Reviewerly/exHarmony huggingface dataset:
Emerging Technologies and Educational Requirements in Engineering Education for the Fourth Industrial Revolution

Higher education institutions and their engineering departments have a vital role in fulfilling the new requirements and opportunities of the information and communication technologies (ICT). Therefore, understanding the guidelines to adapt to the new ICT innovations in relation with the Fourth Industrial Revolution such as the internet of things, cloud computing, virtual reality, and artificial intelligence is vital to determine the emerging patterns in their development, delivery, implementation, and assessment. This study aims to define the new educational requirements in engineering education based on the developments of the Fourth Industrial Revolution. Conducting an in-depth analysis of the current literature revealed that although these emerging technologies have been widely used, there are some challenges being faced for their effective use in engineering education. Therefore, the authors provide some guidelines and discuss possible research directions for the use of these technologies in near future.
Customer requests matter

Estimating the software development effort associated with a customer request, immediately after the request has been made, is quite challenging for project managers. Studies on software effort estimation often utilize expert knowledge to build statistical or machine learning models, although it is subjective and human-dependent. Rich text, natural language based descriptions provided by the customers are mostly not incorporated into the models during estimation.
Character Recognition Based on Multi-scale Feature Fusion Transfer Learning

Video surveillance has been using in daily life widely and has significant impact in security field. Reading time information in video has becoming an essential part. Because of the complex background of timestamp and the high similarity between targets, the features extracted by traditional machine learning algorithm cannot meet the requirements and cannot achieve the required detection speed and accuracy. In this paper, we improved the algorithm with multi-scale features. Features extracted from convolution layers of different scales are used to predict, which could improve the recognition accuracy of small targets. In addition, because the size of the collected dataset is 2860 and the amount of data is not large enough, we use data augmentation and transfer learning to train. Pre-training on the public dataset SVHN firstly, and then trains on the special dataset. Experiments show that the algorithm can recognize timestamps accurately.
Use of Mobile Phones in the Classroom by College Students and Their Perceptions in Relation to Gender: A Case Study in China

This study investigates the uses of mobile phone in the classroom and compares the attitudes of students in different genders towards the use of mobile phones in a Chinese university, to explore the influence of gender.The mixed research strategy was adopted to collect both quantitative and qualitative data.One hundred fifty-three undergraduates from a university in southwest China participated in the survey through questionnaires, and nine of them were interviewed in groups.The results indicated that there was no difference between men and women in their uses of mobile phones in the classroom and attitudes.However, girls were more likely than boys to use the affordances of content generation and education of mobile phones.The results showed that mobile phones were minimally used in education.Implications are presented for guiding students' use of mobile phones to assist the classroom learning.Suggestions for future research are provided.
Energy and Exergy Analysis of the Solar Radiation Incident over Iraq

Abstract The solar radiation is renewable energy and great useful energy of in Iraq. This paper presents a study to compare the daily solar exergy, daily solar radiation, and daily solar that measured by Iraqi Agro-meteorological network in Baghdad. Then, a comparison between the solar energy and solar radiation in six cities in Iraq (Basra, Najaf, Baghdad, Anbar, Mosul, and Sulaymaniyah). The solar exergy estimated using the model of Nayak and Tiwari, while the solar radiation estimated using linear correlation which represented by (Angstrom correlation). The results showed that there is an agreement between daily solar radiation and daily solar which measured in Baghdad with mean error throughout the whole year from January to December (7.2%, 2.4%, 12.5%, 9.1%, 3.2%, 5.1%, 3.7%, 5.4%, 10.9%, 11.4%, 11.1%, and 3.7%), respectively. While, the daily solar exergy had very good agreement with the daily solar measured in Baghdad with mean error through the same 12-months (1.4%, 9.4%, 6.1%, 2.6%, 10.8%, 1.9%, 3.5%, 1.6%, 4.4%, 5.1%, 5%, and 2.7%), respectively. Also, higher daily solar radiation and daily solar exergy were in Basra, but the lowest was in Mosul. The daily solar exergy was (10.27, 10.09, 10, 10, 9.69, and 9.81 MJ/m 2 ) in Basra, Najaf, Baghdad, Anbar, Mosul and Sulaimaniya, respectively. While, the percentage difference between daily solar radiation and daily solar exergy was (7.09%, 7.04, 7%, 6.99%, 6.96%, and 6.85 %) in Basra, Najaf, Anbar, Baghdad, Mosul and Sulaimaniya, respectively.
Regulation, Institutions and Economic Growth in Advanced, Emerging and Developing Countries

