Reviewerly/exHarmony huggingface dataset:
Random-Index PIR and Applications

Private information retrieval (PIR) lets a client retrieve an entry from a database without the server learning which entry was retrieved. Here we study a weaker variant that we call random-index PIR (RPIR), where the retrieved index is an output rather than an input of the protocol, and is chosen at random. RPIR is clearly weaker than PIR, but it suffices for some interesting applications and may be realized more efficiently than full-blown PIR.We report here on two lines of work, both tied to RPIR but otherwise largely unrelated. The first line of work studies RPIR as a primitive on its own. Perhaps surprisingly, we show that RPIR is in fact equivalent to PIR when there are no restrictions on the number of communication rounds. On the other hand, RPIR can be implemented in a “noninteractive” setting (with pre-processing), which is clearly impossible for PIR. For two-server RPIR we even show a truly noninteractive solution, offering information-theoretic security without any pre-processing.The other line of work, which was the original motivation for our work, uses RPIR to improve on the recent work of Benhamouda et al. (TCC’20) for maintaining secret values on public blockchains. Their solution depends on a method for selecting many random public keys from a PKI while hiding most of the selected keys from an adversary. However, the method they proposed is vulnerable to a double-dipping attack, limiting its resilience. Here we observe that a RPIR protocol, where the client is implemented via secure MPC, can eliminate that vulnerability. We thus get a secrets-on-blockchain protocol (and more generally large-scale MPC) which is resilient to any fraction \(f < 1/2\) of corrupted parties, resolving the main open problem left from the work of Benhamouda et al.As the client in this solution is implemented via secure MPC, it really brings home the need to make it as efficient as possible. We thus strive to explore whatever efficiency gains we can get by using RPIR rather than PIR. We achieve more gains by using batch RPIR where multiple indexes are retrieved at once. Lastly, we observe that this application can make do with a weaker security guarantee than full RPIR, and show that this weaker variant can be realized even more efficiently. We discuss one protocol in particular that may be attractive for practical implementations.
Practical Aspects on Non-profiled Deep-learning Side-channel Attacks against AES Software Implementation with Two Types of Masking Countermeasures including RSM

Deep-learning side-channel attacks (DL-SCAs), applying deep neural networks (DNNs) to SCAs, are known that can easily attack some existing SCA countermeasures such as masking and random jitter. While there have been many studies on profiled DL-SCAs, a new approach that involves applying deep learning to non-profiled attacks was proposed in 2018. In our study, we investigate the structure of DNN models and attack points (PoI: Points of Interests) for non-profiled DL-SCAs using the ANSSI SCA database with a masking countermeasure. The results of investigations indicate that it is better to use a simple network model, apply regularization to prevent over-fitting, and select a wide range of power traces that contain side-channel information as the PoI. We also implemented AES-128 software implementation protected with the RSM (Rotating Sboxes Masking) countermeasure, which has never been attacked by non-profiled DL-SCAs, on the Xmega128 microcontroller and carried out non-profiled DL-SCAs against it. Non-profiled DL-SCAs successfully recovered all partial keys while the conventional power analysis could not. We conducted two types of experimental analyses to clarify that DL-SCAs learn mask-values used in the masking countermeasure. One is the-gradient visualization used in previous studies, and the other is a new analysis method using partial removal of power traces.
Convergence analysis of AdaBound with relaxed bound functions for non-convex optimization

Clipping on learning rates in Adam leads to an effective stochastic algorithm-AdaBound. In spite of its effectiveness in practice, convergence analysis of AdaBound has not been fully explored, especially for non-convex optimization. To this end, we address the convergence of the last individual output of AdaBound for non-convex stochastic optimization problems, which is called individual convergence. We prove that, with the iteration of the AdaBound, the cost function converges to a finite value and the corresponding gradient converges to zero. The novelty of this proof is that the convergence conditions on the bound functions and momentum factors are much more relaxed than the existing results, especially when we remove the monotonicity and convergence of the bound functions, and only keep their boundedness. The momentum factors can be fixed to be constant, without the restriction of monotonically decreasing. This provides a new perspective on understanding the bound functions and momentum factors of AdaBound. At last, numerical experiments are provided to corroborate our theory and show that the convergence of AdaBound extends to more general bound functions.
On Exploiting Message Leakage in (Few) NIST PQC Candidates for Practical Message Recovery Attacks

