Reviewerly/exHarmony huggingface dataset:
FPGA based Quantum Key Distribution Electronic System for Multipurpose Optical Setup and Protocols

Abstract The purpose of the FPGA electronic control system for Quantum Key Distribution (QKD) is to improve the analog electronic system in optoelectronic conversion. In the existence of the Mezzanine FPGA board, the main conversion of optical laser signal and optical detector signal can be improved. Mezzanine board can reduce the noise errors during the transmission and receiving a signal. Commonly, the optical signal needs to interface with an analog electronic system. This process requires the conversion process and interfacing with a digital signal to control the optical device and produce a good quality optical signal. The Mezzanine board can reduce the uncertainty during signal conversion and gives the digital capability to control the optical devices. The optical modulation signal for phase modulator control can achieve a full complete phase shift in the Mezzanine board. Plus, the voltage phase has capability up to 10 V with an accuracy of 2.4 µV. The time conversion for phase modulator can be extended in the Mezzanine board based on the digital control signal from FPGA. The system is capable to extend into multiple timing control to handle all the signal and to synchronize the signal in receiving, transmission or in between of the transmission. Thus, the system can handle one way or two-way QKD system accordingly.
The Use of Spectral Characteristics of Satellite Images to Assess Water Pollution after Dike Breaking on the Irelyakh River

Abstract An urgent problem in the modern world is the pollution of water bodies with waste water that enters the reservoir after insufficient filtration, or with industrial effluents, in our case related to the mining works. In any case, this pollution negatively affects both the state of the environment and the health of the population forced to use water from polluted sources. And for inhabitants of the Far North remote areas this is fraught with poisoning of fishery resources. The method of remote sensing and processing of satellite images allows you quickly assessing the degree of contamination of reservoirs after man-made emergencies and timely warning the population from exposure to dangerous pollutions. On the basis of decoding the spectral characteristics of pollution, it is possible to track timely the size and speed of its spread, significantly reduce the number of samples taken, field research and ensure regular monitoring of a risen extreme situation. In this work, we used methods of preliminary digital processing of space images: geometric, radiometric and atmospheric correction. The thematic processing of the ERS was carried out. The ERS is a method of image enhancement that includes modification of contrast, noise reduction, boundary detection. An assessment of water pollution in the territory of the Republic of Sakha (Yakutia) after the dyke breaking on the Irelyakh River in August 2018 was carried out using the interpretation of satellite images by spectral analysis on the ENVI software system. The use of this method is relevant for remote hard-to-reach areas and, if necessary, rapid assessments of pollution, as well as for tracing the forecast of the process flow.
Lecturer sets up in-house studio to boost PdP digital content

KUANTAN: Embracing the new norm of online teaching and learning (PdP) programmes in universities due to the Covid-19 pandemic requires a different teachingskillset.
Introduction to the MDATA Model

Knowledge is human’s high-level understanding and summary of massive data. Intelligence is based on knowledge, and many works aim at representing knowledge and understanding intelligence. Although many models could represent static knowledge efficiently, it is still difficult to represent dynamic knowledge, especially which changes with time and space factors. In this chapter, we introduce a new knowledge representation model, Multi-dimensional Data Association and inTelligent Analysis (MDATA for short). We introduce three main parts in the MDATA model, knowledge representation, knowledge acquisition, and knowledge usage. We also discuss some potential applications that MDATA could be adopted and works greatly to improve the efficiency by the stronger ability of representing knowledge.
Description of the composition of gold to silver minerals in the rock containing gold

Abstract This study aimed to determine the composition of gold to silver minerals in rocks originating from Dongi-Dongi at 2 stations and to determine the composition of gold to silver minerals in rocks fixed to definite proportion. This study used secondary data, which were the result of rock analysis from Dongi-Dongi. The analysis showed that the gold composition from the three samples at Station 1 ranged from 0.01 to 0.99%, and the composition of silver ranged from 0.02 to1.46%. While the composition of gold at Station 2 ranged from 0.01to 2.55%, and the composition of silver ranged from 0.07 to3.02%. The results of the similarity analysis test of two variances and more than two variances showed that the composition of gold to silver has the same ratio in each sample, with Fcount &lt;F table , then H 0 is accepted. Therefore, it can be concluded that the ratio of gold to silver in each sample is the same, and follows the Law of Definite Proportion as stated by Proust that a chemical compound consists of elements with a mass ratio always remains the same. In other words, each sample or compound has a fixed composition of elements
A study of the separating property in Reed-Solomon codes by bounding the minimum distance

