Reviewerly/exHarmony huggingface dataset:
Evolution of Push-Communication Towards the Rich Web-Based Applications

Push Communication is an integral requirement in modern Rich Web-based Applications, to implement the features like push notifications or real-time updates. Aspects like push-communication related concepts and their development technologies – focusing on the roots of them and the rationale behind their advancements – are not collectively discussed in any available forum. An intensive literature survey was conducted on identifying the very roots of the push-communication and its evolution towards understanding the abstract architectural formalism of the push-communication in Rich Web-based Applications, also focusing on the aforementioned aspects. We collected and documented the literature regarding the evolution of the push-communication, for archiving and also for reviewing and comparing the reasoning behind the improvements of them over time. We also tried to capture the knowledge to answer some important questions like is push-communication important and how difficult to integrate push-communication into the Rich Web-based Applications? We expect to study the artefacts identified through the survey to identify the abstract characteristics of the push-communication to realize the integration of the push-communication into the Rich Web-based Applications in the form of Delta-Communication.
Smart GOALA: An Alternative Marketing Channel for Connecting the Peri-urban Marginal Dairy Farmers with the Urban Consumers in Bangladesh

The objective of this paper is to present a mobile-based conceptual model of marketing channel for connecting the peri-urban marginal dairy farmers with the urban milk consumers in developing countries, particularly in Bangladesh. It reports the results of one quantitative survey and four qualitative focus group discussions. The survey reveals that the farmers are deprived of getting fair prices of their product due to inefficient marketing channel. The study also explores that a technology-based marketing channel might help the farmers to overcome the problems. Based on these findings, the study proposes a mobile-based channel—‘Smart GOALA’—for connecting the peri-urban farmers with the urban consumers which will ensure them to get a better and fair price.
Economic Assessment of Rooftop Photovoltaic Investment by Considering Uncertainty Solar Irradiation: A case Study of Semarang City, Central Java, Indonesia

Rooftop Photovoltaic (PV) built by the private sector is an option to reduce the government’s burden in achieving the new and renewable energy target. However, the fluctuating global horizontal irradiation (GHI) value causes uncertainty in the profit of rooftop PV investment and causes the private sector to doubt. Therefore, this research analyses the impact of the GHI uncertainty on the economics of rooftop PV investments. This research used Semarang city as a case study. The uncertainty was modelled using the Monte Carlo simulation. The results show that the levelized cost of electricity (LCOE) is always below the tariff and above the generation cost. It implies that it is more profitable if it is built by the private sector (PLN’s customers) rather than build by PT. PLN itself. The LCOE of rooftop PV in Semarang has an average value of 8.23 cents USD/kWh with an average net present of 11,265 USD.
Multi-style image transfer system using conditional cycleGAN

This paper aims to extend the capability of Cycle-Consistent Adversarial Network (CycleGAN) by equipping it with a conditional constraint and extend it into a multi-style image transfer system that can transfer images among more than two image domains. The conditional constraint is given in the form of the target style feature map instead of a one-hot vector, and has shown to provide better transfer results. The proposed system offers greater flexibility for users to choose the style for image transfer. Experimental results show that such an architecture is not only feasible but also yields good results. The proposed architecture can be extended to other transformation applications, such as facial expressions transfer, face aging, and synthesis of various features.
Motivación del alumno como manifestación de la dimensión espiritual del ser humano: la acción de estudiar y la vita activa

After the 1960s, with the release of the Coleman report, the interest of governments, researchers and universities over the influence of socio-economic variables on academic performance of students grew, to the point where most of the studies about the determinants of school performance correlates income, schooling, quality of employment, and other characteristics of parents, with performance of students. Still, the most recent research shows that some characteristics of students, such as their motivation, greatly influence their reading and study activities at school and at home and, consequently, ultimately influence their performance. This preliminary study proposes a simplified model of human being, regarding its capacity for learning and creativity, which can be used later for an analysis of the influences of the teacher's performance on the Motivation of the Students and, consequently, on their school performance. The method used here is eminently the review of literature, on propositions over the human being, typical of the 20th century, carried out on the works of Arendt (2007) and Scheler (2003), from which it is composed a simplified model. It is preliminarily concluded on the validity of the model here presented, from which a complex investigation about the influence of the teacher on motivation will be constructed later.
Prediction Stability as a Criterion in Active Learning

