Reviewerly/exHarmony huggingface dataset:
A General Proof Framework for Recent AES Distinguishers

In this paper, a new framework is developed for proving and adapting the recently proposed multiple-of-8 property and mixture-differential distinguishers. The above properties are formulated as immediate consequences of an equivalence relation on the input pairs, under which the difference at the output of the round function is invariant. This approach provides a further understanding of these newly developed distinguishers. For example, it clearly shows that the branch number of the linear layer does not influence the validity of the property, on the contrary of what was previously believed. We further provide an extension of the mixture-differential distinguishers and multiple-of-8 property to any SPN and to a larger class of subspaces. These adapted properties can then be exhibited in a systematic way for other ciphers than the AES. We illustrate this with the examples of Midori, Klein, LED and Skinny.
An improved form of the ant lion optimization algorithm for image clustering problems

Thispaperproposesanimprovedformoftheantlionoptimizationalgorithm(IALO)tosolveimageclustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and K- means algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.
The Synthesis of the Student Relationship Management System Using the Internet of Things to Collect the Digital Footprint for Higher Education Institutions

&lt;p class="0abstract"&gt;The purpose of this research were to synthesize and to evaluate the element of student relationship management system using the internet of things to collect digital footprint for higher education institutions. The results of the research showed that the student relationship management system using the internet of things had five elements which were 1)identify module, 2)alert module, 3)tracking module, 4)SRM module and 5)analytic module. The evaluation result of the student relationship management system using the internet of things to collect digital footprint for higher education institutions had the overall appropriateness at very much level and the overall appropriateness of usability result at very much level.&lt;/p&gt;
Obstacle Avoidance of Mobile Robot Based on HyperOmni Vision

In this paper, we present a method that combines an improved dynamic window approach (IDWA) and an artificial potential field to avoid obstacles for autonomous wheeled robots with HyperOmni Vision.The experimental environment follows the rule of RoboSot avoidance challenge in the Federation of International Robot-soccer Association (FIRA) competition.There are three parts of this study, namely, (1) image processing, (2) an IDWA, and (3) an improved artificial potential field.In the avoidance challenge in FIRA, the robot must avoid eight obstacles that are placed randomly in the playing field.The results indicate that the threewheeled robot can avoid obstacles autonomously by using the method proposed in this paper.Our team won the FIRA avoidance challenge championship this year by the method proposed in this paper.
Dual supervised learning for non-native speech recognition

Current automatic speech recognition (ASR) systems achieve over 90–95% accuracy, depending on the methodology applied and datasets used. However, the level of accuracy decreases significantly when the same ASR system is used by a non-native speaker of the language to be recognized. At the same time, the volume of labeled datasets of non-native speech samples is extremely limited both in size and in the number of existing languages. This problem makes it difficult to train or build sufficiently accurate ASR systems targeted at non-native speakers, which, consequently, calls for a different approach that would make use of vast amounts of large unlabeled datasets. In this paper, we address this issue by employing dual supervised learning (DSL) and reinforcement learning with policy gradient methodology. We tested DSL in a warm-start approach, with two models trained beforehand, and in a semi warm-start approach with only one of the two models pre-trained. The experiments were conducted on English language pronounced by Japanese and Polish speakers. The results of our experiments show that creating ASR systems with DSL can achieve an accuracy comparable to traditional methods, while simultaneously making use of unlabeled data, which obviously is much cheaper to obtain and comes in larger sizes.
Formal ontologies in manufacturing

Since the early days of ontology engineering, manufacturing is one of the main areas where ontologies have traditionally been applied (Guarino et al., 1997; Uschold and Gruninger, 1996). The interest in ontologies has been motivated, first, by the massive exploitation of computer-based technologies in manufacturing organizations, which need to manage and share data in a robust way, and second, by the need to harmonize different terminologies to facilitate communication. The two motivations are strictly related, since shared terminologies and models are needed to enable computer systems to interact effectively. In addition, in the new landscape of Industry 4.0 (Lu, 2017), guided and informed by big data and machine learning, ontologies find their place to organize the data upon which learning algorithms run....
Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

