Reviewerly/exHarmony huggingface dataset:
Keystroke Identifier Using Fuzzy Logic to Increase Password Security

Cybersecurity is a major issue today. It is predicted that cybercrime will cost the world $6 trillion annually by 2021. It is important to make logins secure as well as to make advances in security in order to catch cybercriminals. This paper will design and create a device that will use Fuzzy logic to identify a person by the rhythm and frequency of their typing. The device will take data from a user from a normal password entry session. This data will be used to make a Fuzzy system that will be able to identify the user by their typing speed. An application of this project could be used to make a more secure log-in system for a user. The log-in system would not only check that the correct password was entered but also that the rhythm of how the password was typed matched the user. Another application of this system could be used to help catch cybercriminals. A cybercriminal may have a certain rhythm at which they type at and this could be used like a fingerprint to help officials locate cybercriminals.
Bernstein-type bounds for beta distribution

This work obtains sharp closed-form exponential concentration inequalities of Bernstein type for the ubiquitous beta distribution, improving upon sub-Gaussian and sub-gamma bounds previously studied in this context. The proof leverages a novel handy recursion of order 2 for central moments of the beta distribution, obtained from the hypergeometric representations of moments; this recursion is useful for obtaining explicit expressions for central moments and various tail approximations.
Application of Inquiry Learning Model Using Android to Improve Media Literacy of Junior High School Students

This research is actually an evaluation of the implementation of the 2013 curriculum conducted in 2019. However, the results are still very relevant to the condition of the co-19 pandemic in early 2020. The evaluation was conducted to find out the application of inquiry learning models using Android. The population of this study were 200 junior high schools which had implemented the 2013 curriculum in 2018. The data were collected by using surveys. Data were processed using Excel and analyzed using simple descriptive statistical techniques. The result of the study is that Android is very suitable to be used as an alternative media in inquiry learning models. In the end it can be seen that the use of android in inquiry-based learning can improve students' media literacy skills. an increase in media literacy skills is due to students being required to be able to find learning resources independently. Of course the learning resources are in accordance with the topic being discussed by the teacher. This is indicated in the evaluation conducted in May related to the use of online learning resources and the percentage was very significant, above 85%.
Faster Rotation-Based Gauss Sieve for Solving the SVP on General Ideal Lattices

The hardness in solving the shortest vector problem (SVP) is a fundamental assumption for the security of lattice-based cryptographic algorithms. In 2010, Micciancio and Voulgaris proposed an algorithm named the Gauss Sieve, which is a fast and heuristic algorithm for solving the SVP. Schneider presented another algorithm named the Ideal Gauss Sieve in 2011, which is applicable to a special class of lattices, called ideal lattices. The Ideal Gauss Sieve speeds up the Gauss Sieve by using some properties of the ideal lattices. However, the algorithm is applicable only if the dimension of the ideal lattice n is a power of two or n+1 is a prime. Ishiguro et al. proposed an extension to the Ideal Gauss Sieve algorithm in 2014, which is applicable only if the prime factor of n is 2 or 3. In this paper, we first generalize the dimensions that can be applied to the ideal lattice properties to when the prime factor of n is derived from 2, p or q for two primes p and q. To the best of our knowledge, no algorithm using ideal lattice properties has been proposed so far with dimensions such as: 20, 44, 80, 84, and 92. Then we present an algorithm that speeds up the Gauss Sieve for these dimensions. Our experiments show that our proposed algorithm is 10 times faster than the original Gauss Sieve in solving an 80-dimensional SVP problem. Moreover, we propose a rotation-based Gauss Sieve that is approximately 1.5 times faster than the Ideal Gauss Sieve.
Transformation of Learning in the Covid-19 Pandemic

Employee development is a strategic asset for every successful company. The COVID-19 pandemic has significantly transformed established learning technologies. The aim of the study is to analyze the education industry in the context of distance interaction. The article discusses options for educational technologies using virtual classes, ideas for the formation of sustainable memorization of material. The role of surveys for synchronizing online learning is emphasized. The importance of democratizing the relations between the teacher and students for the popularization of new ideas, developments and exchange of experience was noted. The post-learning technology for retaining the information received is considered. The most important resources for the development of positive leadership in the context of virtual communication have been identified. The increasing role of electronic resources for the effective development of company employees is noted.
DISTANCE FORMS OF LEARNING THROUGH THE EYES OF THE STUDENTS – THE FUTURE TEACHERS