Recent breakthroughs made by deep learning rely heavily on a large number of annotated samples. To overcome this shortcoming, active learning is a possible solution. Besides the previous active learning algorithms that only adopted information after training, we propose a new class of methods named sequential-based method based on the information during training. A specific criterion of active learning called prediction stability is proposed to prove the feasibility of sequential-based methods. We design a toy model to explain the principle of our proposed method and pointed out a possible defect of the former uncertainty-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability was effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperformed them on CIFAR-100.
DeCStor: A Framework for Privately and Securely Sharing Files Using a Public Blockchain

Cloud services have become increasingly popular during the past few years. Through these services, users can store their data remotely and access them any time and from anywhere. These services are offered by centralized systems where an organization or company usually offers their resources to users. The centralized nature of these systems causes several problems; a single point of failure exists, security issues might provide unwarranted access to intruders and there are privacy issues to consider as well. A solution to these problems is the decentralization of the system. A core technology that can help in this respect is the blockchain. It does not require any centralized control and its security model is based on the nodes of the blockchain network to share and verify transactions. This work aims to develop a secure decentralized cloud service, which does not expose the users' personal data. To this effect, a framework that implements a cloud service using the Ethereum blockchain ecosystem and the Swarm decentralized storage platform was developed. In this, file access is provided through user-specific decryption keys. By developing a decentralized cloud, using a secure encryption model for the data, a service which is more secure, and where the users have full control over their data is possible.
Hardware Design of Integrated Platform of Optical Fiber Sensor Based on Temperature Distribution

The distributed fiber sensing technology can calculate the temperature and strain distribution on the fiber by detecting the scattered light intensity along each position of the fiber. Brillouin optical time domain analyzer is a distributed optical fiber sensing technology, which is suitable for long distance, large range and high sensitivity distributed optical fiber sensing system, and can achieve accurate real-time measurement of temperature and strain on the optical fiber. It has important research significance and practical value. In this paper, brillouin scattering effect and BOTDA sensing principle in optical fiber are studied. Based on the signal characteristics of BOTDA sensing system, a distributed optical fiber sensing system test platform is designed.
Clustering and Regression to Impute Missing Values of Robot Performance

It is widely claimed that a major challenge in Robotics is to get reliable systems while both response and down times are minimized. In keeping with this idea, present paper proposes the application of a Hybrid Artificial Intelligence System (HAIS) to preprocess data with the aim of improving the detection of performance anomalies. One of the main problems when analyzing real-life data is the presence of missing values. It is usually solved by removing incomplete data, what causes a loss of information that may be critical in some domains. As an alternative, present paper proposes the application of regression models to impute those missing values. Prediction is optimized by generating personalized models on previously clustered data. Experiments are run on a public and up-to-date dataset that contains information about anomalies affecting the component-based software of a robot. The obtained results validate the proposed HAIS, as it successfully imputes missing values from the different features in the original dataset.
A Meta-Search Engine Ranking Based on Webpage Information Quality Evaluation

This paper demonstrates a meta-search engine developed by the authors, which ranks the results based on web page information quality evaluation algorithm. The web page information quality score is calculated based on the title of the web page, the abstract of the web page and the source of the web page. The quality of web page can be evaluated by these factors. When a user submits an input, the proposed meta-search engine system collects the results from some general search engines like Baidu, Bing, Sogou and so on, and rank the web pages according to their information quality scores. Because we do not need a local database to store a large amount of data, all operations are completed in the cache, which greatly reduces system consumption. The system is evaluated by three kinds of representative queries, and the results show that its search accuracy and user experience are obviously better than the current general search engines.
Space Complexity Analysis in Hybrid Principal Component Analysis

Conventional linear PCA approaches do not adequately handle the issues such as intrinsic spatial structural information of pattern, the complexity of calculating covariance matrix, variations restricted to some parts of a pattern, and small sample size problem, etc. To handle the above challenges, several PCAs in feature partitioning framework have been developed to improve the recognition accuracy. Recently developed approaches such as extended cross-correlation sub-pattern principal component analysis (ESubXPCA) operate on the sub-pattern and whole pattern at a time and captures the local and global variation of patterns by maintaining cross-correlations across sub-pattern and whole pattern sets. Along with recognition accuracy and time, space complexity is also an important parameter to study the relative effectiveness of a PCA approach. In this paper, we have formulated the space complexities of various PCAs in feature partitioning framework and compare them with some similar methods both theoretically and experimentally. The experiment conducted on ORL and YALE face datasets with multiple image resolutions. The experimental results show the technique ESubXPCA exhibits minimum feature dimensionality; SpPCA exhibits minimum space requirements for the covariance matrix and to design the complete recognition system.
Restoration for fiber bundle endomicroscopy using a fast iterative shrinkage-thresholding algorithm

