Reviewerly/exHarmony huggingface dataset:
High-Performance Cryptanalysis: A Comparative Study of Code-Breaking Techniques

The experience in the cryptanalysis field shows that evaluating the security level of a cryptosystem involves not only understanding the mathematical concept, but also applying the best possible cryptographic attacks on this cryptosystem by using the best available techniques. By applying the appropriate attack on the appropriate hardware platform, we can determine the security limits of the studied cryptographic algorithm, and this will enable us to recommend adjustments to the related parameters of this algorithm. On the other hand, although ordinary computers are capable of performing security services, cryptanalytical tasks may require massive computations that go beyond the capabilities of the available computing platforms and this raises the need of using high-performance platforms. In this paper, we will present a comparison between the most popular high-performance computing systems used to perform cryptanalytical tasks. This study will be of a particular interest to researchers who are interested in using high-performance computing units for testing cryptographic systems.
Local Interpretable Classifier Explanations with Self-generated Semantic Features

Explaining predictions of classifiers is a fundamental problem in eXplainable Artificial Intelligence (XAI). LIME (for Local Interpretable Model-agnostic Explanations) is a recently proposed XAI technique able to explain any classifier by providing an interpretable model which approximates the black-box locally to the instance under consideration. In order to build interpretable local models, LIME requires the user to explicitly define a space of interpretable components, also called artefacts, associated with the input instance. To reconstruct local black-box behaviour, the instance neighbourhood is explored by generating instance neighbours as random subsets of the provided artefacts. In this work we note that the above depicted strategy has two main flaws: first, it requires user intervention in the definition of the interpretable space and, second, the local explanation is limited to be expressed in terms the user-provided artefacts. To overcome these two limitations, in this work we propose \(\mathcal {S}\text {-LIME}\), a variant of the basic LIME method exploiting unsupervised learning to replace user-provided interpretable components with self-generated semantic features. This characteristics enables our approach to sample instance neighbours in a more semantic-driven fashion and to greatly reduce the bias associated with explanations. We demonstrate the applicability and effectiveness of our proposal in the text classification domain. Comparison with the baseline highlights superior quality of the explanations provided adopting our strategy.
Absolute measurement approach for crystal growth height based on a polarization-synchronized phase-shifting interferometer

The quality of the solid deuterium–deuterium (D-D) layer in the inertial confinement fusion (ICF) target plays a vital role in the success of fusion experiments. A good understanding of how the quality is affected by the unstable growth of D-D crystal is required. This article provides an approach of measuring D-D layer absolute height in real time by combining monitoring algorithms and a synchronous phase-shifting interferometer. In the approach taken, a real-time monitoring technology, in which an antivibration algorithm is added, is used to get an absolute height of monitoring zone, overcoming the inability to accurately detect the saltus step in the interferometric measurement. Meanwhile, the polarization-synchronized phase-shifting technology is propitious to retrieve the D-D height distribution in a whole interferogram. Consequently, the categorical altitude of the D-D layer in entire crystalline regions can be obtained. Simulation analysis together with experiments have proved that a non-contact, rapid, and high-precision measurement of the D-D crystal absolute height can be realized by using the interferometer and method proposed.
Quantum Codes Do Not Fix Qubit Independent Errors

The goal of this work is to analyse the performance of quantum error correction codes in regard to fixing independent errors in several qubits.We say that a quantum code does not fix a quantum computing error if its application does not reduce the variance of the error.We restrict our study to the classical 5-qubit quantum error correcting code (proposed by Laflamme and some collaborators), which is able to correct arbitrary errors in a single qubit and is also fault-tolerant.We show that this code does not fix qubit independent errors, even assuming that the correction circuit does not introduce new errors.We also prove, for qubit independent errors, that if the correction circuit of the 5-qubit quantum code detects an error, then the corrected state has central symmetry and, as a consequence, its variance is maximal.We have been able to obtain these results thanks to the high symmetry of the 5-qubit quantum code.Although the calculations needed to extend our proofs to less symmetric codes seem to be extremely complicated, we nevertheless think that the results obtained for the 5-qubit quantum code reveal a general behavior pattern of quantum error correcting codes against qubit independent errors.
Obstacle Avoidance with Kinetic Energy Buffer

This paper presents Kinetic Energy Difference (KED) as a metric for collision proximity. The calculation of KED for differentially driven robots is explained, along with an example obstacle avoidance algorithm that utilizes it. This example algorithm is computationally efficient and simulations show that it is capable of guiding robots with slow dynamics through narrow corridors.
Reproducibility Companion Paper

