Reviewerly/exHarmony huggingface dataset:
High-speed Secure Key Distribution with Polarization Modulation Boosted by Broadband Optical Chaos

We report a secure key distribution (SKD) scheme based on broadband opto-electronic Boolean chaos and polarization modulation for higher secure key generation rate (KGR). An error-free SKD with KGR of 3.6 Gb/s is experimentally demonstrated.
ВИКОРИСТАННЯ ХМАРНИХ СЕРВІСІВ GOOGLE НА УРОКАХ МАТЕМАТИКИ

The authors identify the prospects for the introduction into the educational process of general secondary education in the study of mathematics in Google’s cloud services. The positive influence on mathematics lessons from the involvement of Google cloud services — Google-form, which contributes to the intensification of the educational process.
Multi-branch Fusion Fully Convolutional Network for Person Re-Identification

Building effective CNN architectures with light weight has become an increasing application demand for person re-identification (Re-ID) tasks. However, most of the existing methods adopt large CNN models as baseline, which is complicated and inefficient. In this paper, we propose an efficient and effective CNN architecture named Multi-branch Fusion Fully Convolutional Network (MBF-FCN). Firstly, multi-branch feature extractor module focusing on different receptive field sizes is designed to extract low-level features. Secondly, basic convolution block units (CBU) are used for constructing candidate network module to obtain deep-layer feature presentation. Finally, head structures consisted of multi-branches will be adopted, combining not only global and local features but also lower-level and higher-level features with fully convolutional layer. Experiments demonstrate our superior trade-off among model size, speed, computation, and accuracy. Specifically, our model trained from scratch, only has 2.1 million parameters, 0.84 GFLOPs and 384-dimensional features, reaching the state-of-the-art result on Market-1501 and DuckMTMCreID dataset of Rank-1/mAP = 94.5\(\%\)/84.3\(\%\), Rank-1/mAP = 86.6\(\%\)/73.5\(\%\) without re-ranking, respectively.
Realization of DXF File Analysis and Vectorgraph Overlay Based on Embedded Linux Platform

This paper analyzes the structural differences of various elements in the DXF file, and uses the pure C++ library dxflib to develop a DXF file analysis and drawing program with Qt Creator. The program supports translation, rotation and zoom operations of the DXF vector graphics. Integrating the program into our embedded Linux camera can realize positioning and preliminary shape judging of the workpiece, which achieved good results.
Cell Image Incremental Classification with Memory-Augmented Convolutional Neural Networks

The traditional manual analysis method in the field of cell image recognition is time-consuming and laborious, and Convolutional Neural Networks (CNNs) can already handle this type of work well. However, subject to the catastrophic forgetting problem, current CNNs cannot adapt to incremental learning scenarios, which is quite common in cell image classification. In this paper, we propose a new method to handle this problem. Our method mainly consists of an autoencoder and a fixed-capacity memory bank. On one hand, the autoencoder adopts the popular CNN structure, the encoder encodes the training image as feature vectors which will be saved into the memory bank during training. The decoder can generate old categories knowledge based on the memory bank to against catastrophic forgetting when learning new categories of cells. On the other hand, the memory bank saves the feature vectors of each learned category and can be a nearest-neighbor classifier to better adapt to the incremental scenario. We design a specific updated algorithm for the memory bank to make full use of its limited capacity. We conduct comparative experiments with other state-of-the-art methods on cell image classification datasets and the results demonstrate the superiority of our method.
On-campus Workshop on Occupation Implemented in Cooperation with Industry and Academia

This paper describes on-campus workshop on occupation implemented in cooperation with industry and academia. In order for students to experience internships appropriately, an on-campus occupation workshop was held at the Department of Electrical and Electronic Engineering, Faculty of Engineering, Fukuoka Institute of Technology as an opportunity for them to consider their occupations early. The content of the workshop that appeals to the awareness of students considering electrical work as an option was considered through industry-academia collaboration. At the occupation workshop, explanations and practical exercises were conducted while showing concrete materials such as photographs and drawings so that students could imagine what the work of electrical construction management would be like. The students answered in the questionnaire that they were able to understand the outline of the four main tasks of construction management: “schedule control”, “quality control”, “cost control”, and “safety control”. Therefore, it is considered that the on-campus occupational workshop was able to provide students with useful information and experiences.
Assessing Deep Neural Networks as Probability Estimators