Optical endomicroscope via fiber bundles has been widely used to provide cellular-level visualization for clinical applications. However, improving the quality and spatial resolution of the under-sampling images obtained by fiber bundle with the inter-core spacing remains a challenging problem. In this paper, we address the problem of deconvolution and restoration in fiber-bundle-based imaging. We propose a fast iterative shrinkage-thresholding algorithm (FISTA) solve this problem, and a high-resolution (HR) image without honeycomb patterns is restored from a low-resolution (LR) fiber bundle image. The feasibility of this mothed is verified by experimental results, which shows a promising and wide applications for fiber bundle imaging.
Lifting Path Planning of Mobile Cranes Based on RRT Algorithm

Lifting operations of mobile cranes are always based on the experience of operators, leading to low efficiency as well as high accident rate because of the dynamic and complex construction environment. Thus, there is a strong need to develop an appropriate approach to guide the crane operations. In this paper, an improved rapidly-exploring random tree (RRT) algorithm is proposed for the automated lifting path planning of mobile crane. Considering the critical role of nearest neighbor search (NNS) in the implementation of RRT algorithm, an improved strategy consisted of generalized distance method and cell method for searching the nearest neighbor is developed. Both two methods have been tested in simulation-based experiments, with obvious improvements in computing time, number of nodes, and frequency of collision. Therefore, this improved RRT algorithm enables the rapid path planning of mobile cranes in a dynamic and complex construction environment, making it possible to enhance the efficiency and safety in crane lifting practices.
Research on IT Management and Service of Small and Medium Sized Libraries Based on Cloud Computing and Virtualization Technology

Abstract At present, digital resources have become an important part of the collection resources of modern small and medium-sized libraries. As a new technology, cloud computing technology has been widely used in the field of small and medium-sized libraries, and has just begun to enter the vision of professional researchers. This paper studies the application of cloud computing and virtualization technology in IT management and service of small and medium-sized libraries. In order to better understand the application and help of cloud computing and virtualization technology in IT management and service of small and medium-sized libraries, this paper applies cloud computing and virtualization technology to it management and service in a university library, and then carries out a survey and statistics of the figure The library has developed a questionnaire to investigate the library staff and readers’ feelings about the application. The results show that under the support of cloud computing and virtualization technology, IT management and service for small and medium-sized libraries can significantly improve the management efficiency and service quality of libraries.
Chinese Emergency Event Recognition Based on BiGRU-AM Model

Abstract A Chinese emergency event recognition based on bidirectional gated recurrent unit BiGRU-AM model with attention mechanism is proposed to resolve the limitation of traditional event recognition methods and the poor interpretability of general recurrent neural networks in respect of information features with different degrees of importance. Firstly, text corpus was trained to generate word vectors, and contextual information features were extracted through BiGRU, and then attention mechanism was introduced into BiGRU network to make feature extraction more selective. Finally, the learned features were activated by softmax function to output recognition results. Simulation results show that this method improves the accuracy and recall rate of emergency recognition, and the F value is superior to other methods.
A Research on Encryption Methods and Account Security

Nowadays, the virtual world is widely used by increasing of the precautions for the global pandemic. Therefore, the membership systems that are created on the basis of the user accounts have an important role in order to meet the increasing requirements. The most important requirements of the current systems are privacy and delivering of the datas as seamlessly for sending of the datas as security and receiving of the end users datas. The security of an account is enhanced by additional measures such as sms systems, authentication, security question, and robot control along with password complexity to prevent cyber attacks. Symmetric and asymmetric encryption algorithms are composed of easy and convenient methods for data privacy and integrity. In this study, the validity of the used encryption methods in today for the security of user accounts, which are becoming widespread in every field, is analyzed and the role of password complexity in account security is investigated.
Power Grid Classification through Electrical Network Frequency and its Multiple Frequency Components

Monitoring and optimizing the power grids are one of the most important tasks in electrical engineering. In this paper we would present an approach to detect power grid locations using Electrical Network Frequency and multiple frequency components associated with it. It is based on frequency domain method. We have used some different approach to detect the grids. We have build the system using machine learning simulation on Matlab. Performance evaluation in terms of number of correct detection and false alarms reveals the effect of system performance.
SENTIPUBLIKO: Sentiment Analysis of Repost Jejemon Messages using Hybrid Approach Algorithm