Abstract According to their strength, the tracing properties of a code can be categorized as frameproof, separating, IPP and TA. It is known that, if the minimum distance of the code is larger than a certain threshold then the TA property implies the rest. Silverberg et al. ask if there is some kind of tracing capability left when the minimum distance falls below the threshold. Under different assumptions, several papers have given a negative answer to the question. In this paper, further progress is made. We establish values of the minimum distance for which Reed-Solomon codes do not posses the separating property.
Input-Aware Neural Knowledge Tracing Machine

Knowledge Tracing (KT) is the task of tracing evolving knowledge state of each student as (s)he engages with a sequence of learning activities and can provide personalized instructions. However, exiting methods such as Bayesian Knowledge Tracing (BKT) and Deep Knowledge Tracing (DKT) either cannot capture the relationship among different concepts or lack of interpretability. Although Knowledge Tracing Machines (KTM) makes up for these shortcomings, it only uses a linear function to model students' knowledge states, which cannot capture more information contained in each feature. To solve above problems, this work introduces a novel model called Input-aware Neural Knowledge Tracing Machine (INKTM) which can enhance the interpretability to some extent and capture more complex structure information of real-world data to improve prediction performance. Unlike standard FM-based methods that focus on the feature interactions, our model focuses more on the information contained in each feature itself and retains all 2-order feature interactions. By converting weights of each feature to a multidimensional vector, our model can use the vectors to learn a unique attention weight of each feature in different instances by an attention network, so as to highlight important features and then enhance interpretability. At last, we input re-weighted features to a deep neural network to capture the non-linear and complex inherent structure of data. Experiment results show our model can consistently outperform existing models in a range of KT datasets.
Design of identification and printing module circuit for garbage collection system based on Internet of things technology

Under the premise of the implementation of waste classification in China, the public has designed a lot of garbage collection systems, but all of them have single use. Therefore, the author designs the identification and printing module, so as to expand the use of the garbage collection system.
Hydroxyurea Dosage Classification for Sickle Cell Disease Patients

One of the most common blood cell disorder is Sickle cell disease (SCD). It is inherited from the parents. Hydroxyurea (HU) is the effective drug which reduces the painful episodes in SCD patients by 50 percent. In the present scenario most of the healthcare professionals use manual approach for deciding the dosage of medication for SCD patients. Manual approach of deciding HU dosage has several limitations such as, the accuracy of dosage prediction is based on the experience and knowledge of healthcare expert making this task time consuming and slow. Artificial intelligence-based system gained significant attention for the classification of dosage of medication. In this study Long short-term memory (LSTM) and Extreme learning machines (ELM) are used for the classification of dosage of HU in case of SCD patients. Also, the performance of two methods has evaluated and compared. The study is conducted on pathological attributes of SCD patients consist of 12 pathological features for predication and single target variable i.e. HU dosage in milligrams discretized into 3 bins: low dose, moderate dose, and high dose. The dataset comprises 1128 sample points. The experimental results show that LS TM performs better than ELM in terms of class wise and overall accuracy for the given dataset.
Description and Implementation of Production Factory Ontology

Abstract The article follows the previous work of the authors. The paper aims to elaborate ontology into a solid form using directly for the production factory. The authors divided the article into two parts. The first part explains the basics of conceptualization, and the second part is devoted to creating and describing ontology using the RDF model. As an example of ontology’s practical use, there are codes in the programming language #C.
Risk Aware Stochastic Placement of Cloud Services

Allocating the right amount of resources to each service in any of the datacenters in a cloud environment is a very difficult task. This task becomes much harder due to the dynamic nature of the workload and the fact that while long term statistics about the demand may be known, it is impossible to predict the exact demand in each point in time. As a result, service providers either over allocate resources and hurt the service cost efficiency, or run into situation where the allocated local resources are insufficient to support the current demand. In these cases, the service providers deploy overflow mechanisms such as redirecting traffic to a remote datacenter or temporarily leasing additional resources (at a higher price) from the cloud infrastructure owner. The additional cost is in many cases proportional to the amount of overflow demand. In this paper we study this approach and develop a novel mechanism to assign services to datacenters based on the available resources in each datacenter and the distribution of the demand for each service. We use comprehensive analysis to prove that the overall overflow cost is almost optimal for arbitrary demand distributions, as long as there are no dependencies among the services. We further show, using simulation based on real data that the scheme performs very well on realistic service workloads.
A Metric-Based Approach to Modelling a Virtual Machine for Smart Contract Execution

