Reviewerly/exHarmony huggingface dataset:
Towards Post-Quantum Security for Cyber-Physical Systems: Integrating PQC into Industrial M2M Communication

The threat of a cryptographically relevant quantum computer contributes to an increasing interest in the field of post-quantum cryptography (PQC). Compared to existing research efforts regarding the integration of PQC into the Transport Layer Security (TLS) protocol, industrial communication protocols have so far been neglected. Since industrial cyber-physical systems (CPS) are typically deployed for decades, protection against such long-term threats is needed. In this work, we propose two novel solutions for the integration of post-quantum (PQ) primitives (digital signatures and key establishment) into the industrial protocol Open Platform Communications Unified Architecture (OPC UA): a hybrid solution combining conventional cryptography with PQC and a solution solely based on PQC. Both approaches provide mutual authentication between client and server and are realized with certificates fully compliant to the X.509 standard. Moreover, we implement the two solutions and measure and evaluate their performance across three different security levels. All selected algorithms (Kyber, Dilithium, and Falcon) are candidates for standardization by the National Institute of Standards and Technology (NIST). We show that Falcon is a suitable option—especially—when using floating-point hardware provided by our ARM-based evaluation platform. Our proposed hybrid solution provides PQ security for early adopters but comes with additional performance and communication requirements. Our solution solely based on PQC shows superior performance across all evaluated security levels in terms of handshake duration compared to conventional OPC UA but comes at the cost of increased sizes for handshake messages.
A survey on the animation of signing avatars: From sign representation to utterance synthesis

Signing avatars make it possible for deaf people to access information in their preferred language. However, sign language synthesis represents a challenge for the computer animation community as the motions generated must be realistic and have a precise semantic meaning. In this article, we distinguish the synthesis of isolated signs deprived of any contextual inflections from the generation of full sign language utterances. In both cases, the animation engine takes as input a representation of the synthesis objective to create the final animation. Because of their spatiotemporal characteristics, signs and utterances cannot be described by a sequential representation like phonetics in spoken languages. For this reason, linguistic and gestural studies have aimed to capture the typical and special features of signs and sign language syntax to promote different sign language representations. Those sign representations can then be used to produce an avatar animation thanks to sign synthesis techniques based on keyframes, procedural means or data-driven approaches. Novel utterances can also be generated using concatenative or articulatory techniques. This article constitutes a survey of (i) the challenges specific to sign languages avatars, (ii) the sign representations developed in order to synthesize isolated signs, (iii) the possible sign synthesis approaches, (iv) the different utterance specifications, and (v) the challenges and animation techniques for generating sign language utterances.
Digital Image Encryption Based on Chaotic Cellular Automata

Cellular automata are dynamical systems, discrete in terms of both space and time. Many cellular automata are capable of generating chaos and are well suited for applications like digital image encryption and scrambling. Various cellular automata-based digital image scrambling techniques have been proposed in literature. An adversary may have access to a set of images from which the particular image is scrambled. The problem with these techniques in this particular case is that an adversary may be able to find out the true content of the scrambled image just by comparing its histogram with the histograms of a suspected set of images. In this paper, a secure digital image encryption technique based on outer totalistic cellular automata is proposed that modifies the histogram of the scrambled image so that it is difficult to guess the true content carried in the digital image.
The Cost of Bitcoin Mining Has Never Really Increased

The Bitcoin network is burning a large amount of energy for mining. In this paper, we estimate the lower bound for the global mining energy cost for a period of ten years from 2010 to 2020, taking into account changes in energy costs, improvements in hashing technologies and hashing activity. We estimate energy cost for Bitcoin mining using two methods: Brent Crude oil prices as a global standard and regional industrial electricity prices weighted by the share of hashing activity. Despite a ten-billion-fold increase in hashing activity and a ten-million-fold increase in total energy consumption, we find the cost relative to the volume of transactions has not increased nor decreased since 2010. This is consistent with the perspective that, in order to keep the Blockchain system secure from double spending attacks, the proof or work must cost a sizable fraction of the value that can be transferred through the network. We estimate that in the Bitcoin network this fraction is of the order of 1%.
Predicting Detection Filters for Small Footprint Open-Vocabulary Keyword Spotting

