Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF LEARNING MEDIA BASED ON ARTICULATE STORYLINE

Utilization of technology in education will produce good quality learning, up to date, make critical thinking, have conceptual thinking and easy to access various learning resources. Together with technology, the teacher must realize a learning assisted learning media, so that students’ interest in learning increases. Articulate storyline application is an interactive multimedia application that can be used by teachers or students. Based on observations found that teachers use media objects for learning in class, especially in force material, then based on the results of interviews with teachers, teachers more often use learning media types of objects, visuals and silently projected. The purpose of this study is to validate the articulate storyline as a learning medium on force material. The steps of this research development include initial research and information gathering, planning, developing initial product formats, initial trials and product revisions. Data collected through observation, interviews and questionnaires. The results of the validation of the material experts stated that media is valid with an average value of 79.65%. Furthermore, the results of the validation of the media experts stated that media is valid with an average value of 86.16%. In tested valid media products with an average of 81.93%. From the results of these data the researchers concluded that the articulate storyline-based media are valid, and suitable for use as learning media.
Ensemble Learning of Lightweight Deep Learning Models Using Knowledge Distillation for Image Classification

In recent years, deep learning models have been used successfully in almost every field including both industry and academia, especially for computer vision tasks. However, these models are huge in size, with millions (and billions) of parameters, and thus cannot be deployed on the systems and devices with limited resources (e.g., embedded systems and mobile phones). To tackle this, several techniques on model compression and acceleration have been proposed. As a representative type of them, knowledge distillation suggests a way to effectively learn a small student model from large teacher model(s). It has attracted increasing attention since it showed its promising performance. In the work, we propose an ensemble model that combines feature-based, response-based, and relation-based lightweight knowledge distillation models for simple image classification tasks. In our knowledge distillation framework, we use ResNet−20 as a student network and ResNet−110 as a teacher network. Experimental results demonstrate that our proposed ensemble model outperforms other knowledge distillation models as well as the large teacher model for image classification tasks, with less computational power than the teacher model.
Fast Black-Box Quantum State Preparation

Quantum state preparation is an important ingredient for other higher-level quantum algorithms, such as Hamiltonian simulation, or for loading distributions into a quantum device to be used e.g. in the context of optimization tasks such as machine learning. Starting with a generic "black box" method devised by Grover in 2000, which employs amplitude amplification to load coefficients calculated by an oracle, there has been a long series of results and improvements with various additional conditions on the amplitudes to be loaded, culminating in Sanders et al.'s work which avoids almost all arithmetic during the preparation stage.In this work, we construct an optimized black box state loading scheme with which various important sets of coefficients can be loaded significantly faster than in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msqrt><mml:mi>N</mml:mi></mml:msqrt><mml:mo stretchy="false">)</mml:mo></mml:math> rounds of amplitude amplification, up to only <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> many. We achieve this with two variants of our algorithm. The first employs a modification of the oracle from Sanders et al., which requires fewer ancillas (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>log</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>&amp;#x2061;</mml:mo><mml:mi>g</mml:mi></mml:math> vs <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>g</mml:mi><mml:mo>+</mml:mo><mml:mn>2</mml:mn></mml:math> in the bit precision <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>g</mml:mi></mml:math>), and fewer non-Clifford operations per amplitude amplification round within the context of our algorithm. The second utilizes the same oracle, but at slightly increased cost in terms of ancillas (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>g</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>log</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>&amp;#x2061;</mml:mo><mml:mi>g</mml:mi></mml:math>) and non-Clifford operations per amplification round. As the number of amplitude amplification rounds enters as multiplicative factor, our black box state loading scheme yields an up to exponential speedup as compared to prior methods. This speedup translates beyond the black box case.
Robotic Systems of Systems Based on a Decentralized Service-Oriented Architecture

Multi-robot systems are often static and pre-configured during the design time of their software. Emerging cooperation between unknown robots is still rare and limited. Such cooperation might be basic like sharing sensor data or complex like conjoined motion planning and acting. Robots should be able to detect other robots and their abilities during runtime. When cooperation seems to be possible and beneficial, it should be initiated autonomously. A centralized cloud control shall be avoided. Using software patterns belonging to service-oriented architectures, the robots are able to discover other robots and their abilities during runtime. These abilities are implemented as services and described by their interfaces. Composition of services can be done easily and flexibly. The software patterns originally belonging to cloud computing could be successfully adopted to decentralized multi-robot systems. The developed concept allows autonomous systems to cooperate flexibly and to compose multi-robot systems during runtime.
Assure deletion supporting dynamic insertion for outsourced data in cloud computing