This chapter uses the OECD’s newly assembled Structural Policy Indicators Database for Economic Research (SPIDER) to gauge on the impact of regulation and institutions on economic growth. We investigate for a sample of around 100 countries and for a set of OECD countries the extent to which product and labour market regulations and policies influence per capita income levels. Our estimation results indicate that more business-friendly product market regulations are associated with higher per capita income levels. At the same time, it is more difficult to identify precisely estimated effects of labour market regulation and policies, perhaps with the exception of spending on ALMP, which tend to have a positive relation to per capita income. We also find that the quality of institutions influences a country’s level of development and that institutions interact with product market regulations. Finally, our results also show that labour market regulation and policies may attenuate or accentuate the impact of product market regulations on per capita income levels.
Multiple fragment-level interactive networks for answer selection

Answer selection in question answering (QA) denotes a task which selects the most appropriate one from candidate answers for a given question. Previous researches on answer selection usually conduct it by isolated word-level interaction between questions and answers. In these methods, the abundant contextual information is hardly captured, which affects the choice of the correct answer. To overcome this problem, we propose to exploit a Multiple Fragment-level Interactive Network (MFIN) for this task. The MFIN can extend the search space from word-level to fragment-level, which is conducive to obtaining more contextual information. In MFIN, we apply the multiple fragment-level attention mechanism to select key fragment pairs and achieve multiple fragment-level interaction. Meanwhile, we utilize the recurrent representation encoding to integrate multiple interactive information to reduce noise. The experimental results demonstrate that our proposed model is efficient compared to the existing methods on the WikiQA and SemEval-2016 CQA datasets.
Text-To-Image Synthesis Method Evaluation Based On Visual Patterns

A commonly used evaluation metric for text-to-image synthesis is the Inception score (IS) [1], which has been shown to be a quality metric that correlates well with human judgment. However, IS does not reveal properties of the generated images indicating the ability of a text-to-image synthesis method to correctly convey semantics of the input text descriptions. In this paper, we introduce an evaluation metric and a visual evaluation method allowing for the simultaneous estimation of the realism, variety and semantic accuracy of generated images. The proposed method uses a pre-trained Inception network [2] to produce high dimensional representations for both real and generated images. These image representations are then visualized in a 2-dimensional feature space defined by the t-distributed Stochastic Neighbor Embedding (t-SNE) [3]. Visual concepts are determined by clustering the real image representations, and are subsequently used to evaluate the similarity of the generated images to the real ones by classifying them to the closest visual concept. The resulting classification accuracy is shown to be a effective gauge for the semantic accuracy of text-to-image synthesis methods.
Wall-climbing obstacle-avoiding full-automatic lampblack cleaning device

Abstract With the development of Chinese society and the progress of productive forces, dust and oil stains are easily produced in kitchens and ceilings when living at home. Dirt deposition in many places cannot be carried out well due to the limitation of space location and cleaning methods, which brings great inconvenience to people’s life. At the same time, the existing cleaning robots in the market need to work on a single wall surface, and when encountering obstacles, the positions of the robots need to be adjusted manually to further realize the work and cannot solve the cleaning problems of ceilings and the like. The existing cleaning robots mostly use centrifugal fans to fix on vertical wall surfaces in an adsorption way, thus causing obstacles to be avoided, the connection between different wall surfaces cannot be realized, and the defect that intelligence works on a single wall surface requires manual wall surface replacement, thus reducing automation, intelligence and intelligence. Based on the above background, the members of this group have designed a wall-climbing cleaning machine. The device mainly comprises a walking module, a wall turnover module and a cleaning module [1].
Genetic Algorithm Based Bidirectional Recurrent Neural Network for LIBOR Prediction

London Inter Banking Offered Rate (“LIBOR”) is one of the most important indicators of global currency liquidity. LIBOR market is the most important interest rate market with huge market volume and millions market stake holders from different countries trade in the market with different currencies. Because of the complexity of the stakeholders and the currencies, it is difficult to forecast the LIBOR which is important for the stability of global finance market. To train and predict the time series dataset, the traditional algorithms, including pure genetic algorithm and recurrent neural network, have the performance issues when forecasting the turning point in a one-direction trend. This paper is to propose a new algorithm, which is to combine genetic algorithm with bidirectional recurrent neural network (GA+BRNN) to improve the performance of LIBOR prediction. The pro and cons of the new algorithm will be introduced with the evaluation criteria including Mean Squared Error (“MSE”) and Fitness Value. 50 test cases are executed randomly in the experiment phase to display the performance of prediction for Libor in USD in different test scenarios. The target variance between predication and real value is no more than 0.015.
Primal-Dual Stochastic Subgradient Method For Log-Determinant Optimization