In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, that allows the users to include a customizable voice interface to their device and that does not require task-specific data. We present a keyword detection neural network weighing less than 250KB, in which the topmost layer performing keyword detection is predicted by an auxiliary network, that may be run offline to generate a detector for any keyword. We show that the proposed model outperforms acoustic keyword spotting baselines by a large margin on two tasks of detecting keywords in utterances and three tasks of detecting isolated speech commands. We also propose a method to fine-tune the model when specific training data is available for some keywords, which yields a performance similar to a standard speech command neural network while keeping the ability of the model to be applied to new keywords.
Querying little is enough: Model inversion attack via latent information

As machine learning (ML) technologies evolve, various online intelligent services use ML models to provide predictions. Unfortunately, attackers can obtain the private information of the model by interacting with the online service, namely model inversion attack (MIA). However, MIA requires large data sets to be transferred to an online service to obtain the predictive value of the inference model. Besides, the huge transmission may cause the administrator's active defense. To overcome this drawback, we propose a novel MIA scheme, which leverages latent information extracted by an auxiliary neural network as high-dimensional features to simplify what inversion model should learn. The core idea of our scheme is to reuse some parameters of the local pretraining model. Extensive experiments have verified the effectiveness of our method in convolutional neural networks on LFW, pubFig, MNIST data sets. Experimental results show that even with a few queries, our inversion method still work accurately and is superior to other technologies. It is worth mentioning that our method makes it more difficult for administrators to defend against the attack and elicit more investigations for privacy-preserving.
Guidelines for Quality Assurance of Machine Learning-Based Artificial Intelligence

Significant effort is being put into developing industrial applications for artificial intelligence (AI), especially those using machine learning (ML) techniques. Despite the intensive support for building ML applications, there are still challenges when it comes to evaluating, assuring, and improving the quality or dependability. The difficulty stems from the unique nature of ML, namely, system behavior is derived from training data not from logical design by human engineers. This leads to black-box and intrinsically imperfect implementations that invalidate many principles and techniques in traditional software engineering. In light of this situation, the Japanese industry has jointly worked on a set of guidelines for the quality assurance of AI systems (in the Consortium of Quality Assurance for AI-based Products and Services) from the viewpoint of traditional quality-assurance engineers and test engineers. We report on the second version of these guidelines, which cover a list of quality evaluation aspects, catalogue of current state-of-the-art techniques, and domain-specific discussions in five representative domains. The guidelines provide significant insights for engineers in terms of methodologies and designs for tests driven by application-specific requirements.
Efficient and provable local capability revocation using uninitialized capabilities

Capability machines are a special form of CPUs that offer fine-grained privilege separation using a form of authority-carrying values known as capabilities. The CHERI capability machine offers local capabilities, which could be used as a cheap but restricted form of capability revocation. Unfortunately, local capability revocation is unrealistic in practice because large amounts of stack memory need to be cleared as a security precaution. In this paper, we address this shortcoming by introducing uninitialized capabilities : a new form of capabilities that represent read/write authority to a block of memory without exposing the memory’s initial contents. We provide a mechanically verified program logic for reasoning about programs on a capability machine with the new feature and we formalize and prove capability safety in the form of a universal contract for untrusted code. We use uninitialized capabilities for making a previously-proposed secure calling convention efficient and prove its security using the program logic. Finally, we report on a proof-of-concept implementation of uninitialized capabilities on the CHERI capability machine.
Breast cancer detection using active contour and classification by deep belief network

The abnormal modifications in tissues or cells of the body and growth beyond normal grow and control is called cancer. Breast cancer is one of the kinds of cancer. The existence level of patient suffering from breast cancer can be improved through Prognosis detection of breast cancer. The cancer diagnosis and detection accuracy have upgraded with the machine learning techniques, and the improvement of expertise. A computer-aided diagnosis (CAD) system is used for the mammograms to enable initial breast Cancer recognition, analysis, and treatment. For existing CAD system the accuracy is unacceptable. This research work explores a breast CAD scheme established on feature fusion through Convolution Neural Network deep features. The outcome shows that the random forest algorithm contributes the maximum accuracy (97.51%) with the lowest error rate than CNN classifier (95.65%). The abnormality of the breast images is scrutinized through Deep Belief Network (DBN). The offered work practices active contour segmentation to distinct the abnormal image and it can be categorized by Deep Belief Network. The recommended algorithm is used to eradicate the properties of the point spread function in the method of low-dose medical CT image restoration and recovers the reconstructed image quality. The efficiency and speed of the segmentation process are increased through sparse transform. Our mentioned method is used to afford the enhanced performance, offer a good implementation of the results, evaluated the accuracy and the processing time.
Faults in deep reinforcement learning programs: a taxonomy and a detection approach