This companion paper supports the experimental replication of the paper "Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment'' presented at ACM Multimedia 2020. We provide the software package for replicating the implementation of the "Norm-in-Norm'' loss and the corresponding "LinearityIQA'' model used in the original paper. This paper contains the guidelines to reproduce all the experimental results of the original paper.
Optimizing the Hyper-parameters of Multi-layer Perceptron with Greedy Search

The core of deep learning network is hyper-parameters which are updated through learning process with samples.Whenever a sample is fed into deep learning network, parameters change according to gradient value.At this point, the number of samples and the amount of learning are crucial, which are batch size and learning rate.To find the optimal batch size and learning rate, lots of trial is inevitable so it takes so much time and effort.Therefore, there have been lots of papers to enhance the efficiency of its optimization process by automatically tuning the single parameter.However, global optimization can't be guaranteed by simply combining separately optimized parameters.This paper propose brand new effective method for hyperparameter optimization in which greedy search is adopted to find the optimal batch size and learning rate.In experiment with Fashion MNIST and Kuzushiji MNIST dataset, the proposed algorithm shows the similar performance as compared to complete search, which means the proposed algorithm can be a potential alternative to complete search.
Various DNN-HMM Architectures Used in Acoustic Modeling with Single-Speaker and Single-Channel

In this paper, we discuss some interesting features of training a special acoustic model for only one speaker with a constant acoustic background (acoustic channel). Currently, the LF-MMI method achieves the best results in many speech recognition tasks. A typical LF-MMI training procedure uses a special 1-state HMM topology that has different pdfs at the self-loop and forward transitions. We would like to discuss the replacement of this typical LF-MMI HMM by different types of HMM topologies (1-, 2- and 3-state HMM topologies that have outputs associated with states). Next, we discuss the advantages of using biphone context modeling over using the triphone context or even simpler context-free monophone. We also address the effect of the amount of training data and the context of DNN on WER, and all this with regard to a special acoustic model with one speaker and an almost constant acoustic channel.
Measuring the principal planes and effective focal length of an optical imaging system

We present a simple method to measure the two principal planes and the effective focal length of an imaging optical system. It is based on measurements of transverse magnification of the image and both the object and image positions from two arbitrary reference points. A simple linear equation is derived to find the distances of the principal planes from the reference points, which enables the computation of the effective focal length. Experimentally, the method only needs a standard optical bench, thus avoiding the use of a nodal slide. The accuracy obtained for a practical example with an optical system composed of two achromatic doublets is smaller than 1 mm, or better than 2%, for all three measured values (two principal planes and the one effective focal length) as compared with the results of optical ray tracing performed with OSLO software.
RCKF Cooperative Navigation Algorithm for Tightly Coupled Vehicle Ad Hoc Networks Based on Huber M Estimation

As a core technology of cooperative navigation, relative position sensing is crucial for vehicle intelligent driving. It plays a key role in the cooperative positioning algorithm of vehicle ad hoc networks (VANETs). However, because of system nonlinearity and colored noise, the acquisition of a posteriori information of cooperative positioning under the same hardware platform is usually limited to a certain accuracy. To address these problems, a vehicle cooperative positioning state estimation algorithm based on a robust cubature Kalman filter (RCKF) of Huber M estimation is herein proposed. The algorithm combines the cubature rule for nonlinear updating, converts the measurement equation into a linear regression problem, and uses M estimation to solve it. The colored noise is suppressed by reducing the weight of disturbed measurements by the Huber loss function. The experimental results of tightly coupled vehicle cooperative navigation show that compared with the extended Kalman filter (EKF) and cubature Kalman filter (CKF), the positioning accuracy of the RCKF proposed in this paper is improved by 26.04% and 27.10% respectively, which effectively improves the accuracy and robustness of relative position estimation. It provides a reference system quality control strategy for vehicle cooperative positioning solutions.
Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world applications. In this work, we show how human demonstrations can improve final performance of agents on the Minecraft minigame ObtainDiamond with only 8M frames of environment interaction. We propose a training procedure where policy networks are first trained on human data and later fine-tuned by reinforcement learning. Using a policy exploitation mechanism, experience replay and an additional loss against catastrophic forgetting, our best agent was able to achieve a mean score of 48. Our proposed solution placed 3rd in the NeurIPS MineRL Competition for Sample-Efficient Reinforcement Learning.
Education for Sustainable Development in an Unstable World

