Reviewerly/exHarmony huggingface dataset:
A Framework for Fine-Grained Nonlinearity Optimization of Boolean and Vectorial Boolean Functions

Boolean functions and vectorial Boolean functions (S-boxes) are widely used cryptographic primitives for achieving cryptanalytic resistance of modern block or stream ciphers.In the aspect of information security, one of the most desirable characteristics a given S-box should possess is a high nonlinearity.In this paper, we project the nonlinearity optimization problem to the domain of binary integer programming.Then, we demonstrate how this interconnection could be successfully exploited by SAT solvers.The provided toolbox could serve in cases, where the designer's goal is to increase (or intentionally decrease) the nonlinearity of a given S-box by applying as minimum changes as possible.For example, we demonstrate how the Skipjack S-box, developed by the U.S. National Security Agency (NSA), and the Kuznyechik S-box, developed by the Russian Federation's standardization agency, could be optimized to a higher nonlinearity by tweaking, respectively, just 4 and 12 bits (out of 2048).In the end, we show that bijective (8,8) S-boxes, the eight coordinates of which possess the currently known optimal nonlinearity value of 116, do exist.
A Study on the Key Competencies and Barriers of Korean Hairdressers for Overseas Employment -Focused on Hair Salons in Australia-

The purpose of this study was to verify the key competencies and barriers of Korean hairdressers for overseas employment. This study was conducted between December 10, 2020 through May 18, 2021, using a questionnaire from 42 employers and managers of hair salons in Australia. Frequency analysis, mean(likert 5-point scale) and standard deviation were used to analyze the data. This study results were as follows: First, the salons answered the questions about preferred characteristics of Korean hairdressers: they didn’t care about gender(69.0%), they didn’t think age was important(45.2%), and they didn’t care about education either(88.1%). However, the salons preferred 4~5 years work experienced hairdressers(50%). Second, for the key competencies of Korean hairdressers, the hair salons regarded ‘personality(4.48±0.73)’ as the first priority and they answered that ‘hairdressing skills(4.18±0.89)’, ‘salon works(3.74±0.97)’, ‘language skills(3.70±1.15)’, ‘leadership(3.58±0.96)’, and ‘cultural understanding(3.39±1.20)’ were important as well. Third, the factors of barriers to employ Korean hairdressers were found to be ‘lack of English skills(3.75±1.18)’, ‘difficulty of visa extension(3.75±1.20)’, and ‘lack of work experience(3.25±1.16)’. Forth, the hair salons were satisfied with Korean hairdressers(3.86±0.99) and they would be willing to support Employer-sponsored-visa for hardworking hairdressers(4.64 ±0.60) as well.
An Improved Unsupervised Color Correction Algorithm for Underwater Image

Aiming at the problems of low contrast, dim, noise interference and color distortion caused by the attenuation and scattering of light in underwater propagation, an improved unsupervised color correction algorithm is proposed. Firstly, according to the attenuation characteristics of light R, G and B in underwater environment, color components of R, G and B are balanced and contrast enhanced; secondly, the image is mapped into HSV color model space, and the contrast of S and V components are stretched; and finally, the details and contours of the underwater images are enhanced by a method of limiting contrast adaptive histogram equalization. Experiments are carried out on the underwater image enhancement reference data set (UIEBD), the results show that the proposed algorithm can correct the underwater image color in different complex environments well. And contrast, saturation and image details of the enhanced underwater image are all improved, and the algorithm is evaluated by MSE, PSNR, SSIM UQI and VIF. Compared with deep learning based algorithm (DUIENet), the average value of indexes MSE with the proposed algorithm is decreased by 4.32%, SSIM and VIF are increased by 1.12% and 26.79%, respectively; compared with traditional algorithms, the total average value of indexes MSE with the proposed algorithm is decreased by 62.9%, PSNR, SSIM, VIF and UQI with the proposed algorithm are increased by 16.6%, 8.43%, 14.52% and 16.88%, respectively.
Epileptic EEG Signal Denoising Enhancement Using Improved Threshold Based Wavelet Method

Epilepsy is a disease of the brain characterized by enduring predisposition to generate epileptic seizures. It is one of the most common neurological illness affecting individuals of any age. The diagnosis of epilepsy is made primarily on clinical grounds supporting investigation, which includes Electroencephalography (EEG), Electrocardiography (ECG) and Electrocorticography (ECoG), allowing both lightweight analysis and extensive analysis to provide more accurate and reliable decisions. In this work, two algorithms for EEG signal denoising is proposed. One method uses Savitzky-Golay (S-G) smoothing filter to remove noisy data from EEG signal in time domain and second method uses improved wavelet thresholding function to discard redundant data in wavelet domain. Raw EEG signals are decomposed into different sub bands by using wavelet transform. Only detailed sub bands are filtered by using thresholding method. Performance of the proposed denoising methods are compared by computing statistical value. Results showed that the improved wavelet thresholding method provides better results when compared to S-G smoothing filter. A new algorithm for denoising based on wavelet thresholding function is proposed.
Single image dehazing method via sky-regions segmentation and dark channel prior