The article presents and analyses the results of an online survey on the positives and deficits of distance forms of learning – the only way to ensure the continuity of the educational process in the conditions of the Covid-19 pandemic – based on the answers of 93 students in Preschool Pedagogy and Preschool and Primary School Pedagogy from Episcope Constantine Preslavski University of Shumen. The survey represents the first – research module of a three-module experimental model which also includes an interactive-applied stage (offering interactive models for possible distance work with children from the preparatory groups of kindergarten), and a control-evaluation stage based on the idea of distance learning combined with interactivity.
Sensorless attitude estimation of three-degree-of-freedom actuator for image stabilization

Space recognition is a crucial issue for autonomous systems such as unmanned aerial vehicles, walking robots, and autonomous cars. These systems are necessary to obtain information using a camera while moving. However, reduction in recognition accuracy and system size increase has created problems. To solve these problems, we proposed the three-degree-of-freedom (DOF) actuator for image stabilization. This paper proposes a novel initial position estimation method and sensorless attitude estimation method for the three-DOF actuator to further downsize and reduce weight. The effectiveness of the proposed methods is verified by a magnetic field analysis, using a three-dimensional finite element method and numerical simulations.
Towards Robust Direct Perception Networks for Automated Driving

We consider the problem of engineering robust direct perception neural networks with the output being regression. Such networks take high dimensional input image data, and they produce affordances such as the curvature of the upcoming road segment or the distance to the front vehicle. Our proposal starts by allowing a neural network prediction to deviate from the label with tolerance Δ. The source of tolerance can be either contractual or from limiting factors where two entities may label the same data with slightly different numerical values. The tolerance motivates the use of a non-standard loss function where the loss is set to 0 so long as the prediction-to-label distance is less than Δ. We further extend the loss function and define a new provably robust criterion that is parametric to the allowed output tolerance Δ, the layer index l where perturbation is considered, and the maximum perturbation amount κ. During training, the robust loss is computed by first propagating symbolic errors from the l̃-th layer (with quantity bounded by κ) to the output layer, followed by computing the overflow between the error bounds and the allowed tolerance. The overall concept is experimented in engineering a direct perception neural network for understanding the central position of the ego-lane in pixel coordinates.
Divertor power load predictions based on machine learning

Abstract Machine learning based data-driven approaches to thermal load prediction on the divertor targets of ASDEX upgrade (AUG) are presented. After selecting time averaged data from almost six years of operation of AUG and applying basic physics-motivated cuts to the data we find that we are able to train machine learning models to predict a scalar quantifying the steady state thermal loads on the outer divertor target given scalar operational parameters. With both random forest and neural network based models we manage to achieve decent agreement between the model predictions and the observed values from experiments. Furthermore, we investigate the dependencies of the models and observe that the models manage to extract trends expected from previous physics analyses.
Zero-Pair Image to Image Translation using Domain Conditional Normalization

In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i.e., translating between two domains which have no paired training data available but each have paired training data with a third domain. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target domain output. The validation benchmark uses RGB-depth pairs and RGB-semantic pairs for training and compares performance for the depth-semantic translation task. The proposed approaches improve in qualitative and quantitative terms over the compared methods, while using much fewer parameters.
Recoverable Token: Recovering from Intrusions against Digital Assets in Ethereum

Blockchain systems allow storing digital assets in a tamper-proof, consensus-based, append-only ledger in a decentralized fashion, where no single party has full control. A blockchain is an immutable, append-only, log of transactions. Unfortunately, in some cases there is the need to undo transactions that result from intrusions, e.g., when the private keys of a wallet are stolen, when one of the transaction participants does not comply with what was agreed upon, or when smart contract vulnerabilities are exploited by attackers. There are also accidental scenarios, e.g., when private keys are lost leaving the associated digital assets inaccessible. Although there have been a few proposals which allow modifications to the blockchain, they break the basic guarantees they are supposed to provide. We propose an approach for wallet owners to recover from attacks against their digital assets and accidental loss, while still assuring fundamental properties of the blockchain technology. We implemented the mechanism for Ethereum / EVM.
Area Measuring with PC Camera