The widespread simplistic understanding of the concept of sustainable development (SD) and the strategy of education for sustainable development (ESD) is poorly compatible with the increasing natural and social instability of the world. In fact, SD and ESD allow you to quickly navigate in a crisis. The core of the SD concept is a conscious refusal of a person or society from any actions that may complicate life in the future. The ESD strategy is not limited to explaining environmental problems to the population and possible ways to solve them. ESD is designed to contribute to the intellectual development of wide sections of the population – the ability to foresee the consequences of their actions, the ability to independently find solutions to any problems, the skills of advanced self-organization and self-education. ESD is based on an ‘initiative learning model’, and not on the completeness and accuracy of the reproduction of knowledge and skills, which have long been considered central to education. The ability to acquire any knowledge and skills on your own is the main thing in the initiative learning model. Searching for your own solutions, and not memorizing ready-made ones, allows you to get used to acting independently in nonstandard conditions, which is necessary in an unstable world. The transition to the initiative learning has long been brew, The transition to the initiative learning model (ILE) has long been brew and even partially taking place, but too slowly. Initiative learning takes longer than reproductive learning, so it is difficult to fully implement it without changing traditional education. This is the main reason for the failure of attempts to introduce initiative learning model over the centuries. According to the initiative learning model, which is the basis of ESD, education is becoming continuous and lifelong. At the same time, the reduction in the number of compulsory educational sections occurs with the simultaneous expansion of curricula and the freedom of students to choose courses, sections and tasks that they study in depth. The assimilation of knowledge becomes mosaic, but this deficiency is fully compensated by the depth of the acquired knowledge and the desire to independently fill their gaps.
Stabilization and visual analysis of video-recorded sailing sessions

One common way to aid coaching and seek to improve athletes' performance is by recording training sessions for posterior analysis. In the case of sailing, coaches record videos from another boat, but usually rely on handheld devices, which may lead to issues with the footage and missing important moments. On the other hand, by autonomously recording the entire session with a fixed camera, the analysis becomes challenging owing to the length of the video and possible stabilization issues. In this work, we aim to facilitate the analysis of such full-session videos by automatically extracting maneuvers and providing a visualization framework to readily locate interesting moments. Moreover, we address issues related to image stability. Finally, an evaluation of the framework points to the benefits of video stabilization in this scenario and an appropriate accuracy of the maneuver detection method.
Resisting Face Surveillance with Copyright Law

Face surveillance is animated by deep-rooted demographic and deployment biases that endanger marginalized communities and threaten the privacy of all. But current approaches have not prevented its adoption by law enforcement. Some companies have offered voluntary moratoria on selling the technology, leaving many others to fill in the gaps. Legislators have enacted regulatory oversight at the state and city levels, but a federal ban remains elusive. Both approaches require vast shifts in practical and political will, each with drawbacks. While we wait, face surveillance persists. This Article suggests a new possibility: face surveillance is fueled by unauthorized copies and reproductions of photographs, and resisting face surveillance compels us to consider countering it with copyright law.So why haven’t face surveillance companies been overwhelmed with copyright infringement litigation? Fair use. This Article lays out the litigation landscape before analyzing the recent Supreme Court decision in Google v. Oracle, alongside other key fair use cases, to examine why this complex doctrine may permit many uses of machine learning without allowing face surveillance to copy and reproduce online profile pictures. Some face surveillance companies claim to be transformative search engines, but their business models are more like private subscription services that are rarely found to be fair use. And scraping profile pictures harms the unique licensing market for these photographs, which grows as companies and researchers increasingly reject scraped photos as sources of face analysis training data. This Article concludes that copyright law could curtail face surveillance without waiting for companies or Congress to catch up.
The Relationship Between Preferences for Online Large-Classroom Learning Methods and Self-Directed Learning Readiness

The Covid-19 era has an impact on teaching.The Faculty of Medicine of Universitas Islam Indonesia develops online classrooms with a number of learning strategies.Self-directed learning readiness is one of the factors related to the selection of learning strategies.This study aims to determine the relationship between SDLR and online learning strategy preferences.Methodology This study aims to survey students' preferences for online learning in large classrooms and their reasons as well as to investigate the levels of self-directed learning readiness of the students.Furthermore, a crosstab analysis was carried out to observe the relationship between the self-directed learning readiness of the students and their online learning preferences.Results Out of 132 students, 70 (53%) chose to study using the flipped classroom, 47 (35.6%) chose synchronous lectures, while 15 (11.4%) chose to independently study the lecture materials shared online.A total of 115 students (87,1%) had a high level of SDLR, and 17 students (12,9%) had a moderate level of self-directed learning.There is no relationship between students' preferences for online learning methods and students' self-directed learning levels (p-value >0.05). Conclusion and Recommendation:Self-directed learning does not correlate with choosing the forms of online learning.It is necessary to carry out a further exploration of which factors play a role in online learning selection by students.
Identification of Damage on Stringers By the PSO Method