Abstract Jejemon language becomes a form of communication dialect. It was a form of expression used by a particular social group unknown as Jejemon. However, the Jejemon expression has different formats ranging from basic form of changing letter to number, lowercase letter to uppercase letter, inserting shortcut texts into more complicated format. This paper aims to classify Jejemon tweet whether it is a positive, negative or neutral sentiment through sentiment analysis techniques. Experiment included translation of Jejemon formatted tweet, reduction of sentiment scores on repost tweets and sentiment classification. Analysis of experiment results involves Paired T-Test, confusion matrix, precision, recall, f-score and accuracy. Evidently, translated Jejemon tweet resulted 78.5% similar from the actual message using cosine similarity algorithm. Furthermore, Paired T-Test shows no significant difference between new sentiment scores from translated expression and actual sentiment scores using Hybrid Algorithm. Sentiment analysis metrics such as precision, recall, f-score and accuracy show acceptable values of 71%, 76%, 71% and 73% respectively.
Immersive Storytelling in Augmented Reality: Witnessing the Kindertransport

Although hardware and software for Augmented Reality (AR) advanced rapidly in recent years, there is a paucity and gap on the design of immersive storytelling in augmented and virtual realities, especially in AR. In order to fill this gap, we designed and developed an immersive experience based on HoloLens for the National Holocaust Centre and Museum in the UK to tell visitors the Kindertransport story. We propose an interactive narrative strategy, an input model for Immersive Augmented Reality Environment (IARE), a pipeline for asset development, the design of character behavior and interactive props module and provide guidelines for developing immersive storytelling in AR. In addition, evaluations have been conducted in the lab and in situ at the National Holocaust Centre and Museum and participants’ feedback were collected and analysed.
Instance-Aware Evaluation of Sensitive Columns in Tabular Dataset

Fully discovering knowledge from big data has to publish and share corresponding datasets whenever required. However, the risk for privacy leakage, i.e., record re-identification through some released columns, in the datasets is a fatal problem that prevents these tasks. Therefore, evaluating the sensitivity for different attributes is a prerequisite for dataset desensitization and anonymization, after which datasets can be published and shared in a privacy-preserving way. However, automatically evaluating the sensitivity for attributes is challenging and remains an open problem. In this work, we present a novel-but-simple technique for quantifying the sensitivity in structural database. It automatically evaluates the risks for re-identification for different columns according to Record-linkage Attack. Under the support of our scheme, the output sensitivity for the same attribute in different instances of a relational schema varies. Moreover, our scheme can quantify the risks of the columns no matter the semantics of columns are known or not. We also empirically show that the proposed scheme is effective in dataset sensitivity governance comparing with baselines.
MESMER

In this paper we present MESMER, a work-in-progress tangible conversation tool for playful design. Our work extends the Otherworld Framework (OF) [7] for tangible tools by centering specifically on play as a conversation topic. Here we unpack how early experiments with OF motivated our work and describe the current iteration of the MESMER tool, which comprises persona cards, various boards, and a shared physical token. MESMER is inspired by our findings from early trials with OF: performative playful interaction promoted playful and divergent thinking; embodied non-verbal communication led to shared insights, the board's contents and structure helped scaffold conversations, a diversity of personas and narratives seemed desirable, and role-playing personas encouraged multi-stakeholder empathy. Our ongoing research aims to help designers and researchers to facilitate engaging, fruitful and inspiring conversations where diverse stakeholders can contribute to playful technology design.
Agglomerative Constrained Clustering Through Similarity and Distance Recalculation

Constrained clustering has become a topic of considerable interest in machine learning, as it has been shown to produce promising results in domains where only partial information about how to solve the problem is available. Constrained clustering can be viewed as a semi-supervised generalization of clustering, which is traditionally unsupervised. It is able to leverage a new type of information encoded by constraints that guide the clustering process. In particular, this study focuses on instance-level must-link and cannot-link constraints. We propose an agglomerative constrained clustering algorithm, which combines distance-based and clustering-engine adapting methods to incorporate constraints into the partitioning process. It computes a similarity measure on the basis of distances (in the dataset) and constraints (in the constraint set) to later apply an agglomerative clustering method, whose clustering engine has been adapted to consider constraints and raw distances. We prove its capability to produce quality results for the constrained clustering problem by comparing its performance to previous proposals on several datasets with incremental levels of constraint-based information.