Reviewerly/exHarmony huggingface dataset:
An approach to construct feedforward clock-controlled sequence with high linear complexity

In practical applications, people tend to use feedforward clock control structures to construct stream ciphers. However, it is difficult to estimate the linear complexity lower bound for stream ciphers constructed in this way. This paper proposes a clock-controlled sequence construction method with provably high linear complexity. For a control sequence with period m, the complexity of proof is O(m<sup>2</sup>), independent of the controlled sequence period size. <sup>1</sup>
Improved scene understanding through semantic reasoning and online learning

With the number of sensors constantly increasing, there is a great need for automating the processing of sensor data in order to reduce cognitive load and response time for manned systems and enable greater autonomy in unmanned systems. It is anticipated that the unprecedented access to sensor data (both in volume and variety) will lead to reduced false alarm rates and increased probability of detection of threats and targets. Effectively, this capability will support situational awareness and facilitate mission success. However, current signal and image processing systems largely ignore the scene context which hinders their performance. In this paper, we describe a machine learning- and semantic reasoning-based system for target detection which incorporates the context. It combines the state-of-the-art image and signal processing capability with the leading-edge logic-based semantic reasoning technology. The main focus of this paper is on the value added by the semantic reasoning to machine learning.
Interpolation of the Zech’s Logarithm: Explicit Forms

Zech’s logarithm is a function closely related to the Discrete Logarithm. It has applications in communications, cryptography, and computing. In this paper, we provide polynomial and exponential formulas for Zech’s logarithm over prime fields.
Anomaly detection based on memory-augmented in computer room laboratory

Computer room laboratory is a place to store servers, its security is very import, the way manual monitoring to monitor the abnormal of the computer room monitoring video is time-consuming and laborious, it is efficient to detect the abnormal conditions in the monitoring video of the computer room laboratory based on deep learning. Therefore, this paper proposes anomaly detection based on memory-agumented in computer room laboratory, and collected and processed the anomaly detection dataset of computer room laboratory. The model proposed in this paper uses the automatic encoder as the basic network and the anomaly discrimination method based on reconstruction, but the automatic encoder “generalizes”, in this paper, memory enhancement module is applied in automatic encoders, that suppresses the reconstruction of abnormal sample. In order to extract spatiotemporal features efficiently, interaction-reduced channel-separated convolution network is used as the video feature extraction network, which makes the memory module saves memory items with better performance and accurately reconstruct normal sample. The detection accuracy of the model of this paper is 90.68% in the anomaly detection dataset of computer room laboratory.
Generating Sequences for Online Courses using a GAN based on a small Sample Set

In this paper, we use a Generative Adversarial Network (GAN) as a sequence generator for language learning online courses. Therefore, we cluster a very small dataset of manually created training samples to derive rules. Then, we train a GAN that can mimic rule-based sequences, where we use our derived rules to evaluate generated samples. We enhance our approach by a parameter that course creators can select deviations they want to have in new sequences without manual adjustments. The resulting sequences follow the core structure of the small sample set. Based on deviations of the generated new learning paths, new combinations of methods can be used that course creators did not previously have in mind. This opens up a new way to generate course sequences without the need to model many alternative learning paths for adaptions.
Cryogenic Optical Biasing of a Superconducting Nanowire Single Photon Detector with a Photodiode

We present a method to provide the bias current for an SNSPD via the photocurrent of a photodiode at 0.8K. This offers the prospect for noise reduction, heat load reduction, and bias current pulsing.
Protocol Enhancement in Optical Circuit Switched Distributed Deep Learning System

We propose and experimentally demonstrate a proactive packet loss protocol (PPLP) in order to effectively accelerate concurrent distributed deep learning jobs in the same OCS topology by relieving the congestions among them.
Optimal sensor deployment in complex 3D terrain surface

Three-dimensional (3D) optimal sensor deployment is to plan the least sensors to cover the surface of the 3D terrain under the condition of reaching a certain coverage rate. Since the existing methods do not consider the terrain obscured effect, a 3D sensor deployment method based on viewshed and pattern search is proposed in this paper. Firstly, the problem is mathematically described by using the sensor detection model and terrain obscured effect. Secondly, the detailed implementation steps of the method are given. The viewshed algorithm is used to calculate the coverage rate of the sensors and establish the objective function. The pattern search algorithm is used to optimize the deployment location of sensors. Finally, the proposed method is verified by simulation. The simulation results show that the method can plan the least sensors on the surface of a given 3D complex terrain to reach a certain coverage rate, and avoid falling into the local optimization.
Efficient Multiplication of Somewhat Small Integers Using Number-Theoretic Transforms