A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep reinforcement learning (DRL) is the application of DL in the domain of Reinforcement Learning. Like any software system, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 21 synthetic and real faulty DRL programs. For synthetic samples, we injected faults observed in the analyzed artifacts from Stack Overflow and GitHub. The results show that DRLinter can successfully detect faults in both synthesized and real-world examples with a recall of 75% and a precision of 100%.
Multi-level quantum noise spectroscopy

System noise identification is crucial to the engineering of robust quantum systems. Although existing quantum noise spectroscopy (QNS) protocols measure an aggregate amount of noise affecting a quantum system, they generally cannot distinguish between the underlying processes that contribute to it. Here, we propose and experimentally validate a spin-locking-based QNS protocol that exploits the multi-level energy structure of a superconducting qubit to achieve two notable advances. First, our protocol extends the spectral range of weakly anharmonic qubit spectrometers beyond the present limitations set by their lack of strong anharmonicity. Second, the additional information gained from probing the higher-excited levels enables us to identify and distinguish contributions from different underlying noise mechanisms.
Bitcoin: The Longest Running Mania – Tulips of the 21st Century?

Almost four centuries later, the Dutch tulipmania of the 17th century is always mentioned as a mania and used as a reference point in the aftermath of contemporaneous economic and financial crises since the late 1990s. Studies investigating what drove the tulip speculation throughout 17th and 18th centuries ignored market fundamentals, which we believe were the driving forces in Bitcoin price speculation and the ensuing crash. Bitcoin mania is far from a true madness, the increased frequency in its boom-and-bust cycle since 2017 comes from the cryptocurrency market’s extreme reactionary mode to any good or bad news from regulators and central banks (the Fed and ECB in particular) as well as security issues related to cyberattacks. In a matter of several months, the price of Bitcoin skyrocketed from $2,000 in April 2017 to the intraday high of $20,089 on December 17, 2017. The potential Bitcoin bubble occurred in the end of 2017, Bitcoin price surged from $5,600 to $20,089 in October–December 2017 and crashed in January 2018.
Multi-document extractive text summarization based on firefly algorithm

Extracting relevant information from a large amount of data is a challenging task. Automatic text summarization is a potential solution for obtaining this information. In this paper, a nature inspired swarm intelligence-based algorithm viz. firefly algorithm for multi-document text summarization is proposed. A new fitness function consisting of three features viz. topic relation factor, cohesion factor and readability factor is utilized. The experiments are performed on datasets from Document Understanding Conference i.e. DUC-2002, DUC-2003 and DUC-2004. The performance of the algorithm has been evaluated using ROUGE score. The performance of the proposed algorithm is compared with some other nature inspired ones such as particle swarm optimization (PSO) and genetic algorithm (GA). The performance of the proposed algorithm outperforms the other adopted ones.
EdgeML

In recent years, deep learning algorithms are increasingly adopted by a wide range of data-intensive and time-critical Internet of Things (IoT) applications. As a result, several new approaches, including model partition/offloading and progressive neural architecture, have been proposed to address the challenge of deploying the computation-intensive deep neural network (DNN) models on resource-constrained edge devices. However, the performance of existing approaches is highly affected by runtime dynamics. For example, offloading workload from edge to cloud suffers from communication delays and the efficiency of progressive neural architecture supporting early-exit DNN executions relies on input characteristics. In this paper, we introduce EdgeML, an AutoML framework that provides flexible and fine-grained DNN model execution control by combining workload offloading mechanism and dynamic progressive neural architecture. To achieve desirable latency-accuracy-energy system performance on edge platforms, EdgeML adopts reinforcement learning to automatically update model execution policy in response to runtime dynamics in real-time. We implement EdgeML for several widely used DNN models on the latest edge devices. Comparing to existing approaches, our experiments show that EdgeML achieves up to 8× performance improvement under dynamic environments.
Heralding quantum entanglement between two room-temperature atomic ensembles