Area measurement and registration in different areas of the industry are done using different methods. One example is the production of leather. The different shape, thickness, and color of the leather makes this task a challenge. In the leather industry, a measurement method using photosensors and a video camera method have been successfully implemented. This paper presents the development of software that measures the area of leather with a USB camera. Such a system is implemented but it is necessary to manually set the measurement mode. It works through the method of dark and light spots and so dark (black) or light (white) skins are measured. The proposed new software is based on automatic color recognition against a green background.
The Impact of Fake Reviews on Reputation Systems and Efficiency

Reputation systems, where individuals rate each other, can increase trust and improve efficiency. However, rating fraud is a major problem, especially on online platforms. Using arepeated public good game with a reputation system, we experimentally investigate the circumstances under which rating fraud undermines the functioning of the reputation system and reduces efficiency. We find that the ability to manipulate feedback received from others generally reduces the reliability of average ratings compared to a control treatment where rating fraud is not possible. When manipulation is possible and costless, rating fraud is widespread, average ratings become less reliable, expectations are lower, and both cooperation and efficiencyare significantly reduced. However, when there is a cost to manipulation, fraud is reduced, average ratings are more reliable and contributions and efficiency are not affected.
Detect Turn-takings in Subtitle Streams with Semantic Recall Transformer Encoder

Subtitles are precious dialogue text data because of similarity to human conversation, but the lack of turn structures limits their applications in many NLP tasks. The previous work takes turn-taking detection (TTD) in subtitles as a sentence-pair classification problem to predict if there is a turn-taking happened between the two adjacent utterances. The results are not good enough. For considering the dialogue context information, we innovatively take TTD as a sentence-level sequence labelling problem, predicting the turn-takings through the whole subtitle stream. First, we present a novel fine-tuning method that enables BERT to encode utterances into embedding set with effective turntaking features. Then, we propose the Semantic Recall Transformer (SRT) model to detect the turn-takings among the utterance embedding set, by taking the turn-taking features and context information into account at the same time. Compared with the baselines, it achieves state-of-the-art on both English and Chinese subtitle corpus. Moreover, we explored the impacts of the length of the subtitle stream and the number of conversation participants on our model performance, which show the performance can be further improved in the future.
Khmer WordNet Construction

This paper describes a semi-automatic, translation-based, expansion approach for Khmer WordNet construction. Our approach considers and expands the Princeton English WordNet's synsets and takes into account the existing cross-language machine-readable dictionaries in which the translations between Khmer and English words are provided. The approach automatically developed and deployed a model, which is verified by a small number of experts. As a result, our Khmer WordNet has been constructed with promising coverage and accuracy.
Triggered Measurements in Markov Processes for Entropy-Constrained State Estimation with Application to Precision Agriculture

This paper presents an optimization-based technique for triggering measurements in a hidden Markov model so that the number/rate of measurements is minimized while maintaining accuracy of the state estimates to satisfy a prescribed constraint. Here, the accuracy of a state estimate is characterized by the entropy of the distribution associated with the estimate. After introducing the general approach, this paper also discusses its application to precision agriculture. In particular, we consider site-specific monitoring of soil nitrate levels and show through simulations that the required number of measurements can be reduced with the proposed technique compared to conventional strategies where measurements are taken at a fixed rate.
Hacking Anti-Shoplifting System to Hide Data within Clothes

Steganography has been used to prevent unauthorized access to private information during transmission. It is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, or text. Current steganography methods are working by assigning a cover file then embed the payload within it by making some modifications, creating the stego-file. However, the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload. Aiming to solve this issue, a novel, highly robust steganography method based on hacking anti-shoplifting systems has proposed to hide data within clothes. The anti-Shoplifting system is an anti-theft security system that protects goods and products, leaving the store in an illegal way (i.e., without paying for them). The proposed method works by modifying the default anti-shoplifting system by changing its built-in soft RFID (radio-frequency identification) tags sewn in clothes into NFC (Near Field Communication) tags. These NFC tags are smart tags that can communicate with NFC-Enabled smart-phones using NDEF (NFC Data Exchange Format). NDEF is one of the advancements added to RFID technology by NFC, which allows the data exchange. Every NDEF message has one/more NDEF records that contain record type, a unique ID, a length, and a payload of data that contains the secret message content that can be any type of data that fits in a byte stream. Based on NDEF and NFC-enabled smart-phones, the proposed method will take the secret message from the sender, make use of his NFC-enabled smart-phone to communicate with the NFC tag, then hide the secret message within the NDEF’s payload of the NFC tag stuck in clothes. Finally, to evaluate the proposed method, it has been compared with default (digital) steganography weak points. Such as time, lockable, robustness, attacks, capacity, and a few more points. The results and comparisons showed that the proposed method is more efficient than default (digital) steganography and has many advantages.
Alternative Underwater Image Restoration Based on Unsupervised Learning and Autoencoder with Degradation Block