Applicability of smart contracts showed significant potential to multiple industries by offering data integrity, transparency, non-repudiation, and trust automation. An essential part of programmability within a blockchain is a process virtual machine that performs the execution and outputs new valid states. In this paper, there will be proposed theoretical models that address the problems that arise in the design of a virtual machine in blockchain, by strongly emphasizing the importance of metrics for an efficient halting mechanism. A model aimed towards performing computations and tracking state changes will be presented in order to discuss the key aspects of programmability in blockchains. Also, the virtual machine will be defined as a deterministic state automaton and within the definition, additional metrics for a halting condition will be proposed. We also propose a loosely-coupled architecture such that the virtual machine's interactions with the blockchain are handled in an isolated manner from the blockchain's functionality and also introduce an additional layer on top of the virtual machine designated to add privacy and improve scalability.
Enhancing and Nonenhancing 3D Brain Tumor Segmentation with Modified Swish Activation and Double U-net Architecture

A Brain tumor is a growth of irregular cells in the brain. (Artificial Intelligence) AI can be used for fully automatic extraction of the tumor region from (Magnetic Resonance Imaging) MRI scans. Developing such a model will decrease the dependency on the radiologist's experience and provides a faster way to visualize the tumor without the time-consuming process of manually segmenting the tumor regions from 3 Dimensional MRI scans. Diagnosing the extent of the tumor region in the early stages can be lifesaving. Automated segmentation models can also be integrated with Medical Imaging devices. In this paper, we use a Deep Convolutional Neural Network inspired by the U-Net architecture for the segmentation of a brain tumor. We propose a Double U-Net architecture with a new, custom activation function, modified Swish function. Our proposed model achieves better performance metrics than the U-Net model with the well-known activation function, ReLU, while having to train on half as many parameters. Our proposed network is trained and evaluated on the Decathlon 10 Challenge dataset, which contains multimodal 3D MRI scans, labeled as enhancing tumor and non-enhancing tumor by experts. In this paper, we also propose a new activation function, i.e., Modified Swish Function(MSW).
Error Analysis and Boundary Extension

Before we introduce the applications of the wavelet Galerkin methodGalerkin method to solve the boundary-value problemsBoundary-value problems, in this chapter, we introduce the error analysisError analysis and the boundary extensionBoundary extension technology what we conducted such that we know when the accuracyAccuracy of the applications is ensured.
UMAP Based Data Validity Evaluation for Artificial Intelligence Systems

Abstract Although comparison is one of the most useful data validity evaluation method, it has a few potential drawbacks. One of them is that results from the training or the testing a machine learning model shall be obtained. Another one is that uncertainty due to the machine learning model itself may cause a difficulty in evaluating the data validity. In this paper, a new data validity evaluation method was proposed so that these drawbacks can be made up by the proposed method. The proposed method enables a data validity evaluation to be performed in data collection phase or data pre-processing phase. Uniform Manifold Approximation and Projection (UMAP) offers the methodological background to the proposed method.
Texture descriptors for better face recognition performance

Abstract Due to, the great electronic development, which reinforced the need to define people’s identities, different methods, and databases to identification people’s identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40,401 and 10 subjects taken from ORL, UFI and Self-Created databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database as standard database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance. Hereby, the LBP based biometric system performs excellence recognition accuracy even with difficult face images that contain different expressions, different poses, and varied illumination like the UFI dataset.
On asymptotic fairness in voting with greedy sampling

Abstract The basic idea of voting protocols is that nodes query a sample of other nodes and adjust their own opinion throughout several rounds based on the proportion of the sampled opinions. In the classic model, it is assumed that all nodes have the same weight. We study voting protocols for heterogeneous weights with respect to fairness. A voting protocol is fair if the influence on the eventual outcome of a given participant is linear in its weight. Previous work used sampling with replacement to construct a fair voting scheme. However, it was shown that using greedy sampling, i.e., sampling with replacement until a given number of distinct elements is chosen, turns out to be more robust and performant. In this paper, we study fairness of voting protocols with greedy sampling and propose a voting scheme that is asymptotically fair for a broad class of weight distributions. We complement our theoretical findings with numerical results and present several open questions and conjectures.
CAD System for Detection and Classification of Liver Cancer using Optimization Neural Network &amp; Convolution Neural Network Classifiers

