Reviewerly/exHarmony huggingface dataset:
Learning Color Theory in Elementary School Using Basic Infographic Media During the Covid-19 Pandemic

COVID-19 first appeared in Wuhan, China in December 2019. The virus attacks the airways and causes respiratory tract infections. In a relatively short time, the virus spread to other parts of China and then to other countries. Responding to the impact of the spread of COVID-19, the Government of Indonesia is aggressively conducting learning media, one of which is infographics. Infographics are one of the media that can be used to attract learning to elementary school students. In infographics, there are visual elements that are also important to attract the attention of the target audience and even help convey messages or information. The method in this study uses quantitative research. The results showed that the post-test value was greater than the pre-test value, this shows that students have increased understanding of color theory after being given an understanding of color theory Therefore, infographic media can increase students' absorption of color theory. Students' understanding of color theory can be improved with interactive learning such as relevant infographic media used during the pandemic where learning is carried out online.
Optimization on Replication Performance via Balance Quorum (BQ) and Data Center Selection Method (DCSM) Algorithms in Cloud Environment

The cloud replication environment is an established and prominent technology globally recognized to deal with the issue of high-volume data that users are expected to access from anywhere at any time. Abundant researchers embarked their efforts to develop heterogeneous strategies to complement the ambiguities of cloud platform and system requirements. Regardless the resilient and durable service technologies provided by cloud providers, the limitation in respective cloud replication strategies are inevitable. The key issue is when acquired data demanded to be always accessible to users irrespective of time and location, appears to be crucial problem which is relative to many inefficient system implementations. Therefore, we selected existing research work named “Dynamic Popularity aware Replication Strategy (DPRS) and concentrated on finding the research strength, gaps and limitations. A thorough case study was conducted via establishing re-simulation on DPRS algorithm and rigorous review was focused on the algorithm process flow. Subsequently, this study specifically reveals the limitations in particular algorithm and proposed potential area for improvements. The case study was simulated using simulation tools called, CloudSim. In order to optimize multi-performance in cloud replication environments, algorithms are presented as part of the proposed model. Finally, analytical results and discussion are shared to evidence proposed algorithms are explicitly contributes betterments in storage consumptions, data availability, network usage and replication frequency.
An Empirical Study on Effect of Semantic Measures in Cross-Domain Recommender System in User Cold-Start Scenario

Cold-start problem is one of the fatal problems in recommender system. The development of cross-domain recommender system (CDRS) provides feasibility to deal with the problem, while it needs to handle heterogeneous information when linking different domains. Some existing semantic measures based on Linked Open Data (LOD) are likely to play a positive role in this research area. According to the different LOD information used, we analyze two classes of semantic measures (similarity and relatedness) to explore the relationship between LOD-based domain correlation and user interests, and study the performance of different semantic measures on a cross-domain recommendation framework. Through experiments on a real-world dataset, this work has identified that the similarity measures can accurately capture the user’s existing interests while the relatedness measures can produce diverse recommendations. Besides, by comparing with some representative methods, the experiment demonstrated that the cross-domain recommendation method could provide users with satisfactory recommendations even in the cold-start scenario.
Implementation of Deep Learning Neural Network In Forecasting of Solar Power

Solar power is a non-conventional cleanest form of energy which is abundant and available gratuitously. Mostly the generated solar PV power is fed to the grid. The power generated from solar depends upon various dynamically changing environmental factors such as irradiation, temperature etc which demands an high spinning reserve over the grid side. In this article, a Deep learning Neural Network based model is implemented to predict the solar power generation of a solar plant by using various environmental parameters by which spinning reserve stress towards the grid can be adhered. The proposed Deep learning model is compared with the conventional neural network model of single & two hidden layers and the results are compared. The results shows promising efficiency of deep learning neural network based system over the conventional neural network for the power predictions on various environmental scenarios.
Liver vessel segmentation based on densely connected three-dimensional fully convolutional neural network

Liver vessel segmentation is an important task of liver tumor resection and ablation. This paper proposes a liver vessel segmentation method based on an improved V-Net network. The dense connection is introduced into the network, which strengthens the feature transfer and reuses between convolutional layers, and can effectively alleviate the disappearance of the gradient of the network. The experimental results on the public data set 3Dircadb shown that the dice, accuracy, and sensitivity are 71.2%, 73.9%, and 97.8% respectively. The final experiment showed that the improved network has good generalization ability and can effectively improve the accuracy of liver vessel segmentation.
Multi-dimensional LSTM: A Model of Network Text Classification