Deep Neural Networks (DNNs) have performed admirably in classification tasks. However, the characterization of their classification uncertainties, required for certain applications, has been lacking. In this work, we investigate the issue by assessing DNNs’ ability to estimate conditional probabilities and propose a framework for systematic uncertainty characterization. Denoting the input sample as x and the category as y, the classification task of assigning a category y to a given input x can be reduced to the task of estimating the conditional probabilities p(y|x), as approximated by the DNN at its last layer using the softmax function. Since softmax yields a vector whose elements all fall in the interval (0, 1) and sum to 1, it suggests a probabilistic interpretation to the DNN’s outcome. Using synthetic and real-world datasets, we look into the impact of various factors, e.g., probability density f(x) and inter-categorical sparsity, on the precision of DNNs’ estimations of p(y|x), and find that the likelihood probability density and the inter-categorical sparsity have greater impacts than the prior probability to DNNs’ classification uncertainty.
Research on similarity algorithm of normal cloud based on double envelope curves

The algorithm of normal cloud similarity based on double envelope curve is presented. Solving the equation sets of inner envelope curve of normal cloud model, regarding the position of the intersection point of the equations, the ratio of the area of overlapping part of two inner envelope curves to the area surrounded by the inner envelope curve and transverse axis is calculated, which is the same for the outer envelope curve. Take the average value of these two ratios as the similarity of two normal clouds, the new algorithm is established. This new normal cloud similarity algorithm not only fully embodies the three digital features of normal cloud, but also has good stability and is not affected by the number of cloud droplets. The comparison between the algorithm and two existing algorithms is given by an example by which the effectiveness and superiority of the algorithm are verified.
Optimization of Hyperparameters in Convolutional Neural Network for Human Activity Recognition

Human Activity Recognition (HAR) is a popular research area due to its diverse applications including smart health. Classification of various human activities mainly relies of manually extracted features based on domain knowledge and different machine learning classification methods. Sometimes it’s very tedious to extract the features manually from high voluminous sensor data. Thus, deep learning methods are gaining popularity due to its automatic feature extraction characteristics. In this research work, Convolutional Neural Network (CNN) is used to identify different human activities. However, tune the values of different hyperparameters associated with CNN to achieve higher classification accuracy is a challenging task. Expert experience with the combination of trial and error optimization is a common solution to tune the hyperparameters. Moreover, it is time consuming. In this work, we use Random Search (RS) to optimize the hyperparameters of CNN. In our experiment, standard public “Wireless Sensor Data Mining” (WISDM) dataset for HAR, is used to show the outperformance of our proposed method.
A comparison study: the impact of age and gender distribution on age estimation

Age estimation from a single facial image is a challenging and attractive research area in the computer vision community. Several facial datasets annotated with age and gender attributes became available in the literature. However, one major drawback is that these datasets do not consider the label distribution during data collection. Therefore, the models training on these datasets inevitably have bias for the age having least number of images. In this work, we analyze the age and gender distribution of previous datasets and publish an Uniform Age and Gender Dataset (UAGD) which has almost equal number of female and male images in each age. In addition, we investigate the impact of age and gender distribution on age estimation by comparing DEX CNN model trained on several different datasets. Our experiments show that UAGD dataset has good performance for age estimation task and also it is suitable for being an evaluation benchmark.
Analysis of Various Clustering Algorithms to Enhance Bag-of-Visual-Words for Drowsiness Prediction

Bag-of-Visual-Words is a technique used to create image vocabularies which describes the best image features. The construction of visual vocabulary is done using various clustering techniques. This work concentrates on various clustering techniques that are implemented on Bag-of-Visual-Words technique so that to analyse the accuracy of vocabulary creation. The clustering techniques such as K-means, Mini-batch K-means, Mean-shift, DBSCAN and OP-TICS are implemented individually to record the efficiency of the model. Features from the input images are extracted using Scale Invariant Feature Transform(SIFT) matched with Fast Library for Approximate Nearest Neighbors(FLANN). Drowsy images are classified based on the occurrence of the visual words. The comparison result indicates that the OPTICS clustering algorithm works well with Bag-of-Visual-Words to output an accuracy rate of 79.01%.
Improved Classification Accuracy for Identification of Cervical Cancer

The major purpose of this research is to forecast cervical cancer, compare which algorithms perform well, and then choose the best algorithm to predict cervical cancer at an early stage. Cervical cancer classification can be automated using a machine learning system. This study evaluates multiple machine learning techniques for cervical cancer classification. For this classification, algorithms such as Decision Tree, Naive Bayes, KNN, SVM, and MLP are proposed and evaluated. The cervical cancer Dataset, which was retrieved from the UCI machine learning data repository, was used to test these methods. With the help of Sciklit-learn, the algorithms' results were compared in terms of Accuracy, Sensitivity, and Specificity. Sciklit-learn is a Python-based machine learning package that is available for free. Finally, the best model for predicting cervical cancer is developed.
Fuzzy-Weighted Differential Evolution Computing Paradigm for Fractional Order Nonlinear Wiener Systems