In this work, we propose generic and practical side-channel attacks for message recovery in post-quantum lattice-based public key encryption (PKE) and key encapsulation mechanisms (KEM). The targeted schemes are based on the well known Learning With Errors (LWE) and Learning With Rounding (LWR) problem and include three finalists and six semi-finalist candidates of the ongoing NIST’s standardization process for post-quantum cryptography. Notably, we propose to exploit inherent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ciphertext malleability</i> properties of LWE/LWR-based PKEs as a powerful tool for side-channel assisted message recovery attacks. The use of ciphertext malleability widens the scope of previous attacks with the ability to target multiple operations for message recovery. Moreover, our attacks are adaptable to different implementation variants and are also applicable to implementations protected with concrete <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shuffling</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">masking</i> side-channel countermeasures. Our work mainly highlights the presence of inherent algorithmic properties in LWE/LWR-based schemes that can aid side-channel attacks for message recovery, thereby stressing on the need for strong side-channel countermeasures against message recovery for LWE/LWR-based schemes.
CNN and LSTM based Encoder-Decoder for Anomaly Detection in Multivariate Time Series

The purpose of anomaly detection is to detect data that deviates from the expected, and is widely used in intrusion detection, data preprocessing and so on.For data anomaly detection, we propose a data anomaly detection algorithm based on convolutional neural network and Encoder-Decoder architecture CNN-LSTMED (Convolutional Neural Networks Long Short-Term Encoder-Decoder).First,we use the convolutional neural network to encode the time series data to obtain the encoded sequence,and use the features extracted from the sequence as the input of the nonlinear model long short-term memory network LSTM (Long Short-Term Memory) to decode and output the decoded sequence. Finally, the reconstruction error is calculated and the threshold is set to determine the abnormal point. Through experimental comparison with GRUED (Gated Recurrent Neural Encoder-Decoder), LSTMED (Long Short-Term Memory Encoder-Decoder) ,and other algorithms on the KDD99 data set and credit card fraud data set,it turns out that our algorithm has strong robustness and accuracy .
Pattern Matching Model for Recognition of Stone Inscription Characters

Abstract As there are countless significant works done for handwritten character recognition, very meager effort has been reported for inscription characters especially for Tamil stone inscriptions. The real challenge faced in handling stone inscription is dataset collection and foreground and background discrimination. Till present days, the archeological department follows traditional way of capturing, preserving and deciphering stone inscriptions which is manual, more time consuming and need expert assistance. Hence digitized recognition is essential and efficient pattern matching algorithm is needed to be developed to deal with variations in shape and size of complex structured characters present in Tamil stone inscriptions. In this paper, an automated character recognition by pattern matching approach is developed, where character features were extracted by using pattern matching algorithm that helps achieving good recognition rate. Recognition of ancient Tamil stone inscriptions characters and finding their corresponding contemporary Tamil character is done by Image-based Character Pattern Identification (ICPI) system. Modified Speeded Up Robust Feature with Bag of Grapheme (MSURF-BoG) algorithm is implemented to detect the strongest key points from the input character with different orientations. These key point features were created for training the image as a model called Bag of Grapheme (BoG) with code word creation. Hence unsupervised key point features were extracted and pattern matching is performed. 11th century Tamil stone inscriptions were taken as samples which has 7 vowels and 17 consonants, totally 24 characters were used. Here samples with different orientation from each 24 character were used for training the system. The proposed system is evaluated by recognition accuracy which is reported for character wise at the maximum of 96%.
An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware

With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by Machine Learning (ML) models. However, any increase in the training data results in a linear increase in the space complexity of the ML models. It is highly challenging to deploy such ML models on IoT devices with limited memory (TinyML hardware). To alleviate such memory issues, in this paper, we present an SRAM-optimized classifier porting, stitching, and efficient deployment approach. The proposed method enables large classifiers to be comfortably executed on microcontroller unit (MCU) based IoT devices and perform ultra-fast classifications while consuming 0 bytes of SRAM. We tested our SRAM optimized approach by utilizing it to port and execute 7 dataset-trained classifiers on 7 popular MCU boards, and report their inference time and memory (Flash and SRAM) consumption. It is apparent from the experimental results that; (i) the classifiers ported using our proposed approach are of varied sizes but have constant SRAM consumption. Thus, the approach enabled the deployment of larger ML classifier models even on tiny Atmega328P MCU-based Arduino Nano, which has only 8 kB SRAM; (ii) even the resource-constrained 8-bit MCUs performed faster unit inference (in less than a millisecond) than a NVIDIA Jetson Nano GPU and Raspberry Pi 4 CPU; (iii) the majority of models produced 1-4x times faster inference results in comparison with the models ported by the sklearn-porter, m2cgen, and emlearn libraries.
Image quality evaluation for OLED-based smart-phone displays at various lighting conditions