Deep learning is a recent field of machine learning that has garnered a great deal of attention in recent years. It has been commonly used for a variety of applications and has proved to be a strong machine learning method for many complex problems. Liver cancer appears to be a leading cause of female death, and a lot of money has been spent in the form of preventative screening programs. The use of automatic image processing techniques resulting from deep learning in that same sense represents a promising way of helping to detect liver cancer. In this paper, ADF-USM (Anisotropic diffusion filtering with Unsharp masking) completed the liver malignant growth CT (Computer Tomography) image preprocessing, the shapes and curves were effectively differentiated and upgraded by histogram equalization. It is seen by analyzing the results of the division method that the SSBIC (Superpixel Segmentation Based Iterative Clustering) algorithm produces favorable results than other current strategies. Finally, the process for classification relies mostly on execution of AGWO-CNN (Adaptive Grey Wolf Optimization with Convolution Neural Network classifier) to verify whether the images are benign or malignant.Experimental results on CT images show that the AGWOCNN model achieved high processing efficiency with an accuracy of 97.6 percent in the classification of liver cancer compared to other classification models.
The Powtoon video in Instagram: The learning physics fun in social media

The learning trend in the 21st century is to adopt technology to support the learning process. The rapid development of social media can be used positively to improve the physics learning process outside the classroom. This article presents the results of the research and development of Powtoon-based physics learning videos on social media Instagram. This research and development used the ADDIE model approach. Research has succeeded in developing learning videos on Instagram for the concepts of equilibrium and rotational dynamics. The resulting video has been implemented in Instagram media. Product worthiness was assessed by experts using a valid and reliable Likert scale questionnaire instrument. Besides, the feasibility of the product is also evaluated by the number of users by giving a like statement on Instagram. The results of this study indicate that the Powtoon-based learning video concepts of equilibrium and rotational dynamics displayed on Instagram are appropriate as physics learning materials in 21st century learning.
Translanguaging, intercomprensione e didattica inclusiva in contesti ‘svantaggiati’

The aim of this paper is to propose a reflection on three concepts – translanguaging, inter-comprehension, inclusion – apparently unrelated to each other, trying to establish interconnections for the linguistic integration of migrant subjects, considered ‘disadvantaged’. In fact, many widespread social representations and some L2 learning/teaching practices are opposed, in favour of mimicry and monolingualism, to any manifestation of diversity, plurality, non-conflicting coexistence respecting otherness. Instead, didactic practices based on the recognition of translanguaging and intercomprehensive approaches can constitute a concrete tool of inclusion.
Data Processing: <i>KontoSensor</i> as an Application of Predictive Analytics

This chapter introduces the KontoSensor, a digital service offered by Deutsche Bank since September 2018, as an example of data processing using predictive analytics. We present the motivation behind this digital service, the use cases and methods currently implemented, the way they have been created, and measures to increase the usage of the KontoSensor. With KontoSensor, Deutsche Bank offers a digital service to its clients to analyze their transactions on their current accounts using methods from predictive analytics and to inform them when irregularities are found. Twelve months after the start, 90,000 clients are already using this service and experiencing the results of data science firsthand.
Research on Anti-terrorism Intelligence Mining Method Based on Attention Neural Networks

In order to improve the intelligence and prediction accuracy of anti-terrorism intelligence mining, this paper applies a medium of attraction neural network based on attention mechanism and investors will own a Complete set of open Source Intelligence Data Mining and takes the analysis of casualties and terrorist organizations as examples and a prediction model of casualties and potential about organizations is constructed. It has had been experimentally verified that its prediction ability is significantly improved compared to support vector machine algorithm, logistic regression algorithm, multi-layer perceptron and attention neural network.
Wavelet B-Splines Bases on the Interval for Solving Boundary Value Problems

The use of multiresolution techniques and wavelets has become increa-singly popular in the development of numerical schemes for the solution of differential equations. Wavelet’s properties make them useful for developing hierarchical solutions to many engineering problems. They are well localized, oscillatory functions which provide a basis of the space of functions on the real line. We show the construction of derivative-orthogonal B-spline wavelets on the interval which have simple structure and provide sparse and well-conditioned matrices when they are used for solving differential equations with the wavelet-Galerkin method.
The Design and Implementation of Code Reading and Code Quality Practice Discussion Course

The report introduces the specific content of the design and implementation of code-related practical courses by university teachers and industry professionals with profound software design and development experience. Based on scientific and engineering practice methodology and educational principles, the report introduces curriculum design principles, including the design principles of software engineering curriculum and the requirements of engineering accreditation. The report describes the course's timetable, course objectives, content, learning materials, learning outcomes, teacher activity and student activity, and assessment/evaluation. The report explains in detail how to help students develop the software engineering mindset based on industry best practice and real-world cases. The report summarizes the actual results of the course and proposes the next step to polish the course.