Focusing on the diversified opinion expression form and the explosive growth of information amount in network environment of big data, we propose a text emotion recognition model based on multi-dimensional LSTM to improve classification accuracy of network information by making full use of additional information of text samples. In this paper, we divide the original sample into two parts: the main information sample and the additional information sample. Then multi-dimensional LSTM model is used to extract their features vectors. Finally, according to the results of the two feature vectors, the classification result is carried out by feature fusion and further computation. The multi-dimensional LSTM model is implemented and tested by TensorFlow. The experimental results show that the emotion recognition classification accuracy has been greatly improved by taking advantage of multi-dimensional LSTM in big data environment.
Production of a Small-sized Tandem Rotor Aircraft with Two Tiltable Coaxial Rotors and Its Experiments

In recent years, various types of drones have been paid attention to and are expected to play an active role in many fields. However, when flying forward and backward, or right and left, the attitude of the conventionaldrones changes depending on the situation. In order to prevent the drones from changing its posture, several type of drones with tiltable rotors have been attracting attention in recent years. Among them, a tandem-type rotor with 2-DOF tiltable coaxial rotors has been already proposed. In this study, the servo motor to be used for tilting the pitching angle of the tiltable rotor is exchanged to a multi-turned type to exclude the rotational restriction of the tilt mechanism, so that it enhances the motion ability of the UAV. In addition, the size and weight of the machine is reduced so as to make it suitable for a simple experiment in a narrow space. Some experiments are given to demonstrate the effectiveness of the design and manufacture of the current aircraft on the ground motion.
Compression of DNNs Using Magnitude Pruning and Nonlinear Information Bottleneck Training

As Deep Neural Networks (DNNs) have achieved state-of-the-art performance in various scientific fields and applications, the memory and computational complexity of DNNs have increased concurrently. The increased complexity required by DNNs prohibits them from running on platforms with limited computational resources. This has sparked a renewed interest in parameter pruning. We propose to replace the standard cross-entropy objective - typically used in classification problems - with the Nonlinear Information Bottleneck (NIB) objective to improve the accuracy of a pruned network. We demonstrate, that our proposal outperforms cross-entropy combined with global magnitude pruning for high compression rates on VGG-nets trained on CIFAR10. With approximately 97% of the parameters pruned, we obtain an accuracy of 87.63% and 88.22% for VGG-16 and VGG-19, respectively, where the baseline accuracy is 91.5% for the unpruned networks. We observe that the majority of biases are pruned completely, and pruning parameters globally outperforms layer-wise pruning.
Application of Computer Network Technology in College English Teaching

The innovation of computer network technology provides great opportunities for the development of various industries, and also makes college English teaching face many challenges. Based on the concept and characteristics of computer network technology, this paper fully reveals the problems of single teaching mode, backward method and limited evaluation mechanism in traditional college English teaching. In addition, by comparing the advantages and disadvantages of computer network technology in college English teaching, this paper puts forward some ways for computer network technology to act on college English teaching, then it will provide development ideas for college English teaching and college education reform.
Deep Learning Model for Product Classification

The text classification problem is one of the common machine learning problems, where an input object can be assigned to one or more output labels.
Visual Encryption Using Block based Scrambling Followed by Affine Encryption Technique

This article reports the Block based cipher concept followed by the affine cipher technique. The Image considered was grouped into squared (16, 32 and 64) pixel blocks then each column was shifted by specific values. These values were randomly generated prime numbers and worked as the key for scrambling. These images were investigated for their quality of scrambling using histogram and adjacent pixel correlation. The adjacent pixel correlations for 16, 32 and 64 pixel-based ciphered images were found as 0.7907, 0.7292, and 0.4783 respectively. The analysis gave the information that the level of scrambling was not satisfactory, therefore; the affine cipher technique was applied to each of the images. These images were converted into the matrix format and each element was transformed using the affine cipher. This transformed matrix is again converted inform from the image to visualize. The Histogram and adjacent pixel correction for these images were much improved.
Decision Support System for Online Recruitment

In the past, potential candidates for a job offer were in physical locations that could be reached through the major media that were available at the time, often strongly rooted in their local geographic space. Today, digital media replaced those traditional channels, offering advertisers a broader geographic reach. However digital channels are more and more numerous, making it difficult to target candidates on the web. Existing decision support system on e-recruitment in the literature does not identify the desired profile from a job offer (C1), the relevance of a resume (C2) or the changing environment of recruitment (C3). Thereby, the objective of our research is to optimize the e-recruitment process by designing a decision support system capable of targeting potential candidates at a lower cost and that addresses the challenges (C1), (C2) and (C3).
Edge minimization in de Bruijn graphs