The log-determinant optimization problem with general matrix constraints arises in many applications. The log-determinant term hampers the scalability of existing methods. This paper proposes a highly efficient stochastic method that has time complexity O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), whereas existing methods have complexity O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). In order to achieve the quadratic complexity, the proposed algorithm leverages an efficient stochastic gradient of the augmented Lagrangian form and relies on subgradient descent method. Convergence of this method is analyzed both theoretically and empirically. The resulting primal-dual stochastic subgradient method yields the same accuracy as existing methods yet only requires O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) operations.
Research on Server Health State Prediction Model Based on Support Vector Machine

Abstract With the development of artificial intelligence, machine learning methods have been widely applied to predictive modelling in various fields. Support vector machine is a linear classifier with the largest interval defined on the feature space. In order to improve the current status of server operation and maintenance, and improve the accuracy and efficiency of server health prediction, this paper studies the feature quantity selection based on grey relational analysis, studies the support vector machine applied to data generalization and classification, and establishes a health prediction model. The simulation training of historical data shows that the method has fast learning, high prediction speed, high efficiency, high accuracy and wide application prospects.
Low-Rank Kernelized Graph-based Clustering using Multiple Views

Kernelized methods using multiple kernels have shown better performances in graph-based clustering. But those kernelized methods get affected by the noise present in the data set. Also, only a single view has been used in those kernelized graph-based clustering methods. To address those issues, a novel low-rank multi-view multi-kernel graph-based clustering framework (LRMVMKC) has been proposed in this paper. Where the similarity nature of kernel matrices are exploited by low-rank optimal kernel learning and the clustering performances are boosted by using multiple views that provide different partial information about a given data set. The use of the proposed LRMVMKC framework on different benchmark data sets demonstrates the better performances of the proposed framework over other existing methods.
Protecting Privacy and Security Using Tor and Blockchain and De-anonymization Risks

The huge increase in data usage and the rapid development of new technologies such as cloud, IoT, and has also led to the exponential increase in cyber threats online. Anonymity and privacy services have equally seen an exceptional growth rate since the introduction of Blockchain and Tor network, as more individuals demand anonymous services away from the traditional centralised offerings, but also seek more security and privacy. This chapter will review quantitative analysis undertaken to critically evaluate Tor and Blockchain as emerging technologies, by an in-depth comparison of their security and privacy properties. Further analysis is undertaken by utilising network and data points that highlight the necessity of urgent deployment of innovative methods to protect users’ anonymity utilising Blockchain application over the Tor network. By undertaking experimental analysis, it is possible to determine Tor packets from common packets and raises the question on possibilities of cyberattacks leading to loss of personable identifiable information (PII) and de-anonymization.
Image Restoration Via Data-Dependent Proximal Averaged Optimization

Maximum A Posterior (MAP) acts as one of the most popular modeling scheme in image restoration and is usually reduced to a separable optimization model. Unfortunately, it is challenging to establish exact regularization term and the model with complex priors is hard to optimize. In additionally, it is still hard to incorporate different domain knowledge and data-dependent information into MAP model without changing the property of the objective. To partially address the above issues, we develop a Data-dependent Proximal Averaged (DPA) paradigm through optimizing objective and data-dependent feasibility constraint for the challenging Image Restoration (IR) tasks. Both visual and quantitative comparison results demonstrate that our method outperforms the state of the art.
Partially polarized light and complementarity in quantum mechanics

The polarization properties of light have been studied in classical optics for a long time. Quantum mechanical aspects, however, have only recently drawn attention. We present a compact review of recent studies on a particular quantum mechanical aspect of partially polarized light beams. We discuss the connection between complementarity in quantum mechanics and the phenomenon of partial polarization. Instead of stressing the interpretative differences between classical and quantum theories, we focus on the experimentally verified quantitative differences. To put the topic in context, we start by discussing interference and its relevance to quantum mechanics. We show how the quantum mechanical understanding of interference leads us to a new way of looking at optical polarization.
Generalization and Visual Comprehension of CNN Models on Chromosome Images

Abstract Computer-aided image classification has achieved start-of-the-art performance since Convolutional Neural Network structures were employed. Classical neural networks such as AlexNet and VGG-Net inspired several rules of designing network models. Besides benchmark datasets such as MNIST, CIFAR and ImageNet, classification performance of medical images such as chromosome karyotyping images also improved via Convolutional Neural Network. However, there are few studies on generalization among different datasets. In this paper, we designed a neural network with nine layers, and achieved classification accuracy of 0.984, 0.816 and 0.921 on the dataset of MNIST, CIFAR and chromosome karyotype images. We also visualized the output of several layers of the model and explained that smooth output between neural network layers may induce lower accuracy on classification.
Understanding and Using Factor Scoring Methods for Ranking of Countries based on Health Indicators: A Data Science Approach