Conventional wisdom purports that FFT-based integer multiplication methods (such as the Schönhage–Strassen algorithm) begin to compete with Karatsuba and Toom–Cook only for integers of several tens of thousands of bits. In this work, we challenge this belief, leveraging recent advances in the implementation of number-theoretic transforms (NTT) stimulated by their use in post-quantum cryptography. We report on implementations of NTT-based integer arithmetic on two Arm Cortex-M CPUs on opposite ends of the performance spectrum: Cortex-M3 and Cortex-M55. Our results indicate that NTT-based multiplication is capable of outperforming the big-number arithmetic implementations of popular embedded cryptography libraries for integers as small as 2048 bits. To provide a realistic case study, we benchmark implementations of the RSA encryption and decryption operations. Our cycle counts on Cortex-M55 are about $$10\times $$ lower than on Cortex-M3.
A Nonlinear Two-Parameter Model for the Spatial Analysis of Solar Irradiation

Nowadays, energy estimation in various application areas is a major research topic. Additionally, various machine learning techniques, especially regression methods and artificial neural networks, have been developed in recent decades to improve the accuracy of such estimates. This article presents a nonlinear compact regression model for estimating the yearly solar irradiation in Africa and Europe by considering only the latitude and mean temperature of the locations as input parameters. The definition of the values of the coefficients is based on the least-square method constrained by the maximum absolute error. The results of 16 conventional regression models, using the same number of predictors, were compared with the result of the model proposed. Our model minimizes the root mean square error by at least 15%.
An overview on smart buildings

Smart buildings can have a significant impact on human lives and are a key contributor for ensuring sustainable development. As a significant part of our lives is spent inside buildings, they have a strong impact on human life comfort and work efficiency. Moreover, they account for a significant share of the planet resources usage, from the construction phase to the daily activities held in their space and to the required maintenance work. This article addresses technologies and enablers, risks and cultural aspects, interactions, building types, and smart building applications.
Implementation of a Mixed Reality System for the Development of Traditional Crafts Industries

In this study, we implemented a mixed reality (MR) system for developing traditional craft industries. This system has the function of superimposing traditional three-dimensional computer graphics (3DCG) crafts on the real-world space. Furthermore, this system has the function to visualize product information related to traditional crafts. The user can experience traditional crafts by superimposing the traditional crafts gesture operation in any space and can browse much information such as techniques and methods of the traditional crafts.
Comparative Analysis of Disease Buster using Machine learning Algorithms

The field of medical care is one of the premier fields of exploration in the current setting with the quick improvement of innovation and information. It is challenging to deal with a large number of patient data. Because of the wide accessibility of patient information, it has become more straightforward to break down the example through it to arrive at specific clinical results, for example, giving experiences to the specialists with respect to a specific ailment to make treatment simpler [1]. It's not difficult to deal with this information with Big Data Analytics. There are numerous medicines for some illnesses around the world. AI is an arising strategy that predicts, analyze the illness. This paper shows the expectation of illnesses as per the signs that utilization. Patient will likewise get a few wellbeing warnings in light of their report. From the quantitative Analysis of Na&#x00EF;ve Bayes, Random Forest and Decision Tree Algorithms it has been evident that the accuracy of Na&#x00EF;ve Bayes is around (99.1%), Random Forest has the accuracy of (93.5%) whereas the decision Tree algorithm has the accuracy of (91.6%).
Video Feature Tagging and Real-Time Feature Search

A lot of research has gone into the field of computer vision, specifically in the case of video object detection. It was observed that the research mainly focused on the standard datasets and misses out on methods to store it for next level of analysis. In this paper, we bridge the gap between accurate object detection and feature tagging and the ability to store and analyse this information in real time. We propose a system that can annotate exact bounding boxes in the video and store it in Elasticsearch for faster retrieval and further analysis. The proposed system can search for objects from a content of 1000+ hours of video within a few microseconds.
Comparative Study between the Exploration of Diverse Solutions by CVAE and JMVAE for Eyeglass Design

When creating a new product design, a designer chooses design solutions from concepts that combine different impression words. However, there could be more than one optimal design solution. Therefore, a reverse inference system that returns all optimal design solutions is necessary. The purpose of this study is twofold: First, using the eyeglass design as a case study, we clarify the capability to search for diverse design solutions using deep generative models, a solution method for reverse inference. Second, we compare differences in the explored design solutions between two deep generative models, CVAE and JMVAE. The two models generated diverse eyeglass frame designs and yielded results having different characteristics in the search range.
Blind Image Super-Resolution by improved HR-Net with fused Residual Connections