This paper introduces the de Bruijn graph edge minimization problem, which is related to the compression of de Bruijn graphs: find the order-k de Bruijn graph with minimum edge count among all orders. We describe an efficient algorithm that solves this problem. Since the edge minimization problem is connected to the BWT compression technique called “tunneling”, the paper also describes a way to minimize the length of a tunneled BWT in such a way that useful properties for sequence analysis are preserved. Thus, it provides significant progress towards a solution to the open problem of finding optimal disjoint blocks that minimize space, as stated in Alanko et al. (DCC 2019).
Linguistic Peculiarities of the Sicilian Courteous Poetry of the First Half of the XIII Century (by the Material of Stefano Protonotaro’s Canzone “Pir meu cori alligrari”)

The paper aims to reveal linguistic peculiarities of the Sicilian courteous poetry by the material of Stefano Protonotaro’s canzone written in Sicilian volgare. The researcher identifies distinctive features of the dialect under consideration in comparison with the literary Italian language, introduces new factual material into scientific circulation which has not been previously subjected to a linguistic analysis, and herein lies scientific originality of the study. As a result, it is shown that the canzone language is a variant of the Latinized vulgar language enriched by the Gallo-Roman borrowings. The dialect under study manifests deviations from the literary norm at all the language levels (orthographic, phonetic, lexical, grammatical).
Dynamic obstacle avoidance system for the unmanned aerial vehicle (UAV)

Abstract This paper presents a method for collision avoidance for moving obstacle based on ellipsoid geometry. Ellipsoid algorithm itself has been implemented in small unmanned aerial vehicle (UAV) for static obstacle avoidance. In extending the algorithm to dynamic object, two adjustments have been made, first is to calculate safe heading and selection of head or tail contact point. Using these two adjustments, the algorithm succesfully generates safe waypoint for dynamic obstacle avoidance and select which direction to use, whether it is to the head or the tail of obstacle moving direction.
Research on Electronic Ticket System and Key Technology for Road Passenger Transport

Road passenger electronic ticket is a passenger transport contract in the form of electronic data. Road passenger electronic ticket system is an information system with the functions of generating, using and managing electronic tickets which provide more convenient and fast services for passengers. This paper analyzed the significance and necessity of electronic passenger ticket system for road passenger transport, designed the overall scheme of electronic passenger ticket system for road passenger transport based on the existing network ticketing system design, discussed the logical structure, system function and system architecture of the system. Then we studied and implemented the key business processes of the system. Finally, this paper put forward the following research ideas.
Scaling DCN Models for Indoor White Space Exploration

With the fast growth of the wireless spectrum demand, people have been focusing on utilizing indoor white spaces. In the past few years, several indoor white space exploration methods have been proposed. These methods focus on the utilization of spatial and spectral correlations of white spaces. However, these correlations change over time. In this paper, we perform indoor white space synchronous measurement to demonstrate the volatility of white spaces. Then, we propose a DCN (Deep Convolutional Network)-based method to capture the statistical dependencies among the features and combinatorial features extracted from white spaces, which are not limited to spatial or spectral features, and construct the white space availability map. After demonstrating the instability of spectral and spatial correlations, we scale our DCN models to a time-agnostic model. We conduct real-world experiments to evaluate our system. The evaluation results show that our time-specific DCN model and time-agnostic model outperforms the state-of-the-art method.
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation

This paper studies the expressive power of artificial neural networks with rectified linear units. In order to study them as a model of real-valued computation, we introduce the concept of Max-Affine Arithmetic Programs and show equivalence between them and neural networks concerning natural complexity measures. We then use this result to show that two fundamental combinatorial optimization problems can be solved with polynomial-size neural networks. First, we show that for any undirected graph with n nodes, there is a neural network (with fixed weights and biases) of size $$\mathcal {O}(n^3)$$ that takes the edge weights as input and computes the value of a minimum spanning tree of the graph. Second, we show that for any directed graph with n nodes and m arcs, there is a neural network of size $$\mathcal {O}(m^2n^2)$$ that takes the arc capacities as input and computes a maximum flow. Our results imply that these two problems can be solved with strongly polynomial time algorithms that solely uses affine transformations and maxima computations, but no comparison-based branchings.
Exploration and practice of project teaching of dynamic website development based on PHP