In this paper, an optimized contrast enhancement method combining global and local enhancement results is proposed to improve the visual quality of infrared images. Global and local contrast enhancement methods have their merits and demerits, respectively. The proposed method utilizes the complementary characteristics of these two methods to achieve noticeable contrast enhancement without artifacts. In our proposed method, the 2D histogram, which contains both global and local gray level distribution characteristics of the original image, is computed first. Then, based on the 2D histogram, the global and local enhanced results are obtained by applying histogram specification globally and locally. Lastly, the enhanced result is computed by solving an optimization equation subjected to global and local constraints. The pixel-wise regularization parameters for the optimization equation are adaptively determined based on the edge information of the original image. Thus, the proposed method is able to enhance the local contrast while preserving the naturalness of the original image. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.
A new two-level information protection scheme based on visual cryptography and QR code with multiple decryptions

Nowadays, Quick Response (QR) code has been used in many fields due to its advantages, such as reliability, high-speed scanning and large data capacity. However, embedding the privacy information into the QR code lacks adequate security protection. In this paper, a new two-level information protection scheme is designed based on visual cryptography and QR code. Using any standard QR reader device or software, the public-level information can be read out directly from the shares. Moreover, the privacy-level information can be decoded by three different decryptions, which are suitable to non-computation with relative difference 1/4, lightweight computation with relative difference 1/2 and common computation environments with relative difference 1, respectively. Since the proposed scheme keeps the advantages of visual cryptography and QR code, it differs from the related schemes with low computational complexity, robustness against deformations, and high payload. The effectiveness of the proposed scheme has been proved theoretically. Experimental results and analysis demonstrate that the proposed scheme can protect two-level information with multiple decryptions, and has many benefits compared with the previous schemes.
A Novel Privacy Framework for Secure M-Health Applications: The Case of the GDPR

Mobile health (M-health) applications are becoming increasingly popular as an ideal tool to monitor the long-term health conditions of a patient. They play a key role in the emerging monitoring and sensor technologies such as fitness trackers to surgical rehabs that improve the patient's safety and quality of healthcare. However, these applications, if not secure, can pose a significant risk to the privacy issues of the patient's medical data. It is necessary to ensure that there is minimal risk to patient's data privacy. The aim of this paper is to provide a comprehensive insight into the privacy requirements of the m-health application in the context of the new European Data protection regulation (GDPR). It presents a case study of an EU healthcare research project “WELCOME” and evaluates the privacy aspects of this project with regards to the privacy requirements of the law. WELCOME is an integrated care approach for continuous monitoring, early diagnosis, and detection of worsening events and treatment of patients suffering from Chronic Obstructive Pulmonary Disease (COPD). This paper describes the various security and privacy mechanisms implemented in different layers of the WELCOME architecture and proposes recommendations to design a secure and fully compliant mobile health application solution.
Replay Attack Detection Using Magnitude and Phase Information with Attention-based Adaptive Filters

Automatic Speech Verification (ASV) systems are highly vulnerable to spoofing attacks, and replay attack poses the greatest threat among various spoofing attacks. In this paper, we propose a novel multi-channel feature extraction method with attention-based adaptive filters (AAF). Original phase information, discarded by conventional feature extraction techniques after Fast Fourier Transform (FFT), is promising in distinguishing genuine from replay spoofed speech. Accordingly, phase and magnitude information are respectively extracted as phase channel and magnitude channel complementary features in our system. First, we make discriminative ability analysis on full frequency bands with F-ratio methods. Then attention-based adaptive filters are implemented to maximize capturing of high discriminative information on frequency bands, and the results on ASVspoof 2017 challenge indicate that our proposed approach achieved relative error reduction rates of 78.7% and 59.8% on development and evaluation dataset than the baseline method.
Analog quantum algorithms for the mixing of Markov chains