The image quality of three organic light-emitting diode (OLED) based smart-phone displays was assessed at three levels of ambient lighting conditions corresponding to the darkroom, indoor and outdoor environment, respectively. Seven perceptual attributes, i.e., naturalness, colorfulness, brightness, contrast, sharpness, preference, and overall image quality (IQ), were evaluated in both standard dynamic range (SDR) and high dynamic range (HDR) mode via psychophysical experiments by rank order method, while readability was assessed only in SDR mode and gradation was investigated only in HDR mode. The experimental results demonstrate that, besides the color gamut, the tone reproduction curve is also an important factor affecting the colorfulness of mobile display in the two modes. Higher peak luminance would not mean better performance on brightness and contrast for HDR images, which is opposite to SDR mode. Further analysis of variance (ANOVA) indicates that the ranking results of all perceptual attributes are not significantly affected by the ambient lighting levels in both SDR and HDR modes.
Embedding classical dynamics in a quantum computer

We develop a framework for simulating measure-preserving, ergodic dynamical systems on a quantum computer. Our approach provides an operator-theoretic representation of classical dynamics by combining ergodic theory with quantum information science. The resulting quantum embedding of classical dynamics (QECD) enables efficient simulation of spaces of classical observables with exponentially large dimension using a quadratic number of quantum gates. The QECD framework is based on a quantum feature map that we introduce for representing classical states by density operators on a reproducing kernel Hilbert space, $\mathcal{H}$. Furthermore, an embedding of classical observables into self-adjoint operators on $\mathcal{H}$ is established, such that quantum mechanical expectation values are consistent with pointwise function evaluation. In this scheme, quantum states and observables evolve unitarily under the lifted action of Koopman evolution operators of the classical system. Moreover, by virtue of the reproducing property of $\mathcal{H}$, the quantum system is pointwise-consistent with the underlying classical dynamics. To achieve a quantum computational advantage, we project the state of the quantum system onto a finite-rank density operator on a ${2}^{n}$-dimensional tensor product Hilbert space associated with $n$ qubits. By employing discrete Fourier-Walsh transforms of spectral functions, the evolution operator of the finite-dimensional quantum system is factorized into tensor product form, enabling implementation through an $n$-channel quantum circuit of size $O(n)$ and no interchannel communication. Furthermore, the circuit features a state preparation stage, also of size $O(n)$, and a quantum Fourier transform stage of size $O({n}^{2})$, which makes predictions of observables possible by measurement in the standard computational basis. We prove theoretical convergence results for these predictions in the large-qubit limit, $n\ensuremath{\rightarrow}\ensuremath{\infty}$. In light of these properties, QECD provides a consistent simulator of the evolution of classical observables, realized through projective quantum measurement, which is able to simulate spaces of classical observables of dimension ${2}^{n}$ using circuits of size $O({n}^{2})$. We demonstrate the consistency of the scheme in prototypical dynamical systems involving periodic and quasiperiodic oscillators on tori. These examples include simulated quantum circuit experiments in Qiskit Aer, as well as actual experiments on the IBM Quantum System One.
A transfer learning-based convolutional neural network and its novel application in ship spare-parts classification

Spare parts play an essential role in the logistical support of heavy-asset industries that require highly reliable and efficient operations. This paper focuses on spare-parts classification using transfer learning theory via deep convolutional neural networks. Through the present literature, multi-criteria spare-parts classification is innovatively realized using image-processing methods with a three-phase model. Neural network architecture was reconstructed based on the pre-trained VGG-19 in the first step. And the hierarchical inference model of spare parts was subsequently built with a purpose of data visualization and image conversion. In the final phase, the proposed model was trained and tested using actual data generated from a real-world case which demonstrated its excellent performance with an average accuracy of 96.36% in the five-fold cross-validation. To further verify the model, a comparative scheme was proposed using different convolutional neural network-based learning methods, including well-known AlexNet and ResNet-50. The proposed model's performance is outstanding with an overall accuracy of 95.87%. This novel transfer learning-based application for the multi-criteria classification problem successfully achieves an adjustable solution framework for spare-parts management.
Augmented Reality meets Non-Fungible Tokens: Insights Towards Preserving Property Rights

Non-fungible tokens are one of the newest use-cases of blockchain technology. Ethereum Virtual Machine (EVM)-based blockchains such as Ethereum, Binance Smart Chain (BSC), or Polygon/Matic have standardized this type of tokens using interfaces such as ERC721 and ERC1155. This development fostered a connection between blockchain and Mixed Reality technologies, which can now coexist in cohesive applications. In this paper, we present the opportunities and challenges resulting from using Augmented Reality NFTs, with a particular focus on the preservation of property, whether it is physical or purely digital. We present a methodology that can ensure the protection of privacy and the preservation of intellectual property when transitioning assets from the virtual to the physical world.
A First Look at RISC-V Virtualization from an Embedded Systems Perspective