In the Internet era, there are many learning resources related to various courses or technologies, students' autonomous learning is becoming more and more convenient, and relatively speaking, project-based teaching resources that can cultivate students' project development ability are rare. As a server-side language, PHP plays a more and more important role in various website development, however, from the perspective of teaching, the existing curriculum resources and cases can not meet the needs of cultivating students' project development ability. Taking the online bookstore system as a teaching project, this paper puts forward the ideas and methods of project-based teaching reform. Around the realization process of the bookstore system function, the knowledge is explained. Through the project teaching, the students' project development ability and software engineering idea are cultivated.
JomSnapBuy: Search and Buy Product with a Snap

With the growth of technologies, more and more products have been introduced daily to the world rapidly. There are quite several products that can bring convenience to our life but the people do not know the name, or where to buy it. Sometimes, people have to waste a lot of time finding it, especially for the younger generation as they are more tend to spend on the new product. This is the same as finding a song with its melody, neither lyrics nor song name is tough. So, this study is inspired by this thought and trying to come out with an application that can recognize the product in an image and find the store page online to buy it. Besides, the user can share the interesting product they found with others through this application. In this paper, we will discuss the image recognition technique and the state some survey & interview results collected by the Google Form.
Multi-objective Optimization for Dimension Reduction for Large Datasets

In recent advancement of computational techniques, there is an exponential increase in amount of data. Learning on such large amount of data is a major area of concern with application of machine learning algorithms. Therefore, it is considered to be a complicated task to handle and perform computation on such large, complex, and heterogenous dataset. In this paper, a brief discussion about different dimension reduction or feature selection algorithms is given. A brief review about contribution of researchers for designing feature selection algorithms for large dataset is given. By analyzing exisitng problems, this paper is motivated to design a hybrid, robust, flexible, and dynamic feature selection model for classification of large datasets. For this, multi-objective optimized feature selection is proposed with an objective to minimize the error rate and execution time as well maximize accuracy of problem and to generate solution with high probability.
A Research Study on Running Machine Learning Algorithms on Big Data with Spark

The design and implementation of proactive fault diagnosis systems concerning the bearings during their manufacturing process requires the selection of robust representation learning techniques, which belong to the broader scope of the machine learning techniques. Particular systems, such as those that are based on machine learning libraries like Scikit-learn, favor the actual processing of the data, while essentially disregarding relevant computational parameters, such as the speed of the data processing, or the consideration of scalability as an important design and implementation feature. This paper describes an integrated machine learning-based data analytics system, which processes the large amounts of data that are generated by the bearings manufacturing processes using a multinode cluster infrastructure. The data analytics system uses an optimally configured and deployed Spark environment. The proposed data analytics system is thoroughly assessed using a large dataset that stores real manufacturing data, which is generated by the respective bearings manufacturing processes. The performance assessment demonstrates that the described approach ensures the timely and scalable processing of the data. This achievement is relevant, as it exceeds the processing capabilities of significant existing data analytics systems.
Can Open Source Licenses Help Regulate Lethal Autonomous Weapons?

Lethal autonomous weapon systems (LAWS, ethal autonomous weapon also known as killer robots) are a real and emerging technology that have the potential to radically transform warfare. Because of the myriad of moral, legal, privacy, and security risks the technology introduces, many scholars and advocates have called for a ban on the development, production, and use of fully autonomous weapons [1], [2].
Cross-model consensus of explanations and beyond for image classification models: an empirical study

Existing explanation algorithms have found that, even if deep models make the same correct predictions on the same image, they might rely on different sets of input features for classification. However, among these features, some common features might be used by the majority of models. In this paper, we are wondering what the common features used by various models for classification are and whether the models with better performance may favor those common features. For this purpose, our work uses an explanation algorithm to attribute the importance of features (e.g., pixels or superpixels) as explanations and proposes the cross-model consensus of explanations to capture the common features. Specifically, we first prepare a set of deep models as a committee, then deduce the explanation for every model, and obtain the consensus of explanations across the entire committee through voting. With the cross-model consensus of explanations, we conduct extensive experiments using 80+ models on five datasets/tasks. We find three interesting phenomena as follows: (1) the consensus obtained from image classification models is aligned with the ground truth of semantic segmentation; (2) we measure the similarity of the explanation result of each model in the committee to the consensus (namely consensus score), and find positive correlations between the consensus score and model performance; and (3) the consensus score potentially correlates to the interpretability.