The problem of sampling from the stationary distribution of a Markov chain finds widespread applications in a variety of fields. The time required for a Markov chain to converge to its stationary distribution is known as the classical mixing time. In this article, we deal with analog quantum algorithms for mixing. First, we provide an analog quantum algorithm that, given a Markov chain, allows us to sample from its stationary distribution in a time that scales as the sum of the square root of the classical mixing time and the square root of the classical hitting time. Our algorithm makes use of the framework of interpolated quantum walks and relies on Hamiltonian evolution in conjunction with von Neumann measurements. There also exists a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense. In this scenario, the quantum mixing time is defined as the time required to sample from a distribution that is close to this limiting distribution. Recently, we provided an upper bound on the quantum mixing time for Erd\ifmmode \mbox{\H{o}}\else \H{o}\fi{}s-R\'enyi random graphs [Phys. Rev. Lett. 124, 050501 (2020)]. Here, we also extend and expand upon our findings therein. Namely, we provide an intuitive understanding of the state-of-the-art random matrix theory tools used to derive our results. In particular, for our analysis we require information about macroscopic, mesoscopic, and microscopic statistics of eigenvalues of random matrices which we highlight here. Furthermore, we provide numerical simulations that corroborate our analytical findings and extend this notion of mixing from simple graphs to any ergodic, reversible, Markov chain.
Learning edit cost estimation models for graph edit distance

One of the most popular distance measures between a pair of graphs is the Graph Edit Distance. This approach consists of finding a set of edit operations that completely transforms a graph into another. Edit costs are introduced in order to penalize the distortion that each edit operation introduces. Then, one basic requirement when we design a Graph Edit Distance algorithm, is to define the appropriate edit cost functions. On the other hand, Machine Learning algorithms has been applied in many contexts showing impressive results, due, among other things, its ability to find correlations between input and output values. The aim of this paper is to bring the potentialities of Machine Learning to the Graph Edit Distance problem presenting a general framework in which this kind of algorithms are used to estimate the edit costs values for node substitutions.
Arbitrary phase shifting method for fiber-optic fringe projection profilometry based on temporal sinusoidal phase modulation

A new arbitrary phase shifting method is proposed in this paper for fiber-optic fringe projection profilometry. By introducing a small temporal sinusoidal phase modulation to the phase shift and employing the phase-generated carrier (PGC) method, the time-varying interference phase α(t)can be demodulated from the Michelson interference signal, which is reflected by the fiber end faces and recorded concurrently with the recording of the fringe pattern. Integrate sin [α(t)] and cos [α(t)] respectively, and then the phase shifts between frames can be determined and consequently used to solve the phase map with least-squares PS algorithm. The noise model of this method is deduced from which the optimum modulation amplitude can be selected. In the experiment, four fringe patterns with uncalibrated phase shifts were recorded under strong environmental disturbance, and were analyzed by utilizing the Carre algorithm, the proposed method and the modified Fourier transform (MFT) method respectively. A visual criterion is used to evaluate the performances of these methods. Experimental results indicate that the proposed method is much more robust to phase shift miscalibration than the Carre algorithm, and its performance is comparable to that of the MFT method while its computational complexity is substantially lower. Therefore, the proposed method is suitable for real-time phase shift calibration.
The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions

Although the field of learning automata (LA) has made significant progress in the past four decades, the LA-based methods to tackle problems involving environments with a large number of actions is, in reality, relatively unresolved. The extension of the traditional LA to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and so, most components of the vector will soon have values that are smaller than the machine accuracy permits, implying that they will never be chosen. This paper presents a solution that extends the continuous pursuit paradigm to such large-actioned problem domains. The beauty of the solution is that it is hierarchical, where all the actions offered by the environment reside as leaves of the hierarchy. Furthermore, at every level, we merely require a two-action LA that automatically resolves the problem of dealing with arbitrarily small action probabilities. In addition, since all the LA invoke the pursuit paradigm, the best action at every level trickles up toward the root. Thus, by invoking the property of the “max” operator, in which the maximum of numerous maxima is the overall maximum, the hierarchy of LA converges to the optimal action. This paper describes the scheme and formally proves its E-optimal convergence. The results presented here can, rather trivially, be extended for the families of discretized and Bayesian pursuit LA too. This paper also reports extensive experimental results (including for environments having 128 and 256 actions) that demonstrate the power of the scheme and its computational advantages. As far as we know, there are no comparable pursuitbased results in the field of LA. In some cases, the hierarchical continuous pursuit automaton requires less than 18% of the number of iterations than the benchmark L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R-I</sub> scheme, which is, by all metrics, phenomenal.
Transmobility: Possibilities in Cyborg (Cripborg) Bodies