Super-resolution is an important task in computer vision, which intuitively increases the resolution of images, thereby enriching the information in images. In the current superresolution models, fully fusion and exchange of shallow information and deep information of the original image is lacking, as well as the information of the original image is insufficiently utilized. The HR-Net structure retains the information of the original image for a long time, and can conduct sufficient information interaction between different scales simultaneously. Besides, the residual connection, as one of the most commonly used structures, can accelerate the model convergence and preserve the original information of the data well. Taking these ideas into account, this paper proposes a super-resolution method based on HR-Net structure and residual connection, and adds a global residual connection on the base structure of HR-Net, which further preserves the original image information. The method was validated on Set5, Set10, Urban100 and B100 datasets, respectively. Experiments show that this method enriches the details of restored high-resolution images, especially on Set5, and outper-forms most classical methods
An Intelligent Tutoring System for API Misuse Correction by Instant Quality Feedback

Computer science students have difficulty understanding correct usages of an Application Programming Interface (API) and programming violations that cause compilation or runtime errors. Despite high-quality documentation for programming, the students typically need an instructor's feedback when their programs cause bugs, crashes, and vulnerabilities. This paper presents a pedagogical approach that is based on an Intelligent Tutoring System called I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NT</inf> Tu <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ToR</inf> . Briefly, I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NT</inf> T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UTOR</inf> provides novice students with instant feedback to fix their programming issues or vulnerabilities. We have implemented our approach as a plug-in application in the Integrated Development Environment (IDE) for an interactive educational environment. In our proposed evaluation, we plan to perform empirical studies with CS students to assess how effectively I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NT</inf> T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UTOR</inf> improves their ability to identify and fix potential bugs or vulnerabilities in the cryptography-related programming assignments.
Visualization of Classical Chinese Poetry with Augmented Reality Technology

This study aims to describe the effectiveness of Augmented Reality technology-based Classical Chinese Poetry visualization in literature learning. The population of this study were 46 students of Universitas Negeri Malang and Universitas Negeri Surabaya. This research is a pre-experimental study using the One Group Pretest-Posttest Design. Learning using modules accompanied by Augmented Reality Technology-based applications is carried out in three face-to-face and one post-test. The instrument used in this study was a multiple choice formative test. The results showed a significant change in the mean value between the pre-test and post-test, namely an increase of 14 with an average pre-test of 65.75 to 79.75 on the average post-test. The N-gain calculation is in the medium category. Paired Sample T-test results obtained a significance level (effective). Based on the results of several tests, it was concluded that the visualization of Classical Chinese Poetry based on Augmented Reality could be used as a companion medium for textbooks or as a medium that can help students become learning subjects.
Concept drift detection for distributed multi-model machine learning systems

Many works focus on optimizing machine learning models during their training phase, but fail to account how these models adapt into their model-serving phase once they are deployed into real world applications. In this phase models must process through streams of data that can evolve over time and distort the relationship between incoming data, causing concept drift. This paper proposes leveraging the advantages of emerging features stores in order to improve concept drift detection on unlabeled, dynamic data streams across multiple models. Firstly, we introduce Drift Detection on Distributed Datasets (QuaD), which combines classical drift detectors to make use of labeled and unlabeled data, and create local context (i.e. per live model) and global context (i.e. across multiple models). Secondly, we propose using feature store entities, SHAP values, and Collaborative Filtering (CF) to augment unlabeled data across multiple models. To the best of our knowledge, QuaD is the first work that examines the collective behavior of concept drift across multiple models and discerns associations between models that may share a susceptibility in a dynamic setting. QuaD uses a combination of performance-based and data distribution-based drift detectors and CF to capture varying types of concept drifts for labeled and unlabeled data streams and is modeled around the data abstraction provided by emerging feature stores.
Smart Face Detection and Recognition in Illumination Invariant Images using AlexNet CNN Compare Accuracy with SVM

Aim: In many recent technologies it shows that deep learning based face recognition gets best results even surpassing human capability however many models suffer from illumination images. The aim of this study is to recognize faces in different illumination invariant images using deep learning and machine learning techniques. Materials and methods: 20 sample images of illumination invariant image dataset, totally 40 samples trained using deep learning method Alexnet Convolutional Neural Network (ACNN) and Machine learning method Support vector machine (SVM) with G Power 80% and alpha 0.05. Results: ACNN has achieved 98.4 percent accuracy when compared to SVM which has attained 86 percent in face recognition. Attained significance value for two tailed is 0.0001 (p&#x003C; 0.05). Conclusion: For the given set of images, ACNN performs better compared to SVM.
A New Code-Based Linkable Threshold Ring Signature Scheme