With the rapid development of cloud computing, an increasing number of data owners are willing to employ cloud storage service. In cloud storage, the resource-constraint data owners can outsource their large-scale data to the remote cloud server, by which they can greatly reduce local storage overhead and computation cost. Despite plenty of attractive advantages, cloud storage inevitably suffers from some new security challenges due to the separation of outsourced data ownership and its management, such as secure data insertion and deletion. The cloud server may maliciously reserve some data copies and return a wrong deletion result to cheat the data owner. Moreover, it is very difficult for the data owner to securely insert some new data blocks into the outsourced data set. To solve the above two problems, we adopt the primitive of Merkle sum hash tree to design a novel publicly verifiable cloud data deletion scheme, which can also simultaneously achieve provable data storage and dynamic data insertion. Moreover, an interesting property of our proposed scheme is that it can satisfy private and public verifiability without requiring any trusted third party. Furthermore, we formally prove that our proposed scheme not only can achieve the desired security properties, but also can realize the high efficiency and practicality.
Multi-Target Tracking and Detection Based on Hybrid Filter Algorithm

Wireless sensor network is a fast-growing research field. In recent years, it has attracted considerable research attention. The generation of large-scale sensor networks interconnected hundreds of sensor nodes, opening up some technical challenges and huge application potential. This article mainly introduces multi-target tracking and detection based on wireless sensor network. This paper studies the positioning methods and target tracking algorithms of wireless sensor network nodes. It mainly studies the positioning algorithms based on ranging in the positioning algorithms in detail, analyzes the advantages and disadvantages of the algorithms, and analyzes the system models and related targets in the target tracking algorithms. The filtering has been studied in detail. In addition, a tracking algorithm under the mixed linear and non-linear motion of the moving target is also proposed, namely the hybrid filtering algorithm. This algorithm makes the motion state of the tracked moving target no longer restricted, and can freely switch between linear motion and nonlinear motion. The experimental results in this paper show that Kalman filter can effectively track moving targets without sudden changes in speed. When the mobile robot switches the grid, it will bring about the switching of the observation model. Compared with the least squares positioning algorithm, the smooth switching rate of the Kalman filter positioning algorithm is increased by 24%. When the three robots are running at a speed of 0. 5m/s in the monitoring area, the system can track the target in real time and send the positioning result to the robot to provide position navigation for the next formation feedback control.
An Improved Blind Recognition Method for Synchronization Position and Coding Parameters of k/n Rate Convolutional Codes in a Noisy Environment

In digital communication systems, error correction codes are used to recover data that has been disturbed in a noisy environment. Convolutional code is a commonly used error correction code. In non-cooperative communication systems, it is very meaningful to blindly identify the synchronization position and coding parameters of convolutional codes. Existing reference had proposed a blind recognition algorithm for coding parameters of k/n rate convolutional codes in a noisy environment, but it didn't take into account the case where the received code words were not synchronized. In this paper, this problem is improved by using the quasi-correlation between the receive sequence and the check matrix to estimate the synchronization position, while obtaining the check matrix. It implements blind recognition of the synchronization position and coding parameters of k/n rate convolutional codes in a noisy environment. Simulation experimental results show that the improved algorithm in this paper is effective.
A Text Detection Algorithm for Image of Student Exercises Based on CTPN and Enhanced YOLOv3

Intelligent learning system (ILS) has become a popular learning tool for students.It can collect students' wrong questions in exercises and dig out their unskilled knowledge points so that it can recommend personalized exercises for students.Detecting text accurately from images of students' exercises is significant and essential in an ILS.However, a big challenge of text detection is that traditional text detection algorithms can not detect complete text lines in an exercise scene, and their detection box always splits between Chinese and mathematical symbols.In this article, we propose a deep-learning-based approach for text detection, which improves You Only Look Once version 3 (YOLOv3) by changing the regression object from a single character to a fixed-width text and applies a stitching strategy to construct text lines based on the relation matrix, which improves the accuracy by 9.8%.Experimental results on both RCTW Chinese text detection dataset and real exercise scenario show that our model can improve detection effectiveness.In addition, we compare our method with two state-of-the-art approaches in applications of exercise text detection, and discuss its capability and limitations.We have also provided a platform which has implemented the proposal for detecting text lines in students' daily homework or examination papers, which enhances user experience well.
Real-Time Collision-Free Navigation of Multiple UAVs Based on Bounding Boxes

Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs.
Disaster Recovery in Cloud Computing Systems: An Overview

With the rapid growth of internet technologies, large-scale online services, such as data backup and data recovery are increasingly available.Since these large-scale online services require substantial networking, processing, and storage capacities, it has become a considerable challenge to design equally large-scale computing infrastructures that support these services costeffectively.In response to this rising demand, cloud computing has been refined during the past decade and turned into a lucrative business for organizations that own large datacenters and offer their computing resources.Undoubtedly cloud computing provides tremendous benefits for data storage backup and data accessibility at a reasonable cost.This paper aims at surveying and analyzing the previous works proposed for disaster recovery in cloud computing.The discussion concentrates on investigating the positive aspects and the limitations of each proposal.Also examined are discussed the current challenges in handling data recovery in the cloud context and the impact of data backup plan on maintaining the data in the event of natural disasters.A summary of the leading research work is provided outlining their weaknesses and limitations in the area of disaster recovery in the cloud computing environment.An in-depth discussion of the current and future trends research in the area of disaster recovery in cloud computing is also offered.Several work research directions that ought to be explored are pointed out as well, which may help researchers to discover and further investigate those problems related to disaster recovery in the cloud environment that have remained unresolved.
Multitask Adversarial Learning for Chinese Font Style Transfer

