fault Tolerance (PBFT), the best-known and de-facto standard of BFT consensus protocol, as an example, it requires at least three phases to commit a request. Although some works attempt to shorten the number of phases by proposing a fast-path commitment rule, they either sacrifice resilience or subvert security.
Closing the Loophole: Rethinking Reconstruction Attacks in Federated Learning from a Privacy Standpoint

Federated Learning was deemed as a private distributed learning framework due to the separation of data from the central server. However, recent works have shown that privacy attacks can extract various forms of private information from legacy federated learning. Previous literature describe differential privacy to be effective against membership inference attacks and attribute inference attacks, but our experiments show them to be vulnerable against reconstruction attacks. To understand this outcome, we execute a systematic study of privacy attacks from the standpoint of privacy. The privacy characteristics that reconstruction attacks infringe are different from other privacy attacks, and we suggest that privacy breach occurred at different levels. From our study, reconstruction attack defense methods entail heavy computation or communication costs. To this end, we propose Fragmented Federated Learning (FFL), a lightweight solution against reconstruction attacks. This framework utilizes a simple yet novel gradient obscuring algorithm based on a newly proposed concept called the global gradient and determines which layers are safe for submission to the server. We show empirically in diverse settings that our framework improves practical data privacy of clients in federated learning with an acceptable performance trade-off without increasing communication cost. We aim to provide a new perspective to privacy in federated learning and hope this privacy differentiation can improve future privacy-preserving methods.
Towards Practical Application-level Support for Privilege Separation

Privilege separation (privsep) is an effective technique for improving software’s security, but privsep involves decomposing software into components and assigning them different privileges. This is often laborious and error-prone. This paper contributes the following for applying privsep to C software: (1) a portable, lightweight, and distributed runtime library that abstracts externally-enforced compartment isolation; (2) an abstract compartmentalization model of software for reasoning about privsep; and (3) a privsep-aware Clang-based tool for code analysis and semi-automatic software transformation to use the runtime library. The evaluation spans 19 compartmentalizations of third-party software and examines: Security: 4 CVEs in widely-used software were rendered unexploitable; Approximate Effort Saving: on average, the synthesis-to-annotation code ratio was greater than 11.9 (i.e., 10 × lines of code were generated for each annotation); and Overhead: execution-time overhead was less than 2%, and memory overhead was linear in the number of compartments.
Drone Authentication via Acoustic Fingerprint

As drones become widely used in different applications, drone authentication becomes increasingly important due to various security risks, e.g., drone impersonation attacks. In this paper, we propose an idea of drone authentication based on Mel-frequency cepstral coefficient (MFCC) using an acoustic fingerprint that is physically embedded in each drone. We also point out that the uniqueness of the drone's sound comes from the combination of bodies (motors) and propellers. In the experiment with 8 drones, we compare the authentication accuracy of different feature extraction settings. Three kinds of different sound features are used: MFCC, delta MFCC (DMFCC), and delta-delta MFCC (DDMFCC). We choose the feature extraction settings and the sound features according to the best authentication result. In the experiment with 24 drones, we compare the closed set authentication performance of eight machine learning methods in terms of recall under the influence of additive white Gaussian noise (AWGN) with different levels of signal-to-noise ratio (SNR). Furthermore, we conduct an open set drone authentication experiment. Our results show that Quadratic Discriminant Analysis (QDA) outperforms other methods in terms of the highest average recall (94.19%) in the authentication of registered drones and the third highest average recall (82.35%) in the authentication of unregistered drones.
TyPro: Forward CFI for C-Style Indirect Function Calls Using Type Propagation

Maliciously-overwritten function pointers in C programs often lead to arbitrary code execution. In principle, forward CFI schemes mitigate this problem by restricting indirect function calls to valid call targets only. However, existing forward CFI schemes either depend on specific hardware capabilities, or are too permissive (weakening security guarantees) or too strict (breaking compatibility).
Play the Imitation Game: Model Extraction Attack against Autonomous Driving Localization

The security of the Autonomous Driving (AD) system has been gaining researchers' and public's attention recently. Given that AD companies have invested a huge amount of resources in developing their AD models, e.g., localization models, these models, especially their parameters, are important intellectual property and deserve strong protection.