The aim of this study is to rank the countries based on health indicators using factor scoring methods, a data science approach and also to find the simplest way of understanding the indicators using the factor scores. Different methods of factor scores were used in the study, they are Regression Method, Ordinary Least Square Method, Scoring Mechanism of SPSS, Simple Method and Direct Weighted Method. Direct weighted method is a new method gaining inspiration from the simple method, but both Simple and Direct weighted methods don't satisfy our orthogonality criteria i.e., factor scores are independent of each other. In this paper, we suggest that the direct weighted method is preferred to understand the health indicators in a better way than any other methods. Though orthogonality is present, based on the results, if we look into the factor model of each method, direct weighted method can be easily understood and interpreted.
Friend Recommendation Algorithm Based on Interest and Cognition Combined with Feedback Mechanism

Traditional friend recommendation algorithms are mostly based on common friends or similarity of interests. However, when the user's friend relationship or user's interest is sparse, the recommendation result is unsatisfactory. In this case, other factors need to be combined to make recommendations. This paper proposes a method based on the collaborative filtering algorithm that combines interest and cognition to improve the recommendation effect. The mixed similarity is obtained by calculating interest similarity and cognitive similarity and then assigning different weights to them. Also, the traditional friend recommendation algorithm does not take into account the impact of each recommendation result on users. The algorithm proposed in this paper will dynamically adjust the similarity matrix of users based on the results of each recommendation. After each recommendation, we adopt positive feedback adjustment or negative feedback adjustment, which will make the next recommendation more accurate. The experimental results show that compared with the traditional recommendation methods based on common friends or interests, the recommendation algorithm in this paper has a higher precision and recall rate, and the recommendation model can be automatically adjusted according to the user's behavior.
Demo:Towards the Development of a Differentially Private Lightweight and Scalable Blockchain for IoT

In this work, we demonstrate the design and implementation of a novel privacy-preserving blockchain for the resource-constrained Internet of Things (IoT). Blockchain, by design, ensures trust, provides built-in integrity of information and security of immutability in an IoT system without the need of a centralized entity. However, its slow transaction rate, lack of transaction privacy, and high resource consumption are three of the major hindrances to the practical realization of blockchain in IoT. While directed acyclic graphs (DAG)-based blockchain variants (e.g., hashgraph) improve the transaction rate, the other two problems remain open. To this end, we designed and constructed the prototype of a blockchain by utilizing the benefits of high transaction rate and miner-free transaction validation process from hashgraph. The proposed blockchain, coined as PrivLiteChain, implements the concept of local differential privacy to provide transaction privacy and temporal constraint to the lifecycle of the blockchain to make it lightweight.
Designing Instruction for the Age of Singularity: A Transactional View as to How Knowledge Is Synthesized

Historically, knowledge has been viewed as an asset in that, once it is “owned” (i.e., acquired) by an individual, it can never be taken away (Rowley, 2000). First and foremost, we need to distinguish between the concepts of information acquisition and knowledge acquisition. The latter is the third in a five-step process of deep learning with data, information, understanding, and wisdom filling out the continuum (Ackoff, 1999). For this reason, we suggest that the term “knowledge acquisition” that appears to be so often in the literature may actually be a misnomer (Compton & Jensen, 1990). What many may be referring to is actually information acquisition. For the purposes of this chapter, however, we may interchange the two terms with this caveat in mind.
Fast and Efficient Decoding Algorithm Developed from Concatenation Between a Symbol-by-Symbol Decoder and a Decoder Based on Syndrome Computing and Hash Techniques

The exchange of data is done through a communication channel that is not entirely reliable and these data are most likely to be altered by errors. In order to detect and correct any errors on the receiver side, error-correcting codes are used; their principle is to add control data on the transmitter side. The Hartmann and Rudolph (HR) algorithm is based on a probabilistic study to determine if a bit of the received sequence is equal to 0 or 1. To do this, it exploits all the codewords of the dual code, which makes its complexity very high. The efficiency and the low complexity of the hard-decision decoder based on hash and syndrome decoding (HSDec) algorithm compared with its competitors encouraged us to use it in a serial concatenation with the HR decoder. In this paper, we propose a mutual cooperation between the HR partially exploited (PHR) and HSDec decoders. The simulation results show the efficiency and the rapidity of the proposed cooperation. The best performance qualities obtained using PHR-HSDec against HR alone prove the robustness of this cooperation.