Style transfer between Chinese fonts is challenging due to both the complexity of Chinese characters and the significant difference between fonts. Existing algorithms for this task typically learn a mapping between the reference and target fonts for each character. Subsequently, this mapping is used to generate the characters that do not exist in the target font. However, the characters available for training are unlikely to cover all fine-grained parts of the missing characters, leading to the overfitting problem. As a result, the generated characters of the target font may suffer problems of incomplete or even radicals and dirty dots. To address this problem, this paper presents a multi-task adversarial learning approach, termed MTfontGAN, to generate more vivid Chinese characters. MTfontGAN learns to transfer a reference font to multiple target ones simultaneously. An alignment is imposed on the encoders of different tasks to make them focus on the important parts of the characters in general style transfer. Such cross-task interactions at the feature level effectively improve the generalization capability of MTfontGAN. The performance of MTfontGAN is evaluated on three Chinese font datasets. Experimental results show that MTfontGAN outperforms the state-of-the-art algorithms in a single-task setting. More importantly, increasing the number of tasks leads to better performance in all of them.
College Enquiry Chat-Bot System

Nowadays, many people are using smartphone with many new applications i.e. technology is growing day by day.Today Artificial Intelligence is playing a major role in a variety of fields ranging from industries in product manufacturing, to customer care in public relations.As there are many online Artificial Intelligence (AI) systems or chat bots which are in existence that help people solve their problems.So, we are going to implement a virtual assistant based on AI that can solve any college related query.This will work as a College Oriented Intelligence machine.This virtual machine will respond the queries of students on college related issues.A chat bot has information stored in its database to identify the sentences and making a decision itself as response to answer a given question.The college enquiry chat bot will be built using algorithm that analyses queries and understand user's message.
Virtual Reality and Serious Games Using in Distance Learning in Medicine in Bulgaria

Thе paper presents the inclusion of new innovative methods in medical education in Bulgaria. Recently, schools around the world have had to move to distance learning for their students. The use of modern technologies in the virtual classroom today is the most important part of the educational process. Effective modern devices, user-friendly platforms and appropriate educational pedagogical methods prove to be the key to excellent training. The inclusion of virtual teaching methods and serious educational games contributes to the conduct of high-level training and the formation of future medics of knowledge, skills and experience necessary for the formation of their professional qualities. The paper presents the results of a survey conducted among trainers and trainees at medical universities in Bulgaria. The analysis of the results shows the readiness of teachers and students to apply the methods of virtual learning and serious games in the educational process.
Residual Guided Deblocking With Deep Learning

The block-based coding structure in hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing etc. Recently, neural network based loop filters are proposed to enhance the reconstructed frame. But the coding information has not been full utilized in the design of neural networks. Therefore, in this paper, we propose a Residual-Reconstruction-based Convolutional Neural Network (RRCNN) to improve the coding efficiency to its full extent, where the residual frame is fed into the network as a supplementary input to the reconstructed frame. In essence, the residual signal can provide effective information about block partitions and can help in recognizing smooth, edge and texture regions in a picture, such that more adaptive parameters can be trained to handle different texture characteristics. In addition, the network structure has been carefully designed in the proposed dual-input network to learn useful context information from two signals with their distinct features. To the best of our knowledge, this is the first work that employs the residual signal in the CNN-based in-loop filter for video coding. The experimental results show that our proposed RRCNN approach leads to significant BD-rate savings compared to HEVC and the state-of-the-art CNN-based schemes, indicating residual signal plays an important role in the enhancement of reconstructed video frames.
Efficient Processing of Image Processing Applications on CPU/GPU

Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.
Seeing through the forest: The gaze path to purchase

Eye tracking studies have analyzed the relationship between visual attention to point of purchase marketing elements (price, signage, etc.) and purchase intention. Our study is the first to investigate the relationship between the gaze sequence in which consumers view a display (including gaze aversion away from products) and the influence of consumer (top down) characteristics on product choice. We conducted an in-lab 3 (display size: large, moderate, small) X 2 (price: sale, non-sale) within-subject experiment with 92 persons. After viewing the displays, subjects completed an online survey to provide demographic data, self-reported and actual product knowledge, and past purchase information. We employed a random forest machine learning approach via R software to analyze all possible three-unit subsequences of gaze fixations. Models comparing multiclass F1-macro score and F1-micro score of product choice were analyzed. Gaze sequence models that included gaze aversion more accurately predicted product choice in a lab setting for more complex displays. Inclusion of consumer characteristics generally improved model predictive F1-macro and F1-micro scores for less complex displays with fewer plant sizes Consumer attributes that helped improve model prediction performance were product expertise, ethnicity, and previous plant purchases.