This creative, experimental contribution blends written words and sketches depicting our crip bodies engaging with various mobility technologies, including crutches, walkers, prosthetic limbs, and manual and power wheelchairs. By picturing and describing our crip bodies with varieties of technologies that we use, we use these pictures and corresponding narratives about disabled bodies in technology to tell a larger story about the constitution of disability with technologies, as well as the modes of mobility available to disabled bodies. Our visual and narrative elements serve to argue that disabled bodies have a wider array of mobilities and ways of being than are afforded to non-disabled bodies. We resist super-crippery and insist on cripborgery. Crip bodies are taken as sites of possibility, adaptation, and creative reflection.
Cyberattacks on Critical Infrastructure: an Economic Perspective

The aim of this article is to analyze the economic aspects of cybersecurity of critical infrastructure defined as physical or virtual systems and assets that are vital to a country’s functioning and whose incapacitation or destruction would have a debilitating impact on national, economic, military and public security. The functioning of modern states, firms and individuals increasingly relies on digital or cyber technologies and this trend has also materialized in various facets of critical infrastructure. Critical infrastructure presents a new cybersecurity area of attacks and threats that requires the attention of regulators and service providers. Deploying critical infrastructure systems without suitable cybersecurity might make them vulnerable to intrinsic failures or malicious attacks and result in serious negative consequences. In this article a fuller view of costs and losses associated with cyberattacks that includes both private and external (social) costs is proposed. An application of the cost-benefit analysis or the Return on Security Investment (ROSI) indicator is presented to evaluate the worthiness of cybersecurity efforts and analyze the costs associated with some major cyberattacks in recent years. The “Identify, Protect, Detect, Respond and Recover” (IPDRR) framework of organizing cybersecurity efforts is also proposed as well as an illustration as to how the blockchain technology could be utilized to improve security and efficiency within a critical infrastructure.
Classroom Micro-Expression Recognition Algorithms Based on Multi-Feature Fusion

The extraction of facial features is the key to micro-expression recognition.This paper puts forward a micro-expression recognition algorithm through multi-feature fusion.In this algorithm, the change of local binary pattern (LBP) feature distribution is correlated with projection error.For fast and accurate detection, the research data were all extracted from professional facial expression databases, the images had basically the same positions in each expression library, and the pure face image was acquired through manual segmentation from the selected expression library.Through comparison and its application in an intelligent classroom environment, it is proved that the proposed algorithm clearly outperforms the original LBP algorithm.The proposed method can be extended to other image recognition and classification problems.
Multi-Round Transfer Learning for Low-Resource NMT Using Multiple High-Resource Languages

Neural machine translation (NMT) has made remarkable progress in recent years, but the performance of NMT suffers from a data sparsity problem since large-scale parallel corpora are only readily available for high-resource languages (HRLs). In recent days, transfer learning (TL) has been used widely in low-resource languages (LRLs) machine translation, while TL is becoming one of the vital directions for addressing the data sparsity problem in low-resource NMT. As a solution, a transfer learning method in NMT is generally obtained via initializing the low-resource model (child) with the high-resource model (parent). However, leveraging the original TL to low-resource models is neither able to make full use of highly related multiple HRLs nor to receive different parameters from the same parents. In order to exploit multiple HRLs effectively, we present a language-independent and straightforward multi-round transfer learning (MRTL) approach to low-resource NMT. Besides, with the intention of reducing the differences between high-resource and low-resource languages at the character level, we introduce a unified transliteration method for various language families, which are both semantically and syntactically highly analogous with each other. Experiments on low-resource datasets show that our approaches are effective, significantly outperform the state-of-the-art methods, and yield improvements of up to 5.63 BLEU points.
Application of Mixed Reality Technology in Visualization of Medical Operations

With the continuous progress of virtual simulation technology, medical surgery visualization system has been developed from two-dimensional to three-dimensional, from digital to network and intelligentization. The visualization system with mixed reality technology will also be used in all stage of medical surgery, such as case discussion, surgical planning, intraoperative guidance, post-operative evaluation, rehabilitation, so as to further promote high intelligence, high precision of medical surgery, and consequently improve effectiveness of treatment and quality of medical service. This paper discusses the composition and technical characteristics of medical operation visualization system based on mixed reality technology, and introduces some typical applications of mixed reality technology in medical operation visualization, which provides a new perspective for the application of mixed technology in medical surgery.