Images and videos captured in hazy weather are often suffered from visual quality degradation. Recently many dehazing algorithms based on the dark channel prior theory have been proposed. However, these algorithms fail to achieve good performance if there are large sky regions or point light sources in the imaging scene. This paper proposes a novel dehazing algorithm based on sky region correction to overcome the problem of the dark channel prior which is not reasonable for sky regions. In order to estimate the atmospheric light, we choose the luminance value at the top 1% pixels of the sky area in the transmission map and calculate the average value of brightness by these pixels corresponding to the input image, by which the atmospheric light can be estimated more accurately. Experimental results demonstrate that the proposed algorithm has better performance in haze removing and contrast-enhancing than other methods.
Research on Intelligent Education Method Based on Data Mining of Distance Education System

To solve the problem of single system education mode in network distance education, an intelligent service model of distance education based on Web mining is proposed. This paper first provides the general process of data processing in distance education system, including data preparation and selection, data preprocessing, mining algorithm selection and implementation. Then, through the improvement of Apriori algorithm, we extract part of the experimental data to construct the mining model. Through the application analysis of the model, we acquire the rule set to provide personalized guidance for students. According to the mining results, the system can dynamically generate a set of pages that users may be interested. The practice has proved that the application of Web Mining Technology in the distance learning system improves the personalized service level of the learning system, and provides an intelligent auxiliary means for the decision analysis.
Discrete Tchebichef Transform for Versatile Video Coding

The Discrete Tchebichef Transform (DTT) is a transform method based on discrete orthogonal Tchebichef polynomials, which have applications found in image compression and video coding. Our method is to construct all DTT-related discrete orthogonal transforms in the required size (corresponding to the coding unit supported by H.266/VVC). To investigate the feature of Tchebichef polynomials, we make use of a novel discrete orthogonal matrix generation method with determined DTT roots, and scaling and rounding a DTT that depends on the quantization parameter, instead of integer approximation. We can obtain an accurate integer DTT matrix. Experimental results show that this method can improve the video quality and require fewer bit rates.
Applying DevOps for Distributed Agile Development: A Case Study

Agile software engineering principles and practices have been widely adopted by the software-intensive organizations. There is an increasing interest among organizations in adopting DevOps for improving their distributed agile software environments. However, the challenge is how best to adopt and integrate DevOps in their software development environments – especially in distributed agile environment. This paper presents one such successful case study of DevOps adoption by the distributed agile teams for the development and deployment of a real-time high-performance gaming platform. (1) Small teams, (2) trust, (3) active communication and collaboration culture, (4) shared product vision and roadmap, (5) continuous feedback and learning culture, and (6) appreciation and excellent senior management support are some of the key success factors of DevOps. The experiences and learnings discussed in this paper can be used by other organizations to effectively plan and adopt DevOps for their environment.
Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

To promote reliable and secure communications in the cognitive radio network, the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation. In this paper, we address the classification of superimposed modulations dedicated to 5G multiple-input multiple-output (MIMO) two-way cognitive relay network in realistic channels modeled with Nakagami- distribution. Our purpose consists of classifying pairs of users modulations from superimposed signals. To achieve this goal, we apply the higher-order statistics in conjunction with the MultiBoostAB classifier. We use several efficiency metrics including the true positive (TP) rate, false positive (FP) rate, precision, recall, F-Measure and receiver operating characteristic (ROC) area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification. Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio, including the worst case (i.e., ), where the fading distribution follows a one-sided Gaussian distribution. We also carry out a comparative study between our proposal using MultiBoostAB classifier with the decision tree (J48) classifier. Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier. In addition, we study the impact of the symbols number, path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.
Using Counterfactual Regret Minimization and Monte Carlo Tree Search for Cybersecurity Threats