This article describes the first public implementation and evaluation of the latest version of the RISC-V hypervisor extension (H-extension v0.6.1) specification in a Rocket chip core. To perform a meaningful evaluation for modern multi-core embedded and mixed-criticality systems, we have ported Bao, an open-source static partitioning hypervisor, to RISC-V. We have also extended the RISC-V platform-level interrupt controller (PLIC) to enable direct guest interrupt injection with low and deterministic latency and we have enhanced the timer infrastructure to avoid trap and emulation overheads. Experiments were carried out in FireSim, a cycle-accurate, FPGA-accelerated simulator, and the system was also successfully deployed and tested in a Zynq UltraScale+ MPSoC ZCU104. Our hardware implementation was open-sourced and is currently in use by the RISC-V community towards the ratification of the H-extension specification.
KOSMOS: Vertical and Horizontal Resource Autoscaling for Kubernetes

Cloud applications are increasingly executed onto lightweight containers that can be efficiently managed to cope with highly varying and unpredictable workloads. Kubernetes, the most popular container orchestrator, provides means to automatically scale containerized applications to keep their response time under control. Kubernetes provisions resources using two main components: i) Horizontal Pod Autoscaler (HPA), which controls the amount of containers running for an application, and ii) Vertical Pod Autoscaler (VPA), which oversees the resource allocation of existing containers. These two components have several limitations: they must control different metrics, they use simple threshold-based rules, and the reconfiguration of existing containers requires stopping and restarting them.To overcome these limitations this paper presents KOSMOS, a novel autoscaling solution for Kubernetes. Containers are individually controlled by control-theoretical planners that manage container resources on-the-fly (vertical scaling). A dedicated component is in charge of handling resource contention scenarios among containers deployed in the same node (a physical or virtual machine). Finally, at the cluster-level a heuristic-based controller is in charge of the horizontal scaling of each application.
The Challenges of Evaluating the Usability of Augmented Reality (AR)

Augmented reality (AR) is a new and emerging technology that could benefit from evaluating its usability to better the user’s experience with the device or application. This is often done through usability testing and heuristic evaluations. However, AR technology presents some challenges when completing these usability evaluations. Practitioners need to keep in mind the hardware limitations of AR devices that may not be present with other computerized technology, consistency of the users’ environment plays a larger role in the AR experience, recognize that a novelty effect may occur and affect subjective scores, and choose heuristic sets that will best evaluate AR applications. Practitioners need to be aware of these challenges and overcome them to accurately assess the usability of these products to gain insights about what should be changed to make the overall experience with the product better.
QoS Prediction for Web Services via Combining Multi-component Graph Convolutional Collaborative Filtering and Deep Factorization Machine

QoS prediction for Web Services is becoming increasingly important for various QoS-aware Web Services management tasks. However, the existing methods for QoS prediction of Web Services have some drawbacks, such as poor performance in dealing with data sparsity, insufficient consideration of latent information in user-service interaction behavior, and no consideration on discriminating the weight of latent information. To address these shortcomings, this paper proposes a QoS Prediction approach via combining multi-component graph convolutional collaborative filtering and deep factorization machine. A user-service bipartite graph is constructed, and the edges of the graph are decomposed into multiple latent spaces with node-level attention to identify latent components. Then, the importances of latent components are determined, and they are aggregated to obtain the corresponding user-service embedding vectors. Finally, the embedding vectors are taken as the input of a deep factorization machines model to obtain the prediction of unknown QoS. Extensive experiments are conducted on a real-world dataset. The experimental results demonstrate that MGCCF-DFM achieves superior prediction accuracy in terms of mean absolute error (MAE) and root mean square error (RMSE) compared with the existing QoS prediction techniques.
Somewhere Statistical Soundness, Post-Quantum Security, and SNARGs

The main conceptual contribution of this paper is a unification of two leading paradigms for constructing succinct argument systems, namely Kilian's protocol and the \(\mathsf {BMW}\) (Biehl-Meyer-Wetzel) heuristic. We define the notion of a multi-extractable somewhere statistically binding (\(\mathsf {meSSB}\)) hash family, an extension of the notion of somewhere statistically binding hash functions (Hubacek and Wichs, ITCS 2015), and construct it from \(\mathsf {LWE}\). We show that when instantiating Kilian's protocol with a \(\mathsf {meSSB}\) hash family, the first two messages are simply an instantiation of the \(\mathsf {BMW}\) heuristic. Therefore, if we also instantiate it with a PCP for which the BMW heuristic is sound, e.g., a computational non-signaling PCP, then the first two messages of the Kilian protocol is a sound instantiation of the \(\mathsf {BMW}\) heuristic.