Underwater image restoration represents a challenge to computer vision and image processing based on machine learning. Recent methodologies to tackle this problem are based on learning. However, the lack of paired image datasets leads the researches to synthesize datasets. We present a new unsupervised learning algorithm that no requires paired dataset to train an encoder-decoder-like neural network for underwater image restoration. Encoder-decoder network learns to represent its input data in a latent representation and reconstruct then in the output. During the training stage, our algorithm applies the output image to a degradation block based on image formation model that reinforces its degradation. The degraded and input images are matched using a loss function. After the training process, we are able to obtain a restored image from the decoder. We focus on underwater inspection and our method relies on small dataset and a light neural network. Underwater images are used to evaluate and validate our algorithm. The qualitative and quantitative results show the improvement provided by our method.
Information Hiding in Color Images Utilizing Multi-bit Substitution Patterns

Reversible data hiding (RDH) has become a significant branch of information embedding, as it allows error free recovery of the original media. Reversibility is the paramount requirement in fields like healthcare, forensics, defense and legal. Most of the work contributed in RDH using image as host has been carried out using gray images as host. The multiphase nature of color images makes them more convenient to high payload applications. This chapter presents RDH scheme for color images. The data has been embedded using various multi-bit substitution patterns used to obtain application specific parameters like PSNR and payload. A particular substitution pattern decides the number of bits embedded and the perceptual performance obtained. Multi-bit substitution pattern with bit placement pattern-9 (bpp-9) shows a highest imperceptibility of 52.4 dB and lowest capacity of 589,822 bits while as contrast is observed in bpp-27 with PSNR 19.17 dB and capacity of 1,769,470 bits. The scheme besides being reversible is capable of detecting any tamper caused due to various signal processing and geometric attacks. This has been ensured by embedding a fragile watermark interleaved with the embedding data and scattered throughout various phases of color image. Given the results obtained, it can be used for secure communication of critical data like patient records in smart-health.
Topology-Linked Self-Adaptive Quantum Particle Swarm optimization for Dynamic Environments

The canonical particle swarm optimizer (PSO) is generally unsuitable for use in dynamic environments owing to its loss of diversity, the inability to detect changes in the environment, and the outdated memory of the particles' previous positions following such a change. In an attempt to overcome these challenges, the quantum particle swarm optimizer (QPSO) was developed. However, QPSO introduces an additional problem-dependent parameter called the “cloud radius” that requires problem-specific tuning. A recent variation of the QPSO that does not use the cloud radius as a parameter has been demonstrated to match and at times surpass the performance of QPSO. This variation has however only been tested using the ring topology, despite the self-adaptive mechanism utilized having a strong coupling to the used topology. This study aims to investigate the effects of various network topologies on the performance of self-adaptive QPSO. A topology based on the 3D von Neumann structure has been shown to perform significantly better than other topologies.
Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a synthesis model for high-dimensional DT from a small number of training samples. In this article, we propose a novel DT synthesis method, which makes full use of similarity as prior knowledge to address this issue. Our method is based on the proposed kernel similarity embedding, which can not only mitigate the high dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationships. Specifically, we first put forward two hypotheses that are essential for the DT model to generate new frames using similarity correlations. Then, we integrate kernel learning and the extreme learning machine into a unified synthesis model to learn kernel similarity embeddings for representing DTs. Extensive experiments on DT videos collected from the Internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embeddings can provide discriminative representations for DTs. Further, our method can preserve the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with higher speed and lower computation than the current state-of-the-art methods. The code and more synthesis videos are available at our project page <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://shiming-chen.github.io/Similarity-page/Similarit.html</uri> .