Mitigating cyber threats requires adequate understanding of the attacker characteristics in particular their patterns. Such knowledge is essential in addressing the defensive measures that mitigate the attack. If the attacker enters in the network system, the game tree model generates resources by to counter such threat. This is done by altering the parity in the next game tree iteration which yield an adequate response to counter it. If an attacker enters a network system, and a game tree models the resources he must interface with, then that game tree can be altered, by changing the parity on the next to last iteration. This paper analyzes the sequence of patterns based on incoming attacks. The detection of attacker’s pattern and subsequent changes in iterations to counter threat can be viewed as adequate resource or know how in cyber threat mitigations It was realized that changing the game tree of the hacker deprives the attacker of network resources and hence would represent a defensive measure against the attack; that is changing varying or understanding attacker paths, creates an effective defensive measure to protect the system against the incoming threats.. In this paper we analyze a unique combination of CFR and MCTS that attempts to detect the behavior of a hacker. Counterfactual Regret (CFR) is a game theory concept that helps identify patterns of attacks. The pattern recognition concept of Monte Carlo Tree Search (MCTS) is used in harmony with CFR in order to enhance the detection of attacks.
Sentiment Analysis of Multilingual Mixed-Code, Twitter Data Using Machine Learning Approach

Sentiment Analysis is a process of computing, identifying, and categorizing opinion from a piece of text and discovering if the writer’s attitude toward a particular topic or product is positive or negative. In this paper, we are doing Sentiment Analysis of Twitter Data which are mixed code (English and Hindi). Our model works on different types of Twitter input data, namely Mixed script—the tweet is written in combination of Native script and Roman script, Code-Mixed script—in this type, the tweet is in native language but Roman script was used to write it, and Native script—here, the Regional language tweet is written in Native script. We are developing our model in Jupyter Notebook which is an open-source web application software. Our main aim is to increase the efficiency of the Machine Learning model, so we are building the machine learning model with pipeline techniques and Hyperparameter Tuning technique. We are using the Twitter Training Dataset to train our model. Initial step is to do the Text pre-processing thereby removing the unwanted noise in the data. We are using the cross-validation method to overcome the underfitting and overfitting problems. We build the model with different classifiers, namely Logistic Regression, Decision Tree, Random forest, Multinomial Naive Bayes, Support Vector and vectorizers, namely count and term frequency-inverse document frequency (TFIDF). We tried the different combination of parameter tuning and found the best parameter which gives the maximum accuracy. After the analysis and voting classifier output, we found that the combination of TFIDF vectorizer and Random Forest Classifier pipeline gives the maximum accuracy of 96.57% and Predicts the tweets in real time as Positive or Negative.
Short Text Paraphrase Identification Model Based on RDN-MESIM

In the rapid development of various technologies at the present stage, representative artificial intelligence technology has developed more prominently. Therefore, it has been widely applied in various social service areas. The application of artificial intelligence technology in tax consultation can optimize the application scenarios and update the application mode, thus further improving the efficiency and quality of tax data inquiry. In this paper, we propose a novel model, named RDN-MESIM, for paraphrase identification tasks in the tax consulting area. The main contribution of this work is designing the RNN-Dense network and modifying the original ESIM to adapt to the RDN structure. The results demonstrate that RDN-MESIM obtained a better performance as compared to other existing relevant models and archived the highest accuracy, of up to 97.63%.
“Egungun be careful, na Express you dey go”: Socialising a newcomer-celebrity and co-constructing relational connection on Twitter Nigeria

The paper is a case study on socialising a Nigerian celebrity new to Twitter and the co-construction of relational connection among the interactants on Twitter Nigeria. The data comprise tweets retrieved from the Twitter timeline of the celebrity and subjected to interactional pragmatics analysis. The analysis reveals that the Twitter users deployed the interactional practices of joint fantasising, intertextual allusions and ethnolinguistic repertoire to orient to the newcomer-celebrity's identity, mark their own regular users' identity and co-construct the need for caution by the celebrity. The paper argues that socialising celebrities new to Twitter might include emphasising that their statuses as celebrities might expose them to more savage replies and targeted bullying in the form of 'dragging', perhaps more frequently and at a much larger scale than an 'ordinary' Twitter user; and that they need to be cautious in their deployment of micro-celebrity strategies targeted at amassing followers/fans on Twitter and in their overall interactional and relational behaviours on Twitter as celebrities. Concerning relational connection, as the architecture of Twitter is not built around people who have prior connections, it is argued that relational connection may need to be co-constructed from scratch by interacting Twitter users who may be total strangers.
Chinese Named Entity Recognition Based on Gated Graph Neural Network

Most Chinese Named Entity Recognition (CNER) models based on deep learning are implemented based on long short-term memory networks (LSTM) and conditional random fields (CRF). The serialized structure of LSTM is easily affected by word ambiguity and lack of word boundary information. In this regard, we propose a Chinese named entity recognition model based on a gated graph neural network (GGNN).We use the BERT model to generate pre-training encoding vectors of characters, and introduce global nodes to capture the global information in the sentence. Finally, we exploits multiple interactions between the characters in the graph structure, all matching words, and the entire sentence to solve the problem of word ambiguity.The comparative experimental results on the three CNER datasets show that the GGNN model has a better effect on named entity recognition.
A FIRST-ORDER FRAMEWORK FOR INQUISITIVE MODAL LOGIC