In this paper, we construct a new code-based linkable threshold signature scheme whose security is based on the hardness of the syndrome decoding problem and general syndrome decoding problem. We show that our scheme is secure in terms of existential unforgeability, anonymity, nonslanderability, and linkability. The complexity of the signature size proposed in this paper is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <mi>t</mi> </mrow> </mfenced> </math> . Our method is particularly well suited for large free group voting systems. The greater the number of members in the ring, the more pronounced the advantage of the signature length is when compared to other schemes. That is, our scheme achieves a fixed length signature, independent of the number of ring members. In addition, our scheme has a very short public key, and the size is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <mi>N</mi> </mrow> </mfenced> </math> .
Online and Offline Mixed Teaching Mode Based on Multimedia Computer-Aided Music Lessons during the Epidemic

In recent years, due to the repeated outbreak of the epidemic, the normal teaching of students has been affected, and many schools have conducted mixed online and offline teaching for students, so that students can carry out teaching activities anytime and anywhere without delaying course learning due to class suspension. This paper mainly studies the online and offline mixed teaching mode of multimedia computer-assisted music lessons under the epidemic situation and then selects two classes of students as experimental objects to carry out traditional online and offline teaching behaviors and online and offline teaching as the main, multimedia computer teaching as the main object, and compares the performance differences of the two classes in music courses to reflect the effect of multimedia computer-assisted online and offline teaching. This paper introduced the algorithm use of multimedia computer in detail and then applied it to teaching activities to improve the shortcomings of the existing teaching mode. In order to reflect the effect of the mixed teaching mode assisted by multimedia computer, this paper selected two classes of an experimental middle school to conduct experiments on their music class performance. Before the experiment, the algorithm was evaluated and tested, and the accuracy rate of the algorithm was 91.333%. By carrying out teaching activities in different modes and analyzing the data of the test results, it can be seen that the results of the teaching mode assisted by multimedia computer are much higher than the existing teaching mode. Compared with the traditional classroom learning, the scores of the first class in the music class under the two modes were 0.32 points and 4.74 points higher than those of the traditional classroom learning, and the second class was 0.25 points and 7.01 points higher, respectively. In terms of improvement rate, the improvement rates of Class 1 and Class 2 in the multimedia computer-assisted teaching mode were 6.593% and 9.432%, respectively, which indicated that multimedia computers played a positive role in the assisted mixed teaching mode.
Lewer, D; Tamne, S; Nicholas, O; Anderson, C; Story, A; (2019) Clinic-level variations in contact tracing outcomes: protocol for a cross-sectional study of variation and case-mix adjustment in London, UK.

Contact tracing around patients with infectious tuberculosis is used to identify further cases and interrupt transmission.Approaches to contact tracing are thought to vary between different clinical teams, but the extent of this variation and the importance of case mix are unknown.This project will use data from the London Tuberculosis Register to analyse the extent of variation in the number of contacts identified and the proportion that are assessed/screened, and use hierarchical modelling to test whether differences in case-mix explain any variation.
Predicting Reading Performance based on Eye Movement Analysis with Hidden Markov Models

Reading is an essential medium for learning, but it is challenging to measure learners’ cognitive processes during reading. Eye-tracking, as an approach in multimodal learning analytics (MmLA), can provide fine-grained data that reflect cognitive processes during reading. In this study, we investigated whether eye movements could predict passage reading performance in addition to language proficiency and cognitive abilities. In particular, we assessed learners’ eye movement pattern and consistency through a novel method, Eye Movement analysis with Hidden Markov Models (EMHMM), in addition to traditional eye movement measures. We found that longer saccade length predicted faster reading speed Also, higher English proficiency predicted faster reading speed through the mediation of longer saccade length. In contrast, reading comprehension accuracy was best predicted by a more consistent eye fixation at the beginning of reading engagement, which may result from a better developed visual routine due to higher reading expertise. These findings have important implications for ways to assess and facilitate learners’ reading through eye movement measures and to examine factors influencing reading performance. The methods adopted could further the development of MmLA and serve as an empirical example of understanding learners’ cognitive processes through collecting and modeling critical learner-centered metrics in novel modalities.