The parameter estimation of fractional order nonlinear Wiener system is complex and challenging task due to the presence of significant nonlinearity at the output block, unknown fractional order, unknown parameters of the linear/nonlinear blocks, and the unmeasurable intermediate variables as well as the states. In this study, a novel design of fuzzy-weighted differential evolution is presented for parameter estimation of fractional order nonlinear Wiener (FO-NW) systems which are designed as an extension of conventional Wiener type models subjected to be fractional order system by exploiting Grunwald-Letnikov fractional derivative. The parameter estimation problem of FO-NW systems is constructed by defining a merit/error function between the true and estimated response via knacks of approximation theory in mean square error sense. Fuzzy Weighted Differential evolution algorithm is employed as an optimization mechanism to estimate the parameters of FO-NW systems with various output nonlinearities of polynomial, sinusoidal and sigmoidal kernels for low/high noisy environments in the system dynamics. Comparative studies based on rigorous statistics endorse the accurate, effective, stable and robust performance of fuzzy-weighted differential evolution algorithm.
EXAMPLES OF QUANTUM IT IN NEW TECHNOLOGIES OF COMPUTATION

The paper includes definitions of elements of quantum IT referred to classical technologies of computation. It explains the principles of transformation of calculating algorithms to the domain of quantum computations using the optimisation and matrix calculus. Exemplary applications of classical algorithms are presented with possibilities of their realisation in domain of quantum IT. Autor presents some possibilities for using quantum algorithms in new computation technologies concerning quantum cryptography and data analyses with complex computations.
Lepi-Board: An Infrastructure for the Development of Digital Storytelling Games by Blind-Users

The Digital games and the practice of modding are becoming even more common and popular. However, the target public is still largely homogeneous and does not include people with disabilities. This paper presents a computational solution that involves using a digital game editor for blind end-users. This solution is part of a larger project based on end-user development, which consists of a storytelling desktop editor tool with graphic elements and a web version for cloud storage. Design Science Research was adopted as the methodology employed for this investigation. The research products obtained by this work include a haptic board to design scenes, software to transform the physical scene into a digital scene and a set of instructions. Although it was not possible to carry out an evaluation with the public of interest because of the current health restrictions in Brazil, we demonstrate how the infrastructure created allows the use of a haptic device to create digital storytelling games with the minimum system requirements.
On the number of residues of linear recurrences

For every nonconstant monic polynomial $$g \in \mathbb {Z}[X]$$ , let $$\mathfrak {M}(g)$$ be the set of positive integers m for which there exist an integer linear recurrence $$(s_n)_{n \ge 0}$$ having characteristic polynomial g and a positive integer M such that $$(s_n)_{n \ge 0}$$ has exactly m distinct residues modulo M. Dubickas and Novikas proved that $$\mathfrak {M}(X^2 - X - 1) = \mathbb {N}$$ . We study $$\mathfrak {M}(g)$$ in the case in which g is divisible by a monic quadratic polynomial $$f \in \mathbb {Z}[X]$$ with roots $$\alpha ,\beta $$ such that $$\alpha \beta = \pm 1$$ and $$\alpha / \beta $$ is not a root of unity. We show that this problem is related to the existence of special primitive divisors of certain Lehmer sequences, and we deduce some consequences on $$\mathfrak {M}(g)$$ . In particular, for $$\alpha \beta = -1$$ , we prove that $$m \in \mathfrak {M}(g)$$ for every integer $$m \ge 7$$ with $$m \ne 10$$ and $$4 \not \mid m$$ .
Application of Human mimetic Binaural Sound Source Direction Estimation to Task Realization for Musculoskeletal Humanoid

The recognition of the real environment in detail is significant for humanoids to realize advanced task, and hearing is one of useful senses for recognition of out-of-view in particular. In this study, we conduct task realization experiments for the musculoskeletal humanoid based on recognition of the environment including out-of-view using auditory information processing consisting of human mimetic binaural ear, binaural sound source direction estimation, and binaural environmental sound detection. First, we conduct the appropriate manipulation of car driving tools, such as putting the car into reverse and applying the parking brake. Second, we conduct turning around to a call from out-of-view using visual and acoustic human detection.
Design of Checkers Game Using Alpha-Beta Pruning Algorithm

Checkers is a board game that is played by two people which has a purpose to defeat the opponent by eating all the opponent's pieces or making the opponent unable to make a move. The sophistication of technology at this modern time makes the checkers game can be used on a computer even with a smartphone. The application of artificial intelligence in checkers games makes the game playable anywhere and anytime. Alpha Beta Pruning is an optimization technique from the Minimax Algorithm that can reduce the number of branch/node extensions to get better and faster step search results. In this study, a checkers game based on artificial intelligence will be developed using the alpha-beta pruning method. This research is expected to explain in detail how artificial intelligence works in a game. Alpha-beta pruning was chosen because it can search for the best steps quickly and precisely. This study tested 10 respondents to play this game. The results show that the player's win rate was 60% at the easy level, 40% at the medium level, and 20% at the hard level. Besides that, the level of interest in this game was 80% being entertained and 20% feeling ordinary.
Anomaly Detection for Large-Scale Municipal Data