Abstract We present a natural standard translation of inquisitive modal logic $\mathrm{InqML}$ into first-order logic over the natural two-sorted relational representations of the intended models, which captures the built-in higher-order features of $\mathrm{InqML}$ . This translation is based on a graded notion of flatness that ties the inherent second-order, team-semantic features of $\mathrm{InqML}$ over information states to subsets or tuples of bounded size. A natural notion of pseudo-models, which relaxes the non-elementary constraints on the intended models, gives rise to an elementary, purely model-theoretic proof of the compactness property for $\mathrm{InqML}$ . Moreover, we prove a Hennessy-Milner theorem for $\mathrm{InqML}$ , which crucially uses $\omega $ -saturated pseudo-models and the new standard translation. As corollaries we also obtain van Benthem style characterisation theorems.
Practically Solving LPN

The best algorithms for the Learning Parity with Noise (LPN) problem require sub-exponential time and memory.This often makes memory, and not time, the limiting factor for practical attacks, which seem to be out of reach even for relatively small parameters.In this paper, we try to bring the state-of-the-art in solving LPN closer to the practical realm.We improve upon the existing algorithms by modifying the Coded-BKW algorithm to work under various memory constrains.We correct and expand previous analysis and experimentally verify our findings.As a result we were able to mount practical attacks on the largest parameters reported to date using only 2 39 bits of memory.We thank the authors of [1] for providing the implementation of their chain-finding algorithm and the authors of [2] for providing their complexity calculation script.
YOLO-based Network Fusion for Riverine Floating Debris Monitoring System

Riverine floating debris has been one of the major challenges and a well-known issue across the globe for decades. To mitigate this problem, sources of debris and their pathways to the riverine environment need to be identified and quantified. The scope of this study is to obtain visual information of floating debris which is crucial in developing a robotic platform for riverine management system. Therefore, an object detector using You Only Look Once version 4 (YOLOv4) algorithm is developed to detect floating debris for the riverine monitoring system. The debris detection system is trained on five object classes such as styrofoam, plastic bags, plastic bottle, aluminium can and plastic container. After the first training is conducted, image augmentation technique is implemented to increase training and validation datasets. Finally, the performance of the proposed debris detection system is evaluated based on the highest mean average precision (mAP) weight file, classification accuracy, precision and recall.
Unsupervised Multi-modality Registration Network Based on Spatially Encoded Gradient Information

Multi-modality medical images can provide relevant or complementary information for a target (organ, tumor or tissue). Registering multi-modality images to a common space can fuse these comprehensive information, and bring convenience for clinical application. Recently, neural networks have been widely investigated to boost registration methods. However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training. In this work, we propose a multi-modality registration network (MMRegNet), which can perform registration between multi-modality images. Meanwhile, we present spatially encoded gradient information to train MMRegNet in an unsupervised manner. The proposed network was evaluated on the public dataset from MM-WHS 2017. Results show that MMRegNet can achieve promising performance for left ventricle registration tasks. Meanwhile, to demonstrate the versatility of MMRegNet, we further evaluate the method using a liver dataset from CHAOS 2019. Our source code is publicly available ( https://github.com/NanYoMy/mmregnet ).
Trajectory Planning for Collaborative Transportation by Tethered Multi-UAVs

Collaborative transportation has been widely studied due to its high efficiency, and interesting research problems., specifically in trajectory planning. In this paper, a new trajectory planning scheme for collaborative transportation by tethered multi-UAVs is proposed to avoid obstacles. Firstly, the dynamics and configuration of the system is analyzed to derive the tethered force acting on each UAV, and define the angle between the rope and the vertical direction is calculated. Then the kinodynamic path search method is used to search an initial trajectory of the payload, and the initial trajectory of each UAV is solved. Finally, nonlinear optimization is used to optimize the trajectory of each UAV and the payload to achieve the effect of obstacle avoidance. Both numerical simulations and practical experiments are completed to verify the feasibility and effectiveness of the proposed planning scheme.
Critical Slowing Down Near Topological Transitions in Rate-Distortion Problems

In rate-distortion (RD) problems one seeks reduced representations of a source that meet a target distortion constraint. Such optimal representations undergo topological transitions at some critical rate values, when their cardinality or dimensionality change. We study the convergence time of the Arimoto-Blahut alternating projection algorithms, used to solve such problems, near those critical points, both for the ratedistortion and information bottleneck settings. We argue that they suffer from critical slowing down - a diverging number of iterations for convergence - near the critical points. This phenomenon can have theoretical and practical implications for both machine learning and data compression problems.