Abstract The paper employs the particle swarm optimization (PSO) method for the identification of damage to a stringer web. This method is inspired by a swarm of bees or flock of birds which look for a common solution. It is a heuristic method, that utilizes a particle as a problem-solving candidate. The location of this particle in the search space represents the configuration of all the variables to possible solutions of the problem. As the particle moves into the solution space, the values of the variables change. The particle is defined by a position vector and a velocity vector. The resulting direction of the particle is determined by three components i.e. (1) the moment or inertia, (2) autobiographical or cognitive information and (3) social interaction. The quality of the particle is evaluated by a comparison function. In this study we tried to locate damage by using this method. The damage is represented by a rusted area on a stringer web, which was simulated as a change in the Young’s modulus. We used the modal calculation of the eigenfrequencies as an evaluation function.
One-stage Context and Identity Hallucination Network

Face swapping aims to synthesize a face image, in which the facial identity is well transplanted from the source image and the context (e.g., hairstyle, head posture, facial expression, lighting, and background) keeps consistent with the reference image. The prior work mainly accomplishes the task in two stages, i.e., generating the inner face with the source identity, and then stitching the generation with the complementary part of the reference image by image blending techniques. The blending mask, which is usually obtained by the additional face segmentation model, is a common practice towards photo-realistic face swapping. However, artifacts usually appear at the blending boundary, especially in areas occluded by the hair, eyeglasses, accessories, etc. To address this problem, rather than struggling with the blending mask in the two-stage routine, we develop a novel one-stage context and identity hallucination network, which learns a series of hallucination maps to softly divide the context areas and identity areas. For context areas, the features are fully utilized by a multi-level context encoder. For identity areas, we design a novel two-cascading AdaIN to transfer the identity while retaining the context. Besides, with the help of hallucination maps, we introduce an effectively improved reconstruction loss to utilize unlimited unpaired face images for training. Our network performs well on both context areas and identity areas without any dependency on post-processing. Extensive qualitative and quantitative experiments demonstrate the superiority of our network.
Transport of intensity equation for low-light quantitative phase imaging and security applications

Transport of intensity equation (TIE) is a non-interferometric and non-iterative quantitative phase imaging technique that utilizes multiple intensity recordings along the propagation direction. Apart from many advantages, it has some experimental and numerical challenges as well. The experimental challenge is the requirement of multiple intensity recordings which prohibit TIE for real-time imaging of dynamic samples. The numerical challenge is to work with boundary conditions in post-processing analysis. It is required because phase information is to be retrieved after solving second order partial differential equation relating the intensity derivative with phase. In recent years, we have worked on solving the experimental challenges such as alleviating the need of multiple intensity recordings along-with some applications of TIE.
Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Support Vector Machine classiﬁers are widely used in many classiﬁcation tasks. However, they have two considerable weaknesses, Unclassiﬁable Region (UR) in multiclass classiﬁcation and outliers. In this research, we address these problems by introducing Probabilistic Least Square Twin Support Vector Machine (PLS-TSVM). The proposed algorithm introduces continuous and probabilistic outputs over the model obtained by Least-Square Twin Support Vector Machine (LS-TSVM) method with both linear and polynomial kernel functions. PLS-TSVM not only solves the unclassiﬁable region problem by introducing a continuous output value membership function, but it also reduces the adverse effects of noisy data and outliers. For showing the superiority of our proposed method, we have conducted experiments on various UCI datasets. In the most cases, higher or competitive accuracy to other methods have been obtained such that in some UCI datasets, PLS-TSVM could obtain up to 99.90% of classification accuracy. Moreover, PLS-TSVM has been evaluated against ”one-against-all” and ”one-against-one” approaches on several well-known video datasets such as Weizmann, KTH, and UCF101 for human action recognition task. The results show the higher accuracy of PLS-TSVM compared to its counterparts. Specifically, the proposed algorithm could improve respectively about 12.2%, 2.8%, and 12.1% of classification accuracy in three video datasets compared to the standard SVM and LS-TSVM classifiers. The ﬁnal results indicate that the proposed algorithm could achieve better overall performances than the literature.