Establishing quantum entanglement between individual nodes is crucial for building large-scale quantum networks, enabling secure quantum communications, distributed quantum computing, enhanced quantum metrology, and fundamental tests of quantum mechanics. However, the shared entanglements have been merely observed in either extremely low-temperature or well-isolated systems, which limits quantum networks for real-life applications. Here, we report the realization of heralding quantum entanglement between two atomic ensembles at room temperature, which are contained in two spatially separated, centimeter-sized vapor cells. By mapping the atomic state onto a photonic state after the readout process, we measure the quantum interference of the Raman-scattered photons and reconstruct the entangled state, then we strongly verify the existence of a single excitation delocalized in two atomic ensembles. The demonstrated building block paves the way to construct quantum networks and distributing entanglement across multiple remote nodes at ambient conditions.
Message-Recovery Laser Fault Injection Attack on the Classic McEliece Cryptosystem

Code-based public-key cryptosystems are promising candidates for standardization as quantum-resistant public-key cryptographic algorithms. Their security is based on the hardness of the syndrome decoding problem. Computing the syndrome in a finite field, usually $$\mathbb {F}_{2}$$ , guarantees the security of the constructions. We show in this article that the problem becomes considerably easier to solve if the syndrome is computed in $$\mathbb {N}$$ instead. By means of laser fault injection, we illustrate how to compute the matrix-vector product in $$\mathbb {N}$$ by corrupting specific instructions, and validate it experimentally. To solve the syndrome decoding problem in $$\mathbb {N}$$ , we propose a reduction to an integer linear programming problem. We leverage the computational efficiency of linear programming solvers to obtain real-time message recovery attacks against the code-based proposal to the NIST Post-Quantum Cryptography standardization challenge. We perform our attacks in the worst-case scenario, i.e. considering random binary codes, and retrieve the initial message within minutes on a desktop computer. Our attack targets the reference implementation of the Niederreiter cryptosystem in the NIST PQC competition finalist Classic McEliece and is practically feasible for all proposed parameters sets of this submission. For example, for the 256-bit security parameters sets, we successfully recover the message in a couple of seconds on a desktop computer Finally, we highlight the fact that the attack is still possible if only a fraction of the syndrome entries are faulty. This makes the attack feasible even though the fault injection does not have perfect repeatability and reduces the computational complexity of the attack, making it even more practical overall.
Profile Matching for IoMT: A Verifiable Private Set Intersection Scheme

The rapid development of the Internet of Things (IoTs), 5 G and artificial intelligence (AI) technology have been dramatically incentivizing the advancement of Internet of Medical Things (IoMT) in recent years. Profile matching technology can be used to realize the sharing of medical information between patients by matching similar symptom attributes. However, the symptom attributes are associated with patients' sensitive information such as gender, age, physiological data, and other personal health information, thus the privacy of patients will be revealed during the matching process in the IoMT. To solve the problem, this paper proposes a verifiable private set intersection scheme to achieve fine-grained profile matching. On the one hand, the privacy data of patients can be divided by multi-tag to implement fine-grained operations. On the other hand, re-encryption technique is utilized to protect the privacy of patients. In addition, the cloud server may violate the scheme, thus a verifiable mechanism is leveraged to check the correctness of computation. The analysis of security indicates that our proposed scheme can resist the untrusted cloud server and the performance simulation demonstrates that our scheme improves efficiency by reducing the use of bilinear pairs.
Online Learning in Variable Feature Spaces under Incomplete Supervision

This paper explores a new online learning problem where the input sequence lives in an over-time varying feature space and the ground-truth label of any input point is given only occasionally, making online learners less restrictive and more applicable. The crux in this setting lies in how to exploit the very limited labels to efficiently update the online learners. Plausible ideas such as propagating labels from labeled points to their neighbors through uncovering the point-wise geometric relations face two challenges: (1) distance measurement fails to work as different points may be described by disparate sets of features and (2) storing the geometric shape, which is formed by all arrived points, is unrealistic in an online setting. To address these challenges, we first construct a universal feature space that accumulates all observed features, making distance measurement feasible. Then, we use manifolds to represent the geometric shapes and approximate them in a sparse means, making manifolds computational and memory tractable in online learning. We frame these two building blocks into a regularized risk minimization algorithm. Theoretical analysis and empirical evidence substantiate the viability and effectiveness of our proposal.