In many cities, there is often a disconnect between the government and the people it serves. Many cities have systems in place where citizens can submit ticket requests to help improve communications of their issues. However, the time to complete these tasks satisfactorily can vary drastically depending on a number of factors which can frustrate citizens. To improve the process, a more in-depth understanding of how different attributes affect the completion time of these tickets is needed. For example, in Worcester, Massachusetts, the Department of Public Works and Department of Inspectional Services complete service ticket requests. Like many departments in cities across the world, both of these departments receive a high volume of requests annually and the amount can make managing and analyzing requests difficult. To combat this issue, unsupervised and supervised learning techniques, including autoencoders, angle-based outlier detection, gradient boosting, isolation forests, and neural networks, are applied to identify anomalous tickets. By identifying these outliers the efficiency in which government entities can complete these requests can be greatly increased and thusly improving the relationship between a city and its citizens.
Dear-Net: Learning Diversities for Skeleton-Based Early Action Recognition

Early actionrecognition, i.e., recognizing an action before it is fully performed, is a challenging and important task. Existing works mainly focus on deterministic early action recognition outputting only a single class, and ignore the uncertainty and diversity that essentially exist in this task. Intuitively, when only the early portion of the action is observed, there could be multiple possibilities of the full action, as diversified actions can share almost identical early segments in many scenarios. Thus taking uncertainties and diversities into account, and outputting multiple plausible predictions, instead of a single one, can be important for the sake of authenticity and requirement of many practical applications. To this end, we propose a novel Diversified Early Action Recognition Network (Dear-Net) that is capable of outputting multiple reasonable action classes for each partial sequence by utilizing mode conversion. Specifically, we introduce an effective action diversity learning strategy to drive our network towards predicting diverse and reasonable results, in which each learnable action class is matched with the most suitable mode. Meanwhile, the collapsed modes which fail to receive any action class, are also considered in this strategy in order to ensure diversity. Moreover, we design a sequence decoder within our network to capture latent global information for better early action recognition. It provides a feasible scheme for weakly-supervised setting in which the Dear-Net leverages unlabelled data to improve performance. Experimental results on three challenging datasets clearly show the effectiveness of our approach.
Vision-Based Real-Time Hand Wash Accuracy Prediction

Hands are the primary means of germ transmission, which results in the transmission of deadly diseases. Hand hygiene is thus an important measure to prevent the spread of harmful diseases. The World Health Organization (WHO) has recommended seven steps for proper handwashing hygiene. However, not everyone adheres to the WHO’s handwashing guidelines of seven steps. A proper hand wash is an important factor in protecting people’s health during the corona virus disease (COVID-19) pandemic, especially for healthcare workers who are exposed to bacteria, influenza, and other infectious diseases. There are technologies available like ultraviolet (UV) images to check a person’s hand hygiene. However, there is no real-time system to monitor how efficiently a person makes his or her hand wash. A real-time monitoring system is needed to check people’s hand wash hygiene in public places and hospitals, reducing the risk of spreading communicable diseases. In this paper, we have discussed predicting the accuracy of handwashing actions performed by humans using a deep learning model with transfer learning (VGG16).
Planning and managing projects

Project work is broken into subtasks and then documented with a work breakdown structure. Task sequences and resource constraints are used to develop schedules. Schedules are analyzed and refined using the critical path method and program evaluation and review technique. Project management topics include Gantt charts, budget tracking, task tracking, assessment, and updates.
Intra-and-inter Sentence Attention Model for Enhanced Question Answering System

Task-oriented question answering dialogue systems have been an important branch of conversational systems for oral language, where they first understand the query requested by users, and the models are demanded to seek for answers within the context considering the query information. Previous work models the semantic and syntactic information without taking the interaction into consideration. In this paper, we propose an intra-and-inter sentence attention framework, equipped with self-attention mechanism, which enables the model to focus more on a small part of the context and enhances the model capability of extracting the interactive information. Our experiments are based on the Stanford Question Answering Dataset (SQuAD) and the experimental result shows that our model achieves 68.5 EM and 77.7 F1 score, which verifies how the proposed model improves on the dataset.
Sentiment Analysis Classifiers for Polarity Detection in Social Media Text: A Comparative Study

People often use the internet and microblogging websites to express their thoughts and opinions on products and services. Sentiment analysis of public opinion is much more important because it aids in understanding the public's views on a specific service and the actions of the consumer who is purchasing a product. This area is expected to be extremely useful in analyzing brand awareness in both the private and public sectors. The proposed survey would aid in the comprehension of the different algorithms that are used to categorize the texts posted into various sentimental categories. The study emphasized that the hybrid algorithms outperformed supervised machine learning